A combined passive-active method for diagnosing multiplicative fault

Abstract

Fault detection and diagnosis (FDD) plays an important role in risk and safety management system. According to the ways faults influence the actual system, they can be divided into additive (mainly actuator and sensor faults) and multiplicative (mainly process faults). The two types of faults should be diagnosed with specific methods then handled with different maintenance strategies. This paper presents a combined passive-active fault diagnosis method, which allows a simultaneous consideration of additive and multiplicative faults and to distinguish between them. It also enables detailed diagnosis that assists to enhance the subsequent risk assessment and management. System identification is used as the modeling tool and forms the basis of the method. The passive-active feature of the method reflects in that: it uses online generated residual as a fault indicator for real-time monitoring; it also uses test signals to magnify the fault characteristics and helps to reveal the fault location. Specifically, a method is proposed to distinguish between additive and multiplicative fault according to the different residual behavior after adding test signals. Once fault type is determined, by investigating the identified models with error bounds, a method is further developed to determine the location of multiplicative fault in the multi-variable system. The statistical properties of the identified models are utilized to generate perturbed realizations of the model and derive probabilistic bounds of model errors, enabling both methods to deal with model errors. The proposed method does not require to break the control loops when adding test signals and does not require fault data/model to start with. The effectiveness of the proposed method is validated through a numerical example and Tennessee Eastman process (TEP).