Abstract
As a key player in bearing service life, the lubricant chemistry has a profound effect on bearing reliability. To increase the reliability of bearings, an Industrial Analytics solution is proposed for proactive condition monitoring and this is delivered via a Reliability-as-a-Serviceapplication. The performance predictions of bearings rely on customized algorithms with the main focus on digitalizing lubricant chemistry; the principles behind these processes are outlined in this study. Subsequently, independent testing is performed to confirm the ability of the presented Industrial Analytics solution for such predictions. By deciphering the chemical compounds of lubricants and characteristics of the interface, the Industrial Analytics solution delivers a precise bearing reliability assessment a priori to predict service life of the operation. Bearing tests have shown that the classification system of this Industrial Analytics solution is able to predict 12 out of 13 bearing failures (92%). The described approach provides a proactive bearing risk classification that allows the operator to take immediate action in reducing the failure potential during smooth operation - preventing any potential damage from occurring. For this purpose, a mathematical model is introduced that derives a set of classification rules for oil lubricants, based on linear binary classifiers (support vector machines) that are applied to the chemical compound’s mixture data.
Highlights
Digitalization of processes play a dominant role in most aspects of life
The X-ray fluorescence analysis (XFA) and IR data of 24 different oil lubricants were analyzed independently using SeerWorksTM Reliability to be able to identify which lubricants would lead to bearings with white-etching cracks (WECs) or surface-induced failure (SIF)
The SeerWorksTM Reliability risk classifications performed prior to the FE-8 tests were compared to the actual results from the 13 FE-8 tests; out of the tests matched to their results (Table 4)
Summary
Digitalization of processes play a dominant role in most aspects of life. This has led to the development of a new way on how humans and technology interact, most frequently referred to as the Internet-of-Things (IoT); in industrial applications it is often referred to as Industrial IoT (IIoT) and is part of the 4th Industrial Revolution (Industry 4.0) [1]. Benedek and J.S. Guerin et al / Internet of Things 11 (2020) 100178 there is an ever-increasing push to digitalize applications and knowledge with the goal to create avatars/digital twins that enhance global economic growth, productivity, and competitiveness for financial benefits. The future belongs to those who can combine existing knowledge with digital tools to strategically transform data into action
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