CN115392315B - Gearbox fault detection method based on transferable characteristics - Google Patents
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Abstract
The invention relates to a gearbox fault detection method based on transferable characteristics, which comprises the steps of obtaining a gearbox fault information data set, dividing the data set into a candidate domain data set and a target domain data set, and obtaining a first representative time sequence and a second representative time sequence through preprocessing; obtaining a data set with the most similar mode characteristics in the time domain through calculation based on the correlation between the first representative time sequence and the second representative time sequence; pre-training an LSTM model by using the most similar data set, extracting common features, and then performing time sequence prediction on the target domain data set through the pre-trained model to obtain time domain features of the gearbox; and training a CNN model according to the spectrogram corresponding to the second representative time sequence, acquiring the frequency domain characteristics of the gearbox spectrogram, and combining the time domain characteristics and the frequency spectrum characteristics to obtain a prediction result. The method improves the accuracy of prediction.
Description
Technical Field
The invention relates to the technical field of fault detection, in particular to a gearbox fault detection method based on transferable characteristics.
Background
At present, for the prediction analysis of abundant time sequence data in the same field, a large number of mature technical means exist: the prediction can be completed by using the currently observed characteristic value and historical time sequence information through technologies such as RNN/LSTM/GRU and the like; multi-step prediction can be carried out by the techniques of Seq2Seq and the like; different Attention weights may be added to the temporal data through the Attention mechanism, enhancing multi-feature based prediction; characteristics such as space and the like can be added through a GeoMAN model, so that the prediction accuracy is improved; through a Transformer and a related variant model, a multidimensional long input sequence can be conveniently processed, and the accuracy of a predicted long sequence is improved; historical information of a wider field of view can be captured for predictive analysis through relevant models such as CNN; the accuracy of prediction can be improved by combining a plurality of models and innovating the models. Most of the above technologies need to satisfy the feature of abundant data volume in the same field.
The existing technology mainly comprises the steps of using a model trained in another related field before by methods such as model parameter reuse, source domain and target domain feature conversion and the like through transfer learning, and further achieving the aim of predicting the target domain; and more useful information is extracted by a data enhancement correlation method, the prediction precision is improved, and the like. In the above methods, it is necessary to find the source domain most suitable for the methods for the destination domain, and deeply mine the association relationship between the source domain and the destination domain.
In the mechanical field, a large number of rich theoretical bases are available to aid predictive analysis: for example, it is known that the life of a rolling bearing is predicted to be in positive correlation with a basic dynamic load and in negative correlation with an equivalent dynamic load, and a basic calculation formula and a look-up table are provided. But how to select a data source capable of helping the target domain to predict from the common device data; how to enhance the prediction of data with small data quantity by using a data source with large data quantity; and how to mine hidden information of time series data with small data amount is a relatively difficult problem.
The industry has abundant theoretical mechanism models, basic mechanism knowledge needs to be met in the process of analyzing data, and how to utilize the mechanism knowledge to enhance the accuracy of the data analysis model is a problem to be researched.
Disclosure of Invention
The invention aims to provide a gearbox fault detection method based on transferable characteristics, which aims to solve the problems that the prior art is applicable to a target domain, requires a scene with rich training data, and has little equipment fault data in an industrial environment.
In order to achieve the purpose, the invention provides the following scheme:
a gearbox fault detection method based on transferable features includes:
acquiring a gearbox fault information data set, dividing the data set into a candidate domain data set and a target domain data set, and preprocessing to obtain a first representative time sequence and a second representative time sequence;
obtaining a data set with the most similar mode characteristics in the time domain through calculation based on the correlation between the first representative time sequence and the second representative time sequence;
pre-training an LSTM model by using the most similar data set, extracting common features, and then performing time sequence prediction on the target domain data set by using the pre-trained model to obtain time domain features of the gearbox;
and training a CNN model according to the spectrogram corresponding to the second representative time sequence, acquiring the frequency domain characteristics of the gearbox spectrogram, and combining the time domain characteristics and the frequency spectrum characteristics to obtain a prediction result.
Preferably, the acquiring the gearbox fault information dataset comprises: acquiring a time sequence containing hidden information based on an equipment simulator, and constructing a gearbox fault information data set based on the time sequence containing hidden information, wherein the gearbox fault information data set comprises a plurality of time sequences of hidden information.
Preferably, obtaining the first representative time series and the second representative time series by preprocessing comprises:
and selecting the first representative time sequence and the second representative time sequence by disturbing the partial value of the time sequence of any dimension and sequentially removing the time sequence of any dimension and measuring the importance of the time sequence of different dimensions on the final prediction of the data set, wherein the first representative time sequence is a representative time sequence of the candidate domain data set, and the second representative time sequence is a representative time sequence of the target domain data set.
Preferably, the disturbing of the partial value of the time series of any dimension and the sequential removal of the time series of any dimension comprises:
putting the multidimensional data into the LSTM model to perform multivariate prediction to obtain a first prediction result;
the method comprises the steps of disturbing the partial value of any dimension time sequence, determining the dimension, traversing each time sequence on the same dimension by using a preset threshold value, wherein the front and back sequence of a measuring point is exchanged in each cycle; carrying out classification prediction based on the LSTM model structure to obtain a second prediction result;
sequentially removing any dimension time sequence, removing one time sequence of measuring dimension each time, reducing the input dimension of the LSTM model, and predicting through the LSTM model structure to obtain a third prediction result;
and performing calculation based on the first prediction result, the second prediction result and the third prediction result, and if the calculation result value is larger, representing that the selected dimension contains more fault information, so as to select the first representative time sequence and the second representative time sequence.
Preferably, the obtaining of the most similar data set by calculation comprises:
normalizing the first representative time sequence and the second representative time sequence, summing the normalized first representative time sequence and the normalized second representative time sequence, and performing mean value removing calculation to obtain a calculation result, wherein the calculation result represents the similarity between the candidate domain data set and the target domain data set, and the first k most similar data sets are selected from the candidate domain data sets as the most similar data sets based on the calculation result; wherein smaller calculations represent more similarity.
Preferably, the obtaining the time-domain features comprises:
and taking the most similar data set as an LSTM model pre-training data set, sequentially training the LSTM models, and performing multivariate time sequence prediction on the target domain data set through the trained LSTM models to obtain the time domain characteristics.
Preferably, pre-training the LSTM model comprises:
pre-training the LSTM model based on the most similar data set, modifying the structures of a full connection layer and a softmax layer according to the classification characteristics of different candidate domain data sets, inheriting model parameters before the full connection layer obtained by the previous training before pre-training the model each time, then continuing training, and finally obtaining the pre-trained LSTM model.
Preferably, the multivariate time series prediction of the target domain data set by the pre-trained LSTM model comprises:
transferring the pre-trained LSTM model to the target domain data set for training, modifying the structures of a full connection layer and a SoftMax layer in the LSTM model, introducing a CNN model for merging and training, inheriting model parameters before the full connection layer before the model is trained, training by using the target domain data set, and finally obtaining the time domain characteristics of the gearbox.
Preferably, the training of the CNN model according to the spectrogram corresponding to the second representative time sequence to obtain the frequency domain characteristics of the spectrogram of the gearbox includes:
and generating a corresponding spectrogram according to the second representative time sequence, and extracting the frequency domain characteristics of the gearbox spectrogram by using the information related to the meshing frequency and the side frequency band in the CNN capture spectrogram.
Preferably, obtaining the prediction result comprises:
and combining the result output by the LSTM model and the result output by the CNN model, namely combining the time domain characteristics and the frequency domain characteristics, and outputting the result to the finally modified full-link layer and the softmax layer to obtain the prediction result.
The invention has the beneficial effects that:
(1) The invention provides a transferable feature-based method for a research object with less research data volume, such as a gear box, and the model is pre-trained by using data sets in other similar fields, so that the commonality features are extracted, the robustness of the prediction model is enhanced, and the prediction precision is improved;
(2) For judging the similarity of the fields, the invention provides the method for utilizing the similarity between DTW (dynamic time warping) scale data sets, thereby avoiding the problem of negative migration caused by the migration of unrelated fields;
(3) Aiming at the problems that a plurality of industrial data sets usually have multi-dimensional observation data, but the specific dimension data is difficult to judge and contains more fault information, the invention provides a method for disordering a certain one-dimensional characteristic value and sequentially removing a certain one-dimensional observation characteristic based on a deep learning framework so as to judge the importance of each dimension on the final judgment state of the data set;
(4) In order to improve the prediction accuracy, the method learns the expert knowledge in the related gearbox field, so that the final prediction model structure is modified. And generating a corresponding spectrogram according to the selected original time domain data of the first representative time sequence of the gearbox, capturing information related to the meshing frequency and the side frequency band in the spectrogram by using the CNN, extracting frequency domain features in the time sequence data of the gearbox, and combining the frequency domain features with the time domain features for classification and prediction.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of a gearbox fault detection method based on transferable features in accordance with an embodiment of the present invention;
FIG. 2 is a schematic diagram of the structure of a representative sequence of selected data sets in accordance with an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a method for selecting top k best candidate domains according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a structure of k best candidate domains for model pre-training according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a prediction structure for gearbox data according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
The invention provides a gearbox fault detection method based on transferable characteristics. The technical scheme provided by the invention is as follows (as shown in figure 1):
1) First, the data needs to be preprocessed. Because the device may collect time series of multiple dimensions through multiple sensors, some may contain much failure information to assist in analysis, and some may not be as helpful for analysis. An algorithm is needed from which the dimension containing the most hidden information (see fig. 2) is selected as the representative time series for the second step.
The invention proposes that for each data set, the importance of different dimension time sequences on the final prediction of the data set is measured by disordering the partial value of a dimension time sequence and sequentially removing the dimension time sequence, so as to select the representative time sequence of each data set. The concrete measures are as follows:
for each data set, firstly putting multidimensional data into an LSTM model to perform multivariate prediction to obtain a prediction result 1;
and then shuffle the value of the time series part of a certain dimension. I.e. for each dimension, each time series over this dimension is traversed with step =2, where the back-and-forth order of two measurement points (say 1234- > 2143) is exchanged in each cycle. Then, carrying out classification prediction by using the models with the same structure in the previous step to obtain a prediction result 2;
then one dimension time series is removed in turn. Namely, each time, a time sequence of measuring dimensionality is removed, the input dimensionality of the LSTM model is reduced, and the model with the same structure as the previous step is also used for prediction to obtain a prediction result 3;
it is known that the more the model is lost after human intervention on a certain dimension time series, the more important the corresponding dimension time series (characteristic) is, and more fault information can be contained. So abs ((result 2+ result 3) -result 1 x 2) for each dimension is calculated, and the larger the value, the more fault information this dimension contains, thus selecting a representative time series for each data set.
For each data set, a representative time series is selected, and the correlation calculation of the second step is carried out.
2) For large amounts of industrial source domain data, a method is needed to measure the correlation between all source domain data and target domains, because the smaller the data distribution difference, the more useful knowledge for migration, and the greater the help for target domains, and conversely if the correlation between the two is low, but still performing migration learning, may be worse than the performance without using the source domain knowledge, resulting in "negative migration". This is a troublesome problem.
For industrial fault equipment, the equipment often undergoes three stages of fatigue, namely a fatigue crack initiation stage, a fatigue crack growth stage and a fatigue fracture stage. The points in time at which these phases occur may be different in different devices, but if they have similar pattern sub-sequences, they indicate a high degree of similarity between the two.
The invention provides a DTW-based method for measuring the similarity of time sequence data of all candidate domains and target domains, so that the candidate domain most related to the target domain of a gearbox is selected as a candidate domain for subsequent pre-training.
Similarity between the two data sets is calculated based on DTW between all the candidate domain representative time series and the target domain representative time series respectively. And then selecting the most similar first k data sets from the multiple candidate domain data sets as the designated candidate domains of the subsequently trained prediction model (as shown in FIG. 3). In this embodiment, the first representative time series represents the candidate domain time series, and the second representative time series represents the target domain time series.
3) Due to the fact that the similarity of the selected k candidate domains and the target domain time sequence data is high, the data volume of the candidate domains is larger, and the difficulty that the existing depth model is difficult to use for prediction analysis due to the fact that the data volume of the target domain is small can be solved.
The invention pre-trains the model of the same LSTM time sequence prediction classification by using k candidate domains selected in the second step in sequence, as shown in FIG. 4, extracts some common characteristics and helps the target domain to perform prediction analysis. The specific method comprises the following steps:
structurally, only the structures of the full connection layer and the softmax layer are modified (because the number of fault types of different data sets may be different) according to the classification characteristics of different candidate domain data sets, and the previous model structure is not modified; in terms of parameters, before the model is pre-trained for each time, model parameters before the full connection layer obtained by the last training are inherited, and then training is carried out.
4) And transferring the model obtained by the training in the third step to a target domain for predictive analysis, so that the convergence speed of the model under the target domain can be increased, and the problem of small data volume of the target domain is solved. The specific method is as above:
migrating the pre-trained LSTM model to the target domain data set for training, and structurally positioning 5 computing nodes of a full connection layer and a SoftMax layer (because the label types of the target domain (gearbox) data set are 5 and need to be merged with the introduced CNN model), wherein the previously designed structure is not modified; parametrically, model parameters before a full connection layer are inherited before a model is trained, and then the model is trained by using a target domain (gearbox) data set.
And according to the deep analysis of the mechanism knowledge of the gearbox fault, the transmission error forms the main excitation source of the gear vibration and noise. If the transmission error is large, the collision is intensified when the gears enter and leave the mesh in the running process, a higher vibration peak value is generated, and the amplitude change and the phase change of a short time are formed. The invention mainly researches the gear tooth damage error, namely the gear tooth surface damage caused by various faults in the running of the gear, and the drive error excitation of the gear can be generated in the rotation of the gear.
The process of fault analysis of vibration time series data in industry mainly focuses on the phenotype characteristics of meshing frequency and side frequency bands. There are three major failure analysis methods in industry: power spectrum analysis, side band analysis, cepstrum analysis, which are all based on the transformed spectrogram or power spectrum, etc., and the prediction judgment is performed by mining the characteristics of the meshing frequency and the side band, as shown in fig. 5. The specific method comprises the following steps:
and generating a corresponding spectrogram according to the first representative time sequence of the gearbox data set, and extracting the frequency domain characteristics of the gearbox spectrogram by using the information related to the meshing frequency and the side frequency band in the CNN capture spectrogram. And finally, combining the result output by the LSTM model and the result output by the CNN model, namely combining the time domain characteristic and the frequency domain characteristic, and outputting the result to the modified full-link layer and the softmax layer to obtain a prediction result.
The above-described embodiments are merely illustrative of the preferred embodiments of the present invention, and do not limit the scope of the present invention, and various modifications and improvements of the technical solutions of the present invention can be made by those skilled in the art without departing from the spirit of the present invention, and the technical solutions of the present invention are within the scope of the present invention defined by the claims.
Claims (8)
1. A gearbox fault detection method based on transferable characteristics is characterized by comprising the following steps:
acquiring a gearbox fault information data set, dividing the data set into a candidate domain data set and a target domain data set, and preprocessing to obtain a first representative time sequence and a second representative time sequence; obtaining the first representative time series and the second representative time series through preprocessing comprises:
selecting a first representative time sequence and a second representative time sequence by disturbing partial values of any dimension time sequence, sequentially removing any dimension time sequence and measuring the importance of different dimension time sequences on the final prediction of the data set, wherein the first representative time sequence is a representative time sequence of the candidate domain data set, and the second representative time sequence is a representative time sequence of the target domain data set;
the method for disturbing the partial value of the time sequence of any dimension and sequentially removing the time sequence of any dimension comprises the following steps:
putting the multidimensional data into an LSTM model to perform multivariate prediction to obtain a first prediction result;
the method comprises the steps of disturbing the partial value of any dimension time sequence, determining the dimension, traversing each time sequence on the same dimension by using a preset threshold value, wherein the front and back sequence of a measuring point is exchanged in each cycle; carrying out classification prediction based on the LSTM model structure to obtain a second prediction result;
sequentially removing any dimension time sequence, removing one time sequence of measuring dimension each time, reducing the input dimension of the LSTM model, and predicting through the LSTM model structure to obtain a third prediction result;
calculating based on the first prediction result, the second prediction result and the third prediction result, calculating an abs value of each dimension, and if the calculated result value is larger, representing that the selected dimension contains more fault information, thereby selecting the first representative time series and the second representative time series;
obtaining a data set with the most similar mode characteristics in a time domain through calculation based on the correlation between the first representative time sequence and the second representative time sequence;
pre-training an LSTM model by using the most similar data set, extracting common features, and then performing time sequence prediction on the target domain data set by using the pre-trained model to obtain time domain features of the gearbox;
and training a CNN model according to the spectrogram corresponding to the second representative time sequence, acquiring the frequency domain characteristics of the gearbox spectrogram, and combining the time domain characteristics and the frequency domain characteristics to obtain a prediction result.
2. The transferable feature-based gearbox fault detection method of claim 1 wherein obtaining the gearbox fault information dataset comprises: acquiring a time sequence containing hidden information based on an equipment simulator, and constructing a gearbox fault information data set based on the time sequence containing hidden information, wherein the gearbox fault information data set comprises a plurality of time sequences of hidden information.
3. The transferable feature-based gearbox fault detection method of claim 1 wherein computing the most similar data set comprises:
normalizing the first representative time sequence and the second representative time sequence, summing the normalized first representative time sequence and the normalized second representative time sequence, and performing mean value removing calculation to obtain a calculation result, wherein the calculation result represents the similarity between the candidate domain data set and the target domain data set, and the first k most similar data sets are selected from the candidate domain data sets as the most similar data sets based on the calculation result; wherein smaller calculation results represent more similarity.
4. The transferable feature-based gearbox fault detection method of claim 1 wherein obtaining the time-domain feature comprises:
and taking the most similar data set as the LSTM model pre-training data set, sequentially training the LSTM models, and performing multivariate time sequence prediction on the target domain data set through the trained LSTM models to obtain the time domain characteristics.
5. The transferable feature-based gearbox fault detection method of claim 4, wherein pre-training the LSTM model comprises:
pre-training the LSTM model based on the most similar data set, modifying the structures of a full connection layer and a softmax layer according to the classification characteristics of different candidate domain data sets, inheriting model parameters before the full connection layer obtained by the previous training before pre-training the model each time, then continuing training, and finally obtaining the pre-trained LSTM model.
6. The transferable feature-based gearbox fault detection method of claim 5, wherein multivariate time series prediction is performed on the target domain data set by a pre-trained LSTM model, comprising:
transferring the pre-trained LSTM model to the target domain data set for training, modifying the structures of a full connection layer and a SoftMax layer in the LSTM model, introducing a CNN model for merging and training, inheriting model parameters before the full connection layer before the model is trained, training by using the target domain data set, and finally obtaining the time domain characteristics of the gearbox.
7. The gearbox fault detection method based on transferable features of claim 1, wherein training a CNN model according to the spectrograms corresponding to the second representative time series to obtain the frequency domain features of the spectrograms of the gearbox comprises:
and generating a corresponding spectrogram according to the second representative time sequence, and extracting the frequency domain characteristics of the gearbox spectrogram by using the information related to the meshing frequency and the side frequency band in the CNN capture spectrogram.
8. The transferable feature-based gearbox fault detection method of claim 1, wherein deriving the predicted outcome comprises:
and combining the result output by the LSTM model and the result output by the CNN model, namely combining the time domain characteristics and the frequency domain characteristics, and outputting the result to the finally modified full-link layer and the softmax layer to obtain the prediction result.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021208799A1 (en) * | 2020-04-18 | 2021-10-21 | 华为技术有限公司 | Transfer model training method and apparatus and fault detection method and apparatus |
| CN114034486A (en) * | 2021-10-11 | 2022-02-11 | 中国人民解放军92578部队 | Unsupervised transfer learning-based bearing fault diagnosis method for pump mechanical equipment |
| CN114372491A (en) * | 2021-12-31 | 2022-04-19 | 山东科讯信息科技有限公司 | Fault identification method of rotating equipment and related equipment |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110730046B (en) * | 2019-10-18 | 2022-02-18 | 中国人民解放军陆军工程大学 | Cross-frequency-band spectrum prediction method based on deep migration learning |
| CN113344016A (en) * | 2020-02-18 | 2021-09-03 | 深圳云天励飞技术有限公司 | Deep migration learning method and device, electronic equipment and storage medium |
| CN112507785A (en) * | 2020-11-02 | 2021-03-16 | 北京工商大学 | Rolling bearing fault analysis based on CNN and LSTM |
| CN112380773A (en) * | 2020-11-19 | 2021-02-19 | 华中科技大学鄂州工业技术研究院 | Fuel cell degradation prediction method, device and equipment based on transfer learning |
| CN114677575A (en) * | 2020-12-24 | 2022-06-28 | 华为技术有限公司 | Scene migration method and device and electronic equipment |
| CN113591553B (en) * | 2021-06-18 | 2023-07-18 | 北京航天动力研究所 | Turbine pump transfer learning fault intelligent judgment method based on small sample weight optimization |
| CN113851131A (en) * | 2021-08-17 | 2021-12-28 | 西安电子科技大学广州研究院 | Cross-modal lip language identification method |
| CN113869286B (en) * | 2021-12-01 | 2022-02-25 | 中国工程物理研究院电子工程研究所 | Self-adaptive multi-task intelligent fault diagnosis system and fault diagnosis method |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021208799A1 (en) * | 2020-04-18 | 2021-10-21 | 华为技术有限公司 | Transfer model training method and apparatus and fault detection method and apparatus |
| CN114034486A (en) * | 2021-10-11 | 2022-02-11 | 中国人民解放军92578部队 | Unsupervised transfer learning-based bearing fault diagnosis method for pump mechanical equipment |
| CN114372491A (en) * | 2021-12-31 | 2022-04-19 | 山东科讯信息科技有限公司 | Fault identification method of rotating equipment and related equipment |
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