Semi-Supervised Classification of the State of Operation in Self-Lubricating Journal Bearings Using a Random Forest Classifier

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

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.

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

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.

A review of recent advances and applications of machine learning in tribology

In tribology, a considerable number of computational and experimental approaches to understand the interfacial characteristics of material surfaces in motion and tribological behaviors of materials have been considered to date. Despite being useful in providing important insights on the tribological properties of a system, at different length scales, a vast amount of data generated from these state-of-the-art techniques remains underutilized due to lack of analysis methods or limitations of existing analysis techniques. In principle, this data can be used to address intractable tribological problems including structure-property relationships in tribological systems and efficient lubricant design in a cost and time effective manner with the aid of machine learning. Specifically, data-driven machine learning methods have shown potential in unraveling complicated processes through the development of structure-property/functionality relationships based on the collected data. For example, neural networks are incredibly effective in modeling non-linear correlations and identifying primary hidden patterns associated with these phenomena. Here we present several exemplary studies that have demonstrated the proficiency of machine learning in understanding these critical factors. A successful implementation of neural networks, supervised, and stochastic learning approaches in identifying structure-property relationships have shed light on how machine learning may be used in certain tribological applications. Moreover, ranging from the design of lubricants, composites, and experimental processes to studying fretting wear and frictional mechanism, machine learning has been embraced either independently or integrated with optimization algorithms by scientists to study tribology. Accordingly, this review aims at providing a perspective on the recent advances in the applications of machine learning in tribology. The review on referenced simulation approaches and subsequent applications of machine learning in experimental and computational tribology shall motivate researchers to introduce the revolutionary approach of machine learning in efficiently studying tribology.

Machine Learning in Tribology—More than Buzzwords?

Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives [...]

On the Importance of Temporal Information for Remaining Useful Life Prediction of Rolling Bearings Using a Random Forest Regressor

Rolling bearings are frequently subjected to high stresses within modern machines. To prevent bearing failures, the topics of condition monitoring and predictive maintenance have become increasingly relevant. In order to efficiently and reliably maintain rolling bearings in a predictive manner, an estimate of the remaining useful life (RUL) is of great interest. The RUL prediction quality achieved when using machine learning depends not only on the selection of the sensor data used for condition monitoring, but also on its preprocessing. In particular, the execution of so-called feature engineering has a major impact on prediction quality. Therefore, in this paper, various methods of feature engineering are presented based on rolling–bearing endurance tests and recorded structure-borne sound signals. The performance of these methods is evaluated in the context of a regression-based RUL model. Furthermore, the way in which the quality of RUL prediction can be significantly improved is demonstrated, by adding further processed, time-considering features.

A Semantic Annotation Pipeline towards the Generation of Knowledge Graphs in Tribology

Within the domain of tribology, enterprises and research institutions are constantly working on new concepts, materials, lubricants, or surface technologies for a wide range of applications. This is also reflected in the continuously growing number of publications, which in turn serve as guidance and benchmark for researchers and developers. Due to the lack of suited data and knowledge bases, knowledge acquisition and aggregation is still a manual process involving the time-consuming review of literature. Therefore, semantic annotation and natural language processing (NLP) techniques can decrease this manual effort by providing a semi-automatic support in knowledge acquisition. The generation of knowledge graphs as a structured information format from textual sources promises improved reuse and retrieval of information acquired from scientific literature. Motivated by this, the contribution introduces a novel semantic annotation pipeline for generating knowledge in the domain of tribology. The pipeline is built on Bidirectional Encoder Representations from Transformers (BERT)—a state-of-the-art language model—and involves classic NLP tasks like information extraction, named entity recognition and question answering. Within this contribution, the three modules of the pipeline for document extraction, annotation, and analysis are introduced. Based on a comparison with a manual annotation of publications on tribological model testing, satisfactory performance is verified.

A review of advances in tribology in 2020–2021

Around 1,000 peer-reviewed papers were selected from 3,450 articles published during 2020–2021, and reviewed as the representative advances in tribology research worldwide. The survey highlights the development in lubrication, wear and surface engineering, biotribology, high temperature tribology, and computational tribology, providing a show window of the achievements of recent fundamental and application researches in the field of tribology.