A Bayesian Approach to Fault Identification in the Presence of Multi-component Degradation

Abstract

Fault diagnosis typically consists of fault detection, isolation and identification. Fault detection and isolation determine the presence of a fault in a system and the location of the fault. Fault identification then aims at determining the severity level of the fault. In a practical sense, a fault is a conditional interruption of the system ability to achieve a required function under specified operating condition; degradation is the deviation of one or more characteristic parameters of the component from acceptable conditions and is often a main cause for fault generation. A fault occurs when the degradation exceeds an allowable threshold. From the point a new aircraft takes off for the first time all of its components start to degrade, and yet in almost all studies it is presumed that we can identify a single fault in isolation, i.e. without considering multi-component degradation in the system. This paper proposes a probabilistic framework to identify a single fault in an aircraft fuel system with consideration of multi-component degradation. Based on the conditional probabilities of sensor readings for a specific fault, a Bayesian method is presented to integrate distributed sensory information and calculate the likelihood of all possible fault severity levels. The proposed framework is implemented on an experimental aircraft fuel rig which illustrates the applicability of the proposed method.

Aircraft system-level diagnosis with emphasis on maintenance decisions

“…Finally, diagnostic studies based on multi-component degradation have also been applied in other systems such as the fuel system. The most representative study for the fuel system has been conducted by Lin et al 28 in which a Bayesian network was used to identify the component degradation severity.…”

Ensemble Deep Learning for Detecting Onset of Abnormal Operation in Industrial Multi-component Systems

“…In such cases, researchers have used various other approaches to model and isolate intercomponent interactions. Such approaches include stochastic dependence modeling (Bian & Gebraeel, 2014), adaptive degradation modeling demonstrated for an electrical system (Prakash, Samantaray, Bhattacharyya, & Ghoshal, 2018), and Naïve Bayesian method (Lin, Zakwan, & Jennions, 2020) and model-free clustering analysis (Liu, Zhao, Zaporowska, & Zakwan, 2020) demonstrated for an aircraft fuel rig.…”

A machine learning-based clustering approach to diagnose multi-component degradation of aircraft fuel systems

“…However, they only work well when assuming that the rest untargeted components are healthy, but fails in the realistic scenario with multiple degraded components. Lin et al [22] proposed a probabilistic framework to incorporate multi-component degradation information for the aim of fault diagnosing in aircraft fuel systems, but the identification of degradation has not been addressed. According to the above-stated diagnosis architecture, how to reveal the mist from the whole faulty status and then break through to find the specific degraded component is quite challenging and related studies are limited.…”