Assessing neural networks for sensor fault detection

The idea of “smart sensing” includes a permanent monitoring and evaluation of sensor data related to possible measurement faults. This concept requires a fault detection chain covering all relevant fault types of a specific sensor. Additionally, the fault detection components have to provide a high precision in order to generate a reliable quality indicator. Due to the large spectrum of sensor faults and their specific characteristics these goals are difficult to meet and error prone. The developer manually determines the specific sensor characteristics, indicates a set of detection methods, adjusts parameters and evaluates the composition. In this paper we exploit neural-network approaches in order to provide a general solution covering typical sensor faults and to replace complex sets of individual detection methods. For this purpose, we identify an appropriate set of fault relevant features in a first step. Secondly, we determine a generic neural-network structure and learning strategy adaptable for detecting multiple fault types. Afterwards the approach is applied on a common used sensor system and evaluated with deterministic fault injections.

[1]  Paolo Bellavista,et al.  A survey of context data distribution for mobile ubiquitous systems , 2012, CSUR.

[2]  E. A. Woods,et al.  Fault detection, supervision and safety for technical processes , 1993 .

[3]  S.M. Rovnyak,et al.  Decision tree-based methodology for high impedance fault detection , 2004, IEEE Transactions on Power Delivery.

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

[5]  Ron J. Patton,et al.  FAULT-TOLERANT CONTROL SYSTEMS: THE 1997 SITUATION , 1997 .

[6]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[7]  Frank L. Lewis,et al.  Intelligent Fault Diagnosis and Prognosis for Engineering Systems , 2006 .

[8]  Jörg Kaiser,et al.  Programming abstractions and middleware for building control systems as networks of smart sensors and actuators , 2010, 2010 IEEE 15th Conference on Emerging Technologies & Factory Automation (ETFA 2010).

[9]  Steven X. Ding,et al.  Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .

[10]  Mauro Birattari,et al.  Fault detection in autonomous robots based on fault injection and learning , 2008, Auton. Robots.

[11]  Shambhu Upadhyaya,et al.  Secure and fault-tolerant voting in distributed systems , 2001, 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542).

[12]  Piotr Przystalka,et al.  Model-Based Fault Detection and Isolation Using Locally Recurrent Neural Networks , 2006, ICAISC.

[13]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[14]  Kai Goebel,et al.  A knowledge-based system approach for sensor fault modeling, detection and mitigation , 2012, Expert Syst. Appl..

[15]  Jiafu Wan,et al.  A survey of Cyber-Physical Systems , 2011, 2011 International Conference on Wireless Communications and Signal Processing (WCSP).

[16]  Dong Zhou,et al.  Translation techniques in cross-language information retrieval , 2012, CSUR.

[17]  Lida Xu,et al.  The internet of things: a survey , 2014, Information Systems Frontiers.

[18]  A. Zolghadri.  An algorithm for real-time failure detection in Kalman filters , 1996, IEEE Trans. Autom. Control..

[19]  kim. bjerge.  Model-Driven Development for Embedded Systems , 2008 .

[20]  Hubert-Marcus Piontek.  Self-description mechanisms for embedded components in cooperative systems , 2007 .

[21]  John E. White,et al.  Detection Filter Design: Spectral Theory and Algorithms , 1986, 1986 American Control Conference.

[22]  Walter Lang,et al.  Application of Computational Intelligence for Sensor Fault Detection and Isolation , 2007 .

[23]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[24]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[25]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .