Fault Mechanism Analysis for Manufacturing System Based on Catastrophe Model

Fault analysis is important in both research and industry. Current fault analysis tasks are mainly concerned with fault prediction and classification and do not focus enough on fault evolution mechanisms. In this paper, we propose a fault analysis method based on catastrophe theory for manufacturing system to improve the effectiveness and efficiency of real time monitoring of potential fault and causes analysis. The key advantages of our proposed method are (i) utilizing catastrophe theory and big data analysis to establish the fault cusp catastrophe model of manufacturing system and create the internal fault evolution mechanism of manufacturing system by the cusp catastrophe model and, (ii) with the established catastrophe model, fulfilling fault monitoring and accurate preventive control of the manufacturing system and ensuring the healthy operation of the manufacturing system.

[1]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[2]  Sanjit A. Seshia,et al.  A Theory of Mutations with Applications to Vacuity, Coverage, and Fault Tolerance , 2008, 2008 Formal Methods in Computer-Aided Design.

[3]  Okyay Kaynak,et al.  Improved PLS Focused on Key-Performance-Indicator-Related Fault Diagnosis , 2015, IEEE Transactions on Industrial Electronics.

[4]  H. Odendaal,et al.  Actuator fault detection and isolation: An optimised parity space approach , 2014 .

[5]  Lei Shu,et al.  When Mobile Crowd Sensing Meets Traditional Industry , 2017, IEEE Access.

[6]  Farrokh Sassani,et al.  On-line fault diagnosis of hydraulic systems using Unscented Kalman Filter , 2010 .

[7]  Kaixiang Peng,et al.  Joint Data-Driven Fault Diagnosis Integrating Causality Graph With Statistical Process Monitoring for Complex Industrial Processes , 2017, IEEE Access.

[8]  Chenglin Wen,et al.  Fault Isolation Based on k-Nearest Neighbor Rule for Industrial Processes , 2016, IEEE Transactions on Industrial Electronics.

[9]  Yang Song,et al.  Parity space-based fault detection for linear discrete time-varying systems with unknown input , 2015, Autom..

[10]  Robert X. Gao,et al.  Virtualization and deep recognition for system fault classification , 2017 .

[11]  Weiqiang Chen,et al.  Logic-Based Methods for Intelligent Fault Diagnosis and Recovery in Power Electronics , 2017, IEEE Transactions on Power Electronics.

[12]  Robert X. Gao,et al.  Deep learning and its applications to machine health monitoring , 2019, Mechanical Systems and Signal Processing.

[13]  Wen Chen,et al.  Simultaneous Fault Isolation and Estimation of Lithium-Ion Batteries via Synthesized Design of Luenberger and Learning Observers , 2014, IEEE Transactions on Control Systems Technology.

[14]  Tao Jiang,et al.  Parameter Estimation-Based Fault Detection, Isolation and Recovery for Nonlinear Satellite Models , 2008, IEEE Transactions on Control Systems Technology.

[15]  Guang-Hong Yang,et al.  Observer-based fault diagnosis for a class of non-linear multiple input multiple output uncertain stochastic systems using B-spline expansions , 2011 .

[16]  Hanlin Liu,et al.  A Data-Driven Fault Diagnosis Methodology in Three-Phase Inverters for PMSM Drive Systems , 2017, IEEE Transactions on Power Electronics.

[17]  K. Khorasani,et al.  A Neural Network-Based Multiplicative Actuator Fault Detection and Isolation of Nonlinear Systems , 2013, IEEE Transactions on Control Systems Technology.

[18]  Qinghua Zhang,et al.  An Information Fusion Fault Diagnosis Method Based on Dimensionless Indicators With Static Discounting Factor and KNN , 2016, IEEE Sensors Journal.

[19]  Brian A Weiss,et al.  Developing a hierarchical decomposition methodology to increase manufacturing process and equipment health awareness. , 2018, Journal of manufacturing systems.

[20]  Lihui Wang,et al.  Imbalanced data fault diagnosis of rotating machinery using synthetic oversampling and feature learning , 2018, Journal of Manufacturing Systems.

[21]  Jing Yuan,et al.  Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review , 2016 .

[22]  Jihong Yan,et al.  Improved Hilbert-Huang transform based weak signal detection methodology and its application on incipient fault diagnosis and ECG signal analysis , 2014, Signal Process..

[23]  Hao Ye,et al.  Fault diagnosis based on parameter estimation in closed-loop systems , 2015 .

[24]  D. Mahinda Vilathgamuwa,et al.  A Sensor Fault Detection and Isolation Method in Interior Permanent-Magnet Synchronous Motor Drives Based on an Extended Kalman Filter , 2013, IEEE Transactions on Industrial Electronics.

[25]  Peng Xu,et al.  Decentralized fault detection and diagnosis via sparse PCA based decomposition and Maximum Entropy decision fusion , 2012 .

[26]  Jiafu Wan,et al.  Implementing Smart Factory of Industrie 4.0: An Outlook , 2016, Int. J. Distributed Sens. Networks.

[27]  R. Lange,et al.  Modeling Maher's Attribution Theory of Delusions as a Cusp Catastrophe , 2000 .

[28]  Wei Guo,et al.  Identification of key features using topological data analysis for accurate prediction of manufacturing system outputs , 2017 .