A new approach to quantitative and credible diagnosis for multiple faults of components and sensors

Abstract

Many practical applications of system diagnosis require the credible identification of multiple faults of nonlinear components and sensors in quantitative measures. However, the state of the art of diagnosis technique is considered to be still insufficient to meet these severe requirements. The approach of diagnosis using the traditional linear system identification theory can diagnose the disturbed parameters of a system in detail and evaluate the quantitative amplitude of the disturbance. However, it hardly provides the diagnosis of the multiple faults and the diagnosis of the components having high nonlinearity. On the other hand, some recent model-based diagnosis approaches can diagnose the multiple faults even for highly nonlinear components, though they do not provide the detailed diagnosis of elements indivisibly involved in components and the quantitative amplitudes of the faults. The method proposed in this paper provides an efficient remedy to achieve all of the practical requirements, i.e., the credible, detailed and quantitative diagnosis of multiple faults of nonlinear components and sensors. Our study newly proposes the frameworks of optimal constraints and causal ordering of physical systems. Also, a systematic and strict theory to synthesize these frameworks together with the model-based diagnosis is provided to characterize an optimal consistency checking method in diagnosis and to evaluate quantitative amplitudes of faulty disturbances. First, the detection of faulty behaviors of an objective component is performed based on the quantitative consistency checking between observations and the optimal constraints, called as “minimal overconstraints”, consisting of first principles in the components. Second, once if some inconsistencies are detected, a mathematical operation of model-based diagnosis derives the candidates of faulty elements and functions even under multiple fault conditions. Third, the anomalous quantities directly disturbed by the faulty elements are identified systematically based on causal ordering. Furthermore, the quantitative deviations of these quantities are evaluated by using the minimal over-constraints. The performance of the proposed method is demonstrated through an example to diagnose an electric water heater. The ability of this diagnosis has been confirmed for the multiple faults in nonlinear and dynamic systems.