A self-configurable fault detection system for Industrial Ethernet networks

Abstract In this paper, a self-configurable fault detection system for automated production systems with Industrial Ethernet is proposed. The scope of the proposed fault detection system are process variables, i.e., the observed actuator and sensor signals. Self-configuration of the fault detection system is enabled by recording and analyzing the link connection of the Ethernet network during system start. In a subsequent training phase, a knowledge base is automatically built from the observed process variables. Knowledge-based fault detection is accomplished once the knowledge base is established. Fault detection has been evaluated for a glue production process. In this application case, the knowledge-based fault detection method yielded a balanced accuracy of 99.81%, while a model-based method, which has been used as reference, produced a balanced accuracy of 93.11%.

[1]  Oliver Niggemann,et al.  A generic synchronized data acquisition solution for distributed automation systems , 2012, Proceedings of 2012 IEEE 17th International Conference on Emerging Technologies & Factory Automation (ETFA 2012).

[2]  Peng Li,et al.  Data Driven Modeling for System-Level Condition Monitoring on Wind Power Plants , 2015, DX.

[3]  Gautam Biswas,et al.  Model-Based Diagnosis of Hybrid Systems , 2003, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[4]  Igor Santos,et al.  Anomaly detection for high precision foundries , 2011, 2011 9th IEEE International Conference on Industrial Informatics.

[5]  Giovanni Cutuli,et al.  Performance evaluation of OPC UA , 2010, 2010 IEEE 15th Conference on Emerging Technologies & Factory Automation (ETFA 2010).

[6]  Melvin Michael Henry,et al.  Model-based Estimation of Probabilistic Hybrid Automata , 2002 .

[7]  Danwei Wang,et al.  Model-Based Prognosis for Hybrid Systems With Mode-Dependent Degradation Behaviors , 2014, IEEE Transactions on Industrial Electronics.

[8]  Feng Zhao,et al.  Monitoring and fault diagnosis of hybrid systems , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[9]  Ronald L. Rivest,et al.  Introduction to Algorithms, 3rd Edition , 2009 .

[10]  A.P. Kalogeras,et al.  Integration of Semantic Web Services and Ontologies into the Industrial and Building Automation Layer , 2007, EUROCON 2007 - The International Conference on "Computer as a Tool".

[11]  Raghunathan Rengaswamy,et al.  A Signed Directed Graph and Qualitative Trend Analysis-Based Framework for Incipient Fault Diagnosis , 2007 .

[12]  Bo Sun,et al.  Fault Diagnosis of Hybrid Systems Using Particle Filter Based Hybrid Estimation Algorithm , 2013 .

[13]  Dattatraya Vishnu Kodavade,et al.  A Universal Object Oriented Expert System Frame Work for Fault Diagnosis , 2012 .

[14]  Oliver Niggemann,et al.  Evaluation of Model-Based Condition Monitoring Systems in Industrial Application Cases , 2015, ML4CPS.

[15]  Shen Yin,et al.  On PCA-based fault diagnosis techniques , 2010, 2010 Conference on Control and Fault-Tolerant Systems (SysTol).

[16]  Sauro Longhi,et al.  Multi-scale PCA based fault diagnosis on a paper mill plant , 2011, ETFA2011.

[17]  Oliver Niggemann,et al.  Efficient fault detection for industrial automation processes with observable process variables , 2015, 2015 IEEE 13th International Conference on Industrial Informatics (INDIN).

[18]  R. Dearden,et al.  Detecting and Learning Unknown Fault States in Hybrid Diagnosis , 2009 .

[19]  Oliver Niggemann,et al.  A HMM-based fault detection method for piecewise stationary industrial processes , 2015, 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA).

[20]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

[21]  Kaixiang Peng,et al.  A Novel Scheme for Key Performance Indicator Prediction and Diagnosis With Application to an Industrial Hot Strip Mill , 2013, IEEE Transactions on Industrial Informatics.

[22]  Danwei Wang,et al.  An Integrated Approach to Mode Tracking and Diagnosis of Hybrid Systems , 2014, IEEE Transactions on Industrial Electronics.

[23]  Zhiwei Gao,et al.  Disturbance Attenuation in Fault Detection of Gas Turbine Engines: A Discrete Robust Observer Design , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[24]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[25]  Oliver Niggemann,et al.  Symptomes Classifier Hypotheses Phenomenological Approach to Diagnosis Causality Analysis Causality Model Hypotheses Model-Based Approach to Diagnosis Symptomes Similarity Search Case Database Hypotheses , 2014 .