Intelligent ball screw fault diagnosis using a deep domain adaptation methodology

Abstract Intelligent data-driven fault diagnosis methods have been successfully developed in the recent years. However, as one of the most important machines in the industries, the ball screw health monitoring problem has received less attention, due to the complex operating patterns and sophisticated mechanical structures. In practice, the working conditions of the ball screws usually change, that further makes the fault diagnosis problem more challenging since the data distributions are not the same. In order to address this issue, a deep learning-based domain adaptation method is proposed for the cross-domain ball screw fault diagnosis problem. The deep convolutional neural network is adopted for feature extraction and health condition classification. The maximum mean discrepancy metric is proposed to measure and optimize the data distributions of different operating conditions. A data segmentation method which is specially designed for the ball screw is further integrated. The experiments on the real ball screw condition monitoring data are carried out for validation. The results indicate the proposed approach is promising for the cross-domain diagnostic tasks of the ball screw in the real industries.

[1]  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.

[2]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[3]  Xianchun Song,et al.  Investigation of load distribution and deformations for ball screws with the effects of turning torque and geometric errors , 2019, Mechanism and Machine Theory.

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

[5]  Rama Chellappa,et al.  Visual Domain Adaptation: A survey of recent advances , 2015, IEEE Signal Processing Magazine.

[6]  Yi Wang,et al.  A data-driven method based on deep belief networks for backlash error prediction in machining centers , 2017, Journal of Intelligent Manufacturing.

[7]  Xiang Li,et al.  Cross-Domain Fault Diagnosis of Rolling Element Bearings Using Deep Generative Neural Networks , 2019, IEEE Transactions on Industrial Electronics.

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

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

[10]  Yi-Cheng Huang,et al.  Prognostic experiment for ball screw preload loss of machine tool through the Hilbert-Huang Transform and Multiscale entropy method , 2010, The 2010 IEEE International Conference on Information and Automation.

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

[12]  Liang Gao,et al.  A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[13]  J. F. Cuttino,et al.  Analytical and Experimental Identification of Nonlinearities in a Single-Nut, Preloaded Ball Screw , 1997 .

[14]  Bernhard Schölkopf,et al.  A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..

[15]  Sebastian Thrun,et al.  Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.

[16]  Zhifeng Liu,et al.  An accuracy degradation analysis of ball screw mechanism considering time-varying motion and loading working conditions , 2019, Mechanism and Machine Theory.

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

[18]  Jay Lee,et al.  Detection and diagnosis of bottle capping failures based on motor current signature analysis , 2019 .

[19]  Xiang Li,et al.  Deep residual learning-based fault diagnosis method for rotating machinery. , 2019, ISA transactions.

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

[21]  Trevor Darrell,et al.  Adversarial Discriminative Domain Adaptation , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Xiang Li,et al.  Diagnosing Rotating Machines With Weakly Supervised Data Using Deep Transfer Learning , 2020, IEEE Transactions on Industrial Informatics.

[23]  Haiyang Pan,et al.  A fault diagnosis approach for roller bearing based on symplectic geometry matrix machine , 2019, Mechanism and Machine Theory.

[24]  Jay Lee,et al.  A blockchain enabled Cyber-Physical System architecture for Industry 4.0 manufacturing systems , 2019, Manufacturing Letters.

[25]  Ivor W. Tsang,et al.  Domain Adaptation via Transfer Component Analysis , 2009, IEEE Transactions on Neural Networks.

[26]  Ruqiang Yan,et al.  Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning , 2019, IEEE Transactions on Industrial Informatics.

[27]  Ridha Ziani,et al.  Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher’s criterion , 2017, J. Intell. Manuf..

[28]  Jürgen Fleischer,et al.  Camera Based Ball Screw Spindle Defect Classification System , 2019 .

[29]  Xu Li,et al.  Machinery fault diagnosis with imbalanced data using deep generative adversarial networks , 2020 .

[30]  Haibo He,et al.  A Hierarchical Deep Domain Adaptation Approach for Fault Diagnosis of Power Plant Thermal System , 2019, IEEE Transactions on Industrial Informatics.

[31]  Liang Gong,et al.  State-Wise LSTM-GRU Method for Ball Screw Prediction , 2019, 2019 IEEE Aerospace Conference.

[32]  Wei Zhang,et al.  Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism , 2019, Signal Process..

[33]  Trevor Darrell,et al.  DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.

[34]  Wei Zhang,et al.  Multi-Layer domain adaptation method for rolling bearing fault diagnosis , 2019, Signal Process..

[35]  Wei Zhang,et al.  Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction , 2019, Reliab. Eng. Syst. Saf..

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

[37]  Lorenzo Bruzzone,et al.  Classification of Hyperspectral Images With Regularized Linear Discriminant Analysis , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Li Zhang,et al.  Ball Screw Fault Detection and Location Based on Outlier and Instantaneous Rotational Frequency Estimation , 2019, Shock and Vibration.

[39]  Hongli Gao,et al.  A deep learning-based multi-sensor data fusion method for degradation monitoring of ball screws , 2016, 2016 Prognostics and System Health Management Conference (PHM-Chengdu).

[40]  Xiaodong Jia,et al.  Prognosability study of ball screw degradation using systematic methodology , 2018, Mechanical Systems and Signal Processing.

[41]  Qi Liu,et al.  A deep learning-based recognition method for degradation monitoring of ball screw with multi-sensor data fusion , 2017, Microelectron. Reliab..

[42]  Guo-Hua Feng,et al.  Establishing a cost-effective sensing system and signal processing method to diagnose preload levels of ball screws , 2012 .

[43]  Wei Zhang,et al.  A robust intelligent fault diagnosis method for rolling element bearings based on deep distance metric learning , 2018, Neurocomputing.

[44]  Ruqiang Yan,et al.  Convolutional Discriminative Feature Learning for Induction Motor Fault Diagnosis , 2017, IEEE Transactions on Industrial Informatics.

[45]  Richard S. Zemel,et al.  Generative Moment Matching Networks , 2015, ICML.

[46]  Michael I. Jordan,et al.  Learning Transferable Features with Deep Adaptation Networks , 2015, ICML.

[47]  Xiang Li,et al.  Remaining useful life estimation in prognostics using deep convolution neural networks , 2018, Reliab. Eng. Syst. Saf..

[48]  Yaguo Lei,et al.  Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data , 2019, IEEE Transactions on Industrial Electronics.

[49]  Sivaraman Balakrishnan,et al.  Optimal kernel choice for large-scale two-sample tests , 2012, NIPS.