CN114754018B - Large turbine compressor fault diagnosis method and system - Google Patents
Large turbine compressor fault diagnosis method and system Download PDFInfo
- Publication number
- CN114754018B CN114754018B CN202210344877.7A CN202210344877A CN114754018B CN 114754018 B CN114754018 B CN 114754018B CN 202210344877 A CN202210344877 A CN 202210344877A CN 114754018 B CN114754018 B CN 114754018B
- Authority
- CN
- China
- Prior art keywords
- fault
- historical
- historical operation
- turbine compressor
- matching
- Prior art date
- Legal status (The legal status 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 status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 75
- 238000003745 diagnosis Methods 0.000 title claims abstract description 40
- 238000005070 sampling Methods 0.000 claims abstract description 80
- 238000004458 analytical method Methods 0.000 claims abstract description 53
- 230000008569 process Effects 0.000 claims abstract description 39
- 230000009467 reduction Effects 0.000 claims description 16
- 238000012545 processing Methods 0.000 claims description 10
- 239000011159 matrix material Substances 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 4
- 238000010586 diagram Methods 0.000 description 9
- 238000004891 communication Methods 0.000 description 7
- 230000002159 abnormal effect Effects 0.000 description 6
- 230000000694 effects Effects 0.000 description 5
- 238000013500 data storage Methods 0.000 description 4
- 238000006073 displacement reaction Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000008030 elimination Effects 0.000 description 3
- 238000003379 elimination reaction Methods 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000013480 data collection Methods 0.000 description 2
- 239000012530 fluid Substances 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000009792 diffusion process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000011664 signaling Effects 0.000 description 1
- 238000013024 troubleshooting Methods 0.000 description 1
Classifications
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D27/00—Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids
- F04D27/001—Testing thereof; Determination or simulation of flow characteristics; Stall or surge detection, e.g. condition monitoring
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2260/00—Function
- F05D2260/80—Diagnostics
Landscapes
- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Control Of Positive-Displacement Air Blowers (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention provides a fault diagnosis method and system for a large turbine compressor, and relates to the technical field of fault diagnosis. The method comprises the following steps: and inputting the equipment parameters of the target turbine compressor into a preset database to obtain a historical operation data set. And laying out signal acquisition points according to the historical operation fault parameters. And acquiring operation information through a signal acquisition point according to the sampling period. The operational information is matched with all of the first historical operational events. And acquiring matching characteristic information of the second historical operation event based on the operation information. And inputting the matching characteristic information into a preset fault analysis model to obtain fault cause parameters. The method and the device realize fault diagnosis and fault removal of the turbine compressor aiming at signals in the running process of the whole turbine compressor, so as to accurately position the fault cause, fully and effectively combine state performance signals in the running process of the turbine compressor to analyze the fault cause, and effectively improve the diagnosis precision and diagnosis efficiency of the turbine compressor.
Description
Technical Field
The invention relates to the technical field of fault diagnosis, in particular to a fault diagnosis method and system for a large-scale turbine compressor.
Background
A turbine compressor refers to a power compressor having a high-speed rotating impeller. The basic working principle of the turbine compressor is that a working wheel with blades arranged on a shaft rotates at a high speed under the drive of a driver. The blades do work on the gas to enable the gas to obtain kinetic energy, and the kinetic energy is converted into pressure energy after diffusion flow, so that the pressure of the gas is improved. And the gas temperature is correspondingly increased. The turbine compressor increases the pressure of gas by means of the interaction force between the rotating impeller and the air flow, and simultaneously makes the air flow generate acceleration to obtain kinetic energy, and then the air flow decelerates in the diffuser to convert the kinetic energy into pressure energy, so that the pressure is further increased.
In the operation process of the turbine compressor, because of the multiple parts and the complex structure of the compressor, internal and external causes which cause or influence the normal operation of the turbine compressor are also complex and various. At present, people overhaul and troubleshoot faults through experience according to actual working conditions of the turbine compressor, so that fault troubleshooting error conditions are easy to occur, and diagnosis accuracy and diagnosis efficiency are low.
Disclosure of Invention
The invention aims to provide a fault diagnosis method and system for a large-sized turbine compressor, which can be used for carrying out fault diagnosis and fault removal on the turbine compressor according to signals in the running process of the whole turbine compressor so as to accurately locate fault reasons and effectively improve the diagnosis precision and diagnosis efficiency of the turbine compressor.
Embodiments of the present invention are implemented as follows:
in a first aspect, embodiments of the present application provide a method for diagnosing a fault in a large turbine compressor, comprising the steps of:
acquiring equipment parameters of a target turbine compressor;
inputting the equipment parameters into a preset database to obtain a historical operation data set corresponding to the target turbine compressor, wherein the historical operation data set comprises a first historical operation event, historical operation fault parameters and historical operation characteristics;
laying out signal acquisition points according to the historical operation fault parameters;
setting a sampling period according to the historical operation characteristics, and collecting operation information of the target turbine compressor in the operation process through a signal collecting point according to the sampling period;
matching the operation information with all first historical operation events, and calculating the matching degree of the operation information with any first historical operation event;
acquiring all second historical operation events with the matching degree not smaller than the preset matching degree;
based on the operation information, obtaining matching characteristic information of any second historical operation event;
and inputting the matching characteristic information of all the second historical operation events into a preset fault analysis model for analysis to obtain an analysis result, wherein the analysis result comprises fault cause parameters.
In some embodiments of the present invention, the step of inputting the matching feature information of all the second historical operating events into a preset fault analysis model for analysis, and obtaining an analysis result includes:
according to any matching characteristic information, calculating a corresponding matching numerical value according to a preset characteristic weight;
judging a first fault type corresponding to the operation information according to the matching value;
traversing all the matching characteristic information to obtain all the first fault types, and determining a second fault type according to all the first fault types;
and obtaining fault cause parameters according to the operation information based on the second fault type.
In some embodiments of the present invention, before the step of inputting the plant parameters into the preset database to obtain the historical operation data set corresponding to the target turbocompressor, the method further includes:
the method comprises the steps of collecting and classifying historical data in the operation process of a plurality of turbine compressors in advance, wherein the historical data comprise historical operation events, historical operation fault parameters and historical operation characteristics corresponding to the turbine compressors;
and storing the historical data of all the turbine compressors into a preset database.
In some embodiments of the invention, the step of storing historical data of all turbine compressors in a pre-set database comprises:
Sorting any historical data into a matrix data set;
performing noise reduction processing on the matrix data set to obtain noise-reduced historical data;
and storing the historical data after the noise reduction treatment into a preset database.
In some embodiments of the present invention, the signal acquisition point includes a temperature sampling point, and the step of acquiring operation information of the target turbine compressor during operation through the signal acquisition point according to a sampling period includes:
the operating temperature of the target turbine compressor is sampled by the temperature sampling points to generate a temperature sampling dataset.
In some embodiments of the present invention, the step of collecting the operation information of the target turbine compressor during the operation process through the signal collection point according to the sampling period includes:
the operating frequency of the target turbocompressor is sampled to generate a frequency sampling dataset.
In some embodiments of the invention, the step of sampling the operating frequency of the target turbine compressor to generate a frequency sampled data set includes:
collecting vibration signals and key phase signals in the operation process of the target turbine compressor through a signal collecting point;
and calculating according to the vibration signal and the key phase signal to obtain the operating frequency of the target turbine compressor.
In a second aspect, embodiments of the present application provide a large turbine compressor fault diagnosis system comprising:
the device parameter acquisition module is used for acquiring the device parameters of the target turbine compressor;
the historical operation data set matching module is used for inputting the equipment parameters into a preset database to obtain a historical operation data set corresponding to the target turbine compressor, wherein the historical operation data set comprises a first historical operation event, a historical operation fault parameter and a historical operation characteristic;
the signal acquisition point layout module is used for layout signal acquisition points according to the historical operation fault parameters;
the sampling period setting module is used for setting a sampling period according to the historical operation characteristics and collecting operation information of the target turbine compressor in the operation process through a signal acquisition point according to the sampling period;
the historical operation event matching module is used for matching the operation information with all the first historical operation events and calculating the matching degree of the operation information with any one of the first historical operation events;
the second historical operation event acquisition module is used for acquiring all second historical operation events with the matching degree not less than the preset matching degree;
the matching characteristic information acquisition module is used for acquiring matching characteristic information of any second historical operation event based on the operation information;
The analysis module is used for inputting the matching characteristic information of all the second historical operation events into a preset fault analysis model for analysis, so that an analysis result is obtained, and the analysis result comprises fault cause parameters.
In some embodiments of the invention, the analysis module includes:
the matching numerical value calculation unit is used for calculating a corresponding matching numerical value according to any matching characteristic information and a preset characteristic weight;
the first fault type judging unit is used for judging a first fault type corresponding to the operation information according to the matching numerical value;
the second fault type determining unit is used for traversing all the matching characteristic information to obtain all the first fault types and determining the second fault types according to all the first fault types;
the fault cause parameter obtaining unit is used for obtaining the fault cause parameter according to the operation information based on the second fault type.
In some embodiments of the invention, the above-described large turbine compressor fault diagnosis system further comprises:
the historical data collection module is used for pre-collecting and classifying historical data in the operation process of the plurality of turbine compressors, wherein the historical data comprise historical operation events, historical operation fault parameters and historical operation characteristics corresponding to the turbine compressors;
And the historical data storage module is used for storing the historical data of all the turbine compressors into a preset database.
In some embodiments of the present invention, the history data storage module includes:
the historical data conversion unit is used for sorting any historical data into a matrix-form data set;
the data set noise reduction unit is used for performing noise reduction treatment on the matrix-form data set to obtain noise-reduced historical data;
the noise reduction storage unit is used for storing the history data after the noise reduction processing into a preset database.
In some embodiments of the present invention, the signal sampling point includes a temperature sampling point, and the sampling period setting module includes:
and the temperature sampling unit is used for sampling the operating temperature of the target turbine compressor through the temperature sampling points so as to generate a temperature sampling data set.
In some embodiments of the present invention, the sampling period setting module includes:
and the frequency sampling unit is used for sampling the operating frequency of the target turbine compressor so as to generate a frequency sampling data set.
In some embodiments of the present invention, the frequency sampling unit includes:
the vibration signal acquisition subunit is used for acquiring vibration signals and key phase signals in the operation process of the target turbine compressor through the signal acquisition point;
And the operating frequency calculating subunit is used for calculating the operating frequency of the target turbine compressor according to the vibration signal and the key phase signal.
In a third aspect, embodiments of the present application provide an electronic device comprising a memory for storing one or more programs; a processor. The method of any of the first aspects described above is implemented when one or more programs are executed by a processor.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as in any of the first aspects described above.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects:
the invention provides a fault diagnosis method and system for a large turbine compressor, comprising the following steps: the plant parameters of the target turbine compressor are obtained. And inputting the equipment parameters into a preset database to obtain a historical operation data set corresponding to the target turbine compressor, wherein the historical operation data set comprises a first historical operation event, historical operation fault parameters and historical operation characteristics. And laying out signal acquisition points according to the historical operation fault parameters. Setting a sampling period according to the historical operation characteristics, and collecting operation information of the target turbine compressor in the operation process through a signal collecting point according to the sampling period. And matching the operation information with all the first historical operation events, and calculating the matching degree of the operation information with any one of the first historical operation events. And acquiring all second historical operation events with the matching degree not smaller than the preset matching degree. And based on the operation information, acquiring matching characteristic information of any second historical operation event. And inputting the matching characteristic information of all the second historical operation events into a preset fault analysis model for analysis to obtain an analysis result, wherein the analysis result comprises fault cause parameters. According to the method and the system, firstly, a turbine compressor with the same parameters as the target turbine compressor equipment is searched in a preset database, and then a historical operation data set corresponding to the target turbine compressor is obtained. And then setting signal acquisition points at a plurality of positions of the target turbine compressor according to the historical operation fault parameters in the historical operation data set so as to acquire signals in the operation process of the target turbine compressor more accurately. Meanwhile, a sampling period is set according to historical operation characteristics, so that the sampling period is matched with a target turbine compressor, and a better acquisition effect is achieved. And then, carrying out one-to-one comparison and matching on parameters in the operation information and any first historical operation event to judge the matching degree of the parameters and the first historical operation event, taking the first historical operation event with the matching degree being greater than or equal to the preset matching degree as a second historical operation event, and determining the second historical operation event from all the first historical operation events, wherein the second historical operation event is more consistent with the operation information. And then inputting the matching characteristic information between any second historical operation event and the operation information into a preset fault analysis model for analysis, so as to obtain fault cause parameters corresponding to the operation information by combining the matching characteristic information of all second historical operation events, namely obtaining the fault cause parameters of the target turbine compressor, and further carrying out fault diagnosis and fault elimination on the turbine compressor according to signals in the operation process of the whole turbine compressor, so as to accurately locate the fault cause, analyze the fault cause by fully and effectively combining the state performance signals in the operation process of the turbine compressor, and effectively improve the diagnosis precision and diagnosis efficiency of the turbine compressor.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for diagnosing faults in a large turbine compressor in accordance with an embodiment of the present invention;
FIG. 2 is a flow chart of a preset failure analysis model according to an embodiment of the present invention;
FIG. 3 is a flow chart of another method for diagnosing faults in a large turbine compressor in accordance with embodiments of the present invention;
FIG. 4 is a block diagram of a large turbine compressor fault diagnosis system according to an embodiment of the present invention;
fig. 5 is a schematic block diagram of an electronic device according to an embodiment of the present invention.
Icon: 100-a fault diagnosis system of a large turbine compressor; 110-an equipment parameter acquisition module; 120-a historical operating dataset matching module; 130-a signal acquisition point layout module; 140-a sampling period setting module; 150-a history running event matching module; 160-a second historical operating event acquisition module; 170-a matching feature information acquisition module; 180-an analysis module; 101-memory; 102-a processor; 103-communication interface.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like, if any, are used solely for distinguishing the description and are not to be construed as indicating or implying relative importance.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, the appearances of the element defined by the phrase "comprising one … …" do not exclude the presence of other identical elements in a process, method, article or apparatus that comprises the element.
In the description of the present application, it should be noted that, if the terms "upper," "lower," "inner," "outer," and the like indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, or an azimuth or the positional relationship that the product of the application is commonly put in use, it is merely for convenience of describing the present application and simplifying the description, and does not indicate or imply that the apparatus or element to be referred to must have a specific azimuth, be configured and operated in a specific azimuth, and thus should not be construed as limiting the present application.
In the description of the present application, it should also be noted that, unless explicitly stated and limited otherwise, the terms "disposed," "connected," and "connected" should be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art in a specific context.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The various embodiments and features of the embodiments described below may be combined with one another without conflict.
Examples
Referring to fig. 1, fig. 1 is a flowchart illustrating a fault diagnosis method for a large-scale turbine compressor according to an embodiment of the present invention. The embodiment of the application provides a fault diagnosis method for a large turbine compressor, which comprises the following steps of:
s110: acquiring equipment parameters of a target turbine compressor;
the plant parameters may include, among others, flow, exhaust pressure, power, efficiency, and rotational speed.
S120: inputting the equipment parameters into a preset database to obtain a historical operation data set corresponding to the target turbine compressor, wherein the historical operation data set comprises a first historical operation event, historical operation fault parameters and historical operation characteristics;
specifically, the preset database stores historical data for a plurality of turbocompressors. After the equipment parameters are input into the preset data set, the corresponding historical operation data set is searched in the preset data set according to the equipment parameters.
Wherein the first historical operation event is the historical operation process of the target turbine compressor in actual engineering. The historical operation fault parameters are the fault phenomenon and the fault position of the target turbine compressor in the actual engineering. The historical operation characteristics are the operation period, the rotating speed, the working period and the like of the target turbine compressor in actual engineering.
S130: laying out signal acquisition points according to the historical operation fault parameters;
specifically, signal acquisition points are set at a plurality of positions of the target turbine compressor based on historical operation fault parameters, and then signals in the operation process of the target turbine compressor are accurately acquired through different signal acquisition points.
S140: setting a sampling period according to the historical operation characteristics, and collecting operation information of the target turbine compressor in the operation process through a signal collecting point according to the sampling period;
specifically, setting the sampling period according to the historical operating characteristics can enable the sampling period to be matched with the target turbine compressor, and further a better collection effect is achieved.
The operation information may include an exhaust gas amount, a temperature parameter, a pressure parameter, and a vibration signal.
S150: matching the operation information with all first historical operation events, and calculating the matching degree of the operation information with any first historical operation event;
specifically, the parameters in the operation information are subjected to one-to-one comparison and matching with any one of the first historical operation events, so that the matching degree of the parameters and the first historical operation events is judged.
For example, if the operation information has 10 parameters in total, if the first historical operation event a is consistent with only the exhaust gas amount and temperature parameters in the operation information, the matching degree of the two is 20%. For another example, if the first historical operating event a is consistent with only the displacement, temperature parameter, pressure parameter, and vibration signal in the operating information, the matching degree of the two is 40%.
S160: acquiring all second historical operation events with the matching degree not smaller than the preset matching degree;
For example, the preset matching degree may be 70%, and the first historical operation event with the matching degree greater than or equal to 70% is used as the second historical operation event, so that the second historical operation event is determined from all the first historical operation events, and then the second historical operation event is more consistent with the operation information. S170: based on the operation information, obtaining matching characteristic information of any second historical operation event;
for example, if the second historical operating event is consistent with the displacement, temperature, pressure, and vibration signals of the operating information, the corresponding matching characteristic information is the displacement, temperature, pressure, and vibration signals.
S180: and inputting the matching characteristic information of all the second historical operation events into a preset fault analysis model for analysis to obtain an analysis result, wherein the analysis result comprises fault cause parameters.
Specifically, the preset fault analysis model can analyze the matching characteristic information of all the second historical operation events so as to obtain fault cause parameters corresponding to the operation information by combining the matching characteristic information of all the second historical operation events, namely the fault cause parameters of the target turbine compressor, and further, the fault diagnosis and the fault elimination are carried out on the turbine compressor according to signals in the operation process of the whole turbine compressor so as to accurately locate the fault cause, thereby fully and effectively combining the state performance signals in the operation process of the turbine compressor to analyze the fault cause, and effectively improving the diagnosis precision and the diagnosis efficiency of the turbine compressor.
Referring to fig. 2, fig. 2 is a flow chart illustrating a preset failure analysis model according to an embodiment of the present invention. In some implementations of the present embodiment, the step of inputting the matching feature information of all the second historical operating events into a preset fault analysis model for analysis to obtain an analysis result includes:
s181: according to any matching characteristic information, calculating a corresponding matching numerical value according to a preset characteristic weight;
s182: judging a first fault type corresponding to the operation information according to the matching value;
the first fault type may include, among other things, a thermal performance fault of a fluid nature, a mechanical fault, a tachometer fault, whether the blade is severely rubbed against the fan end cap, etc. The above-mentioned thermal performance failures of fluid properties are manifested as insufficient displacement, temperature and pressure anomalies.
S183: traversing all the matching characteristic information to obtain all the first fault types, and determining a second fault type according to all the first fault types;
s184: and obtaining fault cause parameters according to the operation information based on the second fault type.
Wherein each fault type has a corresponding matching range of values.
In the implementation process, first, for any matching feature information, a matching value of the operation information and a corresponding second historical operation event is calculated, and then the first fault type is determined according to a matching value range meeting the matching value. And traversing all the matching characteristic information, calculating the matching numerical value corresponding to the running information for each matching characteristic information, and further determining all the first fault types. And then the first fault type with the largest number is obtained as a second fault type, wherein the second fault type is the fault type corresponding to the operation information, and the effect of accurately positioning the fault type of the target turbine compressor is achieved. After determining the fault type corresponding to the operation information, searching according to the operation information to obtain fault cause parameters. Therefore, the purpose of analyzing the matching characteristic information of all the second historical operation events through a preset fault analysis model to obtain fault cause parameters is achieved.
Referring to fig. 3, fig. 3 is a flowchart illustrating another fault diagnosis method for a large-scale turbine compressor according to an embodiment of the present invention. In some implementations of this embodiment, before the step of inputting the plant parameters into the preset database to obtain the historical operating dataset corresponding to the target turbocompressor, the method further includes:
the method comprises the steps of collecting and classifying historical data in the operation process of a plurality of turbine compressors in advance, wherein the historical data comprise historical operation events, historical operation fault parameters and historical operation characteristics corresponding to the turbine compressors;
and storing the historical data of all the turbine compressors into a preset database. Thereby ensuring that the preset database stores historical data of a plurality of turbine compressors.
In some implementations of this embodiment, the step of saving historical data for all turbine compressors to a pre-set database includes:
sorting any historical data into a matrix data set;
performing noise reduction processing on the matrix data set to obtain noise-reduced historical data;
and storing the historical data after the noise reduction treatment into a preset database.
Specifically, firstly, any historical data is converted into a matrix-form data set, and as the matrix-form data set can sequentially express the historical data in a row and column mode, blank data in the historical data can be intuitively seen through the matrix-form data set. And then, carrying out noise reduction treatment on the matrix data set to eliminate blank data in the historical data, namely eliminating abnormal values, namely eliminating abnormal data which can influence the diagnosis precision, and ensuring the accuracy of the historical data in the preset database.
In some implementations of this embodiment, the signal acquisition points include temperature sampling points, and the step of acquiring the operation information of the target turbine compressor during operation by the signal acquisition points according to the sampling period includes:
the operating temperature of the target turbine compressor is sampled by the temperature sampling points to generate a temperature sampling dataset.
For example, since the stuffing box and the gas valve of the target turbine compressor may cause temperature anomalies, temperature sampling points may be provided at the stuffing box and the gas valve to sample the operating temperature.
In some implementations of this embodiment, the step of collecting the operation information of the target turbocompressor during operation through the signal collection point according to the sampling period includes:
the operating frequency of the target turbocompressor is sampled to generate a frequency sampling dataset. Thus, whether the operating frequency of the target turbine compressor is abnormal can be further judged through the frequency sampling data set.
In some implementations of the present embodiment, the step of sampling the operating frequency of the target turbine compressor to generate the frequency sampling dataset includes:
collecting vibration signals and key phase signals in the operation process of the target turbine compressor through a signal collecting point;
And calculating according to the vibration signal and the key phase signal to obtain the operating frequency of the target turbine compressor.
Specifically, in the operation process of the target turbine compressor, the vibration signal and the key phase signal are collected through the signal collection point, so that the operation frequency is calculated according to the vibration signal and the key phase signal, and the purpose of sampling the operation frequency of the target turbine compressor is achieved.
Referring to fig. 4, fig. 4 is a block diagram illustrating a fault diagnosis system 100 for a large-scale turbocompressor according to an embodiment of the present invention. Embodiments of the present application provide a large turbine compressor fault diagnosis system 100, comprising:
an equipment parameter acquisition module 110 for acquiring equipment parameters of the target turbine compressor;
the historical operation data set matching module 120 is configured to input the device parameters into a preset database to obtain a historical operation data set corresponding to the target turbine compressor, where the historical operation data set includes a first historical operation event, a historical operation fault parameter and a historical operation feature;
the signal acquisition point layout module 130 is used for layout signal acquisition points according to the historical operation fault parameters;
the sampling period setting module 140 is configured to set a sampling period according to the historical operation characteristics, and collect operation information of the target turbine compressor in the operation process through the signal collection point according to the sampling period;
The historical operation event matching module 150 is configured to match the operation information with all the first historical operation events, and calculate a matching degree between the operation information and any one of the first historical operation events;
a second historical operation event obtaining module 160, configured to obtain all second historical operation events with matching degrees not less than a preset matching degree;
the matching feature information obtaining module 170 is configured to obtain matching feature information of any second historical operation event based on the operation information;
the analysis module 180 is configured to input the matching feature information of all the second historical operating events into a preset fault analysis model for analysis, so as to obtain an analysis result, where the analysis result includes a fault cause parameter.
Specifically, the system searches a turbine compressor consistent with the equipment parameters of the target turbine compressor in a preset database, and further obtains a historical operation data set corresponding to the target turbine compressor. And then setting signal acquisition points at a plurality of positions of the target turbine compressor according to the historical operation fault parameters in the historical operation data set so as to acquire signals in the operation process of the target turbine compressor more accurately. Meanwhile, a sampling period is set according to historical operation characteristics, so that the sampling period is matched with a target turbine compressor, and a better acquisition effect is achieved. And then, carrying out one-to-one comparison and matching on parameters in the operation information and any first historical operation event to judge the matching degree of the parameters and the first historical operation event, taking the first historical operation event with the matching degree being greater than or equal to the preset matching degree as a second historical operation event, and determining the second historical operation event from all the first historical operation events, wherein the second historical operation event is more consistent with the operation information. And then inputting the matching characteristic information between any second historical operation event and the operation information into a preset fault analysis model for analysis, so as to obtain fault cause parameters corresponding to the operation information by combining the matching characteristic information of all second historical operation events, namely obtaining the fault cause parameters of the target turbine compressor, and further carrying out fault diagnosis and fault elimination on the turbine compressor according to signals in the operation process of the whole turbine compressor, so as to accurately locate the fault cause, analyze the fault cause by fully and effectively combining the state performance signals in the operation process of the turbine compressor, and effectively improve the diagnosis precision and diagnosis efficiency of the turbine compressor.
In some implementations of this embodiment, the analysis module 180 includes:
the matching numerical value calculation unit is used for calculating a corresponding matching numerical value according to any matching characteristic information and a preset characteristic weight;
the first fault type judging unit is used for judging a first fault type corresponding to the operation information according to the matching numerical value;
the second fault type determining unit is used for traversing all the matching characteristic information to obtain all the first fault types and determining the second fault types according to all the first fault types;
the fault cause parameter obtaining unit is used for obtaining the fault cause parameter according to the operation information based on the second fault type.
In the implementation process, first, for any matching feature information, a matching value of the operation information and a corresponding second historical operation event is calculated, and then the first fault type is determined according to a matching value range meeting the matching value. And traversing all the matching characteristic information, calculating the matching numerical value corresponding to the running information for each matching characteristic information, and further determining all the first fault types. And then the first fault type with the largest number is obtained as a second fault type, wherein the second fault type is the fault type corresponding to the operation information, and the effect of accurately positioning the fault type of the target turbine compressor is achieved. After determining the fault type corresponding to the operation information, searching according to the operation information to obtain fault cause parameters. Therefore, the purpose of analyzing the matching characteristic information of all the second historical operation events through a preset fault analysis model to obtain fault cause parameters is achieved.
In some implementations of the present embodiment, the above-described large turbine compressor fault diagnosis system 100 further includes:
the historical data collection module is used for pre-collecting and classifying historical data in the operation process of the plurality of turbine compressors, wherein the historical data comprise historical operation events, historical operation fault parameters and historical operation characteristics corresponding to the turbine compressors;
and the historical data storage module is used for storing the historical data of all the turbine compressors into a preset database. Thereby ensuring that the preset database stores historical data of a plurality of turbine compressors.
In some implementations of this embodiment, the historical data storage module includes:
the historical data conversion unit is used for sorting any historical data into a matrix-form data set;
the data set noise reduction unit is used for performing noise reduction treatment on the matrix-form data set to obtain noise-reduced historical data;
the noise reduction storage unit is used for storing the history data after the noise reduction processing into a preset database.
Specifically, firstly, any historical data is converted into a matrix-form data set, and as the matrix-form data set can sequentially express the historical data in a row and column mode, blank data in the historical data can be intuitively seen through the matrix-form data set. And then, carrying out noise reduction treatment on the matrix data set to eliminate blank data in the historical data, namely eliminating abnormal values, namely eliminating abnormal data which can influence the diagnosis precision, and ensuring the accuracy of the historical data in the preset database.
In some implementations of this embodiment, the signal sampling point includes a temperature sampling point, and the sampling period setting module 140 includes:
and the temperature sampling unit is used for sampling the operating temperature of the target turbine compressor through the temperature sampling points so as to generate a temperature sampling data set.
In some implementations of this embodiment, the sampling period setting module 140 includes:
and the frequency sampling unit is used for sampling the operating frequency of the target turbine compressor so as to generate a frequency sampling data set. Thus, whether the operating frequency of the target turbine compressor is abnormal can be further judged through the frequency sampling data set.
In some implementations of this embodiment, the frequency sampling unit includes:
the vibration signal acquisition subunit is used for acquiring vibration signals and key phase signals in the operation process of the target turbine compressor through the signal acquisition point;
and the operating frequency calculating subunit is used for calculating the operating frequency of the target turbine compressor according to the vibration signal and the key phase signal.
Specifically, in the operation process of the target turbine compressor, the vibration signal and the key phase signal are collected through the signal collection point, so that the operation frequency is calculated according to the vibration signal and the key phase signal, and the purpose of sampling the operation frequency of the target turbine compressor is achieved.
Referring to fig. 5, fig. 5 is a schematic block diagram of an electronic device according to an embodiment of the present application. The electronic device comprises a memory 101, a processor 102 and a communication interface 103, wherein the memory 101, the processor 102 and the communication interface 103 are electrically connected with each other directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 101 may be used to store software programs and modules, such as program instructions/modules corresponding to a large turbine compressor fault diagnosis system 100 provided in the embodiments of the present application, and the processor 102 executes the software programs and modules stored in the memory 101, thereby performing various functional applications and data processing. The communication interface 103 may be used for communication of signaling or data with other node devices.
The Memory 101 may be, but is not limited to, a random access Memory (Random Access Memory, RAM), a Read Only Memory (ROM), a programmable Read Only Memory (Programmable Read-Only Memory, PROM), an erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), an electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.
The processor 102 may be an integrated circuit chip with signal processing capabilities. The processor 102 may be a general purpose processor including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processing, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
It will be appreciated that the configuration shown in fig. 5 is merely illustrative, and that the electronic device may also include more or fewer components than shown in fig. 5, or have a different configuration than shown in fig. 5. The components shown in fig. 5 may be implemented in hardware, software, or a combination thereof.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners as well. The apparatus embodiments described above are merely illustrative, for example, flow diagrams and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the same, but rather, various modifications and variations may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. A fault diagnosis method for a large turbine compressor is characterized by comprising the following steps:
acquiring equipment parameters of a target turbine compressor;
inputting the equipment parameters into a preset database to obtain a historical operation data set corresponding to the target turbine compressor, wherein the historical operation data set comprises a first historical operation event, a historical operation fault parameter and a historical operation characteristic;
laying out signal acquisition points according to the historical operation fault parameters;
setting a sampling period according to the historical operation characteristics, and collecting operation information of the target turbine compressor in the operation process through the signal collecting point according to the sampling period;
Matching the operation information with all first historical operation events, and calculating the matching degree of the operation information with any first historical operation event;
acquiring all second historical operation events with the matching degree not smaller than a preset matching degree;
based on the operation information, obtaining matching characteristic information of any second historical operation event;
and inputting the matching characteristic information of all the second historical operation events into a preset fault analysis model for analysis to obtain an analysis result, wherein the analysis result comprises fault cause parameters.
2. The method for diagnosing a fault in a large turbine compressor as set forth in claim 1, wherein the step of inputting the matching characteristic information of all the second historical operating events into a predetermined fault analysis model for analysis, and obtaining the analysis result includes:
according to any matching characteristic information, calculating a corresponding matching numerical value according to a preset characteristic weight;
judging a first fault type corresponding to the operation information according to the matching value;
traversing all the matching characteristic information to obtain all the first fault types, and determining a second fault type according to all the first fault types;
And obtaining fault reason parameters according to the operation information based on the second fault type.
3. The method for diagnosing a fault in a large-scale turbocompressor as recited in claim 1, wherein before the step of inputting the plant parameters into a preset database to obtain the historical operating dataset corresponding to the target turbocompressor, further comprising:
collecting and classifying historical data in the operation process of a plurality of turbine compressors in advance, wherein the historical data comprise historical operation events, historical operation fault parameters and historical operation characteristics of the corresponding turbine compressors;
and storing the historical data of all the turbine compressors into a preset database.
4. The method for diagnosing a fault in a large-sized turbo compressor according to claim 3, wherein the step of saving the history data of all the turbo compressors to a preset database comprises:
sorting any historical data into a matrix data set;
performing noise reduction processing on the matrix data set to obtain noise-reduced historical data;
and storing the historical data after the noise reduction treatment into a preset database.
5. The method of diagnosing a fault in a large-sized turbo compressor according to claim 1, wherein the signal sampling points include temperature sampling points, and the step of collecting operation information during the operation of the target turbo compressor through the signal sampling points according to the sampling period includes:
And sampling the operating temperature of the target turbine compressor through the temperature sampling points to generate a temperature sampling data set.
6. The method of diagnosing a fault in a large-sized turbo compressor according to claim 1, wherein the step of collecting operation information during the operation of the target turbo compressor through the signal collection point according to the sampling period comprises:
the operating frequency of the target turbocompressor is sampled to generate a frequency sampling dataset.
7. The method of diagnosing a fault in a large turbine compressor as set forth in claim 6, wherein the step of sampling the operating frequency of the target turbine compressor to generate a frequency sampling dataset includes:
collecting vibration signals and key phase signals in the operation process of the target turbine compressor through the signal collecting points;
and calculating the operating frequency of the target turbine compressor according to the vibration signal and the key phase signal.
8. A large turbine compressor fault diagnosis system, comprising:
the device parameter acquisition module is used for acquiring the device parameters of the target turbine compressor;
the historical operation data set matching module is used for inputting the equipment parameters into a preset database to obtain a historical operation data set corresponding to the target turbine compressor, wherein the historical operation data set comprises a first historical operation event, a historical operation fault parameter and a historical operation characteristic;
The signal acquisition point layout module is used for layout signal acquisition points according to the historical operation fault parameters;
the sampling period setting module is used for setting a sampling period according to the historical operation characteristics and collecting operation information of the target turbine compressor in the operation process through the signal acquisition point according to the sampling period;
the historical operation event matching module is used for matching the operation information with all the first historical operation events and calculating the matching degree of the operation information with any one of the first historical operation events;
the second historical operation event acquisition module is used for acquiring all second historical operation events with the matching degree not smaller than a preset matching degree;
the matching characteristic information acquisition module is used for acquiring matching characteristic information of any second historical operation event based on the operation information;
the analysis module is used for inputting the matching characteristic information of all the second historical operation events into a preset fault analysis model for analysis, and obtaining an analysis result, wherein the analysis result comprises fault cause parameters.
9. An electronic device, comprising:
a memory for storing one or more programs;
A processor;
the method of any of claims 1-7 is implemented when the one or more programs are executed by the processor.
10. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210344877.7A CN114754018B (en) | 2022-04-02 | 2022-04-02 | Large turbine compressor fault diagnosis method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210344877.7A CN114754018B (en) | 2022-04-02 | 2022-04-02 | Large turbine compressor fault diagnosis method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114754018A CN114754018A (en) | 2022-07-15 |
| CN114754018B true CN114754018B (en) | 2024-02-20 |
Family
ID=82329014
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210344877.7A Active CN114754018B (en) | 2022-04-02 | 2022-04-02 | Large turbine compressor fault diagnosis method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114754018B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110687896A (en) * | 2019-10-24 | 2020-01-14 | 利维智能（深圳）有限公司 | Fault diagnosis method, device, equipment and readable medium |
| CN110879372A (en) * | 2019-12-03 | 2020-03-13 | 中南大学 | Traction system main loop earth fault diagnosis method and system based on feature correlation |
| CN111060337A (en) * | 2019-12-05 | 2020-04-24 | 杭州哲达科技股份有限公司 | Running equipment real-time fault diagnosis method based on expert system |
| CN112346941A (en) * | 2019-08-08 | 2021-02-09 | 北京国双科技有限公司 | Fault diagnosis method and device |
| CN113377564A (en) * | 2021-06-08 | 2021-09-10 | 珠海格力电器股份有限公司 | Fault diagnosis method and device, computer equipment and storage medium |
-
2022
- 2022-04-02 CN CN202210344877.7A patent/CN114754018B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112346941A (en) * | 2019-08-08 | 2021-02-09 | 北京国双科技有限公司 | Fault diagnosis method and device |
| CN110687896A (en) * | 2019-10-24 | 2020-01-14 | 利维智能（深圳）有限公司 | Fault diagnosis method, device, equipment and readable medium |
| CN110879372A (en) * | 2019-12-03 | 2020-03-13 | 中南大学 | Traction system main loop earth fault diagnosis method and system based on feature correlation |
| CN111060337A (en) * | 2019-12-05 | 2020-04-24 | 杭州哲达科技股份有限公司 | Running equipment real-time fault diagnosis method based on expert system |
| CN113377564A (en) * | 2021-06-08 | 2021-09-10 | 珠海格力电器股份有限公司 | Fault diagnosis method and device, computer equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114754018A (en) | 2022-07-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9818242B2 (en) | Gas turbine engine anomaly detections and fault identifications | |
| US6980910B1 (en) | Extensions to dynamically configurable process for diagnosing faults in rotating machines | |
| CN109000921B (en) | Method for diagnosing main shaft fault of wind turbine generator | |
| CN113834657A (en) | Bearing fault early warning and diagnosis method based on improved MSET and frequency spectrum characteristics | |
| CN105866250A (en) | Vibration-based identification method for cracks of ventilating vane | |
| US10168306B2 (en) | Time reference derivation from time of arrival measurements | |
| CN115795744B (en) | Method for compiling aviation turbofan engine component level low-cycle fatigue life load spectrum | |
| CN112443479A (en) | Compressor fault diagnosis method and device, storage medium and electronic equipment | |
| CN112459971A (en) | Abnormal vibration detection method for tower of wind generating set | |
| Fan et al. | Shifting straight-line fitting method to calculate blade vibration based on blade tip timing | |
| Celis et al. | Steady state detection in industrial gas turbines for condition monitoring and diagnostics applications | |
| CN114754018B (en) | Large turbine compressor fault diagnosis method and system | |
| CN117113200B (en) | Rotor fault diagnosis method, device, electronic equipment and medium | |
| CN111241653B (en) | Multi-dimensional blade error performance influence simulation implementation method based on Monte Carlo method | |
| CN102798519A (en) | System and method for monitoring health of airfoils | |
| Ma et al. | An interpretability research of the Xgboost algorithm in remaining useful life prediction | |
| CN114857067B (en) | Full life cycle monitoring and management method and system for large turbine compressor | |
| US20220364478A1 (en) | Training of machine learning models for data-driven decision-making | |
| CN115422714A (en) | Knowledge condition hybrid driving running state monitoring method for gas turbine | |
| CN114239708A (en) | Combustion engine abnormity detection method based on quality control chart theory | |
| CN114509158A (en) | Acoustic-vibration fused blade crack fault detection method and application | |
| CN114295367A (en) | Wind turbine generator gearbox working condition online monitoring method | |
| Zhao et al. | Health indicator selection and health assessment of rolling element bearing | |
| CN115952628B (en) | Method for calculating equivalent stress of disk center of turbine disk of aero-engine | |
| CN112733351B (en) | Method and device for processing axial force data of impeller of centrifugal compressor |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |