Monitoring of friction-related failures using diffusion maps of acoustic time series

Shevchik, Sergey; Zanoli, Silvio; Saeidi, Fatemeh; Meylan, Bastian; Flück, G.; Wasmer, Kilian

doi:10.1016/j.ymssp.2020.107172

Cited by 7 publications

(6 citation statements)

References 35 publications

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“…In works [5,6] it is noted that the classification and prediction of the wear rate in tribosystems is a real industrial problem that has not been solved today. The authors conclude that an online monitoring system capable of categorizing wear rate can be critical for many industries as it can help prevent catastrophic failures.…”

Section: Main Headingmentioning

confidence: 99%

“…Therefore, a system for monitoring such failures in real-time is in great demand. In works [5,6] a probabilistic analysis of acoustic signals is proposed, with their division into levels, synchronously recorded with the wear rate and the friction coefficient in real experiments.…”

Section: Main Headingmentioning

confidence: 99%

“…-for the material of the fixed triboelement: (6) where µmov and µfix -Poisson's ratio of materials of movable and fixed triboelements; σacs -voltage on a single actual contact spot, Pa; Еmov and Еfix -modulus of elasticity of materials of movable and fixed triboelements, Pa; υsl -sliding speed, m/s.…”

Section: Secondary Headingmentioning

confidence: 99%

“…The upper signal bandwidth is 1.5 MHz. This frequency range was selected based on an analysis of the works of other authors who are devoted to acoustic emission from the friction zone [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19].…”

Section: This Choice Of Materials and Their Combination In The Constr...mentioning

confidence: 99%

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Methodical Approach to Using Acoustic Emission Method for Tribosystem Monitoring

Vojtov¹,

Fenenko²,

Voitov³

et al. 2022

Tribol. Ind.

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The method of diagnosing various constructions of tribosystems by acoustic emission method is developed in the work. The development of the methodology is based on the following assumptions. The activity of acoustic emission during the operation of the tribosystem will depend on the rate of deformation of materials on the spots of actual contact. The total acoustic signal from the friction zone is formed as a result of the interference of the primary acoustic signals from the spots of actual contact with the friction surface, which correspond to the coherence condition. Based on these assumptions, the expression for calculating the informative frequencies that will be generated by the tribosystem in the process. The method of diagnosis contains the following steps: determination of informative frequencies on which it is necessary to register amplitudes; determining the magnitude of the amplitudes that carry the maximum information; distribution of the general acoustic signal on clusters and establishment of functional communication of values of a peak factor of clusters with values of coefficient of friction, speed of wear and time of running-in of tribosystems in an online mode. The use of the developed technique will increase the robustness of the method of diagnosing tribosystems and identify surface processes during wear.

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Section: Main Headingmentioning

confidence: 99%

Section: Main Headingmentioning

confidence: 99%

Section: Secondary Headingmentioning

confidence: 99%

Section: This Choice Of Materials and Their Combination In The Constr...mentioning

confidence: 99%

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Methodical Approach to Using Acoustic Emission Method for Tribosystem Monitoring

Vojtov¹,

Fenenko²,

Voitov³

et al. 2022

Tribol. Ind.

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“…They analyze AE data with support vector machines (SVM) based on radial basis kernel and random forest (RF) [45,46]. On the same tribosystem, they also proposed a monitoring system based on diffusion maps to predict the sliding surfaces state, including scuffing regimes [47]. Histogram features extracted from digital images have been used along with Naïve Bayes and decision trees to predict scratches and defects on sheet metal surface [48].…”

Section: Introductionmentioning

confidence: 99%

Identification of abnormal tribological regimes using a microphone and semi-supervised machine-learning algorithm

et al. 2021

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Functional surfaces in relative contact and motion are prone to wear and tear, resulting in loss of efficiency and performance of the workpieces/machines. Wear occurs in the form of adhesion, abrasion, scuffing, galling, and scoring between contacts. However, the rate of the wear phenomenon depends primarily on the physical properties and the surrounding environment. Monitoring the integrity of surfaces by offline inspections leads to significant wasted machine time. A potential alternate option to offline inspection currently practiced in industries is the analysis of sensors signatures capable of capturing the wear state and correlating it with the wear phenomenon, followed by in situ classification using a state-of-the-art machine learning (ML) algorithm. Though this technique is better than offline inspection, it possesses inherent disadvantages for training the ML models. Ideally, supervised training of ML models requires the datasets considered for the classification to be of equal weightage to avoid biasing. The collection of such a dataset is very cumbersome and expensive in practice, as in real industrial applications, the malfunction period is minimal compared to normal operation. Furthermore, classification models would not classify new wear phenomena from the normal regime if they are unfamiliar. As a promising alternative, in this work, we propose a methodology able to differentiate the abnormal regimes, i.e., wear phenomenon regimes, from the normal regime. This is carried out by familiarizing the ML algorithms only with the distribution of the acoustic emission (AE) signals captured using a microphone related to the normal regime. As a result, the ML algorithms would be able to detect whether some overlaps exist with the learnt distributions when a new, unseen signal arrives. To achieve this goal, a generative convolutional neural network (CNN) architecture based on variational auto encoder (VAE) is built and trained. During the validation procedure of the proposed CNN architectures, we were capable of identifying acoustics signals corresponding to the normal and abnormal wear regime with an accuracy of 97% and 80%. Hence, our approach shows very promising results for in situ and real-time condition monitoring or even wear prediction in tribological applications.

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A Review on Dimensionality Reduction for Machine Learning

Coelho

Madureira²,

Pereira

2023

Lecture Notes in Networks and Systems

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Monitoring of friction-related failures using diffusion maps of acoustic time series

Cited by 7 publications

References 35 publications

Methodical Approach to Using Acoustic Emission Method for Tribosystem Monitoring

Methodical Approach to Using Acoustic Emission Method for Tribosystem Monitoring

Identification of abnormal tribological regimes using a microphone and semi-supervised machine-learning algorithm

A Review on Dimensionality Reduction for Machine Learning

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