Symptomes Classifier Hypotheses Phenomenological Approach to Diagnosis Causality Analysis Causality Model Hypotheses Model-Based Approach to Diagnosis Symptomes Similarity Search Case Database Hypotheses

Cyber-Physical Production Systems (CPPSs) are in the focus of research, industry and politics: By applying new IT and new computer science solutions, production systems will become more adaptable, more resource ef- ficient and more user friendly. The analysis and diagnosis of such systems is a major part of this trend: Plants should detect automatically wear, faults and suboptimal configurations. This paper reflects the current state-of- the-art in diagnosis against the requirements of CPPSs, identifies three main gaps and gives application scenarios to outline first ideas for potential solutions to close these gaps.

[1]  Alban Grastien.  A Spectrum of Diagnosis Approaches , 2013 .

[2]  David L. Hall,et al.  Dirty Secrets in Multisensor Data Fusion , 2001 .

[3]  Volker Lohweg,et al.  A NOVEL DATA FUSION APPROACH USING TWO-LAYER CONFLICT SOLVING , 2008 .

[4]  George J. Klir,et al.  Uncertainty Modeling and Analysis in Engineering and the Sciences (Hardcover) , 2006 .

[5]  Vipin Kumar,et al.  Introduction to Data Mining, (First Edition) , 2005 .

[6]  Rolf Isermann.  Model-based fault-detection and diagnosis - status and applications § , 2004 .

[7]  Rui Araújo,et al.  Fault detection and replacement of a temperature sensor in a cement rotary kiln , 2013, 2013 IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA).

[8]  Ali Siadat,et al.  Optimization of a knowledge-based system by a meta-heuristic approach for the automotive diagnosis , 2011, 2011 IEEE International Conference on Industrial Engineering and Engineering Management.

[9]  Benno Stein,et al.  Learning Behavior Models for Hybrid Timed Systems , 2012, AAAI.

[10]  Insup Lee,et al.  Cyber-physical systems: The next computing revolution , 2010, Design Automation Conference.

[11]  Hans Kleine Büning,et al.  Using behavior models for anomaly detection in hybrid systems , 2011, 2011 XXIII International Symposium on Information, Communication and Automation Technologies.

[12]  Rolf Isermann,et al.  Model-based fault-detection and diagnosis - status and applications , 2004, Annu. Rev. Control..

[13]  Uwe Mönks,et al.  Sensor fusion by two-layer conflict solving , 2010, 2010 2nd International Workshop on Cognitive Information Processing.

[14]  Peter Struss,et al.  Diagnosis of Bottling Plants - First Success and Challenges , 2009 .

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

[16]  K. F. Li,et al.  Fault detection using phenomenological models , 2003, CCECE 2003 - Canadian Conference on Electrical and Computer Engineering. Toward a Caring and Humane Technology (Cat. No.03CH37436).

[17]  Marius Kloft,et al.  Toward Supervised Anomaly Detection , 2014, J. Artif. Intell. Res..

[18]  Franz Wotawa,et al.  Model-Based Diagnosis or Reasoning from First Principles , 2003, IEEE Intell. Syst..

[19]  Edward A. Lee.  Cyber Physical Systems: Design Challenges , 2008, 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC).

[20]  V. Richard Benjamins,et al.  Toward a competence theory of diagnosis , 1994, IEEE Expert.

[21]  Alexander Maier,et al.  Online passive learning of timed automata for cyber-physical production systems , 2014, 2014 12th IEEE International Conference on Industrial Informatics (INDIN).

[22]  Ulrich Epple,et al.  Plant asset management functions driven by property models , 2009, 2009 IEEE Conference on Emerging Technologies & Factory Automation.

[23]  Raymond Reiter,et al.  A Theory of Diagnosis from First Principles , 1986, Artif. Intell..

[24]  Daniel T. Larose,et al.  Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .

[25]  Uwe Mönks,et al.  Machine conditioning by importance controlled information fusion , 2013, 2013 IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA).

[26]  Joaquím Meléndez,et al.  A framework for Case-Based Diagnosis of batch processes in the principal components space , 2009, 2009 IEEE Conference on Emerging Technologies & Factory Automation.

[27]  W. M. Wonham,et al.  Distributed diagnosis for qualitative systems , 2002, Sixth International Workshop on Discrete Event Systems, 2002. Proceedings..

[28]  Andreas Sidorow,et al.  Model Based Fault Detection of the Air and Exhaust Path of Diesel Engines Including Turbocharger Models , 2011 .

[29]  Oliver Niggemann,et al.  A stochastic method for the detection of anomalous energy consumption in hybrid industrial systems , 2013, 2013 11th IEEE International Conference on Industrial Informatics (INDIN).

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

[31]  Oliver Niggemann,et al.  Visual Anomaly Detection in Production Plants , 2012, ICINCO.

[32]  Michaela Huhn,et al.  Symptom propagation and transformation analysis: A pragmatic model for system-level diagnosis of large automation systems , 2011, ETFA2011.

[33]  Cesare Alippi,et al.  A Cognitive Fault Diagnosis System for Distributed Sensor Networks , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[34]  Benno Stein,et al.  Using Learned Models for the Root Cause Analysis of Cyber-Physical Production Systems , 2014 .

[35]  Lawrence D. Pohlmann,et al.  The Engineering Design of Systems – Models and Methods , 2000 .

[36]  Oliver Niggemann,et al.  System modeling based on machine learning for anomaly detection and predictive maintenance in industrial plants , 2014, Proceedings of the 2014 IEEE Emerging Technology and Factory Automation (ETFA).

[37]  Zhang Ren,et al.  Test point optimization for model-based fault diagnosis expert system , 2012 .

[38]  Daren Yu,et al.  Anomaly detection for continuous sequence based compression process , 2012, 2012 IEEE International Conference on Computer Science and Automation Engineering.

[39]  Rolf Isermann,et al.  Model-based fault detection and diagnosis for diesel engines , 2002 .

[40]  Sicco Verwer.  Efficient Identification of Timed Automata: Theory and practice , 2010 .

[41]  Peter Struss,et al.  Diagnosing a Dynamic System with (almost) no Observations , 1998 .

[42]  Jörg Neidig,et al.  Improved diagnosis by combining structural and process knowledge , 2011, ETFA2011.