Research And Application Of Unsupervised Deep Learning Method On Gear Fault Diagnosis

Gear is the common component in transmission system,it is of great significance to control the current operation conditions accurately and carry out gear fault diagnosis for ensuring the reliability of gear operation and reducing the great losses caused by gear fault.Feature extraction is the key step in fault diagnosis.The traditional timefrequency domain analysis methods can extract the spectrum features and the physical characteristics related to mechanical parameters,however,it needs complex signal processing methods and a good understanding of physical characteristics of mechanical systems,so all of the methods require the analysis by professional staff in the field,which is bad to realize automation and intellectualization of fault detection.With the advent of big data,using intelligent method is the inevitable choice to diagnose mechanical equipment quickly and accurately.Most of the features extracted by existing machine learning methods are abstract statistical features,which have no practical physical significance,the features extracted by supervised learning method cannot represent the health or fault condition of the machine directly,but must be classified by classifier.Moreover,the supervised model is difficult to be universal to other type machine.Therefore,it is important to research on the decomposition and extracting of fault features based on intelligent algorithm.To solve this problem,the paper proposes an unsupervised deep learning method for early gear fault diagnosis,specifically,it focuses on the analysis of the relationship between signals in different gear fault states,and extracting useful gear fault features.The main contents of the paper are as follows:1.A feature extraction method for signal disentangled based on deep neural networks is proposed.In the paper,a method called DTM(Disentangled tone mining)for feature decomposition and extraction based on unsupervised deep self-encoding is proposed: firstly,the original time-domain signal is transformed by fast Fourier transform,then the spectrum data is used to learn the deep unsupervised network,and the extracted initial features are selected based on QR decomposition to obtain the linear independent feature matrix,which contains the separated and extracted faultrelated feature.In order to verify the effectiveness of DTM method,an experimental platform for simulating gearbox faults is built and relevant fault experiments are carried out.DTM method is compared with shallow network contains PCA and MDS and a traditional signal processing method,also the advantage of deep neural network is explained,and the mechanism of the method is preliminarily explained by gear simulation signal.2.In the paper,a deep embedded clustering algorithm is used to optimize the embedding features of gear fault,and a regularization of clustering centers method is proposed,which indirectly constrains the embedding samples and makes the extracted feature law more obvious.In order to verify the validity of the method,the natural wear of gear experiments are carried out,and the relevant data are collected.The result shows that the self-learning algorithm can effectively optimize the features,it makes the fault features more distinguishable and reflects the trend of the fault more obvious.3.The paper researches on the ability of predicting gear fault degree using autoencoder,and does the preliminary research on the gear remaining useful life prediction based on deep learning method.A semi-supervised learning method based on autoencoder is used to predict the remaining useful life of gear.The result shows that this method can predict the RUL trend gear system.Because the method in the paper does not need a lot of historical training data,it can use limited historical data to do online prediction,which provides a new idea for online gear fault trend prediction.