Fault Detection using Data-driven LPV State Estimation based on Structural Analysis and ANFIS

This paper presents a data-driven fault detection method combining structural analysis (SA) and machine learning data-driven algorithms. Given a graphic (or textual) system description and the available inputs/outputs measurements, the structure of analytical redundancy relations (ARRs) between some inputs and outputs can be determined with the aid of the SA of the system. Then, using a machine learning data-driven approach applied to historical data, analytical relations between inputs and outputs can be obtained. Thereby, instead of finding ARRs from physical mathematical model, ARRs are obtained combining SA and data-driven approaches. In this paper, the adaptive network fuzzy inference system (ANFIS) data-driven approach is used to implement the diagnosis system. Once the ANFIS model has been identified, it is reformulated in linear parameter varying (LPV) form. Then, a fault detection scheme based on a LPV Kalman filter and pole placement method is developed. A well-known case study based on a four-tanks system is used for illustrative purposes.