CN118258606A - Bearing fault diagnosis method for traction motor of electric forklift in complex noise environment - Google Patents
Bearing fault diagnosis method for traction motor of electric forklift in complex noise environment Download PDFInfo
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- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention discloses a bearing fault diagnosis method of an electric forklift traction motor in a complex noise environment, which comprises the following steps: 1. collecting working phase current of an electric fork-lift motor under a normal working environment; 2. constructing a phase current data set and constructing a one-dimensional convolutional neural network, comprising: each hidden layer comprises a one-dimensional convolution unit, a compression unit, a linear rectification unit and a regularization unit; 3. the network processes the data and obtains a fault prediction result; 4. and training and optimizing the convolutional neural network to obtain a trained electric forklift traction motor bearing fault diagnosis model. The invention has better environment noise anti-interference capability, has high accuracy on data acquisition and processing, can timely and accurately reflect the mechanical condition of the traction motor bearing of the electric forklift when the motor normally operates, and has extremely high practical value in engineering practical application.
Description
Technical Field
The invention belongs to the field of bearing fault diagnosis of common motors, and particularly relates to a bearing fault diagnosis method of an electric forklift traction motor in a complex noise environment.
Background
Traction motors are the primary power means for driving the vehicle concerned, the function of which can be summarized as converting electrical energy in a battery pack into mechanical energy for driving the vehicle. Electric forklifts are now widely used in a variety of logistic transportation and industrial production activities, which require reliable, high quality traction motors to maintain their proper operation.
Through investigation, bearing failure is one of the most common failures in the everyday use of electric forklift traction motors. The motor bearing faults are often caused by slow aging of the bearing itself to reach the fatigue limit or slight damage to enlarge cracks after severe vibration, and the slow aging or the slight damage is difficult to monitor and find in normal operation; in addition, the working environment of the forklift motor belongs to a complex noise environment, and for general vibration signal detection, the fault detection result is inaccurate and frequently happens due to excessive interference data. Therefore, it is an extremely complex task to detect bearing faults in a timely and accurate manner by the existing fault detection techniques.
In addition, the neural network commonly used at present can cause conditions of low precision or time consumption and the like in the process of processing related data sequences in the fault detection and analysis of the traction motor of the electric forklift.
Disclosure of Invention
The invention aims to solve the defects of the prior art, and provides a bearing fault diagnosis method for an electric forklift traction motor under a complex noise environment, so that a current signal during fault can be accurately analyzed, whether bearing faults occur and which type of bearing faults belong to can be accurately judged, and the timeliness and the accuracy of fault diagnosis are improved.
In order to achieve the aim of the invention, the invention adopts the following technical scheme:
the invention relates to a bearing fault diagnosis method for an electric forklift traction motor in a complex noise environment, which is characterized by comprising the following steps:
step 1: acquiring a single-phase fault current data set I t＝{i1,t,i2,t,i3,t,...,in,t,...,iN,t of any one of phase A current, phase B current and phase C current of a traction motor of the electric forklift at the time T by using a phase current measurement link, wherein I n,t represents the nth single-phase current at the time T, and T is more than or equal to 0 and less than or equal to T s,Ts and is the total sampling time; n is more than 0 and less than or equal to N, wherein N is the total number of fault phase currents;
Let i n,t have a tag value of b n,t, where b e [1, D ], D is the number of failed species;
step 2: constructing a one-dimensional convolutional neural network, comprising: an input layer, K hidden layers and an output layer; wherein each hidden layer comprises: the device comprises a one-dimensional convolution unit, a compression unit, a linear rectification unit and a regularization unit;
Step 2.1: the input layer carries out convolution processing on the input i n,t to obtain a fault convolution characteristic i n,t *;
Step 2.2: when k=1, i n,t * is input into a kth hidden layer, a one-dimensional convolution unit processes i n,t * to obtain a kth information hidden state W n,t,k, a compression unit of the kth hidden layer processes the kth information hidden state W n,t,k to obtain a kth fault information feature X n,t,k, a linear rectification unit of the kth hidden layer processes X n,t,k to obtain a kth fault correction feature z n,t,k,zn,t,k, and the kth fault correction feature z n,t,k,zn,t,k is input into a regularization unit of the k hidden layer to process the kth fault information feature J n,t,k;
When k=2, 3, … and K, J n,t,k-1 inputs the K hidden layer for processing to obtain a K fault feature J n,t,k, and finally the K hidden layer outputs a K fault feature J n,t,K;
Step 2.3: the output layer processes J n,t,K to obtain a predicted tag value b n,t * of i n,t and outputs the predicted tag value b n,t *;
Step 4: based on b n,t and b n,t *, constructing a cross entropy loss function of a one-dimensional convolutional neural network, calculating the cross entropy loss function to update network parameters when training the convolutional neural network by using a gradient descent method, and stopping training until the cross entropy loss function converges, so as to obtain a trained electric fork truck traction motor bearing fault diagnosis model, and performing fault prediction on abnormal phase current when the electric fork truck traction motor works.
The electronic device of the present invention includes a memory for storing a program for supporting the processor to execute the bearing failure diagnosis method, and a processor configured to execute the program stored in the memory.
The invention relates to a computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being run by a processor, performs the steps of the bearing fault diagnosis method.
Compared with the prior art, the invention has the beneficial effects that:
1. According to the invention, whether the bearing of the traction motor of the electric forklift is faulty or not is indirectly analyzed by analyzing the change or abnormality of the current signal, and monitoring equipment is not required to be directly arranged at the bearing of the traction motor of the electric forklift, so that the complexity and inaccuracy of directly detecting the fault of the bearing are avoided, and the service life of corresponding diagnosis equipment can be prolonged by adopting the fault diagnosis mode;
2. According to the invention, the diagnosis of the bearing faults of the traction motor of the electric forklift can be completed by analyzing the current signals, the coordination with other fault diagnosis models can be realized, other faults of the motor can also cause abnormal changes of certain current signals, and the abnormal changes are different from the current signal law of the bearing faults, so that the model can be upgraded in the future to diagnose various common fault forms of the traction motor of the electric forklift, including the bearing faults, as long as the fault types are strictly distinguished in the model;
3. compared with a two-dimensional convolutional neural network, the method has the advantages that the one-dimensional convolutional neural network is adopted to process the current data set, the network structure is simpler, the efficiency is higher, and the accuracy is guaranteed;
4. The invention trains the bearing fault diagnosis model of the electric forklift traction motor by utilizing the convergence of the cross entropy loss function, and the specific process comprises the steps of calculating the cross entropy loss function to update network parameters, and stopping training until the cross entropy loss function converges, so that the trained electric forklift traction motor bearing fault diagnosis model is obtained, the accuracy of fault diagnosis results is improved, the interference received under the complex noise environment is small, and the bearing fault of the electric forklift traction motor can be diagnosed in time.
Drawings
FIG. 1 is a schematic flow chart of a method for diagnosing bearing faults of an electric fork-lift traction motor in a complex noise environment;
FIG. 2 is a training flow chart of a one-dimensional convolutional neural network model provided by the invention;
Fig. 3 is a schematic diagram of a one-dimensional convolutional neural network according to the present invention.
Detailed Description
In this embodiment, a bearing fault diagnosis method for an electric forklift traction motor in a complex noise environment is based on different current signal waveform change rules caused by various bearing faults of the electric forklift traction motor, and compares a current signal during working with a normal signal to discriminate whether the bearing fault occurs in the electric forklift traction motor. As shown in fig. 1, the bearing fault diagnosis method includes the steps of:
Firstly, measuring line current by using a phase current measuring link, and further obtaining a single-phase fault current data set I t＝{i1,t,i2,t,i3,t,...,in,t,...,iN,t of any one of phase current of an A phase, a B phase and a C phase of a traction motor of an electric forklift at a time T, wherein I n,t represents an nth single-phase current at the time T, and T is more than or equal to 0 and less than or equal to T s,Ts and is the total sampling time; n is more than 0 and less than or equal to N, N is the total number of fault phase currents, and then, the label value of i n,t is b n,t, wherein b epsilon [1, D ], and D is the number of fault types.
Then, a one-dimensional convolutional neural network is constructed, comprising: an input layer, K hidden layers and an output layer; wherein each hidden layer comprises: the device comprises a one-dimensional convolution unit, a compression unit, a linear rectification unit and a regularization unit.
As shown in fig. 2, a model training flowchart of the one-dimensional convolutional neural network used in the present embodiment is shown. Inputting the current data set into a one-dimensional convolutional neural network to be built, setting a network structure environment, configuring parameters, transmitting signals forward into the network, processing initial signals, comparing the initial signals with standard values, calculating errors, smoothly outputting the errors when the requirements are met, resetting the parameters when the requirements are not met, and repeating the processes until all current signals can be smoothly output, namely marking that the model training of the one-dimensional convolutional neural network is finished.
As shown in fig. 3, a schematic structure of the one-dimensional convolutional neural network is shown.
Inputting the current signal data set into an input layer, and carrying out convolution processing on the input i n,t by the input layer to obtain a fault convolution characteristic i n,t *; then, when k=1, i n,t * is input into a kth hidden layer, a one-dimensional convolution unit processes i n,t * to obtain a kth information hidden state W n,t,k, a compression unit of the kth hidden layer processes the kth information hidden state W n,t,k to obtain a kth fault information feature X n,t,k, a linear rectification unit of the kth hidden layer processes X n,t,k to obtain a kth fault correction feature z n,t,k,zn,t,k, and the kth fault correction feature z n,t,k,zn,t,k is input into a regularization unit of the k hidden layer to process the kth fault information feature J n,t,k;
Similarly, when k=2, 3, … and K, J n,t,k-1 inputs the K hidden layer for processing to obtain a K fault feature J n,t,k, and finally the K hidden layer outputs a K fault feature J n,t,K; finally, the output layer processes J n,t,K to obtain the predicted tag value b n,t * of i n,t, and outputs the predicted tag value b n,t *.
Next, based on b n,t and b n,t *, constructing a cross entropy loss function of a one-dimensional convolutional neural network, calculating the cross entropy loss function to update network parameters when training the convolutional neural network by using a gradient descent method, and stopping training until the cross entropy loss function converges, thereby obtaining a trained electric forklift traction motor bearing fault diagnosis model for carrying out fault prediction on abnormal phase current when the electric forklift traction motor works.
In this embodiment, an electronic device includes a memory for storing a program supporting the processor to execute the above method, and a processor configured to execute the program stored in the memory.
In this embodiment, a computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the method described above.
In addition, the neural network of the present invention has made significant progress in both training time and accuracy, and network depth is enhanced to increase the potential for acceptance domains and overall accuracy.
One-dimensional convolutional neural networks not only have many advantages over two-dimensional convolutional neural networks, but also exhibit superior performance over RNNs. Moreover, the causal relationship of the use of the method relieves the general limitations (gradient explosion and insufficient memory) of the circulation model. The neural network used in the invention enables the network to rely on front and back by removing links layer by layer. At the same time, the network adopts a more reliable one-dimensional expansion convolution to expand the receptive field.
Compared with the traditional method, the method has excellent reliability, economy and timeliness.
Claims (3)
1. The bearing fault diagnosis method of the traction motor of the electric forklift in the complex noise environment is characterized by comprising the following steps:
step 1: acquiring a single-phase fault current data set I t＝{i1,t,i2,t,i3,t,...,in,t,...,iN,t of any one of phase A current, phase B current and phase C current of a traction motor of the electric forklift at the time T by using a phase current measurement link, wherein I n,t represents the nth single-phase current at the time T, and T is more than or equal to 0 and less than or equal to T s,Ts and is the total sampling time; n is more than 0 and less than or equal to N, wherein N is the total number of fault phase currents;
Let i n,t have a tag value of b n,t, where b e [1, D ], D is the number of failed species;
step 2: constructing a one-dimensional convolutional neural network, comprising: an input layer, K hidden layers and an output layer; wherein each hidden layer comprises: the device comprises a one-dimensional convolution unit, a compression unit, a linear rectification unit and a regularization unit;
Step 2.1: the input layer carries out convolution processing on the input i n,t to obtain a fault convolution characteristic i n,t *;
Step 2.2: when k=1, i n,t * is input into a kth hidden layer, a one-dimensional convolution unit processes i n,t * to obtain a kth information hidden state W n,t,k, a compression unit of the kth hidden layer processes the kth information hidden state W n,t,k to obtain a kth fault information feature X n,t,k, a linear rectification unit of the kth hidden layer processes X n,t,k to obtain a kth fault correction feature z n,t,k,zn,t,k, and the kth fault correction feature z n,t,k,zn,t,k is input into a regularization unit of the k hidden layer to process the kth fault information feature J n,t,k;
When k=2, 3, … and K, J n,t,k-1 inputs the K hidden layer for processing to obtain a K fault feature J n,t,k, and finally the K hidden layer outputs a K fault feature J n,t,K;
Step 2.3: the output layer processes J n,t,K to obtain a predicted tag value b n,t * of i n,t and outputs the predicted tag value b n,t *;
Step 4: based on b n,t and b n,t *, constructing a cross entropy loss function of a one-dimensional convolutional neural network, calculating the cross entropy loss function to update network parameters when training the convolutional neural network by using a gradient descent method, and stopping training until the cross entropy loss function converges, so as to obtain a trained electric fork truck traction motor bearing fault diagnosis model, and performing fault prediction on abnormal phase current when the electric fork truck traction motor works.
2. An electronic device comprising a memory and a processor, wherein the memory is configured to store a program that supports the processor to execute the bearing fault diagnosis method of claim 1, the processor being configured to execute the program stored in the memory.
3. A computer readable storage medium having a computer program stored thereon, characterized in that the computer program when run by a processor performs the steps of the bearing fault diagnosis method of claim 1.
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