Common spatial pattern-based feature extraction and worm gear fault detection through vibration and acoustic measurements

Karabacak, Yunus Emre; Özmen, Nurhan Gürsel

doi:10.1016/j.measurement.2021.110366

Cited by 22 publications

(8 citation statements)

References 49 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%

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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%

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

Qurthobi

Maskeliūnas

Damaševičius

2022

Sensors

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“…, 2019). ANN exhibits higher efficiency in binary and multi-class problems compared to other machine learning models (Karabacak and Özmen, 2022). ANNs are a good option when dealing with problems related to predictive maintenance of power plants (Lalik and Wątorek, 2021), wind turbines (Santolamazza et al.…”

Section: Rq2: What Are the Commonly Used Methods Used For Anomaly Det...mentioning

confidence: 99%

“…Calculation of RUL of components in the automobile industry is critical for proper predictive maintenance and mechanical component replacement. This includes forecasting faults in worm gears (Karabacak and € Ozmen, 2022) and defects in rolling-element bearings (Manjurul Islam et al, 2021;Bastami and Vahid, 2021). 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).…”

Section: Fault Detection Techniquesmentioning

confidence: 99%

“…They are employed in a wide range of domains, from detecting mechanical equipment system deterioration (Zhang et al, 2019;Markiewicz et al, 2019) to developing computer failure models (Zhang et al, 2019). ANN exhibits higher efficiency in binary and multi-class problems compared to other machine learning models (Karabacak and € Ozmen, 2022). ANNs are a good option when dealing with problems related to predictive maintenance of power plants (Lalik and Wa ˛torek, 2021), wind turbines (Santolamazza et al, 2021) and belt drives of manufacturing systems (Pollak et al, 2021).…”

Section: Supervised Learningmentioning

confidence: 99%

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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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Automatically Abnormal Detection for Radiator Fans Through Sound Signals Using a Deep Learning Technique

Nguyen,

Tran

2024

Lecture Notes in Mechanical Engineering

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Common spatial pattern-based feature extraction and worm gear fault detection through vibration and acoustic measurements

Cited by 22 publications

References 49 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

Review of fault detection techniques for predictive maintenance

Automatically Abnormal Detection for Radiator Fans Through Sound Signals Using a Deep Learning Technique

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