CN111220379B - Fault diagnosis method and device for traction motor transmission system - Google Patents
Fault diagnosis method and device for traction motor transmission system Download PDFInfo
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Abstract
The method is applied to a fault diagnosis system of a traction motor transmission system, the method takes a train traction motor transmission system as an object, and utilizes the fault diagnosis parameters of the traction motor transmission system to call a multi-information fusion diagnosis model on the premise of not additionally adding a sensor and not additionally carrying out equipment transformation on the train traction motor transmission system, so that the failure fault probability of a coupling part of the traction motor transmission system is realized, and the diagnosis of the failure fault of the coupling part of the traction motor transmission system is realized according to the failure fault probability of the coupling part of the traction motor transmission system and the fault diagnosis parameters of the traction motor transmission system, thereby improving the diagnosis accuracy of the failure fault of the traction motor transmission system.
Description
Technical Field
The application relates to the technical field of fault diagnosis, in particular to a fault diagnosis method and device for a traction motor transmission system.
Background
Along with the development of motor train units in China towards stable high speed, a traction motor transmission system is used as one of key components for vehicle transmission and plays a critical role in train operation, so that higher requirements are put forward on safe and stable operation of trains.
At present, the failure fault diagnosis of the coupling parts of a traction motor transmission system is mainly realized by monitoring the temperature and vibration of a gear box, an axle box and a motor on line, and a vibration sensor is additionally arranged on a vehicle for vibration monitoring, but only a small number of motor train unit vehicles are additionally provided with the vibration sensor at present, and after failure faults of the coupling parts such as the gear box, a coupling, a bearing and the like occur in the actual operation process, the vibration has no obvious characteristic, at the moment, the adoption of vibration detection cannot be applied, and only the temperature diagnosis has limitations and low accuracy.
Disclosure of Invention
Based on the problems in the existing fault diagnosis method for the traction motor transmission system, the fault diagnosis method and device for the traction motor transmission system are provided, the train traction motor transmission system is taken as a research object, and the fault diagnosis of the traction motor transmission system is realized on the premise that a sensor is not additionally arranged and the equipment transformation is not additionally carried out on the train traction motor transmission system, so that the fault diagnosis accuracy of the traction motor transmission system is improved.
In order to achieve the above object, the present application provides the following technical solutions:
a fault diagnosis method for a traction motor transmission system is applied to the fault diagnosis system for the traction motor transmission system, and comprises the following steps:
calling a multi-information fusion diagnosis model, taking a traction motor transmission system fault diagnosis parameter as input to carry out fault diagnosis on a coupling part of a traction motor transmission system, and obtaining failure fault probability of the coupling part of the traction motor transmission system, wherein the multi-information fusion diagnosis model is constructed according to traction motor transmission system sample data, the traction motor transmission system sample data comprises traction motor transmission system fault diagnosis parameters corresponding to a coupling failure state of a gear box coupling part, a coupling part and a bearing coupling part, and the traction motor transmission system fault diagnosis parameters comprise: the motor temperature, the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value;
and judging the state of the connecting part of the traction motor transmission system according to the failure fault probability of the connecting part of the traction motor transmission system, the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value.
Preferably, the method for acquiring the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value specifically includes:
obtaining raw control signals for the traction motor drive system, the raw control signals comprising: speed signals, motor slip signals and torque signals;
and decomposing the original control signals respectively, and extracting fluctuation characteristic values corresponding to the control signals corresponding to the third layer to obtain the speed fluctuation characteristic value, the slip fluctuation characteristic value and the moment fluctuation characteristic value.
Preferably, after obtaining the original control signal of the traction motor transmission system, the method further comprises the following steps:
filtering the original control signal to obtain a filtered control signal;
then, the decomposing is performed on the original control signals respectively, and the fluctuation eigenvalues corresponding to the control signals corresponding to the third layer are extracted, specifically:
and taking the filtered control signals as original control signals, decomposing the filtered control signals respectively, and extracting fluctuation characteristic values corresponding to the control signals corresponding to the third layer.
Preferably, the decomposing the original control signals respectively and extracting the fluctuation eigenvalues corresponding to the control signals corresponding to the third layer specifically include:
s1: identifying the original control signal
And performing envelope fitting according to all the maximum value points to obtain an upper envelope, and marking as
；
S2: extracting the original control signal
And performing envelope fitting according to all the minimum value points to obtain a lower envelope, and marking as
；
S3: according to the upper envelope line
And the lower envelope line
Calculate the average of the upper and lower envelopes, note
；
S4: according to the original control signal
And the average value of the upper and lower envelope lines
Build a new control signal, note
；
S5: repeating the steps S1-S4 by using the new control signal as an original control signal, and sequentially decomposing the control signal corresponding to the ith layer;
s6: calculating the fluctuation characteristic value according to a preset calculation formula by using the data value of the control signal corresponding to the third layer to obtain the fluctuation characteristic value corresponding to the control signal corresponding to the third layer, wherein the preset calculation formula is
Wherein, in the step (A),
in order to be a characteristic value of the fluctuation,
is the value of the j-th point of the data,
in order to be the mean value of the data,
is the data length.
Preferably, the process for constructing the multi-information fusion diagnosis model specifically includes:
acquiring sample data of a traction motor transmission system;
and training multi-information fusion diagnosis by adopting a Bayesian failure probability estimation algorithm based on the sample data of the traction motor transmission system, and constructing the multi-information fusion diagnosis model for fault diagnosis and identification of the traction motor transmission system.
A traction motor transmission system fault diagnosis device is applied to a traction motor transmission system fault diagnosis system, and comprises:
the system comprises a first processing unit and a second processing unit, wherein the first processing unit is used for calling a multi-information fusion diagnosis model, the multi-information fusion diagnosis model is used for carrying out traction motor transmission system fault diagnosis by taking traction motor transmission system fault diagnosis parameters as input, and obtaining the failure fault probability of a coupling component of a traction motor transmission system, the multi-information fusion diagnosis model is constructed according to traction motor transmission system sample data, the traction motor transmission system sample data comprises traction motor transmission system fault diagnosis parameters corresponding to the failure fault state of the coupling of a gear box coupling component, a coupling component and a bearing coupling component, and the traction motor transmission system fault diagnosis parameters comprise: the motor temperature, the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value;
and the second processing unit is used for judging the state of the connecting part of the traction motor transmission system according to the failure fault probability of the connecting part of the traction motor transmission system, the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value.
Preferably, the first processing unit is further specifically configured to:
obtaining raw control signals for the traction motor drive system, the raw control signals comprising: speed signals, motor slip signals and torque signals;
and decomposing the original control signal, and extracting a fluctuation characteristic value corresponding to the control signal corresponding to the third layer to obtain the speed fluctuation characteristic value, the slip fluctuation characteristic value and the moment fluctuation characteristic value.
Preferably, the first processing unit is further specifically configured to:
filtering the original control signal to obtain a filtered control signal;
and taking the filtered control signals as original control signals, decomposing the filtered control signals respectively, and extracting fluctuation characteristic values corresponding to the control signals corresponding to the third layer.
A storage medium comprising a stored program, wherein the program, when executed, controls an apparatus in which the storage medium is located to perform a traction motor drive system fault diagnosis method as described above.
An electronic device comprising at least one processor, and at least one memory, bus connected with the processor; the processor and the memory complete mutual communication through the bus; the processor is configured to invoke program instructions in the memory to perform the traction motor drive system fault diagnostic method as described above.
The method for diagnosing the faults of the transmission system of the traction motor is applied to a fault diagnosis system of the transmission system of the traction motor, a multi-information fusion diagnosis model is built according to sample data of the transmission system of the traction motor, fault diagnosis of a coupling component of the transmission system of the traction motor is carried out by calling the multi-information fusion diagnosis model and taking fault diagnosis parameters of the transmission system of the traction motor as input, and the failure fault probability of the coupling component of the transmission system of the traction motor is obtained, wherein the fault diagnosis parameters of the transmission system of the traction motor comprise: the motor temperature, the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value; and judging the state of the connecting part of the traction motor transmission system according to the failure fault probability of the connecting part of the traction motor transmission system, the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value.
According to the method, the train traction motor transmission system is used as a research object, on the premise that a sensor is not additionally arranged and equipment transformation is not additionally carried out on the train traction motor transmission system, a multi-information fusion diagnosis model is called by utilizing the traction motor transmission system failure diagnosis parameters, the failure fault probability of the coupling part of the traction motor transmission system is realized, the diagnosis of the failure fault of the coupling part of the traction motor transmission system is realized according to the failure fault probability of the coupling part of the traction motor transmission system and the traction motor transmission system failure diagnosis parameters, and therefore the diagnosis accuracy of the failure fault of the traction motor transmission system is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of a traction motor drive system fault diagnosis system provided by an embodiment of the present application;
FIG. 2 is a flow chart of a method for diagnosing a fault in a traction motor drive system according to an embodiment of the present disclosure;
FIG. 3 is a flowchart of one embodiment of a method for obtaining fault diagnosis parameters of a traction motor drive system according to an embodiment of the present disclosure;
FIG. 4 is a flowchart of another embodiment of a method for obtaining a fault diagnosis parameter of a traction motor drive system according to an embodiment of the present disclosure;
fig. 5 is a flowchart of an embodiment of the present disclosure, which is used to decompose the original control signals and extract a fluctuation feature value corresponding to a control signal corresponding to a third layer;
FIG. 6 is a flowchart of a specific implementation of a process for constructing a multi-information fusion diagnostic model according to an embodiment of the present disclosure;
FIG. 7 is a schematic structural diagram of a traction motor drive system fault diagnosis device according to an embodiment of the present disclosure;
fig. 8 is a schematic structural diagram of an electronic device disclosed in an embodiment of the present application.
Detailed Description
The application provides a method and a device for diagnosing faults of a traction motor transmission system, which are applied to a fault diagnosis module in the traction motor transmission system fault diagnosis system shown in figure 1, wherein the motor transmission system fault diagnosis system comprises: the system comprises an acquisition module 10, a fault diagnosis module 11 and a state output module 12, wherein the acquisition module 10 is used for acquiring fault diagnosis parameters of a transmission system of the traction motor in real time, and the fault diagnosis parameters of the transmission system of the traction motor comprise: the motor temperature, the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value; the fault diagnosis module 11 is configured to call a multi-information fusion diagnosis model to perform fault diagnosis on a coupling component of a traction motor transmission system, obtain a failure fault probability of the coupling component of the traction motor transmission system, determine a state of a connection component of the traction motor transmission system according to the failure fault probability of the coupling component of the traction motor transmission system, the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value, and the state output module 12 is configured to output the state of the connection component of the traction motor transmission system.
It should be noted that, in the embodiment of the present application, the failure of the coupling component of the traction motor transmission system includes, but is not limited to, the coupling falling or fastening, the gear of the gearbox being stuck, the motor/axle box bearing being fastened, and other failures.
The invention provides a method and a device for diagnosing faults of a traction motor transmission system, and aims to provide the following steps: the method has the advantages that the diagnosis of the failure fault of the coupling part of the traction motor transmission system is realized by taking the train traction motor as an object on the premise of not additionally adding a sensor and not additionally carrying out equipment transformation on the train traction motor transmission system, so that the diagnosis accuracy of the failure fault of the coupling part of the traction motor transmission system is improved.
The technical solutions in 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 obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
As shown in fig. 2, an embodiment of the present application provides a flowchart of a method for diagnosing a fault of a motor drive system, where the method is applied to a system for diagnosing a fault of a motor drive system, and specifically includes the following steps:
step S201: and calling a multi-information fusion diagnosis model, and taking the fault diagnosis parameters of the traction motor transmission system as input to diagnose the fault of the coupling part of the traction motor transmission system to obtain the failure fault probability of the coupling part of the traction motor transmission system.
The multi-information fusion diagnosis model is constructed according to sample data of a traction motor transmission system, the sample data of the traction motor transmission system comprises traction motor transmission system fault diagnosis parameters corresponding to a connection failure fault state of a gear box connecting part, a coupling connecting part and a bearing connecting part, and the traction motor transmission system fault diagnosis parameters comprise: the motor temperature, the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value.
As shown in fig. 3, the method for obtaining the fault diagnosis parameter of the traction motor transmission system specifically includes the following steps:
s301: obtaining raw control signals for the traction motor drive system, the raw control signals comprising: speed signal, motor slip signal and moment signal.
S302: and decomposing the original control signal, and extracting a fluctuation characteristic value corresponding to a control signal corresponding to a third layer to obtain a fault diagnosis parameter of the traction motor transmission system, wherein the fault diagnosis parameter of the traction motor transmission system comprises the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value.
It should be noted that, during the operation of the motor train, when the coupling component of the traction motor transmission system fails to couple, the traction motor transmission system needs to provide torque to the wheel set through the coupling component, so that the operation state of each coupling component can be directly represented by the speed state, and the traction motor is used as the power output unit of the transmission system, and the speed signal and the slip signal of the traction motor include rich information on the operation state of the transmission system, so that when the coupling component of the traction motor transmission system fails to couple, the speed and the slip of the traction motor transmission system will change obviously. Therefore, in the embodiment of the application, the diagnosis of the failure fault of the coupling component of the traction motor transmission system is realized by taking the speed fluctuation characteristic value, the torque fluctuation characteristic value and the slip fluctuation characteristic value of the train traction motor transmission system as objects.
As shown in fig. 4, further, the method for obtaining the fault diagnosis parameter of the traction motor transmission system specifically includes the following steps:
s401: obtaining raw control signals for the traction motor drive system, the raw control signals comprising: speed signal, motor slip signal and moment signal.
S402: and filtering the original control signal to obtain a filtered control signal.
In the embodiment of the application, because the speed signal of the traction motor belongs to a time-varying non-stationary signal, the speed signal fluctuates in a very short time. In the acceleration, deceleration and uniform speed operation states, the overall trend of the speed keeps the increasing, decreasing and stable states, so that in order to eliminate the influence of speed fluctuation on the differential calculation and fault diagnosis, in order to eliminate the speed fluctuation and noise interference components, after the original control signal of the traction motor transmission system is obtained, the Savitzky-Golay convolution smoothing filtering algorithm is adopted in the embodiment of the application to filter the original control signal, and the filtered control signal is obtained.
S403: and taking the filtered control signals as original control signals, decomposing the filtered control signals respectively, and extracting fluctuation characteristic values corresponding to the control signals corresponding to a third layer to obtain fault diagnosis parameters of the traction motor transmission system, wherein the fault diagnosis parameters of the traction motor transmission system comprise the speed fluctuation characteristic values, the slip fluctuation characteristic values and the torque fluctuation characteristic values.
The embodiment of the application adopts Savitzky-Golay convolution smoothing filtering to smooth and denoise the speed signal, and avoids the problem that the speed fluctuates in a short time when the slip signal is calculated in the later period, so that the sensitivity to the running state of a traction motor transmission system is higher according to the slip signal calculated by speed and current, and the failure fault of a coupling part of the traction motor transmission system can be more effectively identified.
As shown in fig. 5, the decomposing the original control signals, extracting the fluctuation eigenvalues corresponding to the control signals corresponding to the third layer, specifically decomposing the speed signal, the moment signal, and the slip signal through EMD (empirical mode decomposition), and calculating the fluctuation eigenvalues of the third layer 3, taking the speed signal as an example, may specifically include the following steps:
s1: and identifying all maximum value points of the original speed signal, and performing envelope fitting according to all the maximum value points to obtain an upper envelope.
S2: and extracting all minimum value points of the original speed signal, and performing envelope fitting according to all the minimum value points to obtain a lower envelope.
The original velocity signal is recorded as
The above upper envelope is denoted as
The lower envelope is denoted by
。
S3: calculating an average value of upper and lower envelope lines from the upper and lower envelope lines.
The average of the upper and lower envelope lines is expressed as
。
S4: and constructing a new speed signal according to the original speed signal and the average value of the upper envelope line and the lower envelope line.
The new velocity signal is recorded as
。
S5: and repeating the steps S1-S4 by using the new speed signal as the original speed signal, and sequentially resolving the speed signal corresponding to the ith layer.
In the embodiment of the present application,
for the i-th layer of decomposition, will
If steps S1 to S4 are repeated as new signals, three-layer decomposition is performed in the embodiment of the present application.
S6: and calculating the fluctuation characteristic value according to a preset calculation formula by using the data value of the speed signal corresponding to the third layer to obtain the fluctuation characteristic value corresponding to the speed signal corresponding to the third layer.
In the embodiment of the present application, the predetermined calculation formula is
Wherein, in the step (A),
in order to be a characteristic value of the speed fluctuation,
is the value of the j-th point of the data,
in order to be the mean value of the data,
is the data length.
It should be noted that, under normal conditions, the fluctuation characteristics of the speed, torque and slip of the traction motor at each decomposition layer are in a stable state, and when an abnormal fluctuation characteristic occurs in a certain traction motor, it can be determined that the traction motor transmission system is abnormal.
The multi-information fusion diagnosis model is constructed according to sample data of a traction motor transmission system, wherein the sample data of the traction motor transmission system comprises traction motor transmission system fault diagnosis parameters corresponding to a connection failure state of a gear box connecting part, a coupling connecting part and a bearing connecting part.
As shown in fig. 6, the process of constructing the multi-information fusion diagnosis model specifically includes the following steps:
s601: and acquiring sample data of the transmission system of the traction motor.
S602: and training multi-information fusion diagnosis by adopting a Bayesian failure probability estimation algorithm based on the sample data of the traction motor transmission system, and constructing the multi-information fusion diagnosis model for fault diagnosis and identification of the traction motor transmission system.
Before fault diagnosis, firstly, sample data (motor temperature, speed fluctuation characteristic value, slip fluctuation characteristic value and torque fluctuation characteristic value) of a transmission system of a traction motor is sent to a diagnosis model, the probability of failure fault of a connecting part of the transmission system of the traction motor is determined by adopting a Bayesian failure probability estimation algorithm, then multi-information fusion diagnosis is corrected through the sample data of the transmission system of the traction motor, and the probability of failure fault of the connecting part of the transmission system of the traction motor can be determined by inputting the motor temperature, the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value in real time in the corrected multi-information fusion diagnosis.
The Bayesian failure probability estimation algorithm is a data probability calculation algorithm, and according to a Bayesian probability estimation theory, the formula is as follows:
wherein, in order to cause the connection failure of the traction motor transmission system,
the event speed fluctuation characteristic value exceeds the standard, the moment fluctuation characteristic value exceeds the standard, the slip fluctuation characteristic value exceeds the standard and the temperature exceeds the standard,
the probability of failure of the traction motor transmission system connection; to determine a priori failure probabilities based on existing traction motor drive train coupling failure data,
is the probability of the occurrence of an event under the current parameters,
is the probability of an event occurring.
It should be noted that the bayesian failure probability estimation algorithm belongs to the technical means commonly used by those skilled in the art, and the specific calculation process can be referred to the related technical data, which is not described in detail herein.
S202: and judging the state of the connecting part of the traction motor transmission system according to the failure fault probability of the connecting part of the traction motor transmission system, the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value.
In the embodiment of the application, the motor with the failure probability of more than 0.6 of the failure of the coupling part of the traction motor transmission system is recorded as
And recording the motors with the maximum torque fluctuation characteristic value, the maximum slip fluctuation characteristic value and the maximum speed fluctuation characteristic value, wherein the sum is a sequence of traction motors, and specifically, when the sum is the same, the failure fault of the coupling part of the traction motor transmission system can be judged.
In the embodiment of the application, a train traction motor transmission system is taken as a research object, on the premise that a sensor is not additionally arranged and equipment transformation is not additionally carried out on the train traction motor transmission system, a multi-information fusion diagnosis model is called by using traction motor transmission system fault diagnosis parameters, the failure fault probability of the coupling part of the traction motor transmission system is utilized, and the diagnosis of the failure fault of the coupling part of the traction motor transmission system is realized according to the failure fault probability of the coupling part of the traction motor transmission system and the traction motor transmission system fault diagnosis parameters, so that the diagnosis accuracy of the failure fault of the traction motor transmission system is improved.
Referring to fig. 7, based on the method for diagnosing a fault of a transmission system of a traction motor disclosed in the above embodiment, the present embodiment correspondingly discloses a device for diagnosing a fault of a transmission system of a traction motor, which is applied to the system for diagnosing a fault of a transmission system of a traction motor, and the device specifically includes: a first processing unit 701 and a second processing unit 702, wherein:
the first processing unit 701 is configured to invoke a multi-information fusion diagnosis model, perform traction motor transmission system fault diagnosis by using a traction motor transmission system fault diagnosis parameter as an input, and obtain a failure fault probability of a coupling component of a traction motor transmission system, where the multi-information fusion diagnosis model is constructed according to traction motor transmission system sample data, the traction motor transmission system sample data includes a traction motor transmission system fault diagnosis parameter corresponding to a coupling failure state of a gear box coupling component, a coupling component, and a bearing coupling component, and the traction motor transmission system fault diagnosis parameter includes: the motor temperature, the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value.
A second processing unit 702, configured to determine a state of the traction motor drive system connection component according to the probability of failure fault of the traction motor drive system coupling component, the speed fluctuation characteristic value, the slip fluctuation characteristic value, and the torque fluctuation characteristic value.
Preferably, the first processing unit 701 is further specifically configured to:
obtaining raw control signals for the traction motor drive system, the raw control signals comprising: speed signals, motor slip signals and torque signals;
and decomposing the original control signal, and extracting a fluctuation characteristic value corresponding to the control signal corresponding to the third layer to obtain the speed fluctuation characteristic value, the slip fluctuation characteristic value and the moment fluctuation characteristic value.
Preferably, the first processing unit 701 is further specifically configured to:
filtering the original control signal to obtain a filtered control signal;
and taking the filtered control signals as original control signals, decomposing the filtered control signals respectively, and extracting fluctuation characteristic values corresponding to the control signals corresponding to the third layer.
The traction motor transmission system fault diagnosis device comprises a processor and a memory, wherein the first processing unit, the second processing unit and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be provided with one or more than one kernel, the train traction motor transmission system is taken as a research object, on the premise that a sensor is not additionally arranged and the train traction motor transmission system is not additionally subjected to equipment transformation, a multi-information fusion diagnosis model is called by using the fault diagnosis parameters of the traction motor transmission system, the failure fault probability of the coupling part of the traction motor transmission system is realized, and the diagnosis of the failure fault of the coupling part of the traction motor transmission system is realized according to the failure fault probability of the coupling part of the traction motor transmission system and the fault diagnosis parameters of the traction motor transmission system, so that the diagnosis accuracy of the failure fault of the traction motor transmission system is improved. .
An embodiment of the present invention provides a storage medium having a program stored thereon, which when executed by a processor implements the traction motor drive system fault diagnosis method.
The embodiment of the invention provides a processor, which is used for running a program, wherein the program executes the fault diagnosis method of the traction motor transmission system when running.
An embodiment of the present invention provides an electronic device, as shown in fig. 8, the electronic device 80 includes at least one processor 801, at least one memory 802 connected to the processor, and a bus 803; the processor 801 and the memory 802 complete communication with each other through the bus 803; the processor 801 is configured to invoke program instructions in the memory 802 to perform the traction motor drive system fault diagnostic method described above.
The electronic device herein may be a server, a PC, a PAD, a mobile phone, etc.
The present application further provides a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device:
calling a multi-information fusion diagnosis model, taking a traction motor transmission system fault diagnosis parameter as input to carry out fault diagnosis on a coupling part of a traction motor transmission system, and obtaining failure fault probability of the coupling part of the traction motor transmission system, wherein the multi-information fusion diagnosis model is constructed according to traction motor transmission system sample data, the traction motor transmission system sample data comprises traction motor transmission system fault diagnosis parameters corresponding to a coupling failure state of a gear box coupling part, a coupling part and a bearing coupling part, and the traction motor transmission system fault diagnosis parameters comprise: the motor temperature, the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value;
and judging the state of the connecting part of the traction motor transmission system according to the failure fault probability of the connecting part of the traction motor transmission system, the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value.
Preferably, the method for acquiring the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value specifically includes:
obtaining raw control signals for the traction motor drive system, the raw control signals comprising: speed signals, motor slip signals and torque signals;
and decomposing the original control signals respectively, and extracting fluctuation characteristic values corresponding to the control signals corresponding to the third layer to obtain the speed fluctuation characteristic value, the slip fluctuation characteristic value and the moment fluctuation characteristic value.
Preferably, after obtaining the original control signal of the traction motor transmission system, the method further comprises the following steps:
filtering the original control signal to obtain a filtered control signal;
then, the decomposing is performed on the original control signals respectively, and the fluctuation eigenvalues corresponding to the control signals corresponding to the third layer are extracted, specifically:
and taking the filtered control signals as original control signals, decomposing the filtered control signals respectively, and extracting fluctuation characteristic values corresponding to the control signals corresponding to the third layer.
Preferably, the decomposing the original control signals respectively and extracting the fluctuation eigenvalues corresponding to the control signals corresponding to the third layer specifically include:
s1: identifying the original control signal
And performing envelope fitting according to all the maximum value points to obtain an upper envelope, and marking as
；
S2: extracting the original control signal
And performing envelope fitting according to all the minimum value points to obtain a lower envelope, and marking as
；
S3: according to the upper envelope line
And the lower envelope line
Calculate the average of the upper and lower envelopes, note
；
S4: according to the original control signal
And the average value of the upper and lower envelope lines
Build a new control signal, note
；
S5: repeating the steps S1-S4 by using the new control signal as an original control signal, and sequentially decomposing the control signal corresponding to the ith layer;
s6: calculating the fluctuation characteristic value according to a preset calculation formula by using the data value of the control signal corresponding to the third layer to obtain the fluctuation characteristic value corresponding to the control signal corresponding to the third layer, wherein the preset calculation formula is
Wherein, in the step (A),
in order to be a characteristic value of the fluctuation,
is the value of the j-th point of the data,
in order to be the mean value of the data,
is the data length.
Preferably, the process for constructing the multi-information fusion diagnosis model specifically includes:
acquiring sample data of a traction motor transmission system;
and training multi-information fusion diagnosis by adopting a Bayesian failure probability estimation algorithm based on the sample data of the traction motor transmission system, and constructing the multi-information fusion diagnosis model for fault diagnosis and identification of the traction motor transmission system.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a device includes one or more processors (CPUs), memory, and a bus. The device may also include input/output interfaces, network interfaces, and the like.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip. The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that 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, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (5)
1. A fault diagnosis method for a traction motor transmission system is applied to the fault diagnosis system for the traction motor transmission system, and comprises the following steps:
calling a multi-information fusion diagnosis model, taking a traction motor transmission system fault diagnosis parameter as input to carry out fault diagnosis on a coupling part of a traction motor transmission system, and obtaining failure fault probability of the coupling part of the traction motor transmission system, wherein the multi-information fusion diagnosis model is constructed according to traction motor transmission system sample data, the traction motor transmission system sample data comprises traction motor transmission system fault diagnosis parameters corresponding to a coupling failure state of a gear box coupling part, a coupling part and a bearing coupling part, and the traction motor transmission system fault diagnosis parameters comprise: the motor temperature, the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value;
judging the state of a connecting part of the traction motor transmission system according to the failure fault probability of the connecting part of the traction motor transmission system, the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value;
the method for acquiring the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value specifically comprises the following steps:
obtaining raw control signals for the traction motor drive system, the raw control signals comprising: speed signals, motor slip signals and torque signals;
decomposing the original control signals respectively, and extracting fluctuation characteristic values corresponding to the control signals corresponding to a third layer to obtain the speed fluctuation characteristic value, the slip fluctuation characteristic value and the moment fluctuation characteristic value;
after obtaining the original control signal of the traction motor transmission system, the method further comprises the following steps:
filtering the original control signal to obtain a filtered control signal;
then, the decomposing is performed on the original control signals respectively, and the fluctuation eigenvalues corresponding to the control signals corresponding to the third layer are extracted, specifically:
taking the filtered control signals as original control signals, decomposing the filtered control signals respectively, and extracting fluctuation characteristic values corresponding to the control signals corresponding to a third layer;
the decomposing of the original control signals and the extracting of the fluctuation eigenvalues corresponding to the control signals corresponding to the third layer are specifically as follows:
s1: identifying the original control signal
And performing envelope fitting according to all the maximum value points to obtain an upper envelope, and marking as
；
S2: extracting the original control signal
And performing envelope fitting according to all the minimum value points to obtain a lower envelope, and marking as
；
S3: according to the upper envelope line
And said lower envelopeThread
Calculate the average of the upper and lower envelopes, note
；
S4: according to the original control signal
And the average value of the upper and lower envelope lines
Build a new control signal, note
；
S5: repeating the steps S1-S4 by using the new control signal as an original control signal, and sequentially decomposing the control signal corresponding to the ith layer;
s6: calculating the fluctuation characteristic value according to a preset calculation formula by using the data value of the control signal corresponding to the third layer to obtain the fluctuation characteristic value corresponding to the control signal corresponding to the third layer, wherein the preset calculation formula is
Wherein, in the step (A),
in order to be a characteristic value of the fluctuation,
is data of
The value of the point is such that,
in order to be the mean value of the data,
is the data length.
2. The method according to claim 1, wherein the construction process of the multi-information fusion diagnosis model specifically comprises:
acquiring sample data of a traction motor transmission system;
and training multi-information fusion diagnosis by adopting a Bayesian failure probability estimation algorithm based on the sample data of the traction motor transmission system, and constructing the multi-information fusion diagnosis model for fault diagnosis and identification of the traction motor transmission system.
3. A traction motor transmission system fault diagnosis device is applied to a traction motor transmission system fault diagnosis system, and comprises:
the system comprises a first processing unit and a second processing unit, wherein the first processing unit is used for calling a multi-information fusion diagnosis model, the multi-information fusion diagnosis model is used for carrying out traction motor transmission system fault diagnosis by taking traction motor transmission system fault diagnosis parameters as input, and obtaining the failure fault probability of a coupling component of a traction motor transmission system, the multi-information fusion diagnosis model is constructed according to traction motor transmission system sample data, the traction motor transmission system sample data comprises traction motor transmission system fault diagnosis parameters corresponding to the failure fault state of the coupling of a gear box coupling component, a coupling component and a bearing coupling component, and the traction motor transmission system fault diagnosis parameters comprise: the motor temperature, the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value;
the second processing unit is used for judging the state of the connecting part of the traction motor transmission system according to the failure fault probability of the connecting part of the traction motor transmission system, the speed fluctuation characteristic value, the slip fluctuation characteristic value and the torque fluctuation characteristic value;
wherein the first processing unit is further configured to:
obtaining raw control signals for the traction motor drive system, the raw control signals comprising: speed signals, motor slip signals and torque signals;
decomposing the original control signals respectively, and extracting fluctuation characteristic values corresponding to the control signals corresponding to a third layer to obtain the speed fluctuation characteristic value, the slip fluctuation characteristic value and the moment fluctuation characteristic value;
the first processing unit is specifically further configured to:
filtering the original control signal to obtain a filtered control signal;
taking the filtered control signals as original control signals, decomposing the filtered control signals respectively, and extracting fluctuation characteristic values corresponding to the control signals corresponding to a third layer;
the decomposing of the original control signals and the extracting of the fluctuation eigenvalues corresponding to the control signals corresponding to the third layer are specifically as follows:
s1: identifying the original control signal
And performing envelope fitting according to all the maximum value points to obtain an upper envelope, and marking as
；
S2: extracting the original control signal
And performing envelope fitting according to all the minimum value points to obtain a lower envelope, and marking as
；
S3: according to the upper envelope line
And the lower envelope line
Calculate the average of the upper and lower envelopes, note
；
S4: according to the original control signal
And the average value of the upper and lower envelope lines
Build a new control signal, note
；
S5: repeating the steps S1-S4 by using the new control signal as an original control signal, and sequentially decomposing the control signal corresponding to the ith layer;
s6: calculating the fluctuation characteristic value according to a preset calculation formula by using the data value of the control signal corresponding to the third layer to obtain the fluctuation characteristic value corresponding to the control signal corresponding to the third layer, wherein the preset calculation formula is
Wherein, in the step (A),
in order to be a characteristic value of the fluctuation,
is data of
The value of the point is such that,
in order to be the mean value of the data,
is the data length.
4. A storage medium characterized in that the storage medium includes a stored program, wherein an apparatus in which the storage medium is located is controlled to execute the traction motor drive system fault diagnosis method according to any one of claims 1 to 2 when the program is executed.
5. An electronic device comprising at least one processor, and at least one memory, bus connected to the processor; the processor and the memory complete mutual communication through the bus; the processor is configured to invoke program instructions in the memory to perform the traction motor drive system fault diagnostic method of any of claims 1-2.
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