image: Changes in the remaining life prediction distribution as defects progress view more
Credit: Osaka University
Osaka, Japan - Scientists from the Institute of Scientific and Industrial Research, and NTN Next Generation Research Alliance Laboratories at Osaka University developed a machine learning method that combines convolutional neural networks and Bayesian hierarchical modeling to precisely predict the remaining useful life of rolling bearings. This work may lead to new industrial monitoring methods that help manage maintenance schedules and maximize efficiency and safety under defect progression.
A rolling bearing consists of two rings separated by rolling elements (balls or rollers). Because of the ease of rolling, the rings can rotate relative to each other with very little friction. Rolling bearings are essential to almost all automated machinery with rotating elements. The bearings eventually fail due to wear and tear, but often potential defects cannot be easily repaired because the rings are in an inaccessible place, or the machine downtime is too costly. Thus, the ability to accurately predict the remaining useful lifetime under defect progression would reduce unnecessary maintenance procedures and prematurely discarded parts without risking breakdown.
Now, a team of researchers at Osaka University have used machine learning to predict the remaining useful lifetime of rolling bearings based on the measured vibration spectrum. It is known that as defects get worse inside a bearing, its vibration amplitude begin to fluctuate. First, the scientists created a spectrogram showing the intensity of different frequencies as a function of time. These two-dimensional graphs were then used to train into a convolutional neural network, which is a machine learning method for image recognition and vision tasks.
"Predicting the remaining useful life curve of rolling bearings under defect progression is usually difficult, owing to irregular fluctuation of vibration features," first author Masashi Kitai says. Because of this, Bayesian hierarchical modeling was used to infer the parameters, including remaining lifetime. This approach allowed the scientists to integrate the results into a single set of predictions, along with associated uncertainties. During testing, the method improved the error of predicted remaining useful life by about 32%.
"More efficient maintenance of industrial machinery based on our technology may lead to reduced environmental burden and economic loss," senior author Ken-ichi Fukui says. Future algorithms may be generalized to work with a wide range of mechanical parts.
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The article, "A framework for predicting remaining useful life curve of rolling bearings under defect progression based on neural network and Bayesian method" was published in IEEE Access at DOI: https://doi.org/10.1109/ACCESS.2021.3073945
About Osaka University
Osaka University was founded in 1931 as one of the seven imperial universities of Japan and is now one of Japan's leading comprehensive universities with a broad disciplinary spectrum. This strength is coupled with a singular drive for innovation that extends throughout the scientific process, from fundamental research to the creation of applied technology with positive economic impacts. Its commitment to innovation has been recognized in Japan and around the world, being named Japan's most innovative university in 2015 (Reuters 2015 Top 100) and one of the most innovative institutions in the world in 2017 (Innovative Universities and the Nature Index Innovation 2017). Now, Osaka University is leveraging its role as a Designated National University Corporation selected by the Ministry of Education, Culture, Sports, Science and Technology to contribute to innovation for human welfare, sustainable development of society, and social transformation.
Website: https://resou.osaka-u.ac.jp/en
Journal
IEEE Access