Detecting external measurement disturbances based on statistical analysis for smart sensors

The transducer process of a sensor is interference-prone to environmental conditions or external disturbances depending on sensor type, measurement procedure etc. Dependable sensors are characterized by a broad independence of those factors or/and they can both detect situations that make a correct measurement impossible and validate the measurement result. In this paper we describe a statistical approach for the detection of faulty measurements caused by external disturbances. Our fault detection algorithm is based on a comparison of faultless reference measurements with current sensing values. Using this enhancement, a sensor becomes a real smart sensing device and supplies an additional validity estimation of each measurement. The approach was implemented and validated in a demonstration setup that integrates an infrared sensor array disturbed by a strong extraneous light.

[1]  Paul M. Frank,et al.  Fuzzy logic and neural network applications to fault diagnosis , 1997, Int. J. Approx. Reason..

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

[3]  David J. Sheskin,et al.  Handbook of Parametric and Nonparametric Statistical Procedures , 1997 .

[4]  Victor P. Nelson.  Fault-tolerant computing: fundamental concepts , 1990, Computer.

[5]  Rolf Isermann,et al.  Fault-Diagnosis Systems , 2005 .

[6]  Fulei Chu,et al.  Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography , 2004 .

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

[8]  Jörg Kaiser,et al.  An approach towards smart fault-tolerant sensors , 2009, 2009 IEEE International Workshop on Robotic and Sensors Environments.

[9]  J. Davis.  Bioinformatics and Computational Biology Solutions Using R and Bioconductor , 2007 .

[10]  Michèle Basseville,et al.  Detection of abrupt changes: theory and application , 1993 .

[11]  R. A. Breckenridge,et al.  Smart sensors for the 80's - The status of smart sensors , 1980 .

[12]  Éric Gaussier,et al.  A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation , 2005, ECIR.

[13]  Maciej Niedzwiecki,et al.  Adaptive scheme for elimination of broadband noise and impulsive disturbances from AR and ARMA signals , 1996, IEEE Trans. Signal Process..