CN114139591A - Mechanical equipment state early warning method - Google Patents
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Abstract
The patent relates to the field of mechanical equipment state monitoring and fault diagnosis, in particular to a mechanical equipment state early warning method. The fixed threshold alarm method in the prior art has the defects that the data change trend of data cannot be sensed, and the change of the data change trend is often an early signal of fault occurrence. The method comprises five steps: the method comprises the following steps: extracting characteristics; step two: dividing data; step three: training a model; step four: self-learning of a threshold value; step five: and (5) state early warning. The invention provides an LSTM-based mechanical equipment state early warning method, which is characterized in that a prediction model is established for normal state data of mechanical equipment by using the LSTM, an alarm threshold value is learned by self, and an alarm is given when the difference between the prediction data and the real data exceeds the self-learning threshold value.
Description
Technical Field
The patent relates to the field of mechanical equipment state monitoring and fault diagnosis, in particular to a mechanical equipment state early warning method.
Background
Mechanical equipment plays an important role in modern industry, people put forward higher and higher requirements on state monitoring of the mechanical equipment along with the advance of industrial internet technology in recent years, abnormality is found at the early stage of the failure of the mechanical equipment, predictive maintenance is carried out, and the method plays an important role in reducing production cost and guaranteeing production safety of enterprises. However, in actual production, an alarm mode with a fixed threshold is still adopted, and when the alarm occurs, the fault is often worsened to a certain degree, so that great threat is brought to safe production, and an effective state early warning method needs to be developed. The fixed threshold alarm method has the defects that the data change trend of data cannot be sensed, and the change of the data change trend is often an early signal of failure, so that the accurate capture of the change of the data change trend is the key of the early warning method of mechanical equipment.
The LSTM is a specially designed Recurrent Neural Networks (RNN), solves the problem of long-term dependence of the traditional RNN, has good performance on time sequence data prediction, and is applied to the industries of language understanding, transportation and the like at present. Based on the self-learning alarm threshold value, the invention provides a mechanical equipment state early warning method based on LSTM, which comprises the steps of establishing a prediction model for normal state data of the mechanical equipment by using the LSTM, self-learning the alarm threshold value, and alarming when the difference between the predicted data and the real data exceeds the self-learning threshold value.
Disclosure of Invention
The invention aims to solve the problem that a fixed threshold alarm method cannot accurately give an early warning, and provides an LSTM-based mechanical equipment state early warning method.
A mechanical equipment state early warning method comprises five steps:
the method comprises the following steps: extracting characteristics, namely acquiring normal state data of the mechanical equipment, and extracting the characteristics of the normal state data to form a normal state characteristic data set of the mechanical equipment;
step two: data partitioning, dividing the normal state feature data set into XMAnd XTRespectively used for model construction and threshold self-learning;
step three: model training, establishing LSTM network structure, and utilizing XMTraining an LSTM, and taking the LSTM model after training as a prediction model;
step four: threshold value self-learning by XTAs test data of a prediction model, calculating the Mahalanobis distance between the predicted value and the true value of each group of test data, and calculating an alarm threshold value T by a box type graph method;
step five: and (3) state early warning, wherein the real-time vibration data of the mechanical equipment is subjected to feature extraction to be used as a real-time real value, a real-time predicted value is obtained based on data in a past period of time, the difference between the real-time real value and the real-time predicted value is calculated and compared with T, when the difference value is greater than T, an alarm is given, and if the difference value is greater than T, the alarm is not given.
The first step is as follows: the feature extraction specifically comprises the following steps: collecting normal state vibration data of mechanical equipment to be detected within a period of time, and dividing the normal state data into N groups of data Z ' ═ Z ' according to certain intervals ',(q)}q＝1,...,NWherein Z',(q)And arranging the samples of each group in Z' from small to large according to the time sequence for the q-th group of data, and performing feature extraction on the normal state data of each group to obtain a feature vector set.
The second step is that: the data division specifically comprises the following steps: (1) predicting data of the current time by a prediction model based on the LSTM through a piece of historical data, recording the length of the historical data required by each prediction as L, wherein L belongs to [30,90], and (2) inputting the historical data with the length of L and outputting the historical data as a predicted value of the current time by the prediction model, and (3) dividing a normal state feature vector set Z to form a sample set of the prediction model.
The sample set is divided as follows: z contains N time-point feature vectors in total, from the L-th feature vector Z(L)Initially, the first and last eigenvectors of the L eigenvectors are respectively used as input data of the sample set and corresponding true values, and the sample composition of the sample set is expressed as D { (X)(r),Y(r))}r＝1,…,N-L，X(r),Y(r)Data and corresponding true values of the r-th sample in the sample set respectively
Dividing a sample set D into
And
two moieties of which DMFor training early warning models, DTFor self-learning alarm thresholds.
The third step is that: model training, including two parts of LSTM network structure determination and prediction model training, wherein the parameters to be determined by the LSTM network structure include input data size, output data size, LSTM hidden layer neuron number and LSTM unit number; after the structure of the LSTM model is determined, D is adoptedMTraining the LSTM model, making the predicted value output by the LSTM model be H (X), adopting the mean square error as the objective function, and defining the objective function as formula 3
Wherein V is the number of samples per training batch, X(p)、Y(p)Respectively the data of the p-th sample in each batch of training samples and the corresponding real value.
And the prediction model training is to adopt a batch training method to minimize O to complete the training of the LSTM model, and the LSTM model after training can be used as a prediction model of the normal state data of the mechanical equipment to be monitored.
The input data size is the length L of historical data and the characteristic dimension S required by each prediction; the size of the output data is a predicted value of S characteristics in each prediction; the number of LSTM hidden layer neurons and the number of LSTM units are determined by combining actual data.
The fourth step is that: the threshold self-learning specifically comprises the following steps: will DTData of inner N-L-P samples
Inputting the prediction model to obtain the predicted value
Calculating predicted value and true value
The difference value between the predicted value and the true value is calculated through the Mahalanobis distance,
the difference value is calculated as formula 4:
wherein Σ is
The superscript T and-1 of the covariance matrix respectively represent transposition operation and matrix inversion;
calculating DTThe difference value d of the inner N-L-P samples is { d ═ d(i)}i＝N-L-PWhen d is arranged from small to large, the value at 25% is denoted as Q1The value at the top 75% is denoted as Q1According to the abnormal value detection method of the boxplot, the definition of the self-learning threshold value is shown as the formula 5
T＝Q3+1.5×(Q3-Q1) (5)。
The fifth step is as follows: the state early warning specifically comprises:
acquiring state data of mechanical equipment to be monitored from time t-L to time t, and extracting S characteristics same in the step I to obtain a characteristic vector z ═ { z ═ z(t-L),z(t-L+1),…,z(t)Is at least one of
As an input of the prediction model, the output of the prediction model is represented as H [ X ](t)](ii) a Calculating the difference between the predicted value and the actual value of the time t according to equation 7, when d(t)If the alarm is greater than T, the alarm is given, otherwise, the alarm is not given
Drawings
FIG. 1 is a flow chart of a method for early warning of the state of a mechanical device based on LSTM according to the present invention;
FIG. 2 is a vibration waveform data segment of normal state data in an embodiment of the present invention;
FIG. 3 is a feature extraction result of normal state data in an embodiment of the present invention;
FIG. 4 is a graph comparing predicted values and actual values in an embodiment of the present invention;
FIG. 5 is a graph showing the variation of each characteristic of test data according to an embodiment of the present invention;
fig. 6 is an experimental result on test data in the example of the present invention.
Detailed Description
The invention provides an LSTM-based mechanical equipment state early warning method which is divided into two stages of model construction and model application, as shown in the attached figure 1, the method comprises the following five steps:
the method comprises the following steps: and (5) feature extraction. And acquiring normal state data of the mechanical equipment, and performing feature extraction on the normal state data to form a normal state feature data set of the mechanical equipment.
Step two: and (4) dividing data. Partitioning a Normal State feature dataset into XMAnd XTRespectively used for model construction and threshold self-learning.
Step three: and (5) training a model. Building LSTM network structure, using XMAnd training the LSTM, and taking the trained LSTM model as a prediction model.
Step four: the threshold value is self-learning. With XTAnd (3) as the test data of the prediction model, calculating the Mahalanobis distance between the predicted value and the true value of each group of test data, and calculating the alarm threshold value T by using a box type graph method.
Step five: and (5) state early warning. And (3) extracting the characteristics of the real-time vibration data of the mechanical equipment to obtain a real-time real value, obtaining a real-time predicted value based on the data of a period of time in the past, calculating the difference between the real-time real value and the real-time predicted value, comparing the difference with T, and giving an alarm if the difference is greater than T, or not giving an alarm if the difference is greater than T.
The five steps of the present invention will be described in detail below.
(1) The method comprises the following steps: feature extraction
Collecting normal state vibration data of mechanical equipment to be detected within a period of time, and dividing the normal state data into N groups of data Z ' ═ Z ' at certain intervals ',(q)}q＝1,…,NWherein Z',(q)The q-th group of data is formed by arranging the samples in Z' from small to large in time sequence. Extracting features of each group of normal state data to obtain a feature vector set, for example, Z',(q)The feature vector obtained by extracting S features is
Wherein
Is Z',(q)The feature value of the jth feature, the normal state feature vector set obtained from Z' is Z ═ { Z ═ Z(q)}q＝1,…,N。
(2) Step two: data partitioning
The prediction model based on LSTM predicts data at the current time from a single piece of history data, and records the length of the history data required for each prediction as L, generally L ∈ [30,90], so the input of the prediction model is the history data with length L, and the output is the predicted value at the current time. In order to complete the training of the prediction model, the normal state feature vector set Z needs to be divided to form a sample set of the prediction model, and the method for dividing the sample set is as follows:
z contains N time-point feature vectors in total, from the L-th feature vector Z(L)Initially, the first L eigenvectors (including the L-th eigenvector) and the next eigenvector are respectively used as input data of the sample set and corresponding true values, as shown in equations 1 and 2. The sample composition of the sample set may thus be denoted as D { (X)(r),Y(r))}r＝1,…,N-L，X(r),Y(r)Respectively, the data of the r-th sample in the sample set and the corresponding true value.
In the construction method of the early warning model, normal state data are required to be respectively used for training of the prediction model and self-learning of the alarm threshold value, and a sample set D is divided into
And
two moieties of which DMFor training early warning models, DTFor self-learning alarm thresholds.
(3) Step three: model training
And step three, comprising two parts of structure determination of the LSTM network and training of a prediction model. The parameters to be determined by the LSTM network structure comprise input data size, output data size, LSTM hidden layer neuron number and LSTM unit number, and in the method, the specific meaning of the input data size is historical data length L and characteristic dimension S required by each prediction; the specific meaning of the output data size is the predicted value of S characteristics in each prediction; the number of neurons in the LSTM hidden layer and the number of LSTM units are related to the characteristics of actual data, the larger the number of the neurons and the LSTM units is, the stronger the fitting capability of the model is, but the training time and the overfitting possibility of the model are also increased, so that the determination needs to be performed by combining the actual data.
After the structure of the LSTM model is determined, D is adoptedMTraining the LSTM model, enabling a predicted value output by the LSTM model to be H (X), adopting a mean square error as an objective function, defining the objective function as formula 3, adopting a batch training method to minimize O to complete the training of the LSTM model, and enabling the trained LSTM model to be used as a prediction model of normal state data of the mechanical equipment to be monitored.
Wherein V is the number of samples per training batch, X(p)、Y(p)Respectively the data of the p-th sample in each batch of training samples and the corresponding real value.
(4) Step four: threshold self-learning
The prediction model has the capability of predicting the actual value at the current moment according to a section of historical data, when the deviation between the predicted value and the actual value of the prediction model is too large, the fact that the alarm needs to be given when the operation state of the mechanical equipment changes is indicated, and when the deviation between the predicted value and the actual value is large, the alarm is the key for judging whether the early warning model can accurately give an early warning or not. To overcome the defectThe invention self-learns the alarm early warning by normal state data and statistical method. Will DTData of inner N-L-P samples
Inputting the prediction model to obtain the predicted value
Calculating predicted value and true value
The difference value of (a). Because each real value comprises a plurality of characteristics and the scales of the characteristics are inconsistent, the difference value between the predicted value and the real value is calculated through the Mahalanobis distance so as to
For example, the difference value is calculated according to equation 4.
Wherein Σ is
The superscript T, -1 of the covariance matrix of (1) represents the transposition operation and matrix inversion, respectively.
Calculating DTThe difference value d of the inner N-L-P samples is { d ═ d(i)}i＝N-L-PThe values of d from small to large are arranged and the value at 25% (lower quartile) is denoted as Q1And the value at the first 75% (lower quartile) is recorded as Q1According to the detection method of abnormal values of the boxed graph, the definition of the self-learning threshold value is shown as a formula 5.
T＝Q3+1.5×(Q3-Q1) (5)
(5) Step five: state warning
Acquiring state data of mechanical equipment to be monitored from time t-L to time t, and extracting S characteristics identical to those of the first stepObtaining a feature vector z ═ z(t-L),z(t-L+1),…,z(t)Is at least one of
As an input of the prediction model, the output of the prediction model is represented as H [ X ](t)]. Calculating the difference between the predicted value and the actual value of the time t according to equation 7, when d(t)If the alarm is greater than T, the alarm is given, otherwise, the alarm is not given.
In order to describe the present invention more specifically, the following will take pump shaft vibration data of a main pump of a nuclear power plant as an example, and in conjunction with the accompanying drawings, describe an embodiment of the LSTM-based mechanical equipment state warning method in detail.
(1) The method comprises the following steps: feature extraction
Fig. 2 shows a vibration waveform diagram of a pump shaft of a nuclear power main pump in a normal state for 1 second, and the vibration waveform data of the pump shaft for 160 minutes in the normal state of the main pump is subjected to feature extraction, and a peak value, a half-frequency multiplication amplitude, a first-frequency multiplication amplitude, a second-frequency multiplication amplitude, a third-frequency multiplication amplitude, a fourth-frequency multiplication amplitude and a fifth-frequency multiplication amplitude are sequentially extracted to obtain 9600 groups of feature vectors, that is, N is 9600 and S is 7 in the content of the invention. The time-varying curves of the features are shown in fig. 3, Feature _1 to Feature _7 are respectively a peak value, a half-doubled frequency, a first-doubled frequency amplitude, a second-doubled frequency amplitude, a third-doubled frequency amplitude, a fourth-doubled frequency amplitude and a fifth-doubled frequency amplitude, and 7 Feature values corresponding to each time point in the graph form a set of Feature vectors.
(2) Step two: data partitioning
In this embodiment, the length L of the history data required for each prediction is 60, so that the input data and the corresponding true value of any sample in the sample set are shown in formulas 8 and 9, respectively, where 9540 ≧ r ≧ 1.
In the formulas 8 and 9, the first and second groups,
the peak-to-peak value, half frequency multiplication, first frequency multiplication amplitude, second frequency multiplication amplitude, third frequency multiplication amplitude, fourth frequency multiplication amplitude and fifth frequency multiplication amplitude of the r group of characteristic vectors in the characteristic vector set are respectively.
This gave a 9540 sample set D { (X)(r),Y(r))}r＝1,…,9540Dividing D into 1:1
And
two moieties of which DMFor training early warning models, DTFor self-learning alarm thresholds.
(3) Step three: model training
The input data size is related to the length of the historical data and the characteristic dimension required by single prediction, so the input data size is 60 × 7 in the embodiment; the output data size is related to the feature dimension, which is 7 in this embodiment; in addition, in this embodiment, the number of LSTM units is 2, the number of hidden layer neurons is 128, and the data is an optimized value obtained through experiments.
By using DMTraining an LSTM model, wherein a predicted value output by the LSTM model is H (X), a mean square error is used as an objective function, the definition of the objective function is as shown in the formula 10, the training of the LSTM model is completed by minimizing O by adopting a batch training method, the number of samples trained in each batch is 1024, 2000 rounds of training are performed in total, and the trained LSTM model can be used as a prediction model of normal state data of mechanical equipment to be monitored, for example, FIG. 4 shows thatPredicted values (dashed lines) versus true values (solid lines) for the prediction model.
In the formula, X(p)、Y(p)∈DMThe data of the p-th sample in each batch of training samples and the corresponding true value are respectively.
(4) Step four: threshold self-learning
DMIn
The covariance matrix of (A) is shown in formula 11. the quartile number Q can be obtained according to formula 4 in the invention12.901, upper quartile Q34.251, the alarm pre-threshold T is 4.251+1.5 × (4.251-2.901) ═ 6.276.
(5) Step five: state warning
Fig. 5 is a time-dependent change curve of 7 features of test data for verifying the effect of the present invention, in which the time at which the change rule of the features changes is marked with an ellipse, and it can be seen from the graph that the peak value, the half-frequency amplitude, the first-frequency amplitude, and the second-frequency amplitude slowly increase after 2000 time points.
And sequentially and slidingly intercepting the feature vectors of 60 time points of the test data as the input of a prediction model to calculate to obtain a predicted value of the next moment, and calculating the difference value between the predicted value and the true value according to the formula 7 in the invention content.
Claims (9)
1. A mechanical equipment state early warning method is characterized in that: the method comprises the following five steps:
the method comprises the following steps: extracting characteristics, namely acquiring normal state data of the mechanical equipment, and extracting the characteristics of the normal state data to form a normal state characteristic data set of the mechanical equipment;
step two: data partitioning, dividing the normal state feature data set into XMAnd XTRespectively used for model construction and threshold self-learning;
step three: model training, establishing LSTM network structure, and utilizing XMTraining an LSTM, and taking the LSTM model after training as a prediction model;
step four: threshold value self-learning by XTAs test data of a prediction model, calculating the Mahalanobis distance between the predicted value and the true value of each group of test data, and calculating an alarm threshold value T by a box type graph method;
step five: and (3) state early warning, wherein the real-time vibration data of the mechanical equipment is subjected to feature extraction to be used as a real-time real value, a real-time predicted value is obtained based on data in a past period of time, the difference between the real-time real value and the real-time predicted value is calculated and compared with T, when the difference value is greater than T, an alarm is given, and if the difference value is greater than T, the alarm is not given.
2. The mechanical equipment state early warning method according to claim 1, characterized in that: the method comprises the following steps: the feature extraction specifically comprises the following steps: collecting normal state vibration data of mechanical equipment to be detected within a period of time, and dividing the normal state data into N groups of data Z' ═ Z according to a certain interval',(q)}q＝1,…,NWherein Z is',(q)And arranging the samples of each group in Z' from small to large according to the time sequence for the q-th group of data, and performing feature extraction on the normal state data of each group to obtain a feature vector set.
3. The mechanical equipment state early warning method according to claim 1, characterized in that: step two: the data division specifically comprises the following steps: the prediction model based on the LSTM predicts data at the current time through a piece of historical data, the length of the historical data required by each prediction is recorded as L, generally, L belongs to [30,90], the input of the prediction model is the historical data with the length of L, the output of the prediction model is the predicted value at the current time, and a normal state feature vector set Z is divided to form a sample set of the prediction model.
4. The mechanical equipment state early warning method according to claim 3, characterized in that: the sample set is divided as follows: z contains N time-point feature vectors in total, from the L-th feature vector Z(L)Initially, the first and last eigenvectors of the L eigenvectors are respectively used as input data and corresponding true values of the sample set, and the sample composition of the sample set is expressed as D { (X)(r),Y(r))}r＝1,…,N-L，X(r),Y(r)Data and corresponding true values of the r-th sample in the sample set respectively
Dividing a sample set D into
And
two moieties of which DMFor training early warning models, DTFor self-learning alarm thresholds.
5. The mechanical equipment state early warning method according to claim 4, wherein: step three: model training, including two parts of LSTM network structure determination and prediction model training, wherein the parameters to be determined by the LSTM network structure include input data size, output data size, LSTM hidden layer neuron number and LSTM unit number; after the structure of the LSTM model is determined, D is adoptedMTraining the LSTM model, making the predicted value output by the LSTM model be H (X), adopting the mean square error as the objective function, and defining the objective function as formula 3
Wherein V is the number of samples per training batch, X(p)、Y(p)Respectively the data of the p-th sample in each batch of training samples and the corresponding real value.
6. The mechanical equipment state early warning method according to claim 5, wherein: and (3) training a prediction model, namely minimizing O by adopting a batch training method to finish the training of the LSTM model, wherein the LSTM model after being trained can be used as the prediction model of the normal state data of the mechanical equipment to be monitored.
7. The mechanical equipment state early warning method according to claim 4, wherein: the input data size is the length L of historical data and the characteristic dimension S required by each prediction; the size of the output data is a predicted value of S characteristics in each prediction; the number of neurons in the LSTM hidden layer and the number of LSTM units are determined according to actual data.
8. The mechanical equipment state early warning method according to claim 1, characterized in that: the fourth step is that: the threshold self-learning specifically comprises the following steps: will DTData of inner N-L-P samples
Inputting the prediction model to obtain the predicted value
Calculating predicted value and true value
The difference value between the predicted value and the true value is calculated through the Mahalanobis distance,
the calculation method of the difference value is as shown in formula 4:
wherein Σ is
The superscript T and-1 of the covariance matrix respectively represent transposition operation and matrix inversion;
calculating DTThe difference value d of the inner N-L-P samples is { d ═ d(i)}i＝N-L-PWhen d is arranged from small to large, the value at 25% is denoted as Q1The value at the top 75% is denoted as Q1According to the abnormal value detection method of the boxplot, the definition of the self-learning threshold value is shown as the formula 5
T＝Q3+1.5×(Q3-Q1) (5)。
9. The mechanical equipment state early warning method according to claim 1, characterized in that: the fifth step is as follows: the state early warning specifically comprises:
acquiring state data of mechanical equipment to be monitored from time t-L to time t, and extracting S characteristics same in the step I to obtain a characteristic vector z ═ { z ═ z(t-L),z(t-L+1),…,z(t)Is at least one of
As an input of the prediction model, the output of the prediction model is represented as H [ X ](t)](ii) a Calculating the difference value between the predicted value and the true value at the moment t according to the formula 7, and calculating the difference value when d is(t)If the alarm is greater than T, the alarm is given, otherwise, the alarm is not given
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Citations (9)
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