CN112067289A - Motor shaft and transmission shaft abnormal vibration early warning algorithm based on neural network - Google Patents

Motor shaft and transmission shaft abnormal vibration early warning algorithm based on neural network Download PDF

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CN112067289A
CN112067289A CN202010846935.7A CN202010846935A CN112067289A CN 112067289 A CN112067289 A CN 112067289A CN 202010846935 A CN202010846935 A CN 202010846935A CN 112067289 A CN112067289 A CN 112067289A
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李珍
吴健
李�浩
戎锡锋
崔异鹏
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Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
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Abstract

The invention relates to an abnormal vibration early warning algorithm of a motor shaft and a transmission shaft based on a neural network, which is mainly technically characterized in that: classifying and sorting long-term historical operating data of the test bed to obtain an offline training set; establishing a BP neural network model; determining parameters of a BP neural network model; training a BP neural network model by using an offline data set until a stopping criterion is reached, and obtaining an initial BP neural network model; training a BP neural network model on line to obtain a trained BP neural network model; and the BP neural network model calculates the vibration value under the current working condition in real time and compares the vibration value with the vibration value measured by the sensor in real time, and the system automatically warns when any monitored motor shaft or transmission shaft generates abnormal vibration. The invention can accurately detect the abnormal vibration of the motor shaft and the rotating shaft through the neural network model and pre-alarm in time, can reduce the accident probability to the maximum extent, and opens up a new way for the abnormal vibration fault diagnosis of the motor shaft system.

Description

Motor shaft and transmission shaft abnormal vibration early warning algorithm based on neural network
Technical Field
The invention belongs to the technical field of test equipment, and particularly relates to an abnormal vibration early warning algorithm for a motor shaft and a transmission shaft based on a neural network.
Background
The transmission test bed plays an important role in test equipment, the motor is indispensable in the transmission test bed, and the abnormal vibration of the motor shaft often causes huge test accidents or property loss.
The motor shaft is used as a rotating apparatus, and abnormal vibration is caused due to unbalance of a rotor, misalignment of a shaft system, vibration of an oil film, bending, cracking, loosening and the like. However, the vibration signal magnitude can also change under different rotating speed and torque working conditions in the operation process, and the vibration has a normal vibration range under different working conditions. Since the normal vibration range is related to the motor speed and torque, but the relationship is not linear, it is difficult to find a function to represent the relationship, which brings certain difficulty to identify the abnormal vibration.
The abnormal vibration condition of the motor shaft is easy to be ignored during the operation of the system. The system runs under abnormal vibration for a long time, so that the service life of the motor is shortened for a light person, and serious accidents such as shaft breakage and the like can occur for a serious person. Therefore, how to accurately detect the abnormal vibration of the motor shaft and the transmission shaft and send out early warning is a problem which needs to be solved urgently at present.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides an abnormal vibration early warning algorithm for a motor shaft and a transmission shaft based on a neural network, and can accurately detect the abnormal vibration of the motor shaft and the transmission shaft and send out early warning information.
The technical problem to be solved by the invention is realized by adopting the following technical scheme:
an abnormal vibration early warning algorithm for a motor shaft and a transmission shaft based on a neural network comprises the following steps:
step 1, classifying and sorting long-term historical operating data of a test bed, marking abnormal fault type data and normal operating data, carrying out normalization processing on the data to obtain dimensionless data, and taking the dimensionless data as an offline training set;
step 2, establishing a BP neural network model for training an off-line training set;
step 3, determining parameters of the BP neural network model;
step 4, training a BP neural network model by using an offline data set until a stop criterion is reached, and obtaining an initial BP neural network model;
step 5, training a BP neural network model on line by using the motor shaft and transmission shaft data collected by the test bed to obtain the trained BP neural network model;
and 6, in the running process of the system, calculating the vibration value under the current working condition in real time by the BP neural network model, comparing the vibration value with the vibration value measured by the sensor in real time, and automatically early warning the system when any monitored motor shaft or transmission shaft generates abnormal vibration.
The long-term historical data of the test bed comprises motor torque, rotating speed and XYZ-axis vibration values, and the data are obtained by measuring from a sensor of a test bed data acquisition system.
The step 1 is to carry out normalization processing on the data by adopting a Min-Max conversion method.
The BP neural network model established in the step 2 is a three-layer BP neural network model, the rotating speed and the torque in the training set are used as the input of the BP neural network model, and the XYZ-axis vibration value is used as the output of the BP neural network model.
The parameters of the BP neural network model comprise the number of neurons in an input layer, the number of neurons in a hidden layer, the number of neurons in an output layer, an activation function, a learning rate, a training mode and a stopping criterion, wherein:
the number of neurons in the input layer is 2, and the neurons comprise rotating speed and torque;
the number of cryptomelane neurons is between 15 and 21;
the number of neurons in the output layer is 3, and the neurons comprise XYZ-axis vibration values;
the termination criteria are: up to 200000 times or the precision up to 1 × 10-23；
The activation function is:
Figure BDA0002643366620000021
where a is the tilt parameter, then when a → ∞ is then
Figure BDA0002643366620000022
v is a function argument;
learning rate: a learning rate of the decay type is used, in which the initial setting is 0.1, the decay rate is 0.9, and the decay rate is 50.
The method for training the BP neural network model in the steps 4 and 5 comprises the following steps:
initializing: randomly selecting a weight value and a threshold value according to uniform distribution with the average value of 0;
a training sample is prepared: presenting one round of the training samples to a BP neural network model, and sequentially performing forward calculation and backward calculation on the training samples according to the third step and the fourth step;
calculating in the forward direction: error signals can be calculated through forward calculation;
fourthly, reverse calculation: and calculating a weight modifier according to the error signal, and modifying the last calculated network weight.
Carrying out iterative processing: and providing the presented new sample for the BP neural network model, and repeatedly calculating the update weight according to the third step and the fourth step until the stop criterion is met.
The specific implementation method of the step 5 is as follows: acquiring motor shaft and transmission shaft data acquired by a test bed, training a BP neural network model on line, updating the network according to a new round of acquired data every time in a cycle until outlet fault data are obtained, and acquiring the trained BP neural network model; or performing a new round of off-line training of batch data when the model is updated regularly to obtain the trained BP neural network model.
And 6, in the process of comparing the vibration values, judging according to a preset early warning sensitivity dead zone, and automatically early warning when the comparison difference value exceeds the early warning sensitivity dead zone.
The invention has the advantages and positive effects that:
1. the method adopts a three-layer BP neural network model to calculate and obtain the normal vibration value range under any rotating speed and torque, compares the obtained normal vibration range of the motor under any working condition with the real-time measurement value of the sensor, considers that abnormal vibration exists when the normal vibration range is exceeded, and gives an alarm in advance in time, can reduce the accident probability to the maximum extent, has ultrahigh capabilities of memory, information processing and fault identification, overcomes the technical bottleneck of the traditional diagnosis method, and opens up a new way for the abnormal vibration fault diagnosis of the motor shafting.
2. The BP neural network model can be updated regularly, the offline learning method is adopted to label data in batches to train the neural network, and the online learning has higher real-time performance and the network model is updated along with the time so that the network model always keeps stronger nonlinear fitting capability and better robustness.
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FIG. 1 is a process flow diagram of the present invention;
FIG. 2 is a graph of error convergence during model training according to the present invention;
FIG. 3 is a graph showing the identification of a failure test data according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
An abnormal vibration early warning algorithm for a motor shaft and a transmission shaft based on a neural network is shown in figure 1 and comprises the following steps:
step 1, classifying and sorting long-term historical operating data of the test bed, marking abnormal fault type data and normal operating data, carrying out normalization processing on the data to obtain dimensionless data, and taking the dimensionless data as an offline training set.
The long-term historical data of the test bed comprises motor torque, rotating speed and XYZ-axis vibration values, and the data are obtained by measuring from a sensor of a test bed data acquisition system.
In this step, the original data x is processed1,x2,x3…xnThe normalization (using Min-Max conversion) process was performed as follows:
Figure BDA0002643366620000031
obtaining dimensionless data: y is1,y2,y3…yn。
In the step, dimensionless data obtained by processing historical operation data is used as an off-line training set for training the neural network, wherein the rotating speed and the torque are used as the input of the neural network, and the XYZ-axis vibration value is used as the output of the neural network.
And 2, establishing a three-layer BP neural network model for training an off-line training set.
The invention uses a three-layer BP neural network (multi-layer feedforward neural network trained according to an error back propagation algorithm) model to train training set data, and error information output by a neuron j of the BP neural network when iterating to an nth training example is defined as follows:
ej(n)＝dj(n)-yj(n)
defining the error energy transient for neuron j as:
Figure BDA0002643366620000032
the sum of the error energies of all neurons of the output layer is thus defined as:
Figure BDA0002643366620000033
assuming that N is the total number of samples in the training set, the average of the error energies can be obtained:
Figure BDA0002643366620000034
in the above formula, the first and second carbon atoms are,avthe cost function is a measure of learning ability, and the weight parameter is finally adjusted through the learning of the networkTo obtainavA minimization is achieved.
Synaptic weight omega of back propagation algorithmji(n) applying a weight modifier Δ ωji(n) which is proportional to (n) vs. ωji(n) partial derivatives of (n). This gradient is expressed according to the differential chain rule as:
Figure BDA0002643366620000035
in the above formula, ωjiCorrection value Δ ω of (n)ji(n) is defined by the rule of delta:
Figure BDA0002643366620000036
bringing it into the formula:
Δωji(n)＝ηj(n)yi(n)
here, the local gradient
Figure BDA0002643366620000041
ηIs the learning efficiency, the minus sign in the formula indicates that the weight change direction which makes (n) fall is sought in the weight space.
And 3, determining parameters of the BP neural network model, including the number of input layer neurons, the number of hidden layer neurons, the number of output layer neurons, an activation function, a learning rate, a training mode and a stopping criterion.
In this step, the parameters of the BP neural network model are defined as follows:
number of input layer neurons: the number of input variables is determined by the number of input variables, which is 2, and the number of input layer neurons is 2, because the number of input variables is the rotational speed and the torque, respectively.
Number of hidden layer neurons: experiments prove that the model precision is better when the number of hidden layer neurons is between 15 and 21.
The number of neurons in the output layer was 3: XYZ axes vibration values.
Network training termination condition: up to 200000 times or the precision up to 1 × 10-23。
Activation function: selecting
Figure BDA0002643366620000042
Because the Sigmoid function is a strictly increasing function and the Sigmoid function has a good balance between linear and non-linear behavior, a in the activation function is a tilt parameter, which is the case when a → ∞
Figure BDA0002643366620000043
v is a function argument.
Learning rate: the attenuation type learning rate is adopted, the initial setting is 0.1, the attenuation rate is 0.9, and the attenuation rate is 50 (namely, the attenuation is carried out once after 50 iterations).
Step 4, off-line training of the BP neural network model: and (3) training the BP neural network model by using the offline data set obtained in the step (1) until a stopping criterion is reached, and obtaining an initial BP neural network model.
FIG. 2 is a series updating method of error convergence curve and back propagation algorithm using weights in training process, which is realized mainly by training samples
Figure BDA0002643366620000044
Performing cyclic training, wherein the specific method comprises the following steps:
(1) initializing, and randomly selecting a weight value and a threshold value according to uniform distribution with the average value of 0.
(2) And (4) training samples, presenting one round of the training samples to the network, and sequentially performing the forward and reverse calculations of the steps (3) and (4) on the training samples.
(3) Forward calculation, by which an error signal e can be calculatedj(n)。
(4) And (4) performing reverse calculation, calculating a weight modifier according to the error signal, and modifying the last calculated network weight.
(5) And (4) iteration, presenting a new sample to the network, and repeatedly calculating the updated weight value according to the steps (3) and (4) until the stop criterion is met.
Step 5, training a BP neural network model on line: and (3) normalizing the motor shaft and transmission shaft data (rotating speed and matrix signals) acquired by the test bed, performing online training on the BP neural network model, and updating the network according to the acquired data of a new round every time in a circulation mode until outlet fault data are obtained, so as to obtain the trained BP neural network model. Or performing a new round of off-line training of batch data when the model is updated regularly to obtain the trained BP neural network model.
After an initial BP neural network model is established, network weight updating is carried out on data collected by a circulation system each time, an updating algorithm is similar to offline batch training, except that an error function is not sample set error accumulation any more, but a current single sample model error, corresponding calculation is carried out according to the current single sample, and the network weight is updated according to a back propagation algorithm.
And 6, in the running process of the system, calculating the vibration value under the current working condition in real time by the BP neural network model, comparing the vibration value with the vibration value measured by the sensor in real time, and automatically warning and prompting the system when any monitored motor shaft or transmission shaft vibrates abnormally.
In the step, in order to avoid false alarm action caused by numerical value fluctuation in a normal range, a sensitivity dead zone of early warning is set, and if the contrast difference exceeds the sensitivity dead zone in the contrast process, automatic early warning is carried out.
Fig. 3 shows the measured data of abnormal vibration of the X-axis of the power motor in the primary test process, after the working condition changes, the measured data of the X-axis vibration sensor and the model calculation data have large deviation, and an alarm is given according to the setting of the program, so that slight faults are found after the shutdown inspection, and larger test accidents are avoided.
It should be emphasized that the embodiments described herein are illustrative rather than restrictive, and thus the present invention is not limited to the embodiments described in the detailed description, but also includes other embodiments that can be derived from the technical solutions of the present invention by those skilled in the art.

Claims (8)

1. The utility model provides a motor shaft and transmission shaft abnormal vibration early warning algorithm based on neural network which characterized in that: the method comprises the following steps:
step 1, classifying and sorting long-term historical operating data of a test bed, marking abnormal fault type data and normal operating data, carrying out normalization processing on the data to obtain dimensionless data, and taking the dimensionless data as an offline training set;
step 2, establishing a BP neural network model for training an off-line training set;
step 3, determining parameters of the BP neural network model;
step 4, training a BP neural network model by using an offline data set until a stop criterion is reached, and obtaining an initial BP neural network model;
step 5, training a BP neural network model on line by using the motor shaft and transmission shaft data collected by the test bed to obtain the trained BP neural network model;
and 6, in the running process of the system, calculating the vibration value under the current working condition in real time by the BP neural network model, comparing the vibration value with the vibration value measured by the sensor in real time, and automatically early warning the system when any monitored motor shaft or transmission shaft generates abnormal vibration.
2. The motor shaft and transmission shaft abnormal vibration early warning algorithm based on the neural network as claimed in claim 1, wherein: the long-term historical data of the test bed comprises motor torque, rotating speed and XYZ-axis vibration values, and the data are obtained by measuring from a sensor of a test bed data acquisition system.
3. The motor shaft and transmission shaft abnormal vibration early warning algorithm based on the neural network as claimed in claim 1, wherein: the step 1 is to carry out normalization processing on the data by adopting a Min-Max conversion method.
4. The motor shaft and transmission shaft abnormal vibration early warning algorithm based on the neural network as claimed in claim 1, wherein: the BP neural network model established in the step 2 is a three-layer BP neural network model, the rotating speed and the torque in the training set are used as the input of the BP neural network model, and the XYZ-axis vibration value is used as the output of the BP neural network model.
5. The motor shaft and transmission shaft abnormal vibration early warning algorithm based on the neural network as claimed in claim 1, wherein: the parameters of the BP neural network model comprise the number of neurons in an input layer, the number of neurons in a hidden layer, the number of neurons in an output layer, an activation function, a learning rate, a training mode and a stopping criterion, wherein:
the number of neurons in the input layer is 2, and the neurons comprise rotating speed and torque;
the number of cryptomelane neurons is between 15 and 21;
the number of neurons in the output layer is 3, and the neurons comprise XYZ-axis vibration values;
the termination criteria are: up to 200000 times or the precision up to 1 × 10-23；
The activation function is:
Figure FDA0002643366610000011
where a is the tilt parameter, then when a → ∞ is then
Figure FDA0002643366610000012
v is a function argument;
learning rate: a learning rate of the decay type is used, in which the initial setting is 0.1, the decay rate is 0.9, and the decay rate is 50.
6. The motor shaft and transmission shaft abnormal vibration early warning algorithm based on the neural network as claimed in claim 1, wherein: the method for training the BP neural network model in the steps 4 and 5 comprises the following steps:
initializing: randomly selecting a weight value and a threshold value according to uniform distribution with the average value of 0;
a training sample is prepared: presenting one round of the training samples to a BP neural network model, and sequentially performing forward calculation and backward calculation on the training samples according to the third step and the fourth step;
calculating in the forward direction: error signals can be calculated through forward calculation;
fourthly, reverse calculation: and calculating a weight modifier according to the error signal, and modifying the last calculated network weight.
Carrying out iterative processing: and providing the presented new sample for the BP neural network model, and repeatedly calculating the update weight according to the third step and the fourth step until the stop criterion is met.
7. The motor shaft and transmission shaft abnormal vibration early warning algorithm based on the neural network as claimed in claim 1, wherein: the specific implementation method of the step 5 is as follows: acquiring motor shaft and transmission shaft data acquired by a test bed, training a BP neural network model on line, updating the network according to a new round of acquired data every time in a cycle until outlet fault data are obtained, and acquiring the trained BP neural network model; or performing a new round of off-line training of batch data when the model is updated regularly to obtain the trained BP neural network model.
8. The motor shaft and transmission shaft abnormal vibration early warning algorithm based on the neural network as claimed in claim 1, wherein: and 6, in the process of comparing the vibration values, judging according to a preset early warning sensitivity dead zone, and automatically early warning when the comparison difference value exceeds the early warning sensitivity dead zone.
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Application publication date: 20201211