Reliability-as-a-Service for bearing risk assessment investigated with advanced mathematical models
| Autor: | Jan-M. Brandt, Márton Benedek, Jörg Fliege, Jeffrey S. Guerin |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Service (systems architecture)
Computer science Interface (computing) 02 engineering and technology law.invention 0203 mechanical engineering Artificial Intelligence law Management of Technology and Innovation Computer Science (miscellaneous) Lubricant Engineering (miscellaneous) Reliability (statistics) Bearing (mechanical) Mathematical model business.industry Condition monitoring 021001 nanoscience & nanotechnology Computer Science Applications Reliability engineering Support vector machine 020303 mechanical engineering & transports Hardware and Architecture Analytics 0210 nano-technology business Software Information Systems |
| Zdroj: | Internet of Things. 11:100178 |
| ISSN: | 2542-6605 |
| DOI: | 10.1016/j.iot.2020.100178 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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