Machine learning and acoustic method applied to leak detection and location in low-pressure gas pipelines

Cruz, Rodolfo Pinheiro; Silva, Flávio Vasconcelos da; Fileti, Ana Maria Frattini

doi:10.1007/s10098-019-01805-x

Cited by 42 publications

(10 citation statements)

References 26 publications

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“…Breakage is another example of a failure that can be detected by acoustic methods, as shown in research performed by Sun et al [ 71 ] involving mechanical breakage analysis on milling machines. Other failure types that can also be found by the acoustic method, based on the selected articles, are corrosion [ 28 ], cracks [ 57 , 72 ], leakage [ 30 , 52 ], wear [ 46 , 55 ], rubbing [ 62 ], pitting [ 53 ], etc. Table 7 also shows that most of the detected failures were in bearings (17 articles) and gears (12 articles).…”

Section: Resultsmentioning

confidence: 99%

“…In general, mechanical failure is a failure type that causes disruption or cessation of the work of a device. This failure can be caused by cracks [ 75 ], deformation, wear [ 46 , 47 ], leakage [ 30 ], bending, etc. Mechanical failure can be recognized by the increase in temperature or the appearance of an unusual sound when the engine is operating [ 80 ].…”

Section: Resultsmentioning

confidence: 99%

“…Artificial neural networks, k-Nearest Neighbors, and SVM are the most common types of machine learning used in these studies. The use of these methods results in a detection system with an accuracy rate ranging from 80% to 100% [ 30 , 34 , 47 , 64 , 72 ].…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Detection of Mechanical Failures in Industrial Machines Using Overlapping Acoustic Anomalies: A Systematic Literature Review

Qurthobi

Maskeliūnas

Damaševičius

2022

Sensors

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One of the most important strategies for preventative factory maintenance is anomaly detection without the need for dedicated sensors for each industrial unit. The implementation of sound-data-based anomaly detection is an unduly complicated process since factory-collected sound data are frequently corrupted and affected by ordinary production noises. The use of acoustic methods to detect the irregularities in systems has a long history. Unfortunately, limited reference to the implementation of the acoustic approach could be found in the failure detection of industrial machines. This paper presents a systematic review of acoustic approaches in mechanical failure detection in terms of recent implementations and structural extensions. The 52 articles are selected from IEEEXplore, Science Direct and Springer Link databases following the PRISMA methodology for performing systematic literature reviews. The study identifies the research gaps while considering the potential in responding to the challenges of the mechanical failure detection of industrial machines. The results of this study reveal that the use of acoustic emission is still dominant in the research community. In addition, based on the 52 selected articles, research that discusses failure detection in noisy conditions is still very limited and shows that it will still be a challenge in the future.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Mechanical Failures in Industrial Machines Using Overlapping Acoustic Anomalies: A Systematic Literature Review

Qurthobi

Maskeliūnas

Damaševičius

2022

Sensors

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“…Automated leak detection has been studied for different industrial applications. Leaks in rigid pipes have been investigated [ 17 ], some of them using machine learning methods [ 18 ]. In [ 19 ], a pneumatic system was analyzed to detect air leakage in the pipes and the pneumatic actuators while the system continues working.…”

Section: Introductionmentioning

confidence: 99%

Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System

Zazo-Manzaneque

Pons

Vidaurre

et al. 2022

Sensors

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Current enteroscopy techniques present complications that are intended to be improved with the development of a new semi-automatic device called Endoworm. It consists of two different types of inflatable cavities. For its correct operation, it is essential to detect in real time if the inflatable cavities are malfunctioning (presence of air leakage). Two classification predictive models were obtained, one for each cavity typology, which must discern between the “Right” or “Leak” states. The cavity pressure signals were digitally processed, from which a set of features were extracted and selected. The predictive models were obtained from the features, and a prior classification of the signals between the two possible states was used as input to different supervised machine learning algorithms. The accuracy obtained from the classification predictive model for cavities of the balloon-type was 99.62%, while that of the bellows-type was 100%, representing an encouraging result. Once the models are validated with data generated in animal model tests and subsequently in exploratory clinical tests, their incorporation in the software device will ensure patient safety during small bowel exploration.

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“…So far, the methods commonly used in pipeline leakage detection include negative pressure wave detection method (Wang et al, 2016), acoustic detection method (Wu et al, 2018), statistical analysis method (Li et al, 2018), and so on. Among them, the acoustic detection method has high efficiency and low false alarm rate (Da Cruz et al, 2020; Liu et al, 2017a; Zhao et al, 2020b); therefore, this paper applies it to the leak detection of natural gas pipeline.…”

Section: Introductionmentioning

confidence: 99%

Application of improved variational mode decomposition method based on two-dimensional sparrow search algorithm in natural gas pipeline leakage signal denoising

Wang

Zhu

Yue

et al. 2021

Transactions of the Institute of Measurement and Control

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To eliminate noise interference in pipeline leakage detection, a signal denoising method based on an improved variational mode decomposition algorithm is proposed. This work adopts a standard variational mode decomposition algorithm with decomposition level K and the penalty factor α. The improvements consist of using a two-dimensional sparrow search algorithm to find K and α. To verify the superiority of the sparrow search algorithm to find K and α, it is compared with three earlier studies. These studies used the firefly algorithm, particle swarm optimization, and whale optimization algorithm to perform the optimization. The main result of this study is to demonstrate that the variational mode decomposition improved by sparrow search algorithm gives a much improved signal-to-noise ratio compared to the other methods. In all other respects, the results are comparable.

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Machine learning and acoustic method applied to leak detection and location in low-pressure gas pipelines

Cited by 42 publications

References 26 publications

Detection of Mechanical Failures in Industrial Machines Using Overlapping Acoustic Anomalies: A Systematic Literature Review

Detection of Mechanical Failures in Industrial Machines Using Overlapping Acoustic Anomalies: A Systematic Literature Review

Classification Predictive Model for Air Leak Detection in Endoworm Enteroscopy System

Application of improved variational mode decomposition method based on two-dimensional sparrow search algorithm in natural gas pipeline leakage signal denoising

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