An audio-based intelligent fault diagnosis method for belt conveyor rollers in sand carrier

Peng, Chen; Li, Zhipeng; Yang, Minjing; Fei, Minrui; Wang, Yulong

doi:10.1016/j.conengprac.2020.104650

Cited by 19 publications

(18 citation statements)

References 16 publications

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“…The movement of the conveyor cb2 is triggered by "cb2x + ". There is a sensor at the end of cb2 that it is able to distinguish between the sheet workpieces of A and B and the cylinder workpieces C and D, based on which workpieces of C and D are transferred to the conveyor cb3 and then go through the punching section which is trig-FIGURE 8 The augmented system G + 1 for the manufacturing model G 1 in Figure 7 gered by "PUC". The conveyor cb3 is triggered by "cb3x + " to the right direction and by "cb3x − " to the left direction.…”

Section: Application Of the Proposed Decentralized Diagnoser To A Manufacturing Systemmentioning

confidence: 99%

“…Generally, the fault diagnosis approaches can be classified to time-driven [1][2][3][4][5] and event-driven [6] approaches. Timedriven approaches can diagnose a fault occurrence by analyzing the states of the system based on the information received from the observation of the system's degraded performance over time [7][8][9]. In contrast, event-driven approaches can capture abrupt changes (fault events) in a system by tracking the sequence of changes (events) in the system.…”

Section: Introductionmentioning

confidence: 99%

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An inferencing structure for MPO‐based decentralized dynamic fault diagnosis

Khaleghi

Yazdi

Karimoddini

et al. 2021

IET Control Theory & Appl

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This paper addresses the problem of fault diagnosis in discrete event systems through a decentralized structure. Within the proposed decentralized diagnosis structure, local most permissive bbservers (MPOs) are employed as diagnosis tools, and an effective protocol is proposed to coordinate local MPOs to infer diagnosis information. It is shown that the proposed technique can diagnose fault occurrences subjected to proper sensing decisions.A sufficient condition on sensing decision policies is derived for diagnosis of faults in a decentralized setting. Further, it is proven that the proposed decentralized structure has less complexity than a centralized structure. The details of the proposed technique are illustrated by applying the developed method to a manufacturing system.

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Section: Application Of the Proposed Decentralized Diagnoser To A Manufacturing Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An inferencing structure for MPO‐based decentralized dynamic fault diagnosis

Khaleghi

Yazdi

Karimoddini

et al. 2021

IET Control Theory & Appl

View full text Add to dashboard Cite

show abstract

“…Observing the vibration responses of belt conveyor idlers can reveal their state, and vibration analysis is widely used for fault detection [ 23 ]. A vibration sensor must be used to capture the state of idlers, which is a contact sensor that can be difficult to install [ 24 ]. Compared to vibration, acoustic recording is a safe and non-destructive method for capturing the sound of faulty idlers.…”

Section: Introductionmentioning

confidence: 99%

“…By utilizing a hand-held thermal imaging camera, faulty idlers can be detected by an operator. However, since the belt conveyor is typically located in a harsh environment, there is a lot of dust on the site, which can interfere with the image information and potentially distort the reading [ 24 ]. On this basis, thermal image-based detection methods are not considered accurate and reliable methods for intelligent FD, and will not be considered in this review.…”

Section: Introductionmentioning

confidence: 99%

A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models

Alharbi

Luo

Zhang

et al. 2023

Sensors

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Due to increasing demands for ensuring the safety and reliability of a system, fault detection (FD) has received considerable attention in modern industries to monitor their machines. Bulk materials are transported worldwide using belt conveyors as an essential transport system. The majority of conveyor components are monitored continuously to ensure their reliability, but idlers remain a challenge to monitor due to the large number of idlers (rollers) distributed throughout the working environment. These idlers are prone to external noises or disturbances that cause a failure in the underlying system operations. The research community has begun using machine learning (ML) to detect idler’s defects to assist industries in responding to failures on time. Vibration and acoustic measurements are commonly employed to monitor the condition of idlers. However, there has been no comprehensive review of FD for belt conveyor idlers. This paper presents a recent review of acoustic and vibration signal-based fault detection for belt conveyor idlers using ML models. It also discusses major steps in the approaches, such as data collection, signal processing, feature extraction and selection, and ML model construction. Additionally, the paper provides an overview of the main components of belt conveyor systems, sources of defects in idlers, and a brief introduction to ML models. Finally, it highlights critical open challenges and provides future research directions.

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“…As a result, sound can be efficiently processed in near real-time by low-cost hardware such as embedded devices [ 13 ]. Audio is particularly useful for providing insight into systems with periodic acoustic emissions, such as the rotating assemblies commonly found in vehicles and other heavy and industrial equipment [ 14 , 15 , 16 , 17 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Improving Misfire Fault Diagnosis with Cascading Architectures via Acoustic Vehicle Characterization

Terwilliger

Siegel

2022

Sensors

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In a world dependent on road-based transportation, it is essential to understand automobiles. We propose an acoustic road vehicle characterization system as an integrated approach for using sound captured by mobile devices to enhance transparency and understanding of vehicles and their condition for non-expert users. We develop and implement novel deep learning cascading architectures, which we define as conditional, multi-level networks that process raw audio to extract highly granular insights for vehicle understanding. To showcase the viability of cascading architectures, we build a multi-task convolutional neural network that predicts and cascades vehicle attributes to enhance misfire fault detection. We train and test these models on a synthesized dataset reflecting more than 40 hours of augmented audio. Through cascading fuel type, engine configuration, cylinder count and aspiration type attributes, our cascading CNN achieves 87.0% test set accuracy on misfire fault detection which demonstrates margins of 8.0% and 1.7% over naïve and parallel CNN baselines. We explore experimental studies focused on acoustic features, data augmentation, and data reliability. Finally, we conclude with a discussion of broader implications, future directions, and application areas for this work.

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An audio-based intelligent fault diagnosis method for belt conveyor rollers in sand carrier

Cited by 19 publications

References 16 publications

An inferencing structure for MPO‐based decentralized dynamic fault diagnosis

An inferencing structure for MPO‐based decentralized dynamic fault diagnosis

A Brief Review of Acoustic and Vibration Signal-Based Fault Detection for Belt Conveyor Idlers Using Machine Learning Models

Improving Misfire Fault Diagnosis with Cascading Architectures via Acoustic Vehicle Characterization

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