CN105737903A - Intelligent pre-diagnosis and maintenance intelligent early warning method for faults of port machinery - Google Patents

Intelligent pre-diagnosis and maintenance intelligent early warning method for faults of port machinery Download PDF

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Publication number
CN105737903A
CN105737903A CN201610268312.XA CN201610268312A CN105737903A CN 105737903 A CN105737903 A CN 105737903A CN 201610268312 A CN201610268312 A CN 201610268312A CN 105737903 A CN105737903 A CN 105737903A
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maintenance
data
intelligent
fault
equipment
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CN201610268312.XA
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Chinese (zh)
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於斌
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於斌
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Priority to CN201610268312.XA priority Critical patent/CN105737903A/en
Publication of CN105737903A publication Critical patent/CN105737903A/en

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    • GPHYSICS
    • G01MEASURING; TESTING
    • G01DMEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D21/00Measuring or testing not otherwise provided for
    • G01D21/02Measuring two or more variables by means not covered by a single other subclass
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management
    • G06Q10/20Product repair or maintenance administration

Abstract

The invention discloses an intelligent pre-diagnosis and maintenance intelligent early warning method for faults of port machinery. The method comprises the steps of 1, mounting a miniature current or voltage transformer, a vibration sensor and a temperature sensor; 2, setting an engineering parameter of an industrial controller analog quantity input module; 3, setting equipment current values, vibration values and temperature values in different voltages and different working conditions in a normal operation state for establishing a basic database; 4, reading current data, vibration data and temperature data of the equipment in different working conditions in real time or every certain time period; 5, after data sampling and analysis, finding data which are deviated from normal data or empirical values; and 6, finishing field early maintenance or maintenance by maintenance personnel according to a pre-diagnosis result. According to the intelligent pre-diagnosis and maintenance intelligent early warning method, pre-diagnosis of the fault which may occur before fault or damage stoppage of key components of port machinery can be realized and early warning of maintenance personnel is performed; and furthermore a fact that a corresponding requires maintenance is reminded.

Description

Port mechanical equipment intelligent fault diagnoses and repair and maintenance intelligent early-warning method in advance
Technical field
The present invention relates to port mechanical equipment fault repair and maintenance field, particularly relate to a kind of port mechanical equipment intelligent fault and diagnose in advance and repair and maintenance intelligent early-warning method.
Background technology
Current undesirably port mechanical equipment disorderly closedown, remains harbour investor, particularly operator, the concrete difficult and problem faced.Have a strong impact on the one hand the efficiency of loading and unloading of harbour, even the pulling in shore the time of boats and ships.On the other hand, it is possible to cause the security incident of serious equipment and personnel.
Current port mechanical equipment is without similar fault pre-diagnosing and repair and maintenance intelligent early-warning system, and existing port mechanical equipment is all just keep in repair after disorderly closedown or device damage.The repair and maintenance of existing port mechanical equipment are to carry out regular repair and maintenance according to the time of regulation in maintenance manual(MM), without similar repair and maintenance method for early warning.Existing this method, it has been found that problem is late, it has been found that during problem, usual port mechanical equipment is to harbour operation and production, and even equipment and personal safety impact or accident.
EMD, empirical mode decomposition (EMDEmpiricalModeDecomposition) algorithm.The method is based on the time scale feature of data self to carry out signal decomposition, need not preset any basic function.The method it is crucial that empirical mode decomposition, it can make sophisticated signal be decomposed into limited intrinsic mode functions (IntrinsicModeFunction is called for short IMF), and each IMF component being decomposed out contains the local feature signal of the different time scales of original signal.
Correlation analysis (correlationanalysis) data analysis method, correlation analysis is whether to there is certain dependence between research phenomenon, and the phenomenon specifically having dependence is inquired into its related direction and degree of correlation.Dependency relation is the relation of a kind of uncertainty, for instance, load weight and the current of electric of port mechanical equipment is remembered respectively with X and Y.Then X and Y obviously has relation, and indefinite in the degree that can go accurately to determine another by one of them.Industrial Computer Control system is by the current X gathered, and the relation of Y data is learnt with time properly functioning or pre-establish the information in X1, Y1 data set, compares.Anticipation goes out the working condition of the port mechanical equipment being currently in operation.
Weighted mean algorithm, is mainly used to port mechanical equipment difference parts under different operating modes.The curtage value gathered in a certain cycle period is weighted average and calculates, and the data under result of calculation and normal condition are compared.Actual numerical value or trend according to comparison are analyzed and are judged whether different parts are likely to will appear from fault or need repair and maintenance.
Micro-transformer of current principle is based on what transformer principle was made.Current transformer is made up of the iron core closed and winding.Its first side winding number of turn is little; go here and there in needing the circuit of electric current of measurement; therefore it often has whole electric currents of circuit to flow through; secondary side winding turn ratio is more; being serially connected in measurement instrument and protective loop, operationally, its secondary side circuit is Guan Bi to current transformer all the time; therefore the impedance measuring instrument and protective loop series coil is only small, and the duty of current transformer is close to short-circuit.Current transformer is big for primary side electric current to be converted to secondary side small area analysis measure, and secondary side can not be opened a way.
Voltage transformer is a transformator with iron core.It is mainly made up of first and second coil, iron core and insulation.When applying a voltage U1 on a winding, iron core just produces a magnetic flux φ, according to the law of electromagnetic induction, then in Secondary Winding, just produce a secondary voltage U2.Changing once or the number of turn of Secondary Winding, it is possible to produce different primary voltages and secondary voltage ratio, this just can form the voltage transformer of difference ratio.Voltage transformer is mainly (capacitance type potential transformer is widely used) of electromagnetic type, separately has non electromagnetic, such as electronic type, photo-electric.
It is that port mechanical equipment is safe efficient, the crucial pith of stable operation as main driving equipment crucial on port mechanical equipment and corollary equipment.If these main driving equipment or corollary equipment go wrong, will result directly in the fault of port mechanical equipment complete machine, shutdown, even security incident.The operating cost of harbour, the time of pulling in shore of boats and ships, the efficiency of stevedoring, safety are impacted.At present, although the technology such as other field real-time state monitoring, intelligent trouble diagnosis have obtained extensive concern, but seldom relate to port mechanical equipment, and existing condition monitoring and fault diagnosis method is general until main driving equipment or corollary equipment substantially or have completely damaged and just can pinpoint the problems.Seldom consider the abundant digging utilization to fragmentary data (namely to the Monitoring Data under normal condition and malfunction).More remind maintainer that relevant device carries out the effective ways of maintenance or maintenance in advance not over data analysis.
Summary of the invention
The purpose of the present invention: provide a kind of port mechanical equipment intelligent fault to diagnose in advance and repair and maintenance intelligent early-warning method, can be diagnosed to be contingent fault early warning maintainer before port mechanical equipment critical component breaks down or damages shutdown in advance;Corresponding component can be reminded accurately the need of carrying out repair and maintenance, improve by maintenance handbook be periodically subject to unnecessary waste that repair and maintenance cause or non-timely, irrationality.
To achieve these goals, the technical scheme is that
A kind of port mechanical equipment intelligent fault diagnoses and repair and maintenance intelligent early-warning method in advance, and the method at least comprises the steps:
Step 1: install miniature electric current or voltage transformer at the chopper of electrical equipment or catalyst or converter lower end, motor base is installed shaking sensor, mounting temperature sensor in gear-box, and independent industrial computer device is installed on port mechanical equipment or uses the original industrial control device of port mechanical equipment.
Step 2: set the engineering parameter of industrial control unit (ICU) Analog input mModule.
Step 3: under setting normal operating conditions, the device current value in different voltage, operating mode situations, shock value, temperature value set up basic database;Or enable industrial control unit (ICU) intellectual learning module, Intelligent reading taking equipment under different operating modes, the electric current under normal operating condition, voltage, vibrations, temperature parameter value.
Step 4: read equipment electric current under different operating modes, vibrations, temperature data in real time or at set intervals.
Step 5: after data sampling is analyzed, data devious with normal data or empirical value, according to comparison historical failure data information bank, draw and be likely to produce the type of fault or need to carry out the parts of repair and maintenance, undertaken forecasting by the man machine interface of industrial control unit (ICU), early warning;Simultaneously, it is possible to send SMS messaging forecast, early warning by industrial control unit (ICU) to maintenance department personnel.
Step 6: maintainer confirms after completing on-the-spot early stage maintenance or repair and maintenance according to pre-diagnostic result in industrial control unit (ICU) man machine interface;Whole intelligent fault diagnoses in advance and repair and maintenance intelligent early-warning enters new cycle of operation.
Above-mentioned port mechanical equipment intelligent fault diagnoses and repair and maintenance intelligent early-warning method in advance, wherein, in described step 2, described engineering parameter include micro mutual inductor, shaking sensor, temperature sensor collection corresponding actual of curtage signal flow through chopper, catalyst, the principal current value of converter and magnitude of voltage, shock value, temperature value.
Above-mentioned port mechanical equipment intelligent fault diagnoses and repair and maintenance intelligent early-warning method in advance, wherein, in described step 4, also includes as follows step by step:
Step 4.1: the electric current of care testing device, voltage, vibrations and temperature information.
Step 4.2: electric current, information of voltage are carried out EMD feature decomposition, extracts characteristic vector.
Step 4.3: the characteristic vector extracted is weighted average method analysis.
Step 4.4: extract the real time data such as supply voltage, load, transfers the model data of correspondence.
Step 4.5: judge whether sampled data is deviation model data, if so, then performs step 5, otherwise, returns step 4.1.
Above-mentioned port mechanical equipment intelligent fault diagnoses and repair and maintenance intelligent early-warning method in advance, wherein, in described step 5, also includes as follows step by step:
Step 5.1: according to inclined difference range, fault trend that anticipation is possible or the need of maintenance.
Step 5.2: judge whether to need emergency maintenance, if so, then performs step 5.3, otherwise performs step 5.4.
Step 5.3: system alarm, note emergency notice maintainer.
Step 5.4: system alert, SMS notification maintenance personnel.
The present invention can be diagnosed to be the running status of equipment, warns possible equipment fault in advance, reminds the equipment that need to carry out timely repair and maintenance in advance.The downtime rate of equipment can be prevented effectively from; avoid the generation of equipment security incident; and fault or fault initial stage and early warning maintainer is not actually occurred at equipment, carry out early stage problem and risk is got rid of, avoids really breaking down, avoids failure propagation, avoided equipment damage to increase the weight of and the generation of security incident.The inventive method can set up fault diagnosis model based on normal condition and various fault state data, reduce the dependency to historical data, it is capable of the intelligent maintenance of the single-phase equipment such as the three-phase equipments such as motor and light fixture, brake, heater, there is very strong versatility and significantly high engineer applied is worth.
Accompanying drawing explanation
Fig. 1 is that port mechanical equipment intelligent fault of the present invention diagnoses and the flow chart of repair and maintenance intelligent early-warning method in advance.
Fig. 2 is that port mechanical equipment intelligent fault of the present invention diagnoses in advance and the motor of repair and maintenance intelligent early-warning method starts under full-load run, brakes, steady-state operation time voltage, the Wave data figure such as electric current.
Fig. 3 is that port mechanical equipment intelligent fault of the present invention diagnoses and the three phase electric equipment connection figure of repair and maintenance intelligent early-warning method in advance.
Fig. 4 is that port mechanical equipment intelligent fault of the present invention diagnoses and the single-phase electric devices connection figure of repair and maintenance intelligent early-warning method in advance.
Detailed description of the invention
Embodiments of the invention are further illustrated below in conjunction with accompanying drawing.
Referring to shown in accompanying drawing 1, a kind of port mechanical equipment intelligent fault diagnoses and repair and maintenance intelligent early-warning method in advance, and the method at least comprises the steps:
Step 1: at the chopper of electrical equipment or catalyst or converter 1 lower end (or chopper, catalyst, converter, internal) miniature electric current or voltage transformer 2 are installed, motor base is installed shaking sensor, mounting temperature sensor in gear-box, and independent industrial computer (control) device 3 is installed on port mechanical equipment or uses the original PLC(industrial control device of port mechanical equipment).
Refer to shown in accompanying drawing 2 to accompanying drawing 4, accompanying drawing 2 be motor start under full-load run, brake, steady-state operation time voltage, the Wave data such as electric current.For the port machinery equipment of relative standard, set up data model based on each stage and the electric current of operating mode (startup, stable operation, braking, underloading, heavy duty, rising, lower degradation), voltage, noise, temperature value under conventional motor Deng Ge mechanism normal operating condition.If, it does not have reference data, it is also possible to set up system reference model by measured values such as the electric current under stage each under new equipment normal operating condition and operating mode, voltage, noise, temperature.
For, single-phase electric devices.Such as lighting, heater etc., it is possible to be multiplied by the number of devices under single chopper/catalyst according to design drawing and equipment nameplate rated current, carry out setting data model.As, with the light fixture that 5 rated current are 2A, rated voltage 220VAC under a single-phase circuit breaker, in data model, can set that the standard value under this chopper is voltage 220VAC corresponding current 10A, consider further that temperature coefficient.
On port mechanical equipment, a converter or chopper or catalyst 1 power to one or more electrical equipments, if our external or built-in miniature curtage transformer 2, the analog output of miniature electric current or voltage transformer 2 is connected on industrial computer (control) device 3.Meanwhile, vibrations are sent to industrial computer (control) device 3 together with temperature information.Industrial computer (control) device 3 is by checking the curtage of electrical equipment 5, in conjunction with signals such as the information on inducer for line outlet end of transformer or power supply busbar and the temperature of motor terminal, vibrations, for different load number, character, by different calculation and analysis methods, come judgment device whether normal, the need of maintaining.When abnormal condition being detected, industrial computer (control) device 3 is by data model default for comparison and fault large database concept, diagnosing this electrical equipment 5 in advance is need maintenance to need for on-call maintenance, and takes different measures by 4 diagnostic messages of man machine interface according to different classifications.Need reporting to the police immediately and sending urgent message to maintainer of emergency maintenance, it is necessary to the showing early warning in man machine interface 4 and send information to maintenance personnel of in time maintenance.
Step 2: set the engineering parameter (EngineeringParameter) of industrial control unit (ICU) Analog input mModule, mainly defines corresponding actual of the curtage signal of the sensor acquisition such as micro mutual inductor, shaking sensor, temperature sensor and flows through chopper, catalyst, the principal current value of converter and magnitude of voltage, shock value, temperature value.
Step 3: set under normal operating conditions the device current value when different voltage, operating modes, shock value, temperature value set up basic database.Or enable industrial control unit (ICU) intellectual learning module, Intelligent reading taking equipment under different operating modes, the electric current under normal operating condition, voltage, vibrations, temperature parameter value.
Step 4: read the data such as equipment electric current under different operating modes, vibrations, temperature in real time or at set intervals.For main driving equipment (adjustable frequency motor even load), use EMD and correlation analysis (correlationanalysis) data analysis method, draw the ratio of load and electric current during steady-state operation under frequency conversion motor different loads and the fluctuation range up and down of electric current.And the data under corresponding in this ratio and fluctuation range and system data library model operating mode, loading condition are contrasted.In conjunction with shock value and temperature value, analysis deviation value, if with pre-diagnostic cast, namely deviation value can determine whether this main driving equipment has problems more than or less than empirical value.Compare with historical failure data again, draw it may happen that fault or be likely to the parts of damage.
Step 5: after data sampling is analyzed, data devious with normal data or empirical value.According to comparison historical failure data information bank, draw and be likely to produce the type of fault or need to carry out the parts of repair and maintenance, undertaken forecasting by the man machine interface of industrial control unit (ICU), early warning;Simultaneously, it is possible to send SMS messaging forecast, early warning by industrial control unit (ICU) to maintenance department personnel.
Step 6: maintainer confirms after completing on-the-spot early stage maintenance or repair and maintenance according to pre-diagnostic result in industrial control unit (ICU) man machine interface.Whole intelligent fault diagnoses in advance and repair and maintenance intelligent early-warning enters new cycle of operation.
In described step 4, also include as follows step by step:
Step 4.1: the electric current of care testing device, voltage, vibrations and temperature information.
Step 4.2: electric current, information of voltage are carried out EMD feature decomposition, extracts characteristic vector.
Step 4.3: the characteristic vector extracted is weighted average method analysis.
Step 4.4: extract the real time data such as supply voltage, load, transfers the model data of correspondence.
Step 4.5: judge whether sampled data is deviation model data, if so, then performs step 5, otherwise, returns step 4.1.
By industrial computer, actual inclined difference range is judged in advance according to preset model.Such as in the anticipation of the three-phase equipments such as motor, temperature value, shock value, noise numerical value and current value all slightly above model data, then system establishing criteria judgment models and Mishap Database, it is possible to be judged to motor in advance or equipment lacks lubrication, it is necessary to maintenance.If, motor or that equipment temperature rise is high especially, shock value strengthens (and regular sudden change), current value increase is bigger, the regular sudden change of current amplitude, then system establishing criteria judgment models and Mishap Database, can be judged to motor in advance or equipment has the trend of pre-fault/damage, such as bearing, braking dish gap are likely to problematic etc., it is necessary to on-call maintenance.
For another example in the anticipation of the single-phase equipment such as light fixture, heater, in the basicly stable situation of ambient temperature, magnitude of voltage is substantially free of deviation, current value has the situation of irregular increase.Then this group light fixture of anticipation or heater electrical connection point in circuit can be had to loosen or indivedual light fixture catabiosis, it is necessary to carry out maintenance in advance.If, current value reduces substantially, (5 light fixtures or 5 heaters under such as normal condition, each light fixture or heater rated current 2A) average is at 10A, but test data mean value only has 8A, then can determine whether that these 5 light fixtures or 5 heaters have a light fixture or heater failure, need on-call maintenance.
In described step 5, also include as follows step by step:
Step 5.1: according to inclined difference range, fault trend that anticipation is possible or the need of maintenance.
Step 5.2: judge whether to need emergency maintenance, if so, then performs step 5.3, otherwise performs step 5.4.
Step 5.3: system alarm, note emergency notice maintainer.
Step 5.4: system alert, SMS notification maintenance personnel.
The present invention is directed to the characteristic of key equipment in port mechanical equipment and signal characteristic, it is proposed to a kind of fault combined based on EMD data analysis, system correlation analysis (correlationanalysis) data analysis method, weighted mean algorithm, micro-current/voltage signal processing collection, vibrations and temperature rise data analysis and the pre-diagnostic method of repair and maintenance.The core concept of the inventive method is by suitable sensor element and industrial computer equipment, the data model that the running status that equipment is current is corresponding with all kinds of malfunctions with normal condition to be contrasted, judge the gap between totally, based on set judgment rule and method, whether can will appear from fault or problem by anticipation current device, it is possible to anticipation current device is the need of the maintenance carrying out necessity.The inventive method utilizes industrial computer, the tiny signal that identification equipment shows before tending to fault, it is achieved that the status real time monitor of port mechanical equipment diagnoses in advance with fault and repair and maintenance, has filled up the blank of the port mechanical equipment field pre-diagnostic techniques of real-time intelligent.
In sum, the present invention can be diagnosed to be the running status of equipment, warns possible equipment fault in advance, reminds the equipment that need to carry out timely repair and maintenance in advance.The downtime rate of equipment can be prevented effectively from; avoid the generation of equipment security incident; and fault or fault initial stage and early warning maintainer is not actually occurred at equipment, carry out early stage problem and risk is got rid of, avoids really breaking down, avoids failure propagation, avoided equipment damage to increase the weight of and the generation of security incident.The inventive method can set up fault diagnosis model based on normal condition and various fault state data, reduce the dependency to historical data, it is capable of the intelligent maintenance of the single-phase equipment such as the three-phase equipments such as motor and light fixture, brake, heater, there is very strong versatility and significantly high engineer applied is worth.
The foregoing is only the preferred embodiments of the present invention; not thereby the scope of the claims of the present invention is limited; every equivalent structure transformation utilizing description of the present invention to make; or directly or indirectly use the technical field being attached to other Related products, all in like manner include in the scope of patent protection of the present invention.

Claims (4)

1. a port mechanical equipment intelligent fault diagnoses and repair and maintenance intelligent early-warning method in advance, it is characterised in that: the method at least comprises the steps:
Step 1: install miniature electric current or voltage transformer at the chopper of electrical equipment or catalyst or converter lower end, motor base is installed shaking sensor, mounting temperature sensor in gear-box, and independent industrial computer device is installed on port mechanical equipment or uses the original industrial control device of port mechanical equipment;
Step 2: set the engineering parameter of industrial control unit (ICU) Analog input mModule;
Step 3: under setting normal operating conditions, the device current value in different voltage, operating mode situations, shock value, temperature value set up basic database;Or enable industrial control unit (ICU) intellectual learning module, Intelligent reading taking equipment under different operating modes, the electric current under normal operating condition, voltage, vibrations, temperature parameter value;
Step 4: read equipment electric current under different operating modes, vibrations, temperature data in real time or at set intervals;
Step 5: after data sampling is analyzed, data devious with normal data or empirical value, according to comparison historical failure data information bank, draw and be likely to produce the type of fault or need to carry out the parts of repair and maintenance, undertaken forecasting by the man machine interface of industrial control unit (ICU), early warning;Simultaneously, it is possible to send SMS messaging forecast, early warning by industrial control unit (ICU) to maintenance department personnel;
Step 6: maintainer confirms after completing on-the-spot early stage maintenance or repair and maintenance according to pre-diagnostic result in industrial control unit (ICU) man machine interface;Whole intelligent fault diagnoses in advance and repair and maintenance intelligent early-warning enters new cycle of operation.
2. port mechanical equipment intelligent fault according to claim 1 diagnoses and repair and maintenance intelligent early-warning method in advance, it is characterized in that: in described step 2, described engineering parameter include micro mutual inductor, shaking sensor, temperature sensor collection corresponding actual of curtage signal flow through chopper, catalyst, the principal current value of converter and magnitude of voltage, shock value, temperature value.
3. port mechanical equipment intelligent fault according to claim 1 diagnoses and repair and maintenance intelligent early-warning method in advance, it is characterised in that: in described step 4, also include as follows step by step:
Step 4.1: the electric current of care testing device, voltage, vibrations and temperature information;
Step 4.2: electric current, information of voltage are carried out EMD feature decomposition, extracts characteristic vector;
Step 4.3: the characteristic vector extracted is weighted average method analysis;
Step 4.4: extract the real time data such as supply voltage, load, transfers the model data of correspondence;
Step 4.5: judge whether sampled data is deviation model data, if so, then performs step 5, otherwise, returns step 4.1.
4. port mechanical equipment intelligent fault according to claim 1 diagnoses and repair and maintenance intelligent early-warning method in advance, it is characterised in that: in described step 5, also include as follows step by step:
Step 5.1: according to inclined difference range, fault trend that anticipation is possible or the need of maintenance;
Step 5.2: judge whether to need emergency maintenance, if so, then performs step 5.3, otherwise performs step 5.4;
Step 5.3: system alarm, note emergency notice maintainer;
Step 5.4: system alert, SMS notification maintenance personnel.
CN201610268312.XA 2016-04-27 2016-04-27 Intelligent pre-diagnosis and maintenance intelligent early warning method for faults of port machinery CN105737903A (en)

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* Cited by examiner, † Cited by third party
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Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070282548A1 (en) * 2004-03-10 2007-12-06 Shih-Fu Ling Method and Apparatus for Assessing Condition of Motor-Driven Mechanical System
CN102023100A (en) * 2010-04-19 2011-04-20 东莞市罗尔机电科技有限公司 Equipment failure early-warning system and method
CN102522705A (en) * 2011-11-22 2012-06-27 广东电网公司电力科学研究院 Transformer maintenance method and device
CN102707232A (en) * 2012-06-01 2012-10-03 深圳市海亿达能源科技股份有限公司 Online monitoring device of state of motor device and monitoring method thereof
CN102788931A (en) * 2012-07-18 2012-11-21 嘉兴学院 Power transformer winding fault diagnosing method
CN104965464A (en) * 2015-07-08 2015-10-07 福建唐力电力设备有限公司 Marine machinery and port machinery smart system and implementation method
CN105022373A (en) * 2015-04-30 2015-11-04 武汉理工大学 Port equipment maintenance system based on zigbee technology
CN105354587A (en) * 2015-09-25 2016-02-24 国网甘肃省电力公司电力科学研究院 Fault diagnosis method for gearbox of wind generation unit

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070282548A1 (en) * 2004-03-10 2007-12-06 Shih-Fu Ling Method and Apparatus for Assessing Condition of Motor-Driven Mechanical System
CN102023100A (en) * 2010-04-19 2011-04-20 东莞市罗尔机电科技有限公司 Equipment failure early-warning system and method
CN102522705A (en) * 2011-11-22 2012-06-27 广东电网公司电力科学研究院 Transformer maintenance method and device
CN102707232A (en) * 2012-06-01 2012-10-03 深圳市海亿达能源科技股份有限公司 Online monitoring device of state of motor device and monitoring method thereof
CN102788931A (en) * 2012-07-18 2012-11-21 嘉兴学院 Power transformer winding fault diagnosing method
CN105022373A (en) * 2015-04-30 2015-11-04 武汉理工大学 Port equipment maintenance system based on zigbee technology
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