Health Monitoring System for Autonomous Vehicles using Dynamic Bayesian Networks for Diagnosis and Prognosis

Gomes, Iago Pachêco; Wolf, Denis F.

doi:10.1007/s10846-020-01293-y

Cited by 29 publications

(10 citation statements)

References 62 publications

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“…Fault diagnosis of dragline (Sahu & Palei, 2020b;Sahu & Palei, 2022), centrifugal compressor (Jun & Kim, 2017), centrifugal chiller (Zhao, Xiao, & Wang, 2013), and autonomous vehicle system (Gomes & Wolf, 2021).…”

Section: Inferencebased 3-layer Bn Modelmentioning

confidence: 99%

Data‐driven fault diagnosis approaches for industrial equipment: A review

Sahu¹,

Palei

Mishra

2023

Expert Systems

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Undetected and unpredicted faults in heavy industrial machines/equipment can lead to unwanted failures. Therefore, prediction of faults puts paramount importance on maintaining the reliability and availability of capital‐intensive equipment. Due to large number of interconnected and interdependent mechanical and electrical components in the machines, fault analysis becomes a complex and challenging task. Under these circumstances, data‐driven fault diagnosis (DDFD) is one of the most powerful, reliable and cost‐effective artificial intelligence tools to detect, isolate, identify and classify the occurrence of faults. This article aims to make a comprehensive literature survey of various DDFD approaches used for analysing faults in industrial machines/equipment; and summarizing the strengths, limitations, and possible applications of existing fault diagnosis methods. Analysing and synthesizing 190 research works conducted on DDFD in last two decades revealed three types of DDFD approaches: supervised‐learning, semi‐supervised‐learning and unsupervised‐learning‐based fault diagnosis. The supervised‐learning is predominantly applied for fault diagnosis contributing to 82% of research works. Therefore, this article puts special emphasis on two supervised‐learning‐based approaches for fault diagnosis: (i) classification‐based artificial neural network approach, and (ii) inference‐based Bayesian network approach. Finally, these fault diagnosis approaches have been briefly discussed with effectiveness of the models and their possible inclusion in future industrial applications.

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Section: Inferencebased 3-layer Bn Modelmentioning

confidence: 99%

Data‐driven fault diagnosis approaches for industrial equipment: A review

Sahu¹,

Palei

Mishra

2023

Expert Systems

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“…However, in order to identify and categorise defects in more intricate aircraft systems (Fujimaki et al, 2005), predictive maintenance systems in commercial aviation systems require exceedingly precise technologies (Ning et al, 2021). These high-efficiency systems can also be utilised in unmanned ariel vehicles for monitoring critical parts of an aircraft (Hr uz et al, 2021;Gomes and Wolf, 2021) and for health monitoring of the air compressor systems (Gribbestad et al, 2021). Incipient fault detection in aircraft systems can improve the RUL of such systems (Wei et al, 2020).…”

Section: Fault Detection Techniquesmentioning

confidence: 99%

“…These high-efficiency systems can also be utilised in unmanned ariel vehicles for monitoring critical parts of an aircraft (Hrúz et al. , 2021; Gomes and Wolf, 2021) and for health monitoring of the air compressor systems (Gribbestad et al. , 2021).…”

Section: Rq1: What Are the Domains That Use Anomaly Detection For Pre...mentioning

confidence: 99%

Review of fault detection techniques for predictive maintenance

Divya

Bhasi

Kumar

2022

JQME

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PurposeThis study aims to bring awareness to the developing of fault detection systems using the data collected from sensor devices/physical devices of various systems for predictive maintenance. Opportunities and challenges in developing anomaly detection algorithms for predictive maintenance and unexplored areas in this context are also discussed.Design/methodology/approachFor conducting a systematic review on the state-of-the-art algorithms in fault detection for predictive maintenance, review papers from the years 2017–2021 available in the Scopus database were selected. A total of 93 papers were chosen. They are classified under electrical and electronics, civil and constructions, automobile, production and mechanical. In addition to this, the paper provides a detailed discussion of various fault-detection algorithms that can be categorised under supervised, semi-supervised, unsupervised learning and traditional statistical method along with an analysis of various forms of anomalies prevalent across different sectors of industry.FindingsBased on the literature reviewed, seven propositions with a focus on the following areas are presented: need for a uniform framework while scaling the number of sensors; the need for identification of erroneous parameters; why there is a need for new algorithms based on unsupervised and semi-supervised learning; the importance of ensemble learning and data fusion algorithms; the necessity of automatic fault diagnostic systems; concerns about multiple fault detection; and cost-effective fault detection. These propositions shed light on the unsolved issues of predictive maintenance using fault detection algorithms. A novel architecture based on the methodologies and propositions gives more clarity for the reader to further explore in this area.Originality/valuePapers for this study were selected from the Scopus database for predictive maintenance in the field of fault detection. Review papers published in this area deal only with methods used to detect anomalies, whereas this paper attempts to establish a link between different industrial domains and the methods used in each industry that uses fault detection for predictive maintenance.

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“…They combined the current PCA method with the ANN algorithm to get better results and achieve 98.70% prediction accuracy. Iago et al [22] developed a system using the Dynamic Bayesian Network use for health monitoring and fault detection, diagnosis, and prognosis in autonomous vehicles. The proposed Dynamic Bayesian Network gives good performance results with different ML metrics.…”

Section: Related Research Workmentioning

confidence: 99%

Intelligent System to Detect Software Defects in Autonomous Cars

Tanwar

Patel²,

Patel³

et al. 2022

Preprint

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Autonomous cars have become increasingly popular in the last decade because of their numerous benefits, such as lower travel time, increased safety, and improved fuel economy. Many car manufacturing companies and tech giants are working on this technology to make fully autonomous automobiles or strengthen their existing driver-less cars. These cars use very complex, advanced, and sophisticated hardware technologies. However, the software is an equally important feature because it must operate all functions seamlessly while working in sync with other vehicle components. The software must analyze a large amount of data to make quick real-time decisions, so any vulnerabilities or bugs can be a severe problem to the vehicle and the passengers riding in it. Many researchers have proposed various software defect prediction schemes for different projects and applications, but most of them have focused on specific software issues and excluded others. Thus, their methods cannot be applied to the software of autonomous cars. In this paper, we propose an improved Artificial Neural Network (ANN) model, called Dropout-Artificial Neural Network (D-ANN), to solve this problem of defect prediction in autonomous cars. This inclusive model can consider all the parameters simultaneously for effective bugs prediction. The proposed model can be used for the software of any autonomous cars, and it is trained and evaluated using standard methods. The results obtained show that the proposed model predicts software defects with higher accuracy than other models.

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Health Monitoring System for Autonomous Vehicles using Dynamic Bayesian Networks for Diagnosis and Prognosis

Cited by 29 publications

References 62 publications

Data‐driven fault diagnosis approaches for industrial equipment: A review

Data‐driven fault diagnosis approaches for industrial equipment: A review

Review of fault detection techniques for predictive maintenance

Intelligent System to Detect Software Defects in Autonomous Cars

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