CN111898686A - Bearing fault identification method based on gated cyclic unit network - Google Patents
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Abstract
The invention designs a high-precision identification method aiming at the current situation that bearing faults are difficult to diagnose. According to the method, the time sequence data of the historical acceleration sensor is processed by innovatively applying a gating cycle unit-based network, so that the fault can be predicted and diagnosed. The method was set up by the Keras tool of PYTHON3.6.5, comprising the following steps: s1, dividing the time sequence data of the acceleration sensor into a training set (containing T group data), a verification set and a test set; s2, inputting the data of the training set into the gated circulation unit network for training until the network converges; s3, optimizing the parameters of the network on the verification set; s4, using the trained model for evaluating the model on the test set to obtain the effect of high accuracy; s5, model saving is applied to the actual situation. The method provides a new solution for the diagnosis of the rotary machine, and is further widely applied to the field of diagnosis of mechanical equipment.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to a bearing fault identification method based on a gated cycle unit network, and belongs to the field of fault diagnosis of rotary machinery.
[ background of the invention ]
Rotating machines are one of the most common types of machinery used in industrial production and life, and therefore fault detection of rotating machinery is of great significance in practical application situations. As one of the important parts of automobile construction, bearings have a significant impact on the load-bearing capacity, the operating performance and the life of the automobile. But traditional methods tend to rely on traditional methods of manually extracting features, which would require a large amount of a priori knowledge. Because the manual feature extraction process is separated from the classification process, the influence of the feature extraction process on the classification process is difficult to judge, and the effect is to be further improved.
Common methods are largely divided into theoretical methods and statistical-based methods. Statistical-based classification methods are increasingly gaining attention because they do not require excessive prior knowledge and can efficiently and accurately obtain mapping relationships between inputs and outputs, such as artificial neural networks, clustering, regression, and the like. The neural network algorithm can realize automatic identification without extracting fault features in advance. In addition, long short term memory network (LSTM) based methods are also applied in the field of bearing fault identification.
In order to solve the gradient disappearance problem of the traditional recurrent neural network, the LSTM can continuously realize the transmission of timing information and the updating of the memory unit through three gates (an input gate, an output gate, and a forgetting gate). The GRU is an improved variant of the LSTM three-gate design, which can efficiently process long and short term timing data and obtain useful information. In contrast to LSTM, GRU has no cellular state and can directly use hidden states for information transmission. Therefore, the GRU has simpler structure, fewer parameters and faster training speed. In recent years, deep learning has shown great advantages in classification tasks, and with the help of this, the applicant proposes a bearing fault identification method based on a gated cycle unit network to solve the above problems, so as to improve the identification accuracy of bearing faults.
[ summary of the invention ]
Aiming at the defects in the prior art, the invention designs a bearing fault identification method based on a gated cyclic unit network. The technical system disclosed by the invention can well utilize the data of the acceleration sensor to realize the diagnosis of the bearing fault and is beneficial to the safe operation of the rotating machinery.
The technical scheme adopted for achieving the purpose of the invention is that a bearing fault identification method based on a gated cyclic unit network mainly comprises the following steps:
step 1: preprocessing is carried out after data acquisition to obtain time sequence data of the acceleration sensor, and a data set is divided into a training set, a verification set and a test set;
step 2: inputting data of a training set into a gated cyclic unit network for training until the network converges;
and step 3: inputting the data of the verification set into a network for verification, and adjusting the parameters of the network to finally obtain the optimal parameters;
and 4, step 4: and storing the final model, inputting the test set for recognition effect test, and using the final model in an actual rotary machine diagnosis link.
Further, the main steps of step 1 are as follows:
step 1.1: data acquisition: an SKF6205 bearing is adopted in an experiment, faults are introduced through electric spark machining, the fault diameters are 0.18mm, 0.36mm and 0.53mm respectively, and 4 kinds of vibration signal data of a normal state, a ball fault, an inner ring fault and an outer ring fault are collected;
step 1.2: data preprocessing: carrying out mean value missing value filling on the index historical time sequence data, and obtaining a final historical time sequence data set after processing and standardizing abnormal values;
step 1.3: data set partitioning: partitioning a historical time series data set into: the training set contains 1900 pieces of data, the validation set contains 500 pieces of data, and the test set contains 500 pieces of data.
Further, the main steps of step 2 are as follows:
step 2.1: and constructing a gating cycle unit network, wherein the gating cycle unit network comprises an input layer, a gating cycle unit layer, a full connection layer and an output layer. The parameters to be adjusted are the number of gating cycle units of each layer, a Dropout value, the selection of an optimizer and the number of neurons of a full connection layer;
step 2.2: initializing the hidden state of a gating cycle unit, and inputting data;
step 2.3: calculating reset gate r of current neurontUpdate gate ztWeight and alternative hidden state of
Comprises the following steps:
zt＝σ(Wxzxt+Whzht-1+bz)
rt＝σ(Wxrxt+Whrht-1+br)
wherein, WhzRepresenting the weight between the last hidden state and the update gate, WxzRepresenting the weight between the current input and the update gate, WhrRepresenting the weight between the last hidden state and the reset gate, WxrRepresenting the weight between the current input and the reset gate, WxhRepresenting the weight between the current input and the current candidate hidden state, and W is a weight matrix; bzIs to update the bias term of the gate, brIs the bias term for the reset gate; "σ (-) denotes a sigmoid function, and" tanh (-) denotes a hyperbolic tangent function; "+" indicates point-by-point multiplication;
step 2.4: computing the hidden state h of the current neurontAnd passed on to the next neuron;
step 2.5: the training loop is cycled through the above steps until the model converges, using Multi-category entry as the loss function.
Further, the main steps of step 3 are as follows:
step 3.1: testing the trained model on the verification set;
step 3.2: adjusting parameters according to the model result;
step 3.3: and (5) completing parameter adjustment and saving the model.
Further, the main steps of step 4 are as follows:
step 4.1: using the test set to test the finally trained classification model;
step 4.2: comparing all the prediction results with the true values to obtain a final model evaluation index;
step 4.3: comparing the obtained result with other methods to obtain the superiority of the model;
step 4.4: the final classification model can be used for a fault detection link of an actual rotating machine.
The invention has the beneficial effects that: the technical scheme of the invention can well utilize the data of the acceleration sensor to realize the diagnosis of the bearing fault and is beneficial to the safe operation of the rotating machinery.
[ description of the drawings ]
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of a gated loop cell network;
FIG. 3 is a GRU model training loss value;
FIG. 4 is GRU model accuracy.
[ detailed description ] embodiments
The present invention is further illustrated by the following examples. Referring to FIG. 1, which is a flow chart of the present invention, a gated loop cell network structure is shown in FIG. 2. The implementation mode mainly comprises the following steps:
step 1: preprocessing is carried out after data acquisition to obtain sensor time sequence data, and a data set is divided into a training set, a verification set and a test set;
step 2: inputting data of a training set into a gated cyclic unit network for training until the network converges;
and step 3: inputting the data of the verification set into a network for verification, and adjusting the parameters of the network to finally obtain the optimal parameters;
and 4, step 4: and storing the final model, inputting the test set for recognition effect test, and using the final model for an actual bearing fault diagnosis link.
The step 1 comprises the following steps:
step 1.1: data acquisition: the implementation method of the patent comprises the following steps of: the implementation method of the patent comprises the following steps of: the SKF6205 bearing is adopted in the experiment, faults are introduced through electric spark machining, and the fault diameters are 0.18mm, 0.36mm and 0.53mm respectively. 4 kinds of vibration signal data of a normal state, a ball fault, an inner ring fault and an outer ring fault are collected;
step 1.2: data preprocessing: and (4) carrying out mean value missing value filling on the index historical time sequence data, and processing and standardizing abnormal values to obtain a final historical time sequence data set.
Step 1.3: data set partitioning: partitioning a historical time series data set into: the training set contains 1900 pieces of data, the validation set contains 500 pieces of data, and the test set contains 500 pieces of data.
The step 2 comprises the following steps:
step 2.1: and constructing a gating cycle unit network, wherein the gating cycle unit network comprises an input layer, a gating cycle unit layer, a full connection layer and an output layer. The parameters to be adjusted are the number of gating cycle units of each layer, a Dropout value, the selection of an optimizer and the number of neurons of a full connection layer;
step 2.2: and initializing the hidden state of the gating cycle unit and inputting data. The dimension of each sample is 1 × 1600, the data dimension input to the GRU is 160 × 10, i.e. 160 time steps, the input dimension for each time step is 10. Training internal parameters of the model by using back propagation;
step 2.3: calculating the reset gate, the weight of the update gate and the alternative hidden state of the current neuron; inputting x of current neurontAnd hidden state of previous neuronht-1When inputting, update the gate ztAnd a reset gate rtIs calculated as:
zt＝σ(Wxzxt+Whzht-1+bz)
rt＝σ(Wxrxt+Whrht-1+br)
wherein, WhzRepresenting the weight between the last hidden state and the update gate, WxzRepresenting the weight between the current input and the update gate, WhrRepresenting the weight between the last hidden state and the reset gate, WxrRepresenting the weight between the current input and the reset gate; bzIs to update the bias term of the gate, brIs the bias term for the reset gate; "σ (-) denotes a sigmoid function.
Step 2.4: calculating the hidden state of the current neuron and transmitting the hidden state to the next neuron;
candidate hidden states
And a final hidden state htIs calculated as:
wherein, WxhRepresenting the weight between the current input and the current candidate hidden state; w is a weight matrix; "tanh (. circle.)" represents a hyperbolic tangent function; "+" indicates point-by-point multiplication.
Step 2.5: the training loop is cycled through the above steps until the model converges, using Multi-category entry as the loss function.
The step 3 comprises the following steps:
step 3.1: verifying the trained model on a verification set;
step 3.2: adjusting parameters according to the model result;
step 3.3: and (5) completing parameter adjustment and saving the model.
The step 4 comprises the following steps:
step 4.1: using the test set to test the finally trained classification model;
step 4.2: comparing all the prediction results with the true values to obtain a final model evaluation index;
step 4.3: comparing the obtained result with other methods to obtain the superiority of the model;
step 4.4: and adopting a final classification model for a fault detection link of the actual rotating machinery.
The comparison result of the final model is shown in table 1, the training loss value of the GRU model is shown in fig. 3, and the accuracy of the GRU model is shown in fig. 4, so that the algorithm in the patent has good practical prospect and generalization capability.
TABLE 1 Fault diagnosis result Table
It should be noted that the above embodiments are only used for explaining the bearing fault diagnosis of the present invention, and are not intended to limit the present invention. It should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention, and all such modifications and equivalents are intended to be included within the scope of the claims of the present invention.
Claims (5)
1. A bearing fault identification method based on a gated cyclic unit network is characterized by mainly comprising the following steps:
step 1: preprocessing is carried out after data acquisition to obtain time sequence data of the acceleration sensor, and a data set is divided into a training set, a verification set and a test set;
step 2: inputting data of a training set into a gated cyclic unit network for training until the network converges;
and step 3: inputting the data of the verification set into a network for verification, and adjusting the parameters of the network to finally obtain the optimal parameters;
and 4, step 4: and storing the final model, inputting the test set for recognition effect test, and using the final model in an actual rotary machine diagnosis link.
2. The gated cyclic unit network-based bearing fault identification method according to claim 1, wherein the main steps of the step 1 are as follows:
step 1.1: data acquisition: an SKF6205 bearing is adopted in an experiment, faults are introduced through electric spark machining, the fault diameters are 0.18mm, 0.36mm and 0.53mm respectively, and 4 kinds of vibration signal data of a normal state, a ball fault, an inner ring fault and an outer ring fault are collected;
step 1.2: data preprocessing: carrying out mean value missing value filling on the index historical time sequence data, and obtaining a final historical time sequence data set after processing and standardizing abnormal values;
step 1.3: data set partitioning: partitioning a historical time series data set into: the training set contains 1900 pieces of data, the validation set contains 500 pieces of data, and the test set contains 500 pieces of data.
3. The gated cyclic unit network-based bearing fault identification method according to claim 1, wherein the main steps of the step 2 are as follows:
step 2.1: and constructing a gating cycle unit network, wherein the gating cycle unit network comprises an input layer, a gating cycle unit layer, a full connection layer and an output layer. The parameters to be adjusted are the number of gating cycle units of each layer, a Dropout value, the selection of an optimizer and the number of neurons of a full connection layer;
step 2.2: initializing the hidden state of a gating cycle unit, and inputting data;
step 2.3: calculating the weight of the current neuronDoor rtUpdate gate ztWeight and alternative hidden state of
Comprises the following steps:
zt＝σ(Wxzxt+Whzht-1+bz)
rt＝σ(Wxrxt+Whrht-1+br)
wherein, WhzRepresenting the weight between the last hidden state and the update gate, WxzRepresenting the weight between the current input and the update gate, WhrRepresenting the weight between the last hidden state and the reset gate, WxrRepresenting the weight between the current input and the reset gate, WxhRepresenting the weight between the current input and the current candidate hidden state, and W is a weight matrix; bzIs to update the bias term of the gate, brIs the bias term for the reset gate; "σ (-) denotes a sigmoid function, and" tanh (-) denotes a hyperbolic tangent function; "+" indicates point-by-point multiplication;
step 2.4: computing the hidden state h of the current neurontAnd passed on to the next neuron;
step 2.5: the training loop is cycled through the above steps until the model converges, using Multi-category entry as the loss function.
4. The gated cyclic unit network-based bearing fault identification method according to claim 1, wherein the main steps of the step 3 are as follows:
step 3.1: testing the trained model on the verification set;
step 3.2: adjusting parameters according to the model result;
step 3.3: and (5) completing parameter adjustment and saving the model.
5. The gated cyclic unit network-based bearing fault identification method according to claim 1, wherein the main steps of the step 4 are as follows:
step 4.1: using the test set to test the finally trained classification model;
step 4.2: comparing all the prediction results with the true values to obtain a final model evaluation index;
step 4.3: comparing the obtained result with other methods to obtain the superiority of the model;
step 4.4: the final classification model can be used for a fault detection link of an actual rotating machine.
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