Exploiting Bayesian networks for fault isolation: A diagnostic case study of diesel fuel injection system

Abstract

Fault isolation is known to be a challenging problem in machinery troubleshooting. It is not only because the isolation of multiple faults contains considerable number of uncertainties due to the strong correlation and coupling between different faults, but often massive prior knowledge is needed as well. This paper presents a Bayesian network-based approach for fault isolation in the presence of the uncertainties. Various faults and symptoms are parameterized using state variables, or the so-called nodes in Bayesian networks (BNs). Probabilistically causality between a fault and a symptom and its quantization are described respectively by a directed edge and conditional probability. To reduce the qualitative and quantitative knowledge needed, particular considerations are given to the simplification of Bayesian networks structures and conditional probability expressions using rough sets and noisy-OR/MAX model, respectively. By adopting the simplified approach, symptoms under multiple-fault are decoupled into the ones under every single fault, while the quantity of the conditional probabilities is simplified into the linear form of the faults quantity. Prior knowledge needed in Bayesian network-based diagnostic model is reduced significantly, which decreases the complexity in establishing and applying this diagnosis model. The computational efficiency is improved accordingly in the simplified BN model, after eliminating the redundant symptoms. The fault isolation methodology is illustrated through an example of diesel engine fuel injection system to verify the developed model.

Physical and Data-Driven Models Hybridisation for Modelling the Dynamic State of a Four-Stroke Marine Diesel Engine

“…Considering the very high accuracy of the developed ANN on this task, the authors concluded that, compared to traditional methods based on signal analysis techniques and shallow classifiers, their approach can automatically learn high-level representative features from the raw vibration signals and eliminate the necessity of the time-consuming manual feature extraction. A similar study was performed by the authors of Wang et al (2019) presenting a Bayesian ANN-based approach for fault isolation in a DE fuel injection system under the presence of uncertainties. With the proposed approach, the authors demonstrated that symptoms under multiple faults could be decoupled into symptoms correspond-ing to each individual fault.…”

Physical, data-driven and hybrid approaches to model engine exhaust gas temperatures in operational conditions

“…A similar study was performed in Wang et al (2019), in which the authors presented a Bayesian network-based approach for fault isolation in a DE fuel injection system, under the presence of uncertainties. Special consideration was given in the simplification of the Bayesian network structures, due to which symptoms under multiple faults could be decoupled into symptoms corresponding to each individual fault.…”

A Belief Network Reasoning Framework for Fault Localization in Communication Networks

“…According to [2,[27][28][29], networks are considered the most powerful fault diagnosis techniques and have been widely used in various fields, such as mine seismic event discrimination [30,31], and some works considering intrusion detection in wireless communication networks, which could also be helpful in fault localization [32]. Bayesian network techniques are also widely used in mechanical equipment [33][34][35], electronic equipment [36,37], thermal power plants [38,39], petrochemical plants [40,41], nuclear power plants [42,43], and medical diagnoses [44][45][46].…”