Unsupervised Gear Fault Diagnosis Using Raw Vibration Signal Based on Deep Learning

Gears are the most common parts of a mechanical transmission system. Gear wearing faults could cause the transmission system to crash and give rise to the economic loss. It is always a challenging problem to diagnose the gear wearing condition directly through the raw signal of vibration. In this paper, a novel method named augmented deep sparse autoencoder (ADSAE) is proposed. The method can be used to diagnose the gear wearing fault with relatively few raw vibration signal data. This method is mainly based on the theory of wearing fault diagnosis, through creatively combining with both data augmentation ideology and the deep sparse autoencoder algorithm for the fault diagnosis of gear wear. The effectiveness of the proposed method is verified by experiments of six types of gear wearing conditions. The results show that the ADSAE method can effectively increase the network generalization ability and robustness with very high accuracy. This method can effectively diagnose different gear wearing conditions and show the obvious trend according to the severity of gear wear faults. This paper provides an important insight into the field of gear fault diagnosis based on deep learning and has a potential practical application value.

[1]  Diego Cabrera,et al.  Hierarchical feature selection based on relative dependency for gear fault diagnosis , 2015, Applied Intelligence.

[2]  Yi Wang,et al.  Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network , 2013, J. Intell. Manuf..

[3]  Nadir Boutasseta,et al.  Vibration-based gearbox fault diagnosis by DWPT and PCA approaches and an adaptive neuro-fuzzy inference system , 2015, 2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA).

[4]  Hongzhi Teng,et al.  Gear fault diagnosis and damage level identification based on Hilbert transform and Euclidean distance technique , 2014 .

[5]  Jianzhong Wu,et al.  Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images , 2016, IEEE Transactions on Medical Imaging.

[6]  Meng Gan,et al.  Multiple-domain manifold for feature extraction in machinery fault diagnosis , 2015 .

[7]  Jong-Won Park,et al.  A study on crack fault diagnosis of wind turbine simulation system , 2014, 2014 10th International Conference on Reliability, Maintainability and Safety (ICRMS).

[8]  Wenli Shang,et al.  An intelligent fault diagnosis system for newly assembled transmission , 2014, Expert Syst. Appl..

[9]  Krishnakumari Aharamuthu,et al.  Gear fault diagnosis using vibration signals based on decision tree assisted intelligent controllers , 2013 .

[10]  Dong Wang,et al.  K-nearest neighbors based methods for identification of different gear crack levels under different motor speeds and loads: Revisited , 2016 .

[11]  C He,et al.  Gear fault detection based on adaptive wavelet packet feature extraction and relevance vector machine , 2011 .

[12]  Shuai Zhang,et al.  Gear fault identification based on Hilbert–Huang transform and SOM neural network , 2013 .

[13]  David He,et al.  Detection of Pitting in Gears Using a Deep Sparse Autoencoder , 2017 .

[14]  Qun He,et al.  A new fault diagnosis model for rotary machines based on MWPE and ELM , 2017 .