Probability based vehicle fault diagnosis: Bayesian network method

McMurran, Ross; Dhadyalla, Gunwant; Jones, R.P.

doi:10.1007/s10845-008-0083-7

Cited by 66 publications

(33 citation statements)

References 7 publications

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“…Bayesian networks (BN) is a probabilistic model using for diagnosis in various domains such as vehicles [40], electrical power systems [41] and network systems [42,43]. BN describes conditional probabilities between the components; given evidence (observations), an inference algorithm is used to compute the probability of each healthy component to propagate the evidence.…”

Section: Diagnosismentioning

confidence: 99%

“PhysIt”—A Diagnosis and Troubleshooting Tool for Physiotherapists in Training

et al. 2020

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Many physiotherapy treatments begin with a diagnosis process. The patient describes symptoms, upon which the physiotherapist decides which tests to perform until a final diagnosis is reached. The relationships between the anatomical components are too complex to keep in mind and the possible actions are abundant. A trainee physiotherapist with little experience naively applies multiple tests to reach the root cause of the symptoms, which is a highly inefficient process. This work proposes to assist students in this challenge by presenting three main contributions: (1) A compilation of the neuromuscular system as components of a system in a Model-Based Diagnosis problem; (2) The PhysIt is an AI-based tool that enables an interactive visualization and diagnosis to assist trainee physiotherapists; and (3) An empirical evaluation that comprehends performance analysis and a user study. The performance analysis is based on evaluation of simulated cases and common scenarios taken from anatomy exams. The user study evaluates the efficacy of the system to assist students in the beginning of the clinical studies. The results show that our system significantly decreases the number of candidate diagnoses, without discarding the correct diagnosis, and that students in their clinical studies find PhysIt helpful in the diagnosis process.

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Section: Diagnosismentioning

confidence: 99%

“PhysIt”—A Diagnosis and Troubleshooting Tool for Physiotherapists in Training

et al. 2020

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“…Nodes Symptoms According to the literatures and the practical experience of domain experts, the causal relationships between these faults and symptoms are shown in Table 2, in which T (T stands for Ture) represents the fault/symptom is present, and F (F stands for False) denotes the fault/symptom is absent [35]. Take row 5 as an example.…”

Section: Nodes Faultsmentioning

confidence: 99%

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

Wang

Stetsyuk

et al. 2019

ISA Transactions

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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.

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“…Saravanan et al 2008 used support vector machine (SVM) for fault diagnosis of spur bevel gear box and this is a popular machine learning application due to its high accuracy and good generalization capabilities [18]. Huang et al(2008) used probability based Bayesian network methods to identify vehicle fault which can be used to diagnose single-fault and multi-fault [19]. Zhixiong Li et al, usedthe back propagation neural network (BPNN) and the radial-basis function neural network (RBFNN) to identify the states of the gearbox.The numeric and experimental test results showed that the ANN classification method has achieved high detection accuracy [20].…”

Section: Data Mining Techniquesmentioning

confidence: 99%

Assessment of Gearbox Fault DetectionUsing Vibration Signal Analysis and Acoustic Emission Technique

Saxena¹

2013

IOSR-JMCE

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Probability based vehicle fault diagnosis: Bayesian network method

Cited by 66 publications

References 7 publications

“PhysIt”—A Diagnosis and Troubleshooting Tool for Physiotherapists in Training

“PhysIt”—A Diagnosis and Troubleshooting Tool for Physiotherapists in Training

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

Assessment of Gearbox Fault DetectionUsing Vibration Signal Analysis and Acoustic Emission Technique

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