WO2012160370A2 - Determining remaining useful life of rotating machinery including drive trains, gearboxes, and generators - Google Patents

Determining remaining useful life of rotating machinery including drive trains, gearboxes, and generators Download PDF

Info

Publication number
WO2012160370A2
WO2012160370A2 PCT/GB2012/051153 GB2012051153W WO2012160370A2 WO 2012160370 A2 WO2012160370 A2 WO 2012160370A2 GB 2012051153 W GB2012051153 W GB 2012051153W WO 2012160370 A2 WO2012160370 A2 WO 2012160370A2
Authority
WO
WIPO (PCT)
Prior art keywords
operating conditions
eoh
component
wind
damage
Prior art date
Application number
PCT/GB2012/051153
Other languages
French (fr)
Other versions
WO2012160370A3 (en
Inventor
Andy POON
Xiaoqin Ma
John Karl Coultate
Evgenia GOLYSHEVA
Original Assignee
Romax Technology Limited
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from GBGB1108476.1A external-priority patent/GB201108476D0/en
Priority claimed from GBGB1110003.9A external-priority patent/GB201110003D0/en
Priority to IN2382MUN2013 priority Critical patent/IN2013MN02382A/en
Priority to CN201280024323.3A priority patent/CN103547977B/en
Priority to US14/119,146 priority patent/US10466138B2/en
Priority to ES12733515T priority patent/ES2911282T3/en
Application filed by Romax Technology Limited filed Critical Romax Technology Limited
Priority to DK12733515.6T priority patent/DK2710437T3/en
Priority to EP12733515.6A priority patent/EP2710437B1/en
Priority to KR1020137030809A priority patent/KR20140128860A/en
Priority to JP2014510891A priority patent/JP6397330B2/en
Publication of WO2012160370A2 publication Critical patent/WO2012160370A2/en
Publication of WO2012160370A3 publication Critical patent/WO2012160370A3/en
Priority to US15/676,147 priority patent/US10527520B2/en

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M13/00—Testing of machine parts
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D1/00—Wind motors with rotation axis substantially parallel to the air flow entering the rotor 
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03B—MACHINES OR ENGINES FOR LIQUIDS
    • F03B15/00—Controlling
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D80/00—Details, components or accessories not provided for in groups F03D1/00 - F03D17/00
    • F03D80/50—Maintenance or repair
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0283—Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL]
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2260/00—Function
    • F05B2260/82—Forecasts
    • F05B2260/821—Parameter estimation or prediction
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2260/00—Function
    • F05B2260/84—Modelling or simulation
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/332—Maximum loads or fatigue criteria
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/20—Hydro energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction

Definitions

  • the present invention relates to methods for determining residual useful life of rotating machinery including drive trains, gearboxes, and generators.
  • approaches relate to determination of an Equivalent Operating Hours limit for the machinery and comparing it with an Equivalent Operating Hours value for the machinery.
  • it relates to methods for determining residual useful life of wind and water turbines and components thereof, and using these data to operate and manage turbine installations.
  • Operating parameters related to the operation of a wind or water turbine or component thereof, and determining when these parameters move outside an operating window, may indicate that some kind of maintenance or investigation is needed.
  • Operating parameters that are monitored could include lubrication temperature, lubrication debris, vibration, and power output.
  • Vibration is commonly measured by Condition Monitoring Systems. Generally speaking, large vibrations compared to a norm is indicative of damage.
  • Vibration analysis generally relies on a measurement provided by a sensor exceeding a predetermined threshold, which is prone to false alarms if the threshold is set too low.
  • the threshold level is not necessarily constant and may vary with frequency (and hence speed).
  • the presence of shocks and extraneous vibrations means that the threshold level must be set sufficiently high to minimise the risk of false-alarms.
  • the threshold must be sufficiently high to avoid any negative effects caused by 'creep' in sensor performance which may occur over its lifetime.
  • Incipient faults such as unbalance
  • An excitation force due to faults can thus be calculated from field operational conditions and used to calculate individual component loads. Deviation from the assumed operating profile can be addressed by using a generic wind simulation model to determine load at the turbine shaft, which allows individual component loads based on the field operational conditions to be calculated. Combining these gives the total load at each component, which can be is used to estimate the remaining life of the individual components and the life of the gearbox.
  • EOH Equivalent Operating Hours
  • the present invention provides a method for predicting remaining useful life of a wind or water turbine or component thereof, the method comprising the steps of: obtaining an EOH limit value for the wind or water turbine or component thereof; determining an EOH for the wind or water turbine or component thereof; and comparing the EOH and the EOH limit.
  • the step of determining an EOH comprises the steps of: providing data relating to one or more operating conditions; and providing one or more EOH coefficients relating to the one or more operating conditions.
  • the step of providing one or more EOH coefficients comprises the steps of: assessing damage to the wind or water turbine or a component thereof under rated operating conditions and under a plurality of field operating conditions; calculating the weighting factor from the damage under rated conditions and the damage caused under the plurality of field operating conditions; wherein the step of assessing damage comprises the step of providing information on the wind or water turbine or a component thereof.
  • the step of providing information includes providing one or more models selected from the group consisting of: a bearing skidding model; a dynamic model ; a life model; a nominal model of the gearbox, drive-train and/or generator; a model unique to the specific gearbox, drive-train and/or generator including
  • the weighting factor is a function of the damage under rated operating conditions and damage under field operating conditions.
  • the weighting factor is a function of a ratio of damage under rated operating conditions to damage under field operating conditions.
  • the weighting factor is a ratio of damage under rated operating conditions to damage under field operating conditions.
  • the step of determining an EOH comprises calculating a value of a function of the data relating to the one or more operating conditions and the one or more EOH coefficients relating to the one or more operating conditions.
  • the step of determining an EOH comprises calculating a sum of a product of the data relating to the one or more operating conditions and the one or more EOH
  • the step of providing data comprises providing historical data.
  • the step of providing data comprises providing data relating to one or more steady state operating conditions.
  • the step of providing data comprises providing data relating to one or more transient state operating conditions.
  • the step of providing data comprises collecting data from one or more sensors monitoring the one or more operating conditions.
  • the step of providing data comprises providing data from a condition monitoring system.
  • the step of providing one or more EOH coefficients comprises providing EOH coefficients relating to one or more steady state operating conditions.
  • the step of providing one or more EOH coefficients comprises providing EOH coefficients relating to one or more transient state operating conditions.
  • the EOH being greater than the EOH limit, additionally comprising the step of: maintaining the wind or water turbine or component thereof.
  • the wind or water turbine or a component thereof has failed, and in which the EOH being less than the EOH limit, additionally comprising the step of: maintaining the wind or water turbine or component thereof.
  • the step of maintaining the wind or water turbine or component thereof comprises investigating for damage to the wind or water turbine or component thereof.
  • the step of investigating for damage is selected from the group consisting of: using an endoscope, performing vibration analysis and performing lubrication analysis.
  • the wind or water turbine or component thereof having damage, scheduling maintenance of the wind or water turbine or component thereof.
  • the wind or water turbine or component thereof having damage, refurbishing the wind or water turbine or component thereof.
  • the wind or water turbine or component thereof having irreparable damage, replacing the wind or water turbine or component thereof.
  • the method comprises the additional step of: setting EOH of the wind or water turbine or component to zero.
  • a method for identifying a wind turbine or component thereof for maintenance comprising the steps of: determining an EOH value for the wind turbine or component thereof; analysing operating data for the wind turbine or component thereof; and comparing the operating data with a threshold related to the EOH value.
  • the step of determining an EOH comprises the steps of: providing data relating to one or more operating conditions; and providing one or more EOH coefficients relating to the one or more operating conditions.
  • the step of providing one or more EOH coefficients comprises the steps of: assessing damage to the wind or water turbine or a component thereof under rated operating conditions and under a plurality of field operating conditions; calculating the weighting factor from the damage under rated conditions and the damage caused under the plurality of field operating conditions; wherein the step of assessing damage comprises the step of providing information on the wind or water turbine or a component thereof.
  • the step of providing information includes providing one or more models selected from the group consisting of: a bearing skidding model; a dynamic model ; a life model; a nominal model of the gearbox, drive-train and/or generator; a model unique to the specific gearbox, drive-train and/or generator including information on one or more manufacturing variations of one or more components of the gearbox, drive-train and/or generator; a fully coupled finite element model comprising nodes with six degrees of freedom unique to the gearbox, drive-train and/or generator; and one or more meta-models, wherein the one or more meta- models are specific for each of the one or more components.
  • the weighting factor is a function of the damage under rated operating conditions and damage under field operating conditions.
  • the weighting factor is a function of a ratio of damage under rated operating conditions to damage under field operating conditions.
  • the weighting factor is a ratio of damage under rated operating conditions to damage under field operating conditions.
  • the step of determining an EOH comprises calculating a value of a function of the data relating to the one or more operating conditions and the one or more EOH coefficients relating to the one or more operating conditions.
  • the step of determining an EOH comprises calculating a sum of a product of the data relating to the one or more operating conditions and the one or more EOH coefficients relating to the one or more operating conditions.
  • the method additionally comprises the step of setting thresholds for operating data according to one or more ranges of EOH values.
  • the operating data is vibration data.
  • identifying a wind turbine or component thereof for maintenance comprises identifying a wind turbine or component thereof in which the operating data is greater than the threshold.
  • the step of providing information includes providing one or more models selected from the group consisting of: a nominal model of the gearbox, drive- train and/or generator; a model unique to the specific gearbox, drive-train and/or generator including information on one or more manufacturing variations of one or more components of the gearbox, drive-train and/or generator; a bearing skidding model ; a dynamic model; a life model; a fully coupled finite element model comprising nodes with six degrees of freedom unique to the gearbox, drive-train and/or generator; and one or more meta-models, wherein the one or more meta- models are specific for each of the one or more components.
  • the weighting factor is a function of the damage under rated operating conditions and damage under field operating conditions.
  • the weighting factor is a function of a ratio of damage under rated operating conditions to damage under field operating conditions.
  • the weighting factor is a ratio of damage under rated operating conditions to damage under field operating conditions.
  • Also disclosed is a computer readable product comprising code means designed for implementing the steps of the method according to any of the methods disclosed above. Also disclosed is a computer system comprising means designed for
  • Figure 1 shows a flow chart for predicting remaining useful life of a wind or water turbine or components thereof
  • Figure 2 shows a flow chart for the determination of damage to a wind or water turbine or component thereof
  • Figure 3 shows the steps in a method for calculating damage to a wind or water turbine or a component thereof using a model-based approach
  • Figures 4, 5 and 6 show stages in the construction of a meta model.
  • Figure 7 shows a flow chart for scheduling maintenance of a wind or water turbine or components thereof based on an EOH analysis
  • Figure 8 shows a flow chart for gearbox refurbishment based on EOH analysis
  • Figure 9 shows a graph combining EOH operating life models with vibration data for a number of turbines operating in a wind farm.
  • Figure 10 illustrates a schematic diagram of an apparatus according to various embodiments of the invention.
  • turbine load data (which may be simulated or measured) is collected and operating conditions, such as temperature of various bearings, oil conditions, and the like are sensed and logged.
  • Operating condition data can be chosen to represent a typical range of conditions, or they can be obtained from historical logged data such as SCADA or a condition monitoring system. This data can be used in step 102 in a damage-determining model or meta model to determine EOH coefficients 108 relating to steady-state operating conditions.
  • Corresponding EOH coefficients 106 for non-steady state (transient) conditions can be determined in step 104 using a dynamic model of wind turbine and
  • step 1 current or historical operating data 1 10 are provided and EOH is derived from this data and the EOH coefficients determined in steps 106 and 108 according to the relationship:
  • EOH limit is determined from field data (failure records and the like). EOH limit is simply the expected life of the wind or water turbine or component thereof.
  • step 1 18 a comparison of EOH limit and EOH yields a value for the remaining useful life (RUL) of the component.
  • EOH weighting factors or coefficients are obtained from assessing damage to the wind or water turbine or a component thereof under field operating conditions and damage to the wind or water turbine or a component thereof under rated operating conditions of the same duration.
  • the wind or water turbine or a component thereof includes any component of the wind or water turbine and includes, for example, the turbine, turbine shaft, gearbox drive train, and generator, as well as any subcomponent, such as a gear, a drive shaft, and the like.
  • the EOH weighting factor or coefficient is a function of the damage under rated operating conditions and damage under field operating conditions of the same duration. It can be a function of a ratio of damage under rated operating conditions to damage under field operating conditions of the same duration.
  • Figure 2 shows a flow chart for the determination of damage 202 to a wind or water turbine or component thereof.
  • Load data 204,206 for example forces and / or moments, acting on the wind or water turbine or component thereof is provided.
  • Load data 204 relates to operation under rated operating conditions (CR), which can be the conditions for which the wind or water turbine or a component thereof was designed. Load data 204 can be obtained or derived from design data 208.
  • CR rated operating conditions
  • Load data 206 relates to loading under field operating conditions (Co).
  • Field operating conditions can be historical sensor data 21 0 or SCADA data obtained from or derived from a CMS.
  • Field operating conditions can be real time sensor data 21 2 from actual operating conditions under which the wind or water turbine or a component thereof is being operated. This means that EOH weighting factors or coefficients can be calculated in real time. These EOH weighting factors or coefficients can be stored and used again when similar field operating conditions are experienced, leading to a reduction in computing capability required over time.
  • Field operating conditions can be a library of anticipated conditions 21 4 which is a range of operating conditions under which the wind or water turbine or a component thereof may be expected to operate. Alternatively or additionally, a library of anticipated conditions 214 can be populated by historical sensor data 21 0 or real time sensor data 21 2. This means that EOH weighting factors or coefficients can be calculated in advance of operation, reducing the amount of computing capability required during operation.
  • Design data 208, historical data 21 0, real time data 21 2 and library data 21 4 can comprise continuous ranges of data, or the data can be stratified into bins to simplify calculations.
  • the operating conditions can be steady state operating conditions or transient operating conditions.
  • Damage 202 under rated and field operating conditions is determined from information relating to the wind or water turbine or a component thereof.
  • the information can be provided by inspection 21 6, or by using a model 21 8 of the wind or water turbine or component thereof.
  • the EOH weighting factor or damage coefficient 220 is a function of the damage under rated operating conditions and damage under field operating conditions of the same duration. It can be a function of a ratio of damage under rated operating conditions to damage under field operating conditions of the same duration. It can be a ratio of damage under rated operating conditions to damage under field operating conditions of the same duration.
  • data 204, 206 can be derived from other specified or measured parameters present in the data.
  • the derivation can be a simple manipulation of the data available, or it can be obtained using model 218 of the wind or water turbine or component thereof (not shown).
  • a unique model may be generated for one or more of each of the components of the wind or water turbine that leaves a production line.
  • Each unique model is generated using the dimensions and
  • the unique model can be used to calculate the loads, for example forces and/or moments, that may act on a component at any location or particular locations in or on the component according to the operating conditions. This in turn permits the calculation of the damage sustained by each component under rated or field operating conditions.
  • Figure 3 shows the steps in a method for calculating damage to a wind or water turbine or a component thereof using a model-based approach.
  • the component may be, for example a gearbox, as indicated.
  • a gearbox is provided. This may include a fully coupled model with six degrees of freedom.
  • the model may also be unique to the gearbox.
  • the information may include information relating to one or more
  • step 38 loads, for example forces and/or moments, acting on the gearbox during field operating conditions can be monitored during operation or provided from historical data (eg SCADA). Alternatively the loads can be calculated from anticipated field operating conditions. Similarly the loads can be calculated from rated operating conditions. Where loads acting on the gearbox are continuously monitored during operation, these measurements may be taken at a regular sampling frequency of e.g. 50 Hz.
  • step 38 may include monitoring one or more loads over time. Monitoring one or more loads may include monitoring outputs of one or more condition monitoring sensors placed in or on the gearbox at predetermined locations.
  • third step 40 the damage caused to each component by the one or more loads, in each sample of data, however determined is calculated.
  • the fully coupled system model described above is used to calculate the system deflections and component loads.
  • the contact between gear teeth is modelled using finite elements taking into account the tooth bending stiffness and gear mesh contact stiffness. These stiffnesses can be calculated or based on empirical data and are taken into account in the static deflection analysis of the full model.
  • the tooth face load distribution, tooth contact stress or bending stress may be calculated for each gear mesh. These values may then be compared with empirical data or empirical methods used to calculate the operating contact stress, e.g. according to methods given in ISO 6336-2.
  • the tooth bending stress may be calculated using finite element models or may be calculated using empirical methods, e.g. methods in ISO 6336-3.
  • S-N curves for gear contact failure and gear bending failure may be employed and may be based on mathematical simulations or may be based on empirical data, e.g. data provided in ISO 6336.
  • a prediction of the cumulative damage on each component is continuously updated, thus allowing the remaining life of each component to be predicted using empirical data e.g. S-N curves and bearing life data available from ISO standards.
  • the calculation of bearing damage can be performed using the
  • the provided gearbox information cannot be analysed at as high a frequency as the data is sampled.
  • the model analysis required to predict the damage due to each sample of data may take 1 second, but the data may be sampled at 50 Hz.
  • an approximation (a meta-model) can be employed so that the damages are predicted more quickly.
  • the meta-model is constructed in three stages:
  • the meta-model may be constructed using only steps 1 ) and 2) above.
  • Figures 4 to 6 show the three stages listed above applied to a two-variable problem.
  • Figure 4 shows the plotted raw data points.
  • Figure 5 shows the
  • the variables in the meta-model can be one or more of the following loads which may be defined anywhere in the gearbox model, drivetrain or generator: force in the x-direction (F x ); force in the y-direction (F y ); force in the z-direction (F z );
  • the variables may include displacements in any of the x, y and z directions or rotations about any of the x, y, and z axes or temperature.
  • the meta-model is constructed from data samples each of which corresponds to a different combination of any of the variables listed above.
  • the accuracy of the meta model can depend on the method used to determine the variables used to generate each data sample. A sampling regime in which the sample points are randomly determined is possible but is not ideal because it can result in some data samples having similar variables which may result in the meta-model being inaccurate. Spacing the data samples uniformly in the design space represented by the meta-model variables is preferred.
  • Uniform sampling of data in the meta-model variables design space is achieved by optimising the sampling strategy using a genetic algorithm.
  • One method is to maximise the minimum distance between any two neighbouring sample points.
  • Many other suitable sampling strategies exist in literature including minimising the maximum distance between any two neighbouring sample points; L2 optimality; latin hypercube sampling.
  • Methodology consists of fitting a polynomial to the sample data using linear regression.
  • the polynomial can be of any order and may include some or all of the possible terms.
  • the number of variables in the polynomial is equal to the number of variables in the meta-model.
  • a transformation can be applied to the sampled data before fitting the polynomial in order to decrease the 'model bias' which can arise due to the assumption that the data follows a polynomial trend. For example, if the behaviour of the response is observed to follow a trend similar to an exponential, then a polynomial can be fitted to the natural log of the variables in order to improve the meta-model accuracy.
  • the Gaussian deviations may be represented by Gaussian functions with a number of dimensions equal to the number of variables in the meta- model.
  • the deviations are not required to be Gaussian functions and may be represented by another mathematical function.
  • the amplitude of each deviation may be equal to or related to the difference between the output of the polynomial model and the response level of the data sample.
  • a unique meta-model is constructed for each component in the gearbox (i.e. for each gear and bearing) to relate the measured variables with the resulting tooth face load distribution factor, KH P , (for gears, as defined in ISO 6336) and load zone factor (for bearings, as defined in ISO 281 ). Any number of loads, for example forces and/or moments, acting at any point on the gearbox, drive train or generator may be related to these factors by the meta-models. The load zone factors and KH P values may then be used to calculate a corresponding amount of damage caused to each component.
  • the meta-models may alternatively relate the measured variables with component stresses, component lives or percentage damages.
  • Figure 7 shows a method for scheduling maintenance of wind or water turbine or components thereof based on an EOH analysis.
  • step 700 the current EOH of the turbines in the turbine farm is determined, for example as disclosed above in relation to Figure 1 .
  • step 702 a turbine or turbines having the highest EOH on one or more components is identified.
  • step 704 the EOH value or values from step 702 are compared with a preset EOH limit for further forensic investigation. If the EOH value is less than this value, then no action is taken and the turbine continues operation.
  • step 706 further re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-re-
  • step 708 the results of the investigation are assessed: if the investigation indicates that the turbine does not have an operational problem, then no action is taken and the turbine continues operation.
  • FIG. 8 shows a method for gearbox refurbishment based on EOH analysis.
  • step 800 a failed turbine gearbox is provided, and in step 802 a
  • steps 804 and 806 an EOH of a component and a corresponding RUL of the component are respectively determined as disclosed above in relation to Figure 1 .
  • step 808 an evaluation is made as to whether or not the RUL for the component indicates that refurbishment of the component may be worthwhile. If it is not, then the component is discarded.
  • step 810 the component is inspected.
  • step 814 if the inspection indicates that refurbishment of the component is not worthwhile, the component is discarded.
  • step 814 if the inspection indicates that component is suitable for
  • the component is retained to provide a refurbished gearbox.
  • step 816 if the component has been replaced, the EOH for the new component is set to zero.
  • a method for operating a wind or water turbine or component thereof is based on a quantitative measure of vibration in relation to EOH for a wind or water turbine or component thereof.
  • the method may be illustrated by a simple example, in which operating parameter levels are stratified into three levels: low, medium and high.
  • EOH can be similarly stratified into three zones, low, medium and high.
  • CMS data which may be used to monitor wind turbines by identifying wind turbines which exceed a threshold value.
  • Figure 9 shows a graph combining EOH operating life models with vibration data for a number of turbines (T01 to T38) operating in a wind farm. Vibration levels in this context can be based on vibration signature analysis
  • Turbines with moderate EOH and vibration typically require routine monitoring and planned inspections over a longer period.
  • Moderate levels of vibration when EOH values are low may indicate that the wind or water turbine or component thereof should be investigated to see if one or more components are suffering damage and need to be repaired or replaced.
  • moderate levels of vibration at median values of EOH are probably normal, and should be merely monitored routinely.
  • Moderate levels of vibration at high values of EOH require no action.
  • High levels of vibration at high EOH values may be indicative of a need for turbine inspection. Turbines with high EOH and high vibration (circled) can clearly be identified, and these require inspection.
  • Turbine T34 in Figure 9 has a similar vibration level to turbine T05, but turbine T34 has a low EOH life.
  • the former turbine is clearly operating better than other turbines of a similar EOH.
  • Using a system for identifying turbines in need of maintenance based on thresholds alone would consider these two turbines to have the same status.
  • an additionally indicator of a requirement for maintenance may be obtained by collecting data relating to vibration of the wind or water turbine or component thereof on a test rig prior to installation. This can be taken as a subsequent baseline: increases in vibration after installation may be due to damage during transport or poor assembly.
  • Figure 10 illustrates a schematic diagram of an apparatus 46 according to various embodiments of the present invention.
  • the apparatus 46 includes means 48 for performing the steps illustrated in Figures 1 to 9.
  • Means 48 includes a processor 50 and a memory 52.
  • the processor 50 e.g. a microprocessor
  • the processor 50 may also comprise an output interface via which data and/or commands are output by the processor 50 and an input interface via which data and/or commands are input to the processor 50.
  • the memory 52 stores a computer program 54 comprising computer program instructions that control the operation of the apparatus 46 when loaded into the processor 50.
  • the computer program instructions 54 provide the logic and routines that enables the apparatus 46 to perform at least some of steps of the methods illustrated in Figures 1 to 9.
  • the processor 50 by reading the memory 52 is able to load and execute the computer program 54.
  • the computer program may arrive at the apparatus 46 via any suitable delivery mechanism 56.
  • the delivery mechanism 56 may be, for example, a computer- readable storage medium, a computer program product, a memory device, a record medium such as a Blue-ray disk, CD-ROM or DVD, an article of manufacture that tangibly embodies the computer program 54.
  • the delivery mechanism may be a signal configured to reliably transfer the computer program 54.
  • the apparatus 46 may propagate or transmit the computer program 54 as a computer data signal.
  • memory 52 is illustrated as a single component it may be implemented as one or more separate components some or all of which may be integrated/removable and/or may provide permanent/semi-permanent/
  • references to 'computer-readable storage medium', 'computer program product', 'tangibly embodied computer program' etc. or a 'controller', 'computer', 'processor' etc. should be understood to encompass not only computers having different architectures such as single /multi-processor architectures and sequential (Von Neumann)/parallel architectures but also specialized circuits such as field- programmable gate arrays (FPGA), application specific circuits (ASIC), signal processing devices and other devices.
  • References to computer program, instructions, code etc. should be understood to encompass software for a programmable processor or firmware such as, for example, the programmable content of a hardware device whether instructions for a processor, or configuration settings for a fixed-function device, gate array or programmable logic device etc.
  • the steps illustrated in the Figures 1 to 9 may represent steps in a method and/or sections of code in the computer program 54.
  • the illustration of a particular order to the steps does not necessarily imply that there is a required or preferred order for the steps and the order and arrangement of the steps may be varied.

Landscapes

  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • General Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Sustainable Energy (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Development (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Wind Motors (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
  • Testing Resistance To Weather, Investigating Materials By Mechanical Methods (AREA)
  • Control Of Water Turbines (AREA)

Abstract

A method for predicting remaining useful life of a wind or water turbine or component determines in step 116 an EOH for the turbine or component and 5 compares this in step 118 to an EOH limit obtained in step114. This provides a simple approach to estimating remaining useful life, giving the turbine operator an indication of the condition of turbines or farms under management.

Description

Determining remaining useful life of rotating machinery including drive trains, gearboxes, and generators
The present invention relates to methods for determining residual useful life of rotating machinery including drive trains, gearboxes, and generators. The
approaches relate to determination of an Equivalent Operating Hours limit for the machinery and comparing it with an Equivalent Operating Hours value for the machinery. In particular it relates to methods for determining residual useful life of wind and water turbines and components thereof, and using these data to operate and manage turbine installations.
Although the design life of a wind turbine gearbox is typically more than twenty years, failures of wind turbine gearboxes within four to five years are not uncommon. This is because residual useful life (RUL) calculation procedures are based on assumed operating profiles, whereas in operation, the actual profile could be very different.
Monitoring operating parameters related to the operation of a wind or water turbine or component thereof, and determining when these parameters move outside an operating window, may indicate that some kind of maintenance or investigation is needed. Operating parameters that are monitored could include lubrication temperature, lubrication debris, vibration, and power output.
Vibration is commonly measured by Condition Monitoring Systems. Generally speaking, large vibrations compared to a norm is indicative of damage.
Vibration analysis generally relies on a measurement provided by a sensor exceeding a predetermined threshold, which is prone to false alarms if the threshold is set too low. The threshold level is not necessarily constant and may vary with frequency (and hence speed). The presence of shocks and extraneous vibrations means that the threshold level must be set sufficiently high to minimise the risk of false-alarms. Furthermore, the threshold must be sufficiently high to avoid any negative effects caused by 'creep' in sensor performance which may occur over its lifetime. In addition, there is no discrimination between vibrations associated with failure or damage and those which are not indicative of failure or damage. Faults developing during operation, such as an imbalance in a rotor, can create loads on a bearing in excess of that expected resulting in a reduction in its design life. Incipient faults, such as unbalance, can be detected from analysis of vibration signatures. This gives the magnitude of an imbalance, and an excitation force due to imbalance is a function of the magnitude of the imbalance and square of the speed. An excitation force due to faults can thus be calculated from field operational conditions and used to calculate individual component loads. Deviation from the assumed operating profile can be addressed by using a generic wind simulation model to determine load at the turbine shaft, which allows individual component loads based on the field operational conditions to be calculated. Combining these gives the total load at each component, which can be is used to estimate the remaining life of the individual components and the life of the gearbox.
However, shortcomings in wind simulation models mean that the load at the turbine shaft may not be reliably or accurately determined.
Equivalent Operating Hours (EOH), in simple terms, defines damage as being equivalent to the damage caused to a wind or water turbine or components thereof by one hour of operation under rated operating conditions. The EOH is equal to a weighting factor related to the operational condition multiplied by a duration (or alternatively, frequency) of that condition. For any operation in which damage caused is the same as that expected to be caused under rated conditions, the EOH of a component after 1 h will be 1 h, and the weighting factor will be 1 .0. If an operational event causes greater damage, then the EOH will be reduced accordingly. Thus, an operational event of duration of 0.2h of duration and having a weighting factor of 0.7, then the EOH after 1 h will be 0.8 x 1 + 0.2 x 0.7 = 0.94.
According to a first aspect, the present invention provides a method for predicting remaining useful life of a wind or water turbine or component thereof, the method comprising the steps of: obtaining an EOH limit value for the wind or water turbine or component thereof; determining an EOH for the wind or water turbine or component thereof; and comparing the EOH and the EOH limit.
Preferably, the step of determining an EOH comprises the steps of: providing data relating to one or more operating conditions; and providing one or more EOH coefficients relating to the one or more operating conditions. Preferably, the step of providing one or more EOH coefficients, comprises the steps of: assessing damage to the wind or water turbine or a component thereof under rated operating conditions and under a plurality of field operating conditions; calculating the weighting factor from the damage under rated conditions and the damage caused under the plurality of field operating conditions; wherein the step of assessing damage comprises the step of providing information on the wind or water turbine or a component thereof.
Preferably, the step of providing information includes providing one or more models selected from the group consisting of: a bearing skidding model; a dynamic model ; a life model; a nominal model of the gearbox, drive-train and/or generator; a model unique to the specific gearbox, drive-train and/or generator including
information on one or more manufacturing variations of one or more components of the gearbox, drive-train and/or generator; a fully coupled finite element model comprising nodes with six degrees of freedom unique to the gearbox, drive-train and/or generator; and one or more meta-models, wherein the one or more meta- models are specific for each of the one or more components.
Preferably, the weighting factor is a function of the damage under rated operating conditions and damage under field operating conditions. Preferably, the weighting factor is a function of a ratio of damage under rated operating conditions to damage under field operating conditions. Preferably, the weighting factor is a ratio of damage under rated operating conditions to damage under field operating conditions.
Preferably, the step of determining an EOH comprises calculating a value of a function of the data relating to the one or more operating conditions and the one or more EOH coefficients relating to the one or more operating conditions. Preferably, the step of determining an EOH comprises calculating a sum of a product of the data relating to the one or more operating conditions and the one or more EOH
coefficients relating to the one or more operating conditions.
Preferably, the step of providing data comprises providing historical data.
Preferably, the step of providing data comprises providing data relating to one or more steady state operating conditions. Preferably, the step of providing data comprises providing data relating to one or more transient state operating conditions. Preferably, the step of providing data comprises collecting data from one or more sensors monitoring the one or more operating conditions. Preferably, the step of providing data comprises providing data from a condition monitoring system.
Preferably, the step of providing one or more EOH coefficients comprises providing EOH coefficients relating to one or more steady state operating conditions. Preferably, the step of providing one or more EOH coefficients comprises providing EOH coefficients relating to one or more transient state operating conditions.
Preferably, the EOH being greater than the EOH limit, additionally comprising the step of: maintaining the wind or water turbine or component thereof.
Preferably, the wind or water turbine or a component thereof has failed, and in which the EOH being less than the EOH limit, additionally comprising the step of: maintaining the wind or water turbine or component thereof.
Preferably, the step of maintaining the wind or water turbine or component thereof comprises investigating for damage to the wind or water turbine or component thereof.
Preferably, the step of investigating for damage is selected from the group consisting of: using an endoscope, performing vibration analysis and performing lubrication analysis.
Preferably, the wind or water turbine or component thereof having damage, scheduling maintenance of the wind or water turbine or component thereof.
Preferably, the wind or water turbine or component thereof having damage, refurbishing the wind or water turbine or component thereof.
Preferably, the wind or water turbine or component thereof having irreparable damage, replacing the wind or water turbine or component thereof.
Preferably, the method comprises the additional step of: setting EOH of the wind or water turbine or component to zero.
Additionally disclosed is a method for identifying a wind turbine or component thereof for maintenance, the method comprising the steps of: determining an EOH value for the wind turbine or component thereof; analysing operating data for the wind turbine or component thereof; and comparing the operating data with a threshold related to the EOH value. Preferably, the step of determining an EOH comprises the steps of: providing data relating to one or more operating conditions; and providing one or more EOH coefficients relating to the one or more operating conditions.
Preferably, the step of providing one or more EOH coefficients, comprises the steps of: assessing damage to the wind or water turbine or a component thereof under rated operating conditions and under a plurality of field operating conditions; calculating the weighting factor from the damage under rated conditions and the damage caused under the plurality of field operating conditions; wherein the step of assessing damage comprises the step of providing information on the wind or water turbine or a component thereof.
Preferably, the step of providing information includes providing one or more models selected from the group consisting of: a bearing skidding model; a dynamic model ; a life model; a nominal model of the gearbox, drive-train and/or generator; a model unique to the specific gearbox, drive-train and/or generator including information on one or more manufacturing variations of one or more components of the gearbox, drive-train and/or generator; a fully coupled finite element model comprising nodes with six degrees of freedom unique to the gearbox, drive-train and/or generator; and one or more meta-models, wherein the one or more meta- models are specific for each of the one or more components.
Preferably, the weighting factor is a function of the damage under rated operating conditions and damage under field operating conditions. Preferably, the weighting factor is a function of a ratio of damage under rated operating conditions to damage under field operating conditions. Preferably, the weighting factor is a ratio of damage under rated operating conditions to damage under field operating conditions.
Preferably, the step of determining an EOH comprises calculating a value of a function of the data relating to the one or more operating conditions and the one or more EOH coefficients relating to the one or more operating conditions.
Preferably, the step of determining an EOH comprises calculating a sum of a product of the data relating to the one or more operating conditions and the one or more EOH coefficients relating to the one or more operating conditions. Preferably, the method additionally comprises the step of setting thresholds for operating data according to one or more ranges of EOH values.
Preferably, the operating data is vibration data.
Preferably, identifying a wind turbine or component thereof for maintenance comprises identifying a wind turbine or component thereof in which the operating data is greater than the threshold.
Also disclosed is a method for calculating a EOH weighting factor for a wind or water turbine or a component thereof, the method comprising the steps of: assessing damage to the wind or water turbine or a component thereof under rated operating conditions and under a plurality of field operating conditions; calculating the weighting factor from the damage under rated conditions and the damage caused under the plurality of field operating conditions; wherein the step of assessing damage comprises the step of providing information on the wind or water turbine or a component thereof.
Preferably, the step of providing information includes providing one or more models selected from the group consisting of: a nominal model of the gearbox, drive- train and/or generator; a model unique to the specific gearbox, drive-train and/or generator including information on one or more manufacturing variations of one or more components of the gearbox, drive-train and/or generator; a bearing skidding model ; a dynamic model; a life model; a fully coupled finite element model comprising nodes with six degrees of freedom unique to the gearbox, drive-train and/or generator; and one or more meta-models, wherein the one or more meta- models are specific for each of the one or more components.
Preferably, the weighting factor is a function of the damage under rated operating conditions and damage under field operating conditions. Preferably, the weighting factor is a function of a ratio of damage under rated operating conditions to damage under field operating conditions. Preferably, the weighting factor is a ratio of damage under rated operating conditions to damage under field operating conditions.
Also disclosed is a computer readable product comprising code means designed for implementing the steps of the method according to any of the methods disclosed above. Also disclosed is a computer system comprising means designed for
implementing the steps of the method according to any of the methods disclosed above.
The present invention will now be described, by way of example only, with reference to the accompanying drawing, in which:
Figure 1 shows a flow chart for predicting remaining useful life of a wind or water turbine or components thereof;
Figure 2 shows a flow chart for the determination of damage to a wind or water turbine or component thereof;
Figure 3 shows the steps in a method for calculating damage to a wind or water turbine or a component thereof using a model-based approach;
Figures 4, 5 and 6 show stages in the construction of a meta model.
Figure 7 shows a flow chart for scheduling maintenance of a wind or water turbine or components thereof based on an EOH analysis;
Figure 8 shows a flow chart for gearbox refurbishment based on EOH analysis; and;
Figure 9 shows a graph combining EOH operating life models with vibration data for a number of turbines operating in a wind farm; and
Figure 10 illustrates a schematic diagram of an apparatus according to various embodiments of the invention.
Methods for determining damage to a rotating machine, such as a gearbox, drive train, generator, wind turbine or a water turbine, or individual components of these rotating machines, is illustrated in Figure 1 .
In a first step 100 turbine load data (which may be simulated or measured) is collected and operating conditions, such as temperature of various bearings, oil conditions, and the like are sensed and logged. Operating condition data can be chosen to represent a typical range of conditions, or they can be obtained from historical logged data such as SCADA or a condition monitoring system. This data can be used in step 102 in a damage-determining model or meta model to determine EOH coefficients 108 relating to steady-state operating conditions.
Corresponding EOH coefficients 106 for non-steady state (transient) conditions can be determined in step 104 using a dynamic model of wind turbine and
components, and/or a bearing skidding model, model of the lubrication system or the like.
In step 1 16, current or historical operating data 1 10 are provided and EOH is derived from this data and the EOH coefficients determined in steps 106 and 108 according to the relationship:
EOH = f (operating condition, EOH coefficient)
In steps 1 12 and 1 14, EOH limit is determined from field data (failure records and the like). EOH limit is simply the expected life of the wind or water turbine or component thereof.
In step 1 18, a comparison of EOH limit and EOH yields a value for the remaining useful life (RUL) of the component.
In the present invention, EOH weighting factors or coefficients are obtained from assessing damage to the wind or water turbine or a component thereof under field operating conditions and damage to the wind or water turbine or a component thereof under rated operating conditions of the same duration.
The wind or water turbine or a component thereof includes any component of the wind or water turbine and includes, for example, the turbine, turbine shaft, gearbox drive train, and generator, as well as any subcomponent, such as a gear, a drive shaft, and the like.
The EOH weighting factor or coefficient is a function of the damage under rated operating conditions and damage under field operating conditions of the same duration. It can be a function of a ratio of damage under rated operating conditions to damage under field operating conditions of the same duration.
Figure 2 shows a flow chart for the determination of damage 202 to a wind or water turbine or component thereof. Load data 204,206, for example forces and / or moments, acting on the wind or water turbine or component thereof is provided.
Load data 204 relates to operation under rated operating conditions (CR), which can be the conditions for which the wind or water turbine or a component thereof was designed. Load data 204 can be obtained or derived from design data 208.
Load data 206 relates to loading under field operating conditions (Co).
Field operating conditions can be historical sensor data 21 0 or SCADA data obtained from or derived from a CMS.
Field operating conditions can be real time sensor data 21 2 from actual operating conditions under which the wind or water turbine or a component thereof is being operated. This means that EOH weighting factors or coefficients can be calculated in real time. These EOH weighting factors or coefficients can be stored and used again when similar field operating conditions are experienced, leading to a reduction in computing capability required over time. Field operating conditions can be a library of anticipated conditions 21 4 which is a range of operating conditions under which the wind or water turbine or a component thereof may be expected to operate. Alternatively or additionally, a library of anticipated conditions 214 can be populated by historical sensor data 21 0 or real time sensor data 21 2. This means that EOH weighting factors or coefficients can be calculated in advance of operation, reducing the amount of computing capability required during operation.
Design data 208, historical data 21 0, real time data 21 2 and library data 21 4 can comprise continuous ranges of data, or the data can be stratified into bins to simplify calculations.
The operating conditions can be steady state operating conditions or transient operating conditions.
Damage 202 under rated and field operating conditions is determined from information relating to the wind or water turbine or a component thereof. The information can be provided by inspection 21 6, or by using a model 21 8 of the wind or water turbine or component thereof. The EOH weighting factor or damage coefficient 220 is a function of the damage under rated operating conditions and damage under field operating conditions of the same duration. It can be a function of a ratio of damage under rated operating conditions to damage under field operating conditions of the same duration. It can be a ratio of damage under rated operating conditions to damage under field operating conditions of the same duration.
Where design data 208, historical data 210, real time data 212 or library data 214 does not contain measured or specified load information, data 204, 206 can be derived from other specified or measured parameters present in the data. The derivation can be a simple manipulation of the data available, or it can be obtained using model 218 of the wind or water turbine or component thereof (not shown).
Various models may be used. For example, a unique model may be generated for one or more of each of the components of the wind or water turbine that leaves a production line. Each unique model is generated using the dimensions and
clearances inferred from an end of line test and may remain related to the
corresponding component throughout its operational life. The unique model can be used to calculate the loads, for example forces and/or moments, that may act on a component at any location or particular locations in or on the component according to the operating conditions. This in turn permits the calculation of the damage sustained by each component under rated or field operating conditions.
Figure 3 shows the steps in a method for calculating damage to a wind or water turbine or a component thereof using a model-based approach. The component may be, for example a gearbox, as indicated.
In a first step 36, information on a gearbox is provided. This may include a fully coupled model with six degrees of freedom. The model may also be unique to the gearbox. The information may include information relating to one or more
manufacturing variations in the dimensions and clearances of components of a gearbox.
In a second step 38, loads, for example forces and/or moments, acting on the gearbox during field operating conditions can be monitored during operation or provided from historical data (eg SCADA). Alternatively the loads can be calculated from anticipated field operating conditions. Similarly the loads can be calculated from rated operating conditions. Where loads acting on the gearbox are continuously monitored during operation, these measurements may be taken at a regular sampling frequency of e.g. 50 Hz. In various embodiments of the invention, step 38 may include monitoring one or more loads over time. Monitoring one or more loads may include monitoring outputs of one or more condition monitoring sensors placed in or on the gearbox at predetermined locations.
In third step 40, the damage caused to each component by the one or more loads, in each sample of data, however determined is calculated. To do this, the fully coupled system model described above is used to calculate the system deflections and component loads. The contact between gear teeth is modelled using finite elements taking into account the tooth bending stiffness and gear mesh contact stiffness. These stiffnesses can be calculated or based on empirical data and are taken into account in the static deflection analysis of the full model. The tooth face load distribution, tooth contact stress or bending stress may be calculated for each gear mesh. These values may then be compared with empirical data or empirical methods used to calculate the operating contact stress, e.g. according to methods given in ISO 6336-2. The tooth bending stress may be calculated using finite element models or may be calculated using empirical methods, e.g. methods in ISO 6336-3. S-N curves for gear contact failure and gear bending failure may be employed and may be based on mathematical simulations or may be based on empirical data, e.g. data provided in ISO 6336. A prediction of the cumulative damage on each component is continuously updated, thus allowing the remaining life of each component to be predicted using empirical data e.g. S-N curves and bearing life data available from ISO standards.
The calculation of bearing damage can be performed using the
RomaxDesigner software. This calculation takes into account factors such as bearing internal geometry, stiffness and deformation of bearing components, contact between bearing components and considers the bearing loads and stiffness.
It is possible that the provided gearbox information cannot be analysed at as high a frequency as the data is sampled. For example, the model analysis required to predict the damage due to each sample of data may take 1 second, but the data may be sampled at 50 Hz. In this case an approximation (a meta-model) can be employed so that the damages are predicted more quickly. The meta-model is constructed in three stages:
1 ) a number of data samples are obtained from a gearbox model prior to the start of gearbox operation;
2) an underlying trend is determined using response surface methodology (RSM);
3) Gaussian deviations from this trend are introduced using a Gaussian kernel centred on each sample point.
The meta-model may be constructed using only steps 1 ) and 2) above.
Figures 4 to 6 show the three stages listed above applied to a two-variable problem. Figure 4 shows the plotted raw data points. Figure 5 shows the
approximation function constructed from a second order polynomial. Figure 5 shows the approximation function including Gaussian kernels.
The variables in the meta-model can be one or more of the following loads which may be defined anywhere in the gearbox model, drivetrain or generator: force in the x-direction (Fx); force in the y-direction (Fy); force in the z-direction (Fz);
moment about the x-axis (Mx); moment about the y-axis (My), moment about the z- axis (Mz). Alternatively, the variables may include displacements in any of the x, y and z directions or rotations about any of the x, y, and z axes or temperature.
The meta-model is constructed from data samples each of which corresponds to a different combination of any of the variables listed above. The accuracy of the meta model can depend on the method used to determine the variables used to generate each data sample. A sampling regime in which the sample points are randomly determined is possible but is not ideal because it can result in some data samples having similar variables which may result in the meta-model being inaccurate. Spacing the data samples uniformly in the design space represented by the meta-model variables is preferred.
Uniform sampling of data in the meta-model variables design space is achieved by optimising the sampling strategy using a genetic algorithm. One method is to maximise the minimum distance between any two neighbouring sample points. Many other suitable sampling strategies exist in literature including minimising the maximum distance between any two neighbouring sample points; L2 optimality; latin hypercube sampling.
The process of identifying the underlying trend using Response Surface
Methodology (RSM) consists of fitting a polynomial to the sample data using linear regression. The polynomial can be of any order and may include some or all of the possible terms. The number of variables in the polynomial is equal to the number of variables in the meta-model. A transformation can be applied to the sampled data before fitting the polynomial in order to decrease the 'model bias' which can arise due to the assumption that the data follows a polynomial trend. For example, if the behaviour of the response is observed to follow a trend similar to an exponential, then a polynomial can be fitted to the natural log of the variables in order to improve the meta-model accuracy.
The Gaussian deviations (step 3 above) may be represented by Gaussian functions with a number of dimensions equal to the number of variables in the meta- model. The deviations are not required to be Gaussian functions and may be represented by another mathematical function. The amplitude of each deviation may be equal to or related to the difference between the output of the polynomial model and the response level of the data sample.
A unique meta-model is constructed for each component in the gearbox (i.e. for each gear and bearing) to relate the measured variables with the resulting tooth face load distribution factor, KHP, (for gears, as defined in ISO 6336) and load zone factor (for bearings, as defined in ISO 281 ). Any number of loads, for example forces and/or moments, acting at any point on the gearbox, drive train or generator may be related to these factors by the meta-models. The load zone factors and KHP values may then be used to calculate a corresponding amount of damage caused to each component. The meta-models may alternatively relate the measured variables with component stresses, component lives or percentage damages.
Figure 7 shows a method for scheduling maintenance of wind or water turbine or components thereof based on an EOH analysis.
In step 700, the current EOH of the turbines in the turbine farm is determined, for example as disclosed above in relation to Figure 1 . In step 702, a turbine or turbines having the highest EOH on one or more components is identified.
In step 704, the EOH value or values from step 702 are compared with a preset EOH limit for further forensic investigation. If the EOH value is less than this value, then no action is taken and the turbine continues operation.
If the EOH value is higher than this value, then in step 706 further
investigations of the turbine are undertaken, for example endoscope inspection, vibration analysis, oil analysis and the like.
In step 708, the results of the investigation are assessed: if the investigation indicates that the turbine does not have an operational problem, then no action is taken and the turbine continues operation.
If the investigation indicates that the turbine does have an operational problem, then maintenance is scheduled and the turbine may be concomitantly down-rated.
Figure 8 shows a method for gearbox refurbishment based on EOH analysis. In step 800, a failed turbine gearbox is provided, and in step 802 a
corresponding gearbox and/or gearbox component history is provided.
In steps 804 and 806, an EOH of a component and a corresponding RUL of the component are respectively determined as disclosed above in relation to Figure 1 .
In step 808, an evaluation is made as to whether or not the RUL for the component indicates that refurbishment of the component may be worthwhile. If it is not, then the component is discarded.
If it is, then in step 810, the component is inspected.
In step 814, if the inspection indicates that refurbishment of the component is not worthwhile, the component is discarded.
In step 814, if the inspection indicates that component is suitable for
refurbishment, the component is retained to provide a refurbished gearbox.
In step 816, if the component has been replaced, the EOH for the new component is set to zero. According to a further aspect of the invention a method for operating a wind or water turbine or component thereof is based on a quantitative measure of vibration in relation to EOH for a wind or water turbine or component thereof.
The method may be illustrated by a simple example, in which operating parameter levels are stratified into three levels: low, medium and high.
As mentioned above, the danger or damage from increased vibration is dependent to a certain extent to the age of the wind or water turbine or component thereof, in other words, to EOH. EOH can be similarly stratified into three zones, low, medium and high.
This simple approach enables the wind or water turbine operator to prioritise maintenance activities based on EOH and CMS data, as for example in Table 1 .
Table 1. Action needed according to a value for EOH and a level of an operating parameter
Figure imgf000017_0001
The same approach may be adopted for other CMS data which may be used to monitor wind turbines by identifying wind turbines which exceed a threshold value.
Figure 9 shows a graph combining EOH operating life models with vibration data for a number of turbines (T01 to T38) operating in a wind farm. Vibration levels in this context can be based on vibration signature analysis
Turbines with moderate EOH and vibration typically require routine monitoring and planned inspections over a longer period.
Moderate levels of vibration when EOH values are low, for example turbine T02 in Figure 9, may indicate that the wind or water turbine or component thereof should be investigated to see if one or more components are suffering damage and need to be repaired or replaced. However, moderate levels of vibration at median values of EOH are probably normal, and should be merely monitored routinely. Moderate levels of vibration at high values of EOH require no action.
High levels of vibration at high EOH values may be indicative of a need for turbine inspection. Turbines with high EOH and high vibration (circled) can clearly be identified, and these require inspection.
Turbine T34 in Figure 9 has a similar vibration level to turbine T05, but turbine T34 has a low EOH life. The former turbine is clearly operating better than other turbines of a similar EOH. Using a system for identifying turbines in need of maintenance based on thresholds alone would consider these two turbines to have the same status.
In addition to the approaches above, an additionally indicator of a requirement for maintenance may be obtained by collecting data relating to vibration of the wind or water turbine or component thereof on a test rig prior to installation. This can be taken as a subsequent baseline: increases in vibration after installation may be due to damage during transport or poor assembly.
Figure 10 illustrates a schematic diagram of an apparatus 46 according to various embodiments of the present invention. The apparatus 46 includes means 48 for performing the steps illustrated in Figures 1 to 9. Means 48 includes a processor 50 and a memory 52. The processor 50 (e.g. a microprocessor) is configured to read from and write to the memory 52. The processor 50 may also comprise an output interface via which data and/or commands are output by the processor 50 and an input interface via which data and/or commands are input to the processor 50.
The memory 52 stores a computer program 54 comprising computer program instructions that control the operation of the apparatus 46 when loaded into the processor 50. The computer program instructions 54 provide the logic and routines that enables the apparatus 46 to perform at least some of steps of the methods illustrated in Figures 1 to 9. The processor 50 by reading the memory 52 is able to load and execute the computer program 54.
The computer program may arrive at the apparatus 46 via any suitable delivery mechanism 56. The delivery mechanism 56 may be, for example, a computer- readable storage medium, a computer program product, a memory device, a record medium such as a Blue-ray disk, CD-ROM or DVD, an article of manufacture that tangibly embodies the computer program 54. The delivery mechanism may be a signal configured to reliably transfer the computer program 54. The apparatus 46 may propagate or transmit the computer program 54 as a computer data signal.
Although the memory 52 is illustrated as a single component it may be implemented as one or more separate components some or all of which may be integrated/removable and/or may provide permanent/semi-permanent/
dynamic/cached storage.
References to 'computer-readable storage medium', 'computer program product', 'tangibly embodied computer program' etc. or a 'controller', 'computer', 'processor' etc. should be understood to encompass not only computers having different architectures such as single /multi-processor architectures and sequential (Von Neumann)/parallel architectures but also specialized circuits such as field- programmable gate arrays (FPGA), application specific circuits (ASIC), signal processing devices and other devices. References to computer program, instructions, code etc. should be understood to encompass software for a programmable processor or firmware such as, for example, the programmable content of a hardware device whether instructions for a processor, or configuration settings for a fixed-function device, gate array or programmable logic device etc.
The steps illustrated in the Figures 1 to 9 may represent steps in a method and/or sections of code in the computer program 54. The illustration of a particular order to the steps does not necessarily imply that there is a required or preferred order for the steps and the order and arrangement of the steps may be varied.
Furthermore, it may be possible for some steps to be omitted.

Claims

Claims
1 . A method for predicting remaining useful life of a wind or water turbine or
component thereof, the method comprising the steps of:
obtaining an EOH limit value for the wind or water turbine or component thereof;
determining an EOH for the wind or water turbine or component thereof; and comparing the EOH and the EOH limit.
A method according to claim 1 , in which the step of determining an EOH comprises the steps of:
providing data relating to one or more operating conditions; and
providing one or more EOH coefficients relating to the one or more operating conditions.
3. A method according to claim 2, in which the step of providing one or more EOH coefficients, comprises the steps of:
assessing damage to the wind or water turbine or a component thereof under rated operating conditions and under a plurality of field operating conditions; calculating the weighting factor from the damage under rated conditions and the damage caused under the plurality of field operating conditions;
wherein the step of assessing damage comprises the step of providing information on the wind or water turbine or a component thereof.
4. A method according to claim 3 in which the step of providing information
includes providing one or more models selected from the group consisting of: a bearing skidding model;
a dynamic model;
a life model ;
a nominal model of the gearbox, drive-train and/or generator;
a model unique to the specific gearbox, drive-train and/or generator including information on one or more manufacturing variations of one or more
components of the gearbox, drive-train and/or generator; a fully coupled finite element model comprising nodes with six degrees of freedom unique to the gearbox, drive-train and/or generator; and
one or more meta-models, wherein the one or more meta-models are specific for each of the one or more components.
A method according to claim 3 or claim 4 in which the weighting factor is a function of the damage under rated operating conditions and damage under field operating conditions.
A method according to claim 5 in which the weighting factor is a function of a ratio of damage under rated operating conditions to damage under field operating conditions.
A method according to claim 5 in which the weighting factor is a ratio of damage under rated operating conditions to damage under field operating conditions.
8. A method according to any of claims 2 to 7, in which the step of determining an EOH comprises calculating a value of a function of the data relating to the one or more operating conditions and the one or more EOH coefficients relating to the one or more operating conditions.
9. A method according to any of claims 2 to 7, in which the step of determining an EOH comprises calculating a sum of a product of the data relating to the one or more operating conditions and the one or more EOH coefficients relating to the one or more operating conditions.
10. A method according to any of claims 2 to 9, in which the step of providing data comprises providing historical data.
1 1 . A method according to any of claims 2 to 10, in which the step of providing data comprises providing data relating to one or more steady state operating conditions.
12. A method according to any of claims 2 to 10, in which the step of providing data comprises providing data relating to one or more transient state operating conditions.
13. A method according to any of claims 2 to 12, in which the step of providing data comprises collecting data from one or more sensors monitoring the one or more operating conditions.
14. A method according to any of claims 2 to 12, in which the step of providing data comprises providing data from a condition monitoring system.
15. A method according to any of claims 2 to 14, in which the step of providing one or more EOH coefficients comprises providing EOH coefficients relating to one or more steady state operating conditions.
16. A method according to any of claims 2 to 14, in which the step of providing one or more EOH coefficients comprises providing EOH coefficients relating to one or more transient state operating conditions.
17. A method according to any preceding claim, in which the EOH being greater than the EOH limit, additionally comprising the step of: maintaining the wind or water turbine or component thereof.
18. A method according to any of claims 1 to 16, in which the wind or water turbine or a component thereof has failed, and in which the EOH being less than the EOH limit, additionally comprising the step of: maintaining the wind or water turbine or component thereof.
19. A method according to claim 17 or claim 18, in which the step of maintaining the wind or water turbine or component thereof comprises investigating for damage to the wind or water turbine or component thereof.
20. A method according to claim 17 or claim 18, in which the step of investigating for damage is selected from the group consisting of: using an endoscope, performing vibration analysis and performing lubrication analysis.
21 . A method according to claim 19 or claim 20, in which the wind or water turbine or component thereof having damage, scheduling maintenance of the wind or water turbine or component thereof.
22. A method according to claim 19 or claim 20, in which the wind or water turbine or component thereof having damage, refurbishing the wind or water turbine or component thereof.
23. A method according to claim 19 or claim 20, in which the wind or water turbine or component thereof having irreparable damage, replacing the wind or water turbine or component thereof.
24. A method according to claim 22 or claim 23, comprising the additional step of: setting EOH of the wind or water turbine or component to zero.
25. A method for identifying a wind turbine or component thereof for maintenance, the method comprising the steps of:
determining an EOH value for the wind turbine or component thereof;
analysing operating data for the wind turbine or component thereof; and comparing the operating data with a threshold related to the EOH value.
26. A method according to claim 25, in which the step of determining an EOH
comprises the steps of:
providing data relating to one or more operating conditions; and
providing one or more EOH coefficients relating to the one or more operating conditions.
27. A method according to claim 26, in which the step of providing one or more EOH coefficients, comprises the steps of: assessing damage to the wind or water turbine or a component thereof under rated operating conditions and under a plurality of field operating conditions; calculating the weighting factor from the damage under rated conditions and the damage caused under the plurality of field operating conditions;
wherein the step of assessing damage comprises the step of providing information on the wind or water turbine or a component thereof.
28. A method according to claim 27 in which the step of providing information
includes providing one or more models selected from the group consisting of: a bearing skidding model;
a dynamic model;
a life model ;
a nominal model of the gearbox, drive-train and/or generator;
a model unique to the specific gearbox, drive-train and/or generator including information on one or more manufacturing variations of one or more
components of the gearbox, drive-train and/or generator;
a fully coupled finite element model comprising nodes with six degrees of freedom unique to the gearbox, drive-train and/or generator; and
one or more meta-models, wherein the one or more meta-models are specific for each of the one or more components.
29. A method according to claim 27 or claim 28 in which the weighting factor is a function of the damage under rated operating conditions and damage under field operating conditions.
30. A method according to claim 27 or claim 28 in which the weighting factor is a function of a ratio of damage under rated operating conditions to damage under field operating conditions.
31 . A method according to according to claim 27 or claim 28, in which the
weighting factor is a ratio of damage under rated operating conditions to damage under field operating conditions.
32. A method according to any of claims 26 to 31 , in which the step of determining an EOH comprises calculating a value of a function of the data relating to the one or more operating conditions and the one or more EOH coefficients relating to the one or more operating conditions.
33. A method according to any of claims 26 to 31 , in which the step of determining an EOH comprises calculating a sum of a product of the data relating to the one or more operating conditions and the one or more EOH coefficients relating to the one or more operating conditions.
34. A method according to any of claims 25 to 33, additionally comprising the step of setting thresholds for operating data according to one or more ranges of EOH values.
35. A method according to any of claims 25 to 34, in which the operating data is vibration data.
36. A method according to any of claims 25 to 35, in which identifying a wind
turbine or component thereof for maintenance comprises identifying a wind turbine or component thereof in which the operating data is greater than the threshold.
37. A method for calculating a EOH weighting factor for a wind or water turbine or a component thereof, the method comprising the steps of:
assessing damage to the wind or water turbine or a component thereof under rated operating conditions and under a plurality of field operating conditions; calculating the weighting factor from the damage under rated conditions and the damage caused under the plurality of field operating conditions;
wherein the step of assessing damage comprises the step of providing information on the wind or water turbine or a component thereof.
38. A method according to claim 37 in which the step of providing information
includes providing one or more models selected from the group consisting of: a nominal model of the gearbox, drive-train and/or generator; a model unique to the specific gearbox, drive-train and/or generator including information on one or more manufacturing variations of one or more components of the gearbox, drive-train and/or generator;
a bearing skidding model;
a dynamic model;
a life model ;
a fully coupled finite element model comprising nodes with six degrees of freedom unique to the gearbox, drive-train and/or generator; and
one or more meta-models, wherein the one or more meta-models are specific for each of the one or more components.
39. A method according to claim 37 or claim 38 in which the weighting factor is a function of the damage under rated operating conditions and damage under field operating conditions.
40. A method according to claim 39 in which the weighting factor is a function of a ratio of damage under rated operating conditions to damage under field operating conditions.
41 . A method according to claim 39 in which the weighting factor is a ratio of damage under rated operating conditions to damage under field operating conditions.
42 A computer readable product comprising code means designed for
implementing the steps of the method according to any of claims 1 to 41 .
43. A computer system comprising means designed for implementing the steps of the method according to any of claims 1 to 41 .
PCT/GB2012/051153 2011-05-20 2012-05-21 Determining remaining useful life of rotating machinery including drive trains, gearboxes, and generators WO2012160370A2 (en)

Priority Applications (9)

Application Number Priority Date Filing Date Title
JP2014510891A JP6397330B2 (en) 2011-05-20 2012-05-21 Measure the remaining service life of rotating machinery such as drivetrains, gearboxes, and generators
KR1020137030809A KR20140128860A (en) 2011-05-20 2012-05-21 Determining remaining useful life of rotating machinery including drive trains, gearboxes, and generators
CN201280024323.3A CN103547977B (en) 2011-05-20 2012-05-21 The determination of the remaining life of rotating machinery including transmission system, gear-box and generator
US14/119,146 US10466138B2 (en) 2011-05-20 2012-05-21 Determining remaining useful life of rotating machinery including drive trains, gearboxes, and generators
ES12733515T ES2911282T3 (en) 2011-05-20 2012-05-21 Determination of remaining useful life of rotating machinery, including drive trains, gearboxes, and generators
IN2382MUN2013 IN2013MN02382A (en) 2011-05-20 2012-05-21
DK12733515.6T DK2710437T3 (en) 2011-05-20 2012-05-21 DETERMINATION OF REMAINING FURNISHABLE LIFE OF ROTATION MACHINES INCLUDING POWER TRANSMISSION SYSTEMS, GEARBOXES AND GENERATORS
EP12733515.6A EP2710437B1 (en) 2011-05-20 2012-05-21 Determining remaining useful life of rotating machinery including drive trains, gearboxes, and generators
US15/676,147 US10527520B2 (en) 2011-05-20 2017-08-14 Operating wind motors and determining their remaining useful life

Applications Claiming Priority (6)

Application Number Priority Date Filing Date Title
GB1108476.1 2011-05-20
GBGB1108476.1A GB201108476D0 (en) 2011-05-20 2011-05-20 Predicting remaining useful life
GBGB1110003.9A GB201110003D0 (en) 2011-06-14 2011-06-14 Identifying wind turbines for maintenance
GB1110003.9 2011-06-14
GB1110360.3 2011-06-20
GBGB1110360.3A GB201110360D0 (en) 2011-05-20 2011-06-20 Wind or water turbine residual life

Related Child Applications (2)

Application Number Title Priority Date Filing Date
US14/119,146 A-371-Of-International US10466138B2 (en) 2011-05-20 2012-05-21 Determining remaining useful life of rotating machinery including drive trains, gearboxes, and generators
US15/676,147 Continuation US10527520B2 (en) 2011-05-20 2017-08-14 Operating wind motors and determining their remaining useful life

Publications (2)

Publication Number Publication Date
WO2012160370A2 true WO2012160370A2 (en) 2012-11-29
WO2012160370A3 WO2012160370A3 (en) 2013-03-07

Family

ID=46546447

Family Applications (2)

Application Number Title Priority Date Filing Date
PCT/GB2012/051153 WO2012160370A2 (en) 2011-05-20 2012-05-21 Determining remaining useful life of rotating machinery including drive trains, gearboxes, and generators
PCT/GB2012/051154 WO2012160371A2 (en) 2011-05-20 2012-05-21 Determining damage and remaining useful life of rotating machinery including drive trains, gearboxes, and generators

Family Applications After (1)

Application Number Title Priority Date Filing Date
PCT/GB2012/051154 WO2012160371A2 (en) 2011-05-20 2012-05-21 Determining damage and remaining useful life of rotating machinery including drive trains, gearboxes, and generators

Country Status (11)

Country Link
US (3) US10466138B2 (en)
EP (2) EP2710437B1 (en)
JP (3) JP6397330B2 (en)
KR (1) KR20140128860A (en)
CN (3) CN103547976A (en)
DK (2) DK2710438T3 (en)
ES (2) ES2911282T3 (en)
GB (1) GB2491045B (en)
IN (2) IN2013MN02382A (en)
PT (2) PT2710437T (en)
WO (2) WO2012160370A2 (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015011301A1 (en) 2013-07-22 2015-01-29 Nabla Wind Power, S.L. Method for determining the life of components of a wind turbine or similar according to its location
CN105900146A (en) * 2013-01-16 2016-08-24 通用电气公司 Method and system for predicting a life cycle of an engine

Families Citing this family (50)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DK2710438T3 (en) 2011-05-20 2022-04-11 Insight Analytics Solutions Holdings Ltd DETERMINATION OF DAMAGE AND REMAINING FURNISHABLE LIFE OF ROTATION MACHINES INCLUDING POWER TRANSMISSION SYSTEMS, GEARBOXES AND GENERATORS
EP2674581A1 (en) * 2012-06-15 2013-12-18 Siemens Aktiengesellschaft Machine component for a drive train as well as method for designing and/or for starting-up and/or for operating such a drive train
JP6104643B2 (en) * 2013-03-04 2017-03-29 三菱重工業株式会社 Operation plan creation device, operation plan creation method, and operation plan creation program
US20140288855A1 (en) * 2013-03-20 2014-09-25 United Technologies Corporation Temporary Uprating of Wind Turbines to Maximize Power Output
US10458342B2 (en) * 2013-09-17 2019-10-29 General Electric Company System and method for controlling operation of a gas turbine based power plant
WO2015051801A1 (en) * 2013-10-07 2015-04-16 Vestas Wind Systems A/S Methods and apparatus for controlling wind turbines
US20150198492A1 (en) * 2014-01-13 2015-07-16 Simmonds Precision Products, Inc. Remaining useful life forecasting system
CN104018988B (en) * 2014-06-16 2016-07-06 上海交通大学 The wind power generating set monitoring system of physically based deformation component model and real time data
US9558600B2 (en) * 2014-08-01 2017-01-31 Deere & Company Duty cycle recording system and method for estimation of damage and remaining life of drivetrain components
US10067483B1 (en) * 2014-08-28 2018-09-04 Apple Inc. Controlling electrical device based on temperature and voltage
ES2717122T3 (en) 2014-12-23 2019-06-19 Ore Catapult Development Services Ltd Fatigue tests
US10042964B2 (en) * 2015-03-02 2018-08-07 General Electric Company Method of evaluating a part
US9752956B2 (en) * 2015-03-09 2017-09-05 Caterpillar Inc. Monitoring system for predicting gearbox lifespan
US9970325B2 (en) 2015-04-30 2018-05-15 General Electric Company Jacking assembly for rotor
JP6553399B2 (en) * 2015-05-14 2019-07-31 株式会社日立製作所 Calculation system, wind power generation system, or calculation method of remaining life or fatigue damage of windmill
EP3317523B1 (en) 2015-06-30 2020-09-23 Vestas Wind Systems A/S Methods and systems for generating wind turbine control schedules
CN107709762B (en) * 2015-06-30 2019-10-18 维斯塔斯风力系统集团公司 The control method and system of wind turbine
US10746160B2 (en) 2015-06-30 2020-08-18 Vestas Wind Systems A/S Methods and systems for generating wind turbine control schedules
EP3317525B1 (en) 2015-06-30 2021-09-22 Vestas Wind Systems A/S Methods and systems for generating wind turbine control schedules
DK201570559A1 (en) 2015-08-28 2017-03-27 Vestas Wind Sys As Methods and Systems for Generating Wind Turbine Control Schedules
CN107709765B (en) 2015-06-30 2020-08-07 维斯塔斯风力系统集团公司 Method and system for generating a wind turbine control schedule
CN107810323B (en) * 2015-06-30 2020-04-07 维斯塔斯风力系统集团公司 Method and system for generating a wind turbine control arrangement
EP3345063A1 (en) * 2015-09-01 2018-07-11 Walther Flender GmbH Method for the computer-aided forecasting of future operating states of machine components
US9732838B2 (en) * 2015-09-22 2017-08-15 Caterpillar Inc. Gearbox component and lubricant condition monitoring system
JP6792939B2 (en) * 2015-10-28 2020-12-02 三菱パワー株式会社 Turbine analyzers, turbine analysis methods and programs
JP6710039B2 (en) * 2015-10-28 2020-06-17 三菱日立パワーシステムズ株式会社 Planning device, planning method and program
US10330022B2 (en) 2016-02-12 2019-06-25 General Electric Company Systems and methods for determining operational impact on turbine component creep life
JP6803145B2 (en) * 2016-03-16 2020-12-23 株式会社イシダ Equipment management system
EP3309529B1 (en) * 2016-10-11 2022-02-23 ABB Schweiz AG Prediction of remaining useful lifetime for bearings
GB201617584D0 (en) * 2016-10-17 2016-11-30 Romax Technology Limited Determining loads on a wind turbine
US11155288B2 (en) * 2017-04-28 2021-10-26 Transportation Ip Holdings, Llc Vehicle monitoring system
JP6909131B2 (en) * 2017-11-09 2021-07-28 住友重機械工業株式会社 Life prediction system
DE102017223418B4 (en) * 2017-12-20 2023-05-25 Zf Friedrichshafen Ag Model-based method and system for condition monitoring of a plain bearing, especially for wind turbines
CN110069806A (en) * 2018-01-24 2019-07-30 诺迈士科技有限公司 Digital authenticating
JP7188887B2 (en) * 2018-02-14 2022-12-13 ナブテスコ株式会社 Method for managing automatic door devices, method for managing two or more automatic door devices, and automatic door system
US11373455B2 (en) * 2018-04-23 2022-06-28 Woodward, Inc. Predicting electromechanical actuator health and remaining life
AU2019417827A1 (en) * 2018-12-31 2021-08-19 Acciona Energia, S.A. Methods and systems for predicting risk of observable damage in wind turbine gearbox components
PT3947961T (en) * 2019-04-01 2023-07-28 Sentient Science Corp A method for estimating remaining useful life of components of an operational wind turbine
CN109899225A (en) * 2019-04-02 2019-06-18 三峡大学 A kind of the fast terminal sliding mode controller and design method of Adaptive System of Water-Turbine Engine
US11208986B2 (en) 2019-06-27 2021-12-28 Uptake Technologies, Inc. Computer system and method for detecting irregular yaw activity at a wind turbine
CN110598241B (en) * 2019-07-22 2022-12-13 许昌许继风电科技有限公司 Fan hub strength checking method
US11460006B2 (en) 2019-07-31 2022-10-04 General Electric Company Systems and methods for detecting damage in rotary machines
US10975841B2 (en) * 2019-08-02 2021-04-13 Uptake Technologies, Inc. Computer system and method for detecting rotor imbalance at a wind turbine
CN110907155A (en) * 2019-12-02 2020-03-24 吉林松江河水力发电有限责任公司 Fault monitoring method for rotating shaft of water turbine
CN111780910A (en) * 2020-06-28 2020-10-16 缪新建 Wind turbine bolt fastener pressure detection device and abnormality detection method
EP3936957B1 (en) 2020-07-08 2023-08-23 Bull SAS Error-based method for calculating a remaining useful life of an apparatus
CN112818572B (en) * 2021-01-19 2022-04-22 三峡大学 Optimization method for structural parameters of winding area of oil-immersed transformer
CN113221261B (en) * 2021-02-09 2022-12-13 重庆大学 Method for formulating vibration limit value of aviation transmission system
CN114000988B (en) * 2021-11-01 2024-06-14 西安热工研究院有限公司 Fatigue life prediction device and method for bearing seat of wind turbine generator
CN116432361B (en) * 2021-12-27 2024-05-24 北京金风科创风电设备有限公司 Service life assessment method and device of wind generating set

Family Cites Families (37)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH0640173B2 (en) * 1987-02-05 1994-05-25 オリンパス光学工業株式会社 Endoscope
US5210704A (en) * 1990-10-02 1993-05-11 Technology International Incorporated System for prognosis and diagnostics of failure and wearout monitoring and for prediction of life expectancy of helicopter gearboxes and other rotating equipment
US5530647A (en) * 1994-07-25 1996-06-25 Thermo King Corporation Method of dynamically determining oil change intervals for internal combustion engines
JP3487741B2 (en) 1997-09-09 2004-01-19 新キャタピラー三菱株式会社 Work machine abnormality / failure diagnosis and prediction device and method
US6636813B1 (en) 1999-09-27 2003-10-21 Hitchi, Ltd. Service life management system for high-temperature part of gas turbine
AU752024B2 (en) 2000-04-14 2002-09-05 Kabushiki Kaisha Toshiba Method and equipment for assessing the life of members put under high in-service temperature environment for long period
US7065471B2 (en) * 2001-06-18 2006-06-20 Hitachi, Ltd. Method and system for diagnosing state of gas turbine
EP1316866B1 (en) 2001-11-29 2006-05-31 Abb Research Ltd. Optimal operation of a power plant
JP2004101417A (en) 2002-09-11 2004-04-02 Mitsubishi Heavy Ind Ltd Monitoring device
US6920779B2 (en) * 2002-11-15 2005-07-26 International Truck Intellectual Property Company, Llc Method of estimating engine lubricant condition
JP2004301030A (en) 2003-03-31 2004-10-28 Ebara Corp Windmill and its blade
US7103507B2 (en) * 2004-09-28 2006-09-05 Dimitry Gorinevsky Structure health monitoring system and method
US7243042B2 (en) * 2004-11-30 2007-07-10 Siemens Power Generation, Inc. Engine component life monitoring system and method for determining remaining useful component life
JP2006342766A (en) * 2005-06-10 2006-12-21 Mitsubishi Electric Corp Monitoring system of wind power generating facility
US7914250B2 (en) * 2006-12-08 2011-03-29 General Electric Company Method and system for estimating life of a gearbox
EP1965281A1 (en) 2007-03-02 2008-09-03 Abb Research Ltd. Dynamic maintenance plan for an industrial robot
EP2012209A1 (en) 2007-07-02 2009-01-07 Siemens Aktiengesellschaft Method for determining the durability of a power station component
EP2053241A1 (en) * 2007-10-24 2009-04-29 Ecotecnia Energias Renovables S.L. Method for determining fatigue damage in a power train of a wind turbine
AT504028B1 (en) 2007-11-02 2009-03-15 Avl List Gmbh METHOD FOR THE DAMAGE PRESENTATION OF COMPONENTS OF A MOTOR VEHICLE
AT9862U3 (en) 2007-12-19 2009-01-15 Avl List Gmbh METHOD AND DEVICE FOR ASSESSING THE REST LIFE OF A SENSOR UNIT
JP4782766B2 (en) 2007-12-27 2011-09-28 オークマ株式会社 Machine diagnostic method and apparatus
DK2108830T3 (en) * 2008-01-10 2019-11-25 Siemens Gamesa Renewable Energy As Method for determining fatigue load of a wind turbine and for fatigue load control and corresponding wind turbines
WO2009091335A1 (en) * 2008-01-15 2009-07-23 Vestas Wind Systems A/S System for real time supervision of component wear in a wind turbine population
GB0807775D0 (en) * 2008-04-29 2008-06-04 Romax Technology Ltd Methods for model-based diagnosis of gearbox
US8093737B2 (en) * 2008-05-29 2012-01-10 General Electric Company Method for increasing energy capture in a wind turbine
JP5419472B2 (en) 2009-01-09 2014-02-19 Ｎｔｎ株式会社 Wind turbine generator main shaft bearing monitoring device
US7970556B2 (en) * 2009-01-30 2011-06-28 General Electric System and method for monitoring the condition of a gear assembly
ES2577530T3 (en) * 2009-05-25 2016-07-15 Vestas Wind Systems A/S Procedure and system to control the operation of a wind turbine
EP2267305B1 (en) * 2009-06-24 2016-01-13 Vestas Wind Systems A/S A method and a system for controlling operation of a wind turbine
GB0911597D0 (en) 2009-07-06 2009-08-12 Rolls Royce Plc Valve failure detection
EP2302207A1 (en) 2009-09-23 2011-03-30 Siemens Aktiengesellschaft Power generating machine load control based on consumed fatigue life time and real-time of operation of a structural component
WO2011060424A1 (en) 2009-11-16 2011-05-19 Nrg Systems, Inc. Data acquisition system for condition-based maintenance
US8761912B1 (en) 2010-04-23 2014-06-24 Ashford Technical Software, Inc. System for remotely monitoring a tensioner and providing an alarm for anticipated failure and maintenance
US8510060B2 (en) * 2010-06-07 2013-08-13 General Electric Company Life management system and method for gas turbine thermal barrier coatings
WO2011156609A2 (en) 2010-06-09 2011-12-15 Cummins Filtration Ip Inc. System for monitoring and indicating filter life
JP5439357B2 (en) 2010-12-28 2014-03-12 三菱重工業株式会社 Construction time selection device and construction time selection method
DK2710438T3 (en) 2011-05-20 2022-04-11 Insight Analytics Solutions Holdings Ltd DETERMINATION OF DAMAGE AND REMAINING FURNISHABLE LIFE OF ROTATION MACHINES INCLUDING POWER TRANSMISSION SYSTEMS, GEARBOXES AND GENERATORS

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
None

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105900146A (en) * 2013-01-16 2016-08-24 通用电气公司 Method and system for predicting a life cycle of an engine
CN105900146B (en) * 2013-01-16 2019-10-08 通用电气公司 For predicting the method and system in engine life period
WO2015011301A1 (en) 2013-07-22 2015-01-29 Nabla Wind Power, S.L. Method for determining the life of components of a wind turbine or similar according to its location
EP3575985A1 (en) 2013-07-22 2019-12-04 Nabla Wind Power, S.L. Method for determining the life of components of a wind turbine or similar according to its location

Also Published As

Publication number Publication date
US20140088888A1 (en) 2014-03-27
EP2710438B1 (en) 2022-02-09
IN2013MN02383A (en) 2015-06-12
ES2911282T3 (en) 2022-05-18
JP2014517288A (en) 2014-07-17
CN103547977B (en) 2018-09-25
ES2912102T3 (en) 2022-05-24
PT2710438T (en) 2022-04-20
GB2491045B (en) 2013-10-30
JP2014518978A (en) 2014-08-07
GB2491045A (en) 2012-11-21
US20170350789A1 (en) 2017-12-07
DK2710437T3 (en) 2022-04-11
DK2710438T3 (en) 2022-04-11
EP2710437A2 (en) 2014-03-26
US20140088887A1 (en) 2014-03-27
WO2012160370A3 (en) 2013-03-07
JP2018141804A (en) 2018-09-13
IN2013MN02382A (en) 2015-06-12
EP2710438A2 (en) 2014-03-26
US9360393B2 (en) 2016-06-07
EP2710437B1 (en) 2022-01-26
US10466138B2 (en) 2019-11-05
US10527520B2 (en) 2020-01-07
CN113027703A (en) 2021-06-25
PT2710437T (en) 2022-04-14
KR20140128860A (en) 2014-11-06
CN103547976A (en) 2014-01-29
WO2012160371A2 (en) 2012-11-29
WO2012160371A3 (en) 2013-03-07
GB201208958D0 (en) 2012-07-04
JP6397330B2 (en) 2018-09-26
CN103547977A (en) 2014-01-29

Similar Documents

Publication Publication Date Title
US10527520B2 (en) Operating wind motors and determining their remaining useful life
US20110106510A1 (en) Methods, apparatus and computer readable storage mediums for model-based diagnosis
Tinga Application of physical failure models to enable usage and load based maintenance
US9983576B2 (en) Method and system for monitoring rotor blades in combustion turbine engine
AU2008314694B2 (en) Method for determining fatigue damage in a power train of a wind turbine
WO2018044507A1 (en) Machine learning-aided model-based method for estimating gas turbine component life
JP7240527B2 (en) Method for assessing remaining useful life of operational wind turbine components
CN115023598A (en) Method for determining the remaining useful life of a bearing
US20130338938A1 (en) Identifying wind or water turbines for maintenance
WO2019135747A1 (en) Probabilistic life evaluation algorithm for gas turbine engine components
Zhang et al. A multi-fault modeling approach for fault diagnosis and failure prognosis of engineering systems
Roemer et al. Prognosis of rotating machinery components
Abdallah et al. An autonomous real-time decision tree framework for monitoring & diagnostics on wind turbines
Cao et al. Condition-based maintenance at the University of South Carolina: a smart predictive system
Issahak An Integrated Prognostics Condition-Based Maintenance Approach to Reduce Premature Failure of Wind Turbines' Gearbox Components
Abdallah et al. An Integrated Approach for Smart Monitoring, Inspection and Life-Cycle Assessment of Wind Turbines
Roemer¹ et al. Prognosis of Rotating
Fischer Final project report February 2012

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 12733515

Country of ref document: EP

Kind code of ref document: A2

ENP Entry into the national phase

Ref document number: 2014510891

Country of ref document: JP

Kind code of ref document: A

ENP Entry into the national phase

Ref document number: 20137030809

Country of ref document: KR

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

WWE Wipo information: entry into national phase

Ref document number: 14119146

Country of ref document: US

WWE Wipo information: entry into national phase

Ref document number: 2012733515

Country of ref document: EP