Detection, identification, and quantification of sensor fault in a sensor network

Abstract In structural health monitoring (SHM) and control, the structure can be instrumented with an array of sensors forming a redundant sensor network, which can be utilized in sensor fault diagnosis. In this study, the objective is to detect, identify, and quantify a sensor fault using the structural response data measured with the sensor network. Seven different sensor fault types are investigated and modelled: bias, gain, drifting, precision degradation, complete failure, noise, and constant with noise. The sensor network is modelled as a Gaussian process and each sensor in the network is estimated in turn using the minimum mean square error (MMSE) estimation The sensor fault is identified and quantified using the multiple hypothesis test utilizing the generalized likelihood ratio (GLR). The proposed approach is experimentally verified with an array of accelerometers assembled on a wooden bridge. Different sensor faults are simulated by modifying a single sensor. The method is able to detect a sensor fault, identify and correct the faulty sensor, as well as identify and quantify the fault type.

[1]  Enrique Castillo.  Extreme value theory in engineering , 1988 .

[2]  Michael I. Friswell,et al.  Sensor Validation for Structural Systems with Additive Sensor Faults , 2004 .

[3]  A. Ray,et al.  An introduction to sensor signal validation in redundant measurement systems , 1991, IEEE Control Systems.

[4]  K. C. Daly,et al.  Generalized Likelihood Test for FDI in Redundant Sensor Configurations , 1979 .

[5]  Harold W. Sorenson,et al.  Parameter estimation: Principles and problems , 1980 .

[6]  Carl E. Rasmussen,et al.  Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.

[7]  Sami F. Masri,et al.  Multivariate Statistical Analysis for Detection and Identification of Faulty Sensors Using Latent Variable Methods , 2008 .

[8]  Hoon Sohn,et al.  Damage detection in mechanical structures using extreme value statistics , 2002, SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring.

[9]  Subhash Sharma.  Applied multivariate techniques , 1995 .

[10]  Reza Langari,et al.  Isolability of faults in sensor fault diagnosis , 2011 .

[11]  Jyrki Kullaa,et al.  Eliminating Environmental or Operational Influences in Structural Health Monitoring using the Missing Data Analysis , 2009 .

[12]  S. Kay.  Fundamentals of statistical signal processing: estimation theory , 1993 .

[13]  Michael I. Friswell,et al.  Sensor validation for structural systems with multiplicative sensor faults , 2007 .

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

[15]  Jyrki Kullaa,et al.  Distinguishing between sensor fault, structural damage, and environmental or operational effects in structural health monitoring , 2011 .

[16]  Thomas F. Edgar,et al.  Identification of faulty sensors using principal component analysis , 1996 .

[17]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[18]  Daniel J. Inman,et al.  Sensor Validation for Smart Structures , 1999 .

[19]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[20]  Gaëtan Kerschen,et al.  Sensor validation using principal component analysis , 2005 .

[21]  Steven Kay,et al.  Fundamentals Of Statistical Signal Processing , 2001 .

[22]  Jyrki Kullaa,et al.  Sensor validation using minimum mean square error estimation , 2010 .