Acoustic spectral imaging and transfer learning for reliable bearing fault diagnosis under variable speed conditions

Abstract Incipient fault diagnosis of a bearing requires robust feature representation for an accurate condition-based monitoring system. Existing fault diagnosis schemes are mostly confined to manual features and traditional machine learning approaches such as artificial neural networks (ANN) and support vector machines (SVM). These handcrafted features require substantial human expertise and domain knowledge. In addition, these feature characteristics vary with the bearing’s rotational speed. Thus, such methods do not yield the best results under variable speed conditions. To address this issue, this paper presents a reliable fault diagnosis scheme based on acoustic spectral imaging (ASI) of acoustic emission (AE) signals as a precise health state. These health states are further utilized with transfer learning, which is a machine learning technique, which shares knowledge with convolutional neural networks (CNN) for accurate diagnosis under variable operating conditions. In ASI, the amplitudes of the spectral components of the windowed time-domain acoustic emission signal are transformed into spectrum imaging. ASI provides a visual representation of acoustic emission spectral features in images. This ensures enhanced spectral images for transfer learning (TL) testing and training, and thus provides a robust classifier technique with high diagnostic accuracy.

[1]  O.V. Thorsen,et al.  Failure identification and analysis for high voltage induction motors in petrochemical industry , 1998, Conference Record of 1998 IEEE Industry Applications Conference. Thirty-Third IAS Annual Meeting (Cat. No.98CH36242).

[2]  Jong-Myon Kim,et al.  Bearing Fault Diagnosis Based on Convolutional Neural Networks with Kurtogram Representation of Acoustic Emission Signals , 2017, CSA/CUTE.

[3]  Myeongsu Kang,et al.  Robust condition monitoring of rolling element bearings using de-noising and envelope analysis with signal decomposition techniques , 2015, Expert Syst. Appl..

[4]  Li Li,et al.  A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing Fault Diagnosis Based on Hilbert Huang Transform , 2015, Sensors.

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

[6]  Muhammad Sohaib,et al.  A Robust Deep Learning Based Fault Diagnosis of Rotary Machine Bearings , 2017 .

[7]  Jong-Myon Kim,et al.  A Hybrid Feature Model and Deep-Learning-Based Bearing Fault Diagnosis , 2017, Sensors.

[8]  Adam Glowacz,et al.  Fault diagnosis of single-phase induction motor based on acoustic signals , 2019, Mechanical Systems and Signal Processing.

[9]  Haidong Shao,et al.  A novel deep autoencoder feature learning method for rotating machinery fault diagnosis , 2017 .

[10]  Farid Melgani,et al.  One‐dimensional convolutional neural networks for spectroscopic signal regression , 2018 .

[11]  Adam Glowacz,et al.  Acoustic-Based Fault Diagnosis of Commutator Motor , 2018, Electronics.

[12]  Zhang Zengmeng,et al.  Extraction of fault component from abnormal sound in diesel engines using acoustic signals , 2016 .

[13]  Tielin Shi,et al.  Weighted Kernel Entropy Component Analysis for Fault Diagnosis of Rolling Bearings , 2017, Sensors.

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

[15]  Suraj Prakash Harsha,et al.  Fault diagnosis of rolling element bearing with intrinsic mode function of acoustic emission data using APF-KNN , 2013, Expert Syst. Appl..

[16]  Ran Zhang,et al.  Fault Diagnosis from Raw Sensor Data Using Deep Neural Networks Considering Temporal Coherence , 2017, Sensors.

[17]  Gaoliang Peng,et al.  A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load , 2018, Mechanical Systems and Signal Processing.

[18]  Hubert Razik,et al.  Detection and Diagnosis of Faults in Induction Motor Using an Improved Artificial Ant Clustering Technique , 2013, IEEE Transactions on Industrial Electronics.

[19]  Li Wu,et al.  A Novel Faults Diagnosis Method for Rolling Element Bearings Based on EWT and Ambiguity Correlation Classifiers , 2017, Entropy.

[20]  B ParvathiSangeetha,et al.  Dyadic wavelet transform-based acoustic signal analysis for torque prediction of a three-phase induction motor , 2017, IET Signal Process..

[21]  Jiawei Xiang,et al.  A data indicator-based deep belief networks to detect multiple faults in axial piston pumps , 2018, Mechanical Systems and Signal Processing.

[22]  Jong-Duk Son,et al.  Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine , 2009, Expert Syst. Appl..

[23]  Robert B. Randall,et al.  Application of cepstrum pre-whitening for the diagnosis of bearing faults under variable speed conditions , 2013 .

[24]  Xiwen Qin,et al.  The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest , 2017 .

[25]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[26]  B. Eftekharnejad,et al.  The application of spectral kurtosis on Acoustic Emission and vibrations from a defective bearing , 2011 .

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

[28]  Jianjun Hu,et al.  An Ensemble Deep Convolutional Neural Network Model with Improved D-S Evidence Fusion for Bearing Fault Diagnosis , 2017, Sensors.

[29]  Wahyu Caesarendra,et al.  Condition monitoring of naturally damaged slow speed slewing bearing based on ensemble empirical mode decomposition , 2013 .

[30]  Jong-Myon Kim,et al.  Bearing Fault Diagnosis under Variable Speed Using Convolutional Neural Networks and the Stochastic Diagonal Levenberg-Marquardt Algorithm , 2017, Sensors.

[31]  Ran Zhang,et al.  Transfer Learning With Neural Networks for Bearing Fault Diagnosis in Changing Working Conditions , 2017, IEEE Access.

[32]  Weijie Wang,et al.  Resonance-Based Sparse Signal Decomposition and Its Application in Mechanical Fault Diagnosis: A Review , 2017, Sensors.

[33]  Gurmeet Singh,et al.  Induction motor inter turn fault detection using infrared thermographic analysis , 2016 .

[34]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[35]  Myeongsu Kang,et al.  High-Performance and Energy-Efficient Fault Diagnosis Using Effective Envelope Analysis and Denoising on a General-Purpose Graphics Processing Unit , 2015, IEEE Transactions on Power Electronics.

[36]  Myeongsu Kang,et al.  Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis , 2015, IEEE Transactions on Power Electronics.

[37]  Jong-Myon Kim,et al.  Incipient fault diagnosis in bearings under variable speed conditions using multiresolution analysis and a weighted committee machine. , 2017, The Journal of the Acoustical Society of America.

[38]  Ezio Bassi,et al.  Stator Current and Motor Efficiency as Indicators for Different Types of Bearing Faults in Induction Motors , 2010, IEEE Transactions on Industrial Electronics.

[39]  Lin Liang,et al.  Quantitative diagnosis of a spall-like fault of a rolling element bearing by empirical mode decomposition and the approximate entropy method , 2013 .

[40]  Idriss El-Thalji,et al.  Dynamic modelling of wear evolution in rolling bearings , 2015 .

[41]  H. W. Ngan,et al.  Detection of Motor Bearing Outer Raceway Defect by Wavelet Packet Transformed Motor Current Signature Analysis , 2010, IEEE Transactions on Instrumentation and Measurement.

[42]  Rahman Saidur,et al.  A review on electrical motors energy use and energy savings , 2010 .

[43]  Jong-Myon Kim,et al.  Automated Bearing Fault Diagnosis Using 2D Analysis of Vibration Acceleration Signals under Variable Speed Conditions , 2016 .

[44]  Donghua Zhou,et al.  Diagnosis and Prognosis for Complicated Industrial Systems - Part I , 2016, IEEE Trans. Ind. Electron..

[45]  Jong-Myon Kim,et al.  Time–frequency envelope analysis-based sub-band selection and probabilistic support vector machines for multi-fault diagnosis of low-speed bearings , 2017 .