Automatic feature extraction of time-series applied to fault severity assessment of helical gearbox in stationary and non-stationary speed operation

Abstract Signals captured in rotating machines to obtain the status of their components can be considered as a source of massive information. In current methods based on artificial intelligence to fault severity assessment, features are first generated by advanced signal processing techniques. Then feature selection takes place, often requiring human expertise. This approach, besides time-consuming, is highly dependent on the machinery configuration as in general the results obtained for a mechanical system cannot be reused by other systems. Moreover, the information about time events is often lost along the process, preventing the discovery of faulty state patterns in machines operating under time-varying conditions. In this paper a novel method for automatic feature extraction and estimation of fault severity is proposed to overcome the drawbacks of classical techniques. The proposed method employs a Deep Convolutional Neural Network pre-trained by a Stacked Convolutional Autoencoder. The robustness and accuracy of this new method are validated using a dataset with different severity conditions on failure mode in a helical gearbox, working in both constant and variable speed of operation. The results show that the proposed unsupervised feature extraction method is effective for the estimation of fault severity in helical gearbox, and it has a consistently better performance in comparison with other reported feature extraction methods.

[1]  Yong Qin,et al.  Fault detection of rolling bearing based on FFT and classification , 2015 .

[2]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[3]  Jürgen Schmidhuber,et al.  Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction , 2011, ICANN.

[4]  Mengyan Nie,et al.  Review of condition monitoring and fault diagnosis technologies for wind turbine gearbox , 2013 .

[5]  Diego Cabrera,et al.  Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition , 2015 .

[6]  Yuvin Chinniah,et al.  Analysis and prevention of serious and fatal accidents related to moving parts of machinery , 2015 .

[7]  Diego Cabrera,et al.  A statistical comparison of neuroclassifiers and feature selection methods for gearbox fault diagnosis under realistic conditions , 2016, Neurocomputing.

[8]  Chuan Li,et al.  Time-frequency signal analysis for gearbox fault diagnosis using a generalized synchrosqueezing transform , 2012 .

[9]  Li Li,et al.  Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis , 2013 .

[10]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[11]  M. Zuo,et al.  Gearbox fault detection using Hilbert and wavelet packet transform , 2006 .

[12]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[13]  Diego Cabrera,et al.  Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis , 2015, Neurocomputing.

[14]  Baoping Tang,et al.  A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm , 2013 .

[15]  Diego Cabrera,et al.  Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal , 2015, Sensors.

[16]  Diego Cabrera,et al.  Fault diagnosis in spur gears based on genetic algorithm and random forest , 2016 .

[17]  P. K. Kankar,et al.  A comparison of feature ranking techniques for fault diagnosis of ball bearing , 2016, Soft Comput..

[18]  Ming J. Zuo,et al.  Fault diagnosis of planetary gearboxes via torsional vibration signal analysis , 2013 .

[19]  Tshilidzi Marwala,et al.  Principal Component Analysis and Automatic Relevance Determination in Damage Identification , 2007, ArXiv.

[20]  Nader Sawalhi,et al.  A machine learning approach for the condition monitoring of rotating machinery , 2014 .