Diagnosis of Multiple Faults in Rotating Machinery Using Ensemble Learning

Fault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly popular because features are extracted and selected automatically. However, it is challenging for these models to give superior results with rotating machine components of different scales, single and multiple faults across different rotating components, diverse operating speeds, and diverse load conditions. To address these challenges, this paper proposes a comprehensive learning approach with optimized signal processing transforms for single as well as multiple faults diagnosis across dissimilar rotating machine components: gearbox, bearing, and shaft. The optimized bicoherence, spectral kurtosis and cyclic spectral coherence feature spaces, and deep blending ensemble learning are explored for multiple faults diagnosis of these components. The performance analysis of the proposed approach has been demonstrated through a single joint training of the entire framework on a compound dataset containing multiple faults derived from three public repositories. A comparison with the state-of-the-art approaches that used these datasets, shows that our method gives improved results with different components and faults with nominal retraining.

[1]  Yuzhen Zhang,et al.  A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications , 2022, Applied Sciences.

[2]  Zhi Cao,et al.  A Homogeneous Stacking Ensemble Learning Model for Fault Diagnosis of Rotating Machinery With Small Samples , 2022, IEEE Sensors Journal.

[3]  Yanjun Chen,et al.  Fault diagnosis of train rotating parts based on multi-objective VMD optimization and ensemble learning , 2021, Digit. Signal Process..

[4]  Weihua Zhang,et al.  Enhanced spectral coherence and its application to bearing fault diagnosis , 2021, Measurement.

[5]  Minh-Tuan Pham,et al.  2D CNN-Based Multi-Output Diagnosis for Compound Bearing Faults under Variable Rotational Speeds , 2021, Machines.

[6]  Muhammad Sohaib,et al.  Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review , 2021, Energies.

[7]  Ivan Petrunin,et al.  Health Condition Estimation of Bearings with Multiple Faults by a Composite Learning-Based Approach , 2021, Sensors.

[8]  U. A. Monteiro,et al.  Diagnostic and severity analysis of combined failures composed by imbalance and misalignment in rotating machines , 2021, The International Journal of Advanced Manufacturing Technology.

[9]  Amjad J. Humaidi,et al.  Review of deep learning: concepts, CNN architectures, challenges, applications, future directions , 2021, Journal of Big Data.

[10]  Mien Van,et al.  A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis , 2021, Sensors.

[11]  Herman Augusto Lepikson,et al.  Deep learning for diagnosis and classification of faults in industrial rotating machinery , 2020, Comput. Ind. Eng..

[12]  Reza Hassannejad,et al.  A hybrid fine-tuned VMD and CNN scheme for untrained compound fault diagnosis of rotating machinery with unequal-severity faults , 2020, Expert Syst. Appl..

[13]  Joo-Ho Choi,et al.  A Tutorial for Feature Engineering in the Prognostics and Health Management of Gears and Bearings , 2020 .

[14]  Ruixin Wang,et al.  An enhanced selective ensemble deep learning method for rolling bearing fault diagnosis with beetle antennae search algorithm , 2020 .

[15]  Pietro Borghesani,et al.  Automated assessment of gear wear mechanism and severity using mould images and convolutional neural networks , 2020 .

[16]  Konstantinos Gryllias,et al.  A deep learning method for bearing fault diagnosis based on Cyclic Spectral Coherence and Convolutional Neural Networks , 2020 .

[17]  Xinyu Li,et al.  Ensemble deep contractive auto-encoders for intelligent fault diagnosis of machines under noisy environment , 2020, Knowl. Based Syst..

[18]  T. Y. Kam,et al.  Fault diagnosis method for worm gearbox using convolutional network and ensemble learning , 2020, Journal of Physics: Conference Series.

[19]  Yang Liu,et al.  An Ensemble Convolutional Neural Networks for Bearing Fault Diagnosis Using Multi-Sensor Data , 2019, Sensors.

[20]  Bing Han,et al.  Comparisons of different deep learning-based methods on fault diagnosis for geared system , 2019, Int. J. Distributed Sens. Networks.

[21]  Jagath Sri Lal Senanayaka,et al.  Multiple Classifiers and Data Fusion for Robust Diagnosis of Gearbox Mixed Faults , 2019, IEEE Transactions on Industrial Informatics.

[22]  Byeng D. Youn,et al.  A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: shallow and deep learning , 2019, JMST Advances.

[23]  Hongmei Li,et al.  Bearing Fault Diagnosis with a Feature Fusion Method Based on an Ensemble Convolutional Neural Network and Deep Neural Network , 2019, Sensors.

[24]  Minqiang Xu,et al.  A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery , 2019, Entropy.

[25]  Weiming Shen,et al.  Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning , 2019, Sensors.

[26]  David He,et al.  Gear Pitting Fault Diagnosis Using Integrated CNN and GRU Network with Both Vibration and Acoustic Emission Signals , 2019, Applied Sciences.

[27]  Serkan Kiranyaz,et al.  A Generic Intelligent Bearing Fault Diagnosis System Using Compact Adaptive 1D CNN Classifier , 2018, Journal of Signal Processing Systems.

[28]  Fulei Chu,et al.  Ensemble deep learning-based fault diagnosis of rotor bearing systems , 2019, Comput. Ind..

[29]  Vikas M. Phalle,et al.  A bearing vibration data analysis based on spectral kurtosis and ConvNet , 2018, Soft Comput..

[30]  Yan Han,et al.  Multi-level wavelet packet fusion in dynamic ensemble convolutional neural network for fault diagnosis , 2018, Measurement.

[31]  Shunming Li,et al.  An intelligent fault diagnosis framework for raw vibration signals: adaptive overlapping convolutional neural network , 2018, Measurement Science and Technology.

[32]  Enrico Zio,et al.  Artificial intelligence for fault diagnosis of rotating machinery: A review , 2018, Mechanical Systems and Signal Processing.

[33]  Dazhong Wu,et al.  Deep learning for smart manufacturing: Methods and applications , 2018, Journal of Manufacturing Systems.

[34]  Shuting Wan,et al.  Compound Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by MCDK , 2018 .

[35]  Wenliao Du,et al.  Identification of multi-fault in rotor-bearing system using spectral kurtosis and EEMD , 2017 .

[36]  Zhiqiang Chen,et al.  Deep neural networks-based rolling bearing fault diagnosis , 2017, Microelectron. Reliab..

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

[38]  Levent Eren,et al.  Bearing Fault Detection by One-Dimensional Convolutional Neural Networks , 2017 .

[39]  Rajiv Tiwari,et al.  Multi-class Fault Diagnosis in Gears Using Machine Learning Algorithms Based on Time Domain Data , 2017 .

[40]  Sheng Fu,et al.  Fault severity assessment of rolling element bearings based on bicoherence spectrum , 2016 .

[41]  Liang Chen,et al.  Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis , 2016 .

[42]  Weihua Li,et al.  Gearbox fault classification using S-transform and convolutional neural network , 2016, 2016 10th International Conference on Sensing Technology (ICST).

[43]  Radoslaw Zimroz,et al.  Identification of cyclic components in presence of non-Gaussian noise – application to crusher bearings damage detection , 2015 .

[44]  Idriss El-Thalji,et al.  Problems with using Fast Fourier Transform for rotating equipment: Is it time for an update? , 2014 .

[45]  Le Zhang,et al.  Ensemble deep learning for regression and time series forecasting , 2014, 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL).

[46]  Moacir P. Ponti,et al.  Combining Classifiers: From the Creation of Ensembles to the Decision Fusion , 2011, SIBGRAPI Tutorials.

[47]  N. R. Sakthivel,et al.  Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine , 2011, Expert Syst. Appl..

[48]  Qiao Hu,et al.  Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble , 2007 .

[49]  J. Antoni.  The spectral kurtosis: a useful tool for characterising non-stationary signals , 2006 .

[50]  Lin Ma,et al.  Fault diagnosis of rolling element bearings using basis pursuit , 2005 .

[51]  A. F. Stronach,et al.  The Application of Advanced Signal Processing Techniques to Induction Motor Bearing Condition Diagnosis , 2003 .

[52]  Shouqi Yuan,et al.  Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery , 2020, IEEE Access.

[53]  Mohammad Modarres,et al.  Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings , 2017 .

[54]  Eric Bechhoefer,et al.  A Review of Time Synchronous Average Algorithms , 2009 .

[55]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[56]  R. Randall,et al.  THE APPLICATION OF SPECTRAL KURTOSIS TO BEARING DIAGNOSTICS , 2004 .