Anomaly detection of motors with feature emphasis using only normal sounds

Ono, Yoshiyasu; Onishi, Yoshifumi; Koshinaka, Takafumi; Takata, Soichiro; Hoshuyama, Osamu

doi:10.1109/icassp.2013.6638167

Cited by 18 publications

(4 citation statements)

References 9 publications

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“…The use of a microphone is intended to take sound samples from the device under test when the equipment is working in accordance with its function. The frequency of the sound picked up by the microphone can be in the range of 10 Hz–10 kHz (the range of sound that can be heard by humans) [ 59 ], as well as the signals picked up by the microphone on a mobile phone sampling frequency of 44.1 kHz [ 47 , 67 ]. The advantage of using a microphone over other methods is the ease of installation and data collection [ 22 ].…”

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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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%

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

Qurthobi

Maskeliūnas

Damaševičius

2022

Sensors

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“…At present, deep learning is widely used in various tasks in the field of speech [1][2] . In actual situations, because the probability of normal sounds is much higher than abnormal sounds, the amount of data between positive and abnormal sounds is extremely unbalanced, which makes it difficult to implement general deep learning methods such as supervised learning [3] . Because of the large amount of normal sound data, Scott [4] et al proposed an outlier detection method, which firstly modeled the normal sound, then used the model to extract and reconstruct the detected sound, and finally compared the difference between the reconstructed data and the input data (called reconstruction error).…”

Section: Introductionmentioning

confidence: 99%

Research on unsupervised abnormal sound detection based on self-coding model

Wang,

Zhang,

Liu

et al. 2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

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In view of the difficulty of machine abnormal sound detection under the condition that abnormal sound samples are difficult to collect, this paper proposes an unsupervised abnormal sound detection model based on a self-coding model, which effectively improves the accuracy of abnormal sound detection under this condition. In this paper, location coding in Transformer is replaced with relational awareness self-attention to improve the representation capability of location coding. Secondly, the relevance scores in multi-head attention are mixed to enhance the understanding of context in the attention matrix. At the same time, Layer Normalization was replaced with Batch Normalization to speed up model training, and improved Transformer was introduced into the encoders and decoders of self-coding models. Finally, the improved self-coding model is used for unsupervised learning of the machine's normal sound to obtain the potential feature distribution of its normal sound. ToyADMOS and MIMII open data sets are used for experiments. Compared with traditional autoencoders and two improved self-coding models, The AUC score of toycar, Toycar, fan, slider and valve machines increased by 2.1%, 1.97%, 3.06%, 0.34% and 2.99%, respectively.

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“…Their goal is to identify and extract features of signals that correspond to faults [18]. For this purpose, techniques like the Fourier transform has been used to analyze the noise [43]. However, it presents the fundamental disadvantage hat the power spectrum is not immune to noise.…”

Section: Introductionmentioning

confidence: 99%

Higher-Order Spectral Analysis of Stray Flux Signals for Faults Detection in Induction Motors

Martínez

Antonino-Daviu

Córdoba

et al. 2020

Applied Mathematics and Nonlinear Sciences

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AbstractThis work is a review of current trends in the stray flux signal processing techniques applied to the diagnosis of electrical machines. Initially, a review of the most commonly used standard methods is performed in the diagnosis of failures in induction machines and using stray flux; and then specifically it is treated and performed the algorithms based on statistical analysis using cumulants and polyspectra. In addition, the theoretical foundations of the analyzed algorithms and examples applications are shown from the practical point of view where the benefits that processing can have using HOSA and its relationship with stray flux signal analysis, are illustrated.

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Anomaly detection of motors with feature emphasis using only normal sounds

Cited by 18 publications

References 9 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

Research on unsupervised abnormal sound detection based on self-coding model

Higher-Order Spectral Analysis of Stray Flux Signals for Faults Detection in Induction Motors

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