An intelligent fault diagnosis method based on domain adaptation for rolling bearings under variable load conditions

To identify rolling bearing faults under variable load conditions, a method named DISA-KNN is proposed in this paper, which is based on the strategy of feature extraction-domain adaptation-classification. To be specific, the time-domain and frequency-domain indicators are used for feature extraction. Discriminative and domain invariant subspace alignment (DISA) is used to minimize the data distributions’ discrepancies between the training data (source domain) and testing data (target domain). K-nearest neighbor (KNN) is applied to identify rolling bearing faults. DISA-KNN’s validation is proved by the experimental signal collected under different load conditions. The identification accuracies obtained by the DISA-KNN method are more than 90% on four datasets, including one dataset with 99.5% accuracy. The strength of the proposed method is further highlighted by comparisons with the other 8 methods. These results reveal that the proposed method is promising for the rolling bearing fault diagnosis in real rotating machinery.

[1]  Andrew Lewis,et al.  Grasshopper Optimisation Algorithm: Theory and application , 2017, Adv. Eng. Softw..

[2]  Yongbo Li,et al.  A new rolling bearing fault diagnosis method based on multiscale permutation entropy and improved support vector machine based binary tree , 2016 .

[3]  Fei Shen,et al.  Knowledge Transfer for Rotary Machine Fault Diagnosis , 2020, IEEE Sensors Journal.

[4]  Hai Qiu,et al.  Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics , 2006 .

[5]  Jafar Tahmoresnezhad,et al.  Discriminative and domain invariant subspace alignment for visual tasks , 2019, Iran Journal of Computer Science.

[6]  Jinde Zheng,et al.  A GOA-MSVM based strategy to achieve high fault identification accuracy for rotating machinery under different load conditions , 2020 .

[7]  Jian Ma,et al.  Rolling bearing fault diagnosis under variable conditions using LMD-SVD and extreme learning machine , 2015 .

[8]  A. Kraskov,et al.  Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Bo Zhang,et al.  Intelligent Fault Diagnosis Under Varying Working Conditions Based on Domain Adaptive Convolutional Neural Networks , 2018, IEEE Access.

[11]  Tinne Tuytelaars,et al.  Unsupervised Visual Domain Adaptation Using Subspace Alignment , 2013, 2013 IEEE International Conference on Computer Vision.

[12]  Li Long,et al.  Prediction of bearing damage in wind turbines based on the quadratic root mean square of sub-band manifold , 2018 .

[13]  David,et al.  A study on helicopter main gearbox planetary bearing fault diagnosis , 2017, Applied Acoustics.

[14]  Chuanjiang Li,et al.  A New Fuzzy Logic Classifier Based on Multiscale Permutation Entropy and Its Application in Bearing Fault Diagnosis , 2019, Entropy.

[15]  Wang Zhenya,et al.  Rolling bearing fault diagnosis using generalized refined composite multiscale sample entropy and optimized support vector machine , 2020 .

[16]  Jun Yu,et al.  Fault severity identification of roller bearings using flow graph and non-naive Bayesian inference , 2019, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science.

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

[18]  Jafar Tahmoresnezhad,et al.  Visual domain adaptation via transfer feature learning , 2017, Knowledge and Information Systems.

[19]  Xiang Li,et al.  Intelligent ball screw fault diagnosis using a deep domain adaptation methodology , 2020 .

[20]  Yong Qin,et al.  Understanding and Learning Discriminant Features based on Multiattention 1DCNN for Wheelset Bearing Fault Diagnosis , 2020, IEEE Transactions on Industrial Informatics.

[21]  Kate Saenko,et al.  Correlation Alignment for Unsupervised Domain Adaptation , 2016, Domain Adaptation in Computer Vision Applications.

[22]  Jing Tian,et al.  Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis , 2016, IEEE Transactions on Industrial Electronics.

[23]  Yuan Shi,et al.  Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[24]  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.

[25]  Yaguo Lei,et al.  Applications of machine learning to machine fault diagnosis: A review and roadmap , 2020 .

[26]  Ke Lu,et al.  Coupled local-global adaptation for multi-source transfer learning , 2018, Neurocomputing.

[27]  Lin Bo,et al.  High-speed train wheel set bearing fault diagnosis and prognostics: A new prognostic model based on extendable useful life , 2021 .

[28]  Minping Jia,et al.  Fault diagnosis of rolling bearing based on feature reduction with global-local margin Fisher analysis , 2018, Neurocomputing.

[29]  E. Cross,et al.  Detection of sub-surface damage in wind turbine bearings using acoustic emissions and probabilistic modelling , 2020, Renewable Energy.

[30]  Robert B. Randall,et al.  Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study , 2015 .

[31]  Kalyana Chakravarthy Veluvolu,et al.  Rotor Speed-Based Bearing Fault Diagnosis (RSB-BFD) Under Variable Speed and Constant Load , 2015, IEEE Transactions on Industrial Electronics.

[32]  Tao Zhang,et al.  Deep Model Based Domain Adaptation for Fault Diagnosis , 2017, IEEE Transactions on Industrial Electronics.

[33]  Xiang Li,et al.  Deep Learning-Based Machinery Fault Diagnostics With Domain Adaptation Across Sensors at Different Places , 2020, IEEE Transactions on Industrial Electronics.

[34]  Jun Zhang,et al.  Detection for weak fault in planetary gear trains based on an improved maximum correlation kurtosis deconvolution , 2020 .

[35]  Taghi M. Khoshgoftaar,et al.  A survey of transfer learning , 2016, Journal of Big Data.

[36]  Philip S. Yu,et al.  Transfer Feature Learning with Joint Distribution Adaptation , 2013, 2013 IEEE International Conference on Computer Vision.

[37]  Jinrui Wang,et al.  A novel geodesic flow kernel based domain adaptation approach for intelligent fault diagnosis under varying working condition , 2020, Neurocomputing.

[38]  S.A.V. Satya Murty,et al.  Roller element bearing fault diagnosis using singular spectrum analysis , 2013 .

[39]  Yiqiang Chen,et al.  Balanced Distribution Adaptation for Transfer Learning , 2017, 2017 IEEE International Conference on Data Mining (ICDM).

[40]  Minqiang Xu,et al.  Intelligent Fault Identification Based on Multisource Domain Generalization Towards Actual Diagnosis Scenario , 2020, IEEE Transactions on Industrial Electronics.

[41]  Chao Liu,et al.  Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application , 2018, ISA transactions.

[42]  Brigitte Chebel-Morello,et al.  Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals , 2015 .