Classification of operational states in porous journal bearings using a semi-supervised multi-sensor Machine Learning approach

Prost, Josef; Boidi, G.; Puhwein, A.M.; Varga, M.; Vorlaufer, Georg

doi:10.1016/j.triboint.2023.108464

Cited by 5 publications

(4 citation statements)

References 51 publications

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Section: Introductionmentioning

confidence: 99%

“…Therefore, we considered that machine learning (ML) could be used to investigate the friction mechanism in a comprehensive manner.ML has been applied across various academic fields and has contributed significantly to many studies. Within the field of tribology, ML models have been constructed to predict fault diagnosis [16][17][18][19][20][21] , estimate life 22,23 , determine lubrication regimes 24 , and analyze wear properties 25 from sensor datasets. In addition, ML models have been developed to predict wear properties [26][27][28][29][30][31][32] , friction coefficients 26,31 , and surface morphologies 29,33 from laboratory-scale experimental datasets.…”

mentioning

confidence: 99%

“…Furthermore, dimension reductions were used to classify the image data of the elemental distributions of tribofilms in this study. Dimension reductions have been used not only to increase ML learning speed 17,20 but also to visualize the distribution of high-dimensional data 43,44 . However, dimension reductions have not yet visualized the distribution of tribofilm image data to investigate the relationship between the elemental distribution of a tribofilm and its friction coefficient.Therefore, the purpose of this study is to investigate the possibility of using the CNN model, Grad-CAM, and dimension reductions to evaluate the relationship between the friction coefficient and the elemental distribution of a tribofilm that contains multiple lubricant additives.…”

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confidence: 99%

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Application of a neural network model in estimation of frictional features of tribofilms derived from multiple lubricant additives

Noma,

Aoki,

Kobayashi

2024

Sci Rep

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In the field of tribology, many studies now use machine learning (ML). However, ML models have not yet been used to evaluate the relationship between the friction coefficient and the elemental distribution of a tribofilm formed from multiple lubricant additives. This study proposed the possibility of using ML to evaluate that relationship. Friction tests revealed that, calcium tribofilms formed on the friction surface, with the friction coefficient increasing as a result of the addition of OBCS. Therefore, we investigated whether the convolutional neural network (CNN) model could recognize the tribofilms formed from OBCS and classify image data of the elemental distributions of these tribofilms into high and low friction-coefficient groups. The CNN model classifies only output values, and it’s difficult to see how the model has learned. Gradient-weighted class activation mapping (Grad-CAM) was performed using a CNN-based model, and this allowed the visualization of the areas important for classifying elemental distributions into friction coefficient groups. Furthermore, dimension reductions enabled the visualization of these distributions for classification into the groups. The results of this study suggested that the CNN model, the Grad-CAM, and the dimension reductions are useful for evaluating frictional features of tribofilms formed from multiple lubricant additives.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Application of a neural network model in estimation of frictional features of tribofilms derived from multiple lubricant additives

Noma,

Aoki,

Kobayashi

2024

Sci Rep

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“…In some work, the abnormal conditions are classified into different types of abnormal condition such as Run-in, Steady1, Steady2, Pre-critical and critical [15], or the flow regime is classified from the measured data [16,17]. Other studies focus on areas such as the anomaly detection of force signals [18], the classification of operational states [15,17,[19][20][21], load prediction [22], the estimation of model-based remaining useful life and wear prediction [23], and supervised wear volume estimation [24]. Data-driven regression models have been recently employed to assess the influence of temperature, bearing load, and rotational speed on the variation in friction torque and friction coefficient [25].…”

Section: Introductionmentioning

confidence: 99%

Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders

Ates,

Höfchen,

Witt

et al. 2023

Sensors

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Predictive maintenance is considered a proactive approach that capitalizes on advanced sensing technologies and data analytics to anticipate potential equipment malfunctions, enabling cost savings and improved operational efficiency. For journal bearings, predictive maintenance assumes critical significance due to the inherent complexity and vital role of these components in mechanical systems. The primary objective of this study is to develop a data-driven methodology for indirectly determining the wear condition by leveraging experimentally collected vibration data. To accomplish this goal, a novel experimental procedure was devised to expedite wear formation on journal bearings. Seventeen bearings were tested and the collected sensor data were employed to evaluate the predictive capabilities of various sensors and mounting configurations. The effects of different downsampling methods and sampling rates on the sensor data were also explored within the framework of feature engineering. The downsampled sensor data were further processed using convolutional autoencoders (CAEs) to extract a latent state vector, which was found to exhibit a strong correlation with the wear state of the bearing. Remarkably, the CAE, trained on unlabeled measurements, demonstrated an impressive performance in wear estimation, achieving an average Pearson coefficient of 91% in four different experimental configurations. In essence, the proposed methodology facilitated an accurate estimation of the wear of the journal bearings, even when working with a limited amount of labeled data.

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The Role of Automated Classification in Preserving Indonesian Folk and National Songs

Prasetya Wibawa,

Rofi’uddin,

Dreżewski

et al. 2024

Lecture Notes in Computer Science

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Classification of operational states in porous journal bearings using a semi-supervised multi-sensor Machine Learning approach

Cited by 5 publications

References 51 publications

Application of a neural network model in estimation of frictional features of tribofilms derived from multiple lubricant additives

Application of a neural network model in estimation of frictional features of tribofilms derived from multiple lubricant additives

Vibration-Based Wear Condition Estimation of Journal Bearings Using Convolutional Autoencoders

The Role of Automated Classification in Preserving Indonesian Folk and National Songs

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