Feature extraction for fault diagnosis based on wavelet packet decomposition: An application on linear rolling guide

Linear rolling guide is increasingly being used as the transmission system in computer numerical control machine tools due to its high stiffness, low friction, good ability of precision retaining, and so on. The lubrication of rolling linear guide affects significantly its performance and hence monitoring the lubrication condition during its operation is of great importance. In this article, the relation between different lubrication conditions of linear rolling guide and their corresponding vibration signals is studied. Three lubrication conditions labeled as “Poor,”“Medium,” and “Good” are simulated to represent the actual working conditions. A data acquisition system is set up to acquire the vibration signals corresponding to different conditions. The wavelet packet decomposition is employed to perform time–frequency analysis of the raw signal, after which the energy distribution of the decomposed signals is extracted as the feature. Two linear rolling guides manufactured by different companies are used in the experiments. The results demonstrate that the relation between the energy distribution extracted from vibration signals and lubrication conditions follows a certain rule. A typical feedforward backpropagation neural network is used as the classifier to verify the effectiveness of energy distribution. The average classification accuracy of the network with energy distribution as input is more than 95%. The results show that the lubrication conditions can be characterized by “energy” hidden in the vibration signals and the energy distribution is an appropriate feature that can be used for fault diagnosis of linear rolling guide.

[1]  Mangesh B. Chaudhari,et al.  Compound gear-bearing fault feature extraction using statistical features based on time-frequency method , 2018, Measurement.

[2]  Nader Sawalhi,et al.  Vibration signal processing for spall size estimation in rolling element bearings using autoregressive inverse filtration combined with bearing signal synchronous averaging , 2017 .

[3]  Jui-Pin Hung,et al.  Load effect on the vibration characteristics of a stage with rolling guides , 2009 .

[4]  Liang Guo,et al.  Methodology for ball screw support bearing fault analysis with screw nut vibration signal , 2015, 2015 Prognostics and System Health Management Conference (PHM).

[5]  Miao He,et al.  Deep Learning Based Approach for Bearing Fault Diagnosis , 2017, IEEE Transactions on Industry Applications.

[6]  Feng Jia,et al.  An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data , 2016, IEEE Transactions on Industrial Electronics.

[7]  Liang Guo,et al.  A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines , 2018, Neurocomputing.

[8]  Zhixin Yang,et al.  Fault diagnosis of rotating machinery based on multiple probabilistic classifiers , 2018, Mechanical Systems and Signal Processing.

[9]  D. Bianchi,et al.  Wavelet packet transform for detection of single events in acoustic emission signals , 2015 .

[10]  Joo-Ho Choi,et al.  Gear fault diagnosis using transmission error and ensemble empirical mode decomposition , 2018, Mechanical Systems and Signal Processing.

[11]  Hu-Tian Feng,et al.  Model for wear prediction of roller linear guides , 2013 .

[12]  Juan Antonio Ortega-Redondo,et al.  Multiple-Fault Detection Methodology Based on Vibration and Current Analysis Applied to Bearings in Induction Motors and Gearboxes on the Kinematic Chain , 2016 .

[13]  Wentao Mao,et al.  Online sequential prediction of bearings imbalanced fault diagnosis by extreme learning machine , 2017 .

[14]  Raphael T. Haftka,et al.  Predictive airframe maintenance strategies using model-based prognostics , 2018 .

[15]  Pavan Kumar Kankar,et al.  Nonlinear dynamic investigations on rolling element bearings: A review , 2018 .

[16]  E. García Plaza,et al.  Application of the wavelet packet transform to vibration signals for surface roughness monitoring in CNC turning operations , 2018 .

[17]  B.H.M. Sadeghi,et al.  A BP-neural network predictor model for plastic injection molding process , 2000 .

[18]  Nam H. Kim,et al.  A cost driven predictive maintenance policy for structural airframe maintenance , 2017 .

[19]  Adam Glowacz,et al.  Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals , 2018 .

[20]  Andrew Ball,et al.  Gear wear monitoring by modulation signal bispectrum based on motor current signal analysis , 2017 .

[21]  Xiaodong Wang,et al.  Fault diagnosis method based on wavelet packet-energy entropy and fuzzy kernel extreme learning machine , 2018 .

[22]  Zhencai Zhu,et al.  Design of Online Monitoring and Fault Diagnosis System for Belt Conveyors Based on Wavelet Packet Decomposition and Support Vector Machine , 2013 .

[23]  Baolin Wang,et al.  Investigation of the contact stiffness variation of linear rolling guides due to the effects of friction and wear during operation , 2015 .

[24]  Sheng-wei Fei.  Fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm for feature selection and parameter optimization , 2017 .

[25]  Enrico Zio,et al.  A Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach in an evolving environment , 2017 .