Deep Learning-Based Acoustic Emission Scheme for Nondestructive Localization of Cracks in Train Rails under a Load

Suwansin, Wara; Phasukkit, Pattarapong

doi:10.3390/s21010272

Cited by 24 publications

(10 citation statements)

References 44 publications

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“…MLbased methods can also use acoustic emission signals to detect and diagnose failures. The reader can consult the recent work by Suwansin and Phasukkit [81] or the review by Muir et al [82] to have more details and references, being that the latter focus on diagnosis of composites structures.…”

Section: Failure Detection and Diagnosismentioning

confidence: 99%

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

Cunha¹,

Droz²,

Zine³

et al. 2022

Preprint

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The use of Machine Learning (ML) has rapidly spread across several fields, having encountered many applications in Structural Dynamics and Vibroacoustic (SD&V). The increasing capabilities of ML to unveil insights from data, driven by unprecedented data availability, algorithms advances and computational power, enhance decision making, uncertainty handling, patterns recognition and real-time assessments. Three main applications in SD&V have taken advantage of these benefits. In Structural Health Monitoring, ML detection and prognosis lead to safe operation and optimized maintenance schedules. System identification and control design are leveraged by ML techniques in Active Noise Control and Active Vibration Control. Finally, the so-called ML-based surrogate models provide fast alternatives to costly simulations, enabling robust and optimized product design. Despite the many works in the area, they have not been reviewed and analyzed. Therefore, to keep track and understand this ongoing integration of fields, this paper presents a survey of ML applications in SD&V analyses, shedding light on the current state of implementation and emerging opportunities. The main methodologies, advantages, limitations, and recommendations based on scientific knowledge were identified for each of the three applications. Moreover, the paper considers the role of Digital Twins and Physics Guided ML to overcome current challenges and power future research progress. As a result, the survey provides a broad overview of the present landscape of ML applied in SD&V and guides the reader to an advanced understanding of progress and prospects in the field.

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Section: Failure Detection and Diagnosismentioning

confidence: 99%

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

Cunha¹,

Droz²,

Zine³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…The results of the testing indicate that the proposed model NA-AE performs well on the rail condition assessment task based on AE data, with a high macro-F1 score of 97.5 percent and a reasonably quick computation time. This study [52] presents a nondestructive single-sensor AE method for detecting and localizing cracks in steel rail under stress. AE signals were recorded by the AE sensor and converted to digital signal data by the AE data collection module throughout the operation.…”

Section: Ementioning

confidence: 99%

IoT Based Railway Track Faults Detection and Localization Using Acoustic Analysis

et al. 2022

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Rail is one of the most energy efficient and economical modes of transportation amongst many. Regular inspection of railway track health is essential for ensuring robust and secure train operations. Investigations and delayed discovery pose a serious risk to the safe functioning of rail transportation. The traditional method of manually examining the rail track using a railway cart is both inefficient and susceptible to mistakes and bias. It is imperative to automate inspection in order to avert catastrophes and save countless lives particularly in zones where train accidents are numerous. The purpose of this research is to develop an Internet of Things (IoT)-based autonomous railway track fault detection system to enhance the existing railway cart system in order to address the aforementioned issues. In addition to data collection on Pakistani railway lines, this work contributes significantly to railway track fault identification and classification based on acoustic analysis, as well as fault localization. Due to their frequency of occurrences, six types of track's faults were first targeted: wheel burnt, loose nuts and bolts, crash sleeper, creep, low joint, and point and crossing. Support vector machines, logistic regression, random forest, extra tree classifier, decision tree classifier, multilayer perceptron and ensemble with hard and soft voting were among the machine learning methods used. The results indicate that acoustic data can successfully assist in discriminating the track defects and localizing the defects in real time. The results show that MLP achieved the best results, with an accuracy of 98.4 percent.

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“…Such machinery can be targeted for noise reduction, where its noise footprint can be analyzed and compared between diverse workflows or product life spans [27]. In the context of predictive maintenance, one can find applications for preventing structural failure [28], leak localization [29], or nondestructive localization of cracks [30]. In its early stages, the PSO algorithm [9,10] was also used for solving some acoustic localization problems, namely, those related to the localization of partial discharge sources in power transformers [31,32].…”

Section: Introductionmentioning

confidence: 99%

Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study

Fé

Correia

Tomic

et al. 2022

Sensors

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In the last decades, several swarm-based optimization algorithms have emerged in the scientific literature, followed by a massive increase in terms of their fields of application. Most of the studies and comparisons are restricted to high-level languages (such as MATLAB®) and testing methods on classical benchmark mathematical functions. Specifically, the employment of swarm-based methods for solving energy-based acoustic localization problems is still in its inception and has not yet been extensively studied. As such, the present work marks the first comprehensive study of swarm-based optimization algorithms applied to the energy-based acoustic localization problem. To this end, a total of 10 different algorithms were subjected to an extensive set of simulations with the following aims: (1) to compare the algorithms’ convergence performance and recognize novel, promising methods for solving the problem of interest; (2) to validate the importance (in convergence speed) of an intelligent swarm initialization for any swarm-based algorithm; (3) to analyze the methods’ time efficiency when implemented in low-level languages and when executed on embedded processors. The obtained results disclose the high potential of some of the considered swarm-based optimization algorithms for the problem under study, showing that these methods can accurately locate acoustic sources with low latency and bandwidth requirements, making them highly attractive for edge computing paradigms.

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Deep Learning-Based Acoustic Emission Scheme for Nondestructive Localization of Cracks in Train Rails under a Load

Cited by 24 publications

References 44 publications

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic

IoT Based Railway Track Faults Detection and Localization Using Acoustic Analysis

Swarm Optimization for Energy-Based Acoustic Source Localization: A Comprehensive Study

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