A General Diagnostic Methodology for Sensor Fault Detection, Classification and Overall Health State Assessment

Abstract

Sensor fault detection and classification is a key challenge for machine monitoring and diagnostics. To this purpose, a comprehensive approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (named DCIDS), previously developed by the authors, is improved in this paper to detect and classify different fault classes. For a single sensor or redundant/correlated sensors, the improved diagnostic tool, called I-DCIDS, can identify seven classes of fault, i.e. out of range, stuck signal, dithering, standard deviation, trend coherence, spike and bias. Fault detection is performed by means of basic mathematical laws that require some user-defined input parameters, i.e. acceptability thresholds and windows of observation. This paper presents in detail the I-DCIDS methodology for sensor fault detection and classification. Moreover, this paper reports some examples of application of the methodology to simulated data to highlight its capability to detect sensor faults which can be commonly encountered in field applications.