Lifetime assessment of porous journal bearings using joint time-frequency analysis of real-time sensor data

Prost, Josef; Boidi, G.; Varga, M.; Vorlaufer, Georg; Eder, Stefan J.

doi:10.1016/j.triboint.2022.107488

Cited by 7 publications

(2 citation statements)

References 35 publications

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“…Some scholars have conducted relevant research in this respect. For example, Prost et al [47] used time-frequency to analyze the remaining life of bearings. In addition, some scholars have optimized the squirrel algorithm to improve its search ability.…”

Section: Discussionmentioning

confidence: 99%

Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM Algorithm

Xiao

Shi

et al. 2022

Machines

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Rolling bearings are an important part of rotating machinery, and are of great significance for fault diagnosis and life monitoring of rolling bearings. Analyzing fault signals, extracting effective degradation information and establishing corresponding models are the premise of residual life prediction of rolling bearings. In this paper, first, the time-domain features were extracted to form the eigenvector of the vibration signal, and then the index representing the bearing degradation was found. It was found that the time-domain index could effectively describe the degradation information of the bearing, and the multi-dimensional time-domain characteristic information could effectively describe the attenuation trend of the vibration signal of the rolling bearing. On this basis, appropriate feature vectors were selected to describe the degradation characteristics of bearings. Aiming at the problems of large amounts of data, large amounts of information redundancy and unclear performance index of multi-dimensional feature vectors, the dimensionality of multi-dimensional feature vectors was reduced with principal component analysis, thus, simplifying the multi-dimensional feature vectors and reducing the information redundancy. Finally, in view of the support vector machine (SVM)’s needs to determine kernel function parameters and penalty factors, the squirrel optimization algorithm (SOA) was used to adaptively select parameters and establish the state-life evaluation model of rolling bearings. In addition, mean absolute error (MAE) and root mean squared error (RMSE) were used to comprehensively evaluate SOA. The results showed that the SOA reduced the errors by 5.1% and 13.6%, respectively, compared with a genetic algorithm (GA). Compared with particle swarm optimization (PSO), the error of SOA was reduced by 7.6% and 15.9%, respectively. It showed that SOA-SVM effectively improved the adaptability and regression performance of SVM, thus, significantly improving the prediction accuracy.

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Section: Discussionmentioning

confidence: 99%

Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM Algorithm

Xiao

Shi

et al. 2022

Machines

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“…AE and ML algorithms have been gaining attention in tribology [18][19][20][21][22]. AE has been used to monitor wear in real-time and gain insights into the wear mechanisms involved [20,33]. Supervised, semi-supervised, and unsupervised ML techniques have been employed to perform prognosis of the type of failure mechanism as well as classify and predict wear mechanisms such as scuffing, fretting, abrasive wear, etc.…”

Section: Introductionmentioning

confidence: 99%

Classification of Progressive Wear on a Multi-Directional Pin-on-Disc Tribometer Simulating Conditions in Human Joints-UHMWPE against CoCrMo Using Acoustic Emission and Machine Learning

Deshpande,

Wasmer,

Imwinkelried

et al. 2024

Lubricants

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Human joint prostheses experience wear failure due to the complex interactions between Ultra-High-Molecular-Weight Polyethylene (UHMWPE) and Cobalt-Chromium-Molybdenum (CoCrMo). This study uses the wear classification to investigate the gradual and progressive abrasive wear mechanisms in UHMWPE. Pin-on-disc tests were conducted under simulated in vivo conditions, monitoring wear using Acoustic Emission (AE). Two Machine Learning (ML) frameworks were employed for wear classification: manual feature extraction with ML classifiers and a contrastive learning-based Convolutional Neural Network (CNN) with ML classifiers. The CNN-based feature extraction approach achieved superior classification performance (94% to 96%) compared to manual feature extraction (81% to 89%). The ML techniques enable accurate wear classification, aiding in understanding surface states and early failure detection. Real-time monitoring using AE sensors shows promise for interventions and improving prosthetic joint design.

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Technological Features of Manufacturing a High Precision Spool Pair

Ardashev,

Zhukov

2024

Lecture Notes in Mechanical Engineering

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Lifetime assessment of porous journal bearings using joint time-frequency analysis of real-time sensor data

Cited by 7 publications

References 35 publications

Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM Algorithm

Remaining Useful Life Estimation of Rolling Bearing Based on SOA-SVM Algorithm

Classification of Progressive Wear on a Multi-Directional Pin-on-Disc Tribometer Simulating Conditions in Human Joints-UHMWPE against CoCrMo Using Acoustic Emission and Machine Learning

Technological Features of Manufacturing a High Precision Spool Pair

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