Novel Adaptive Search Method for Bearing Fault Frequency Using Stochastic Resonance Quantified by Amplitude-Domain Index

Rotating machinery devices are prone to failure due to severe working conditions, whose failures will further induce other mechanical faults. Therefore, the fault diagnosis of rotating machinery is important, especially in the case of unknown fault types and fault characteristics. A novel adaptive search method is proposed for the bearing fault frequency by using stochastic resonance (SR) with general scale transformation. The amplitude-domain indices, which are independent of the specific fault frequency of vibration signal, are applied to quantify SR response. Simulated bearing fault signal is used to illustrate the good performance of these indices in evaluating SR output. Then, an adaptive search procedure for bearing fault frequency is presented in detail and verified by different vibration signals collected from the multiple working conditions. The searching results demonstrate that the proposed adaptive search method is accurate, effective, and sensitive for detecting unknown failure frequencies of rolling bearings. The proposed method might have significant application value in the condition monitoring of rolling bearings.

[1]  Ashkan Moosavian,et al.  Fault diagnosis and classification of water pump using adaptive neuro-fuzzy inference system based on vibration signals , 2015 .

[2]  C. A. Kitio Kwuimy,et al.  Bifurcation analysis of a nonlinear pendulum using recurrence and statistical methods: applications to fault diagnostics , 2014 .

[3]  Y. Lei,et al.  An underdamped stochastic resonance method with stable-state matching for incipient fault diagnosis of rolling element bearings , 2017 .

[4]  Feng Jia,et al.  An adaptive unsaturated bistable stochastic resonance method and its application in mechanical fault diagnosis , 2017 .

[5]  Feng Jia,et al.  Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution , 2015, Sensors.

[6]  Jianshe Kang,et al.  Bearing fault diagnosis and degradation analysis based on improved empirical mode decomposition and maximum correlated kurtosis deconvolution , 2015 .

[7]  Niaoqing Hu,et al.  Stochastic resonance in multi-scale bistable array , 2013 .

[8]  Yongbo Li,et al.  Application of Bandwidth EMD and Adaptive Multiscale Morphology Analysis for Incipient Fault Diagnosis of Rolling Bearings , 2017, IEEE Transactions on Industrial Electronics.

[9]  Zhiwen Liu,et al.  LMD Method and Multi-Class RWSVM of Fault Diagnosis for Rotating Machinery Using Condition Monitoring Information , 2013, Sensors.

[10]  Yu Zhang,et al.  Incipient Fault Diagnosis of Roller Bearing Using Optimized Wavelet Transform Based Multi-Speed Vibration Signatures , 2017, IEEE Access.

[11]  Jay Lee,et al.  Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .

[12]  Giansalvo Cirrincione,et al.  Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks , 2013, IEEE Transactions on Industrial Electronics.

[13]  Robert B. Randall,et al.  A Stochastic Model for Simulation and Diagnostics of Rolling Element Bearings With Localized Faults , 2003 .

[14]  Houguang Liu,et al.  An improved adaptive stochastic resonance method for improving the efficiency of bearing faults diagnosis , 2018 .

[15]  Ruoyu Li,et al.  Rotational Machine Health Monitoring and Fault Detection Using EMD-Based Acoustic Emission Feature Quantification , 2012, IEEE Transactions on Instrumentation and Measurement.

[16]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[17]  A. Sutera,et al.  The mechanism of stochastic resonance , 1981 .

[18]  Weidong Cheng,et al.  Intelligent Fault Classification of Rolling Bearing at Variable Speed Based on Reconstructed Phase Space , 2014, J. Robotics Netw. Artif. Life.

[19]  Fanrang Kong,et al.  Adaptive Multiscale Noise Tuning Stochastic Resonance for Health Diagnosis of Rolling Element Bearings , 2015, IEEE Transactions on Instrumentation and Measurement.

[20]  Yu Xu,et al.  Quantum Particle Swarm Optimization Algorithm , 2011 .

[21]  Yi Qin,et al.  Adaptive bistable stochastic resonance and its application in mechanical fault feature extraction , 2014 .

[22]  Thomas G. Habetler,et al.  An amplitude modulation detector for fault diagnosis in rolling element bearings , 2002, IEEE 2002 28th Annual Conference of the Industrial Electronics Society. IECON 02.

[23]  Jiangtao Wen,et al.  Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning , 2018, IEEE Transactions on Instrumentation and Measurement.

[24]  Zhengjia He,et al.  Adaptive stochastic resonance method for impact signal detection based on sliding window , 2013 .

[25]  Gregoire Nicolis,et al.  Stochastic resonance , 2007, Scholarpedia.

[26]  Wiesenfeld,et al.  Theory of stochastic resonance. , 1989, Physical review. A, General physics.

[27]  Weihua Li,et al.  Multisensor Feature Fusion for Bearing Fault Diagnosis Using Sparse Autoencoder and Deep Belief Network , 2017, IEEE Transactions on Instrumentation and Measurement.

[28]  Xiang Wang,et al.  Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding , 2015, Sensors.

[29]  Siliang Lu,et al.  A review of stochastic resonance in rotating machine fault detection , 2019, Mechanical Systems and Signal Processing.

[30]  Komi Midzodzi Pekpe,et al.  Bearings fault detection in helicopters using frequency readjustment and cyclostationary analysis , 2013 .

[31]  A. S. Sekhar,et al.  Application of Artificial Neural Networks for Identification of Unbalance and Looseness in Rotor Bearing Systems , 2013 .

[32]  Zhigang Wang,et al.  A Resonance Demodulation Method Based on Harmonic Wavelet Transform for Rolling Bearing Fault Diagnosis , 2010 .

[33]  Bartłomiej Dybiec,et al.  Stochastic Resonance: the Role of alpha -Stable Noises , 2006 .

[34]  Jun Wang,et al.  Effects of multiscale noise tuning on stochastic resonance for weak signal detection , 2012, Digit. Signal Process..

[35]  Wang Taiyong,et al.  Numerical research of twice sampling stochastic resonance for the detection of a weak signal submerged in a heavy Noise , 2003 .

[36]  M. S. Safizadeh,et al.  Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell , 2014, Inf. Fusion.

[37]  Grzegorz Litak,et al.  Improving the bearing fault diagnosis efficiency by the adaptive stochastic resonance in a new nonlinear system , 2017 .

[38]  Yanyang Zi,et al.  Study of frequency-shifted and re-scaling stochastic resonance and its application to fault diagnosis , 2009 .