Degradation state prediction of rolling bearings using ARX-Laguerre model and genetic algorithms
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ORIGINAL ARTICLE
Degradation state prediction of rolling bearings using ARX-Laguerre model and genetic algorithms Taoufik Najeh 1 & Jan Lundberg 1 Received: 23 April 2020 / Accepted: 24 November 2020 # The Author(s) 2020
Abstract This study is motivated by the need for a new advanced vibration-based bearing monitoring approach. The ARX-Laguerre model (autoregressive with exogenous) and genetic algorithms (GAs) use collected vibration data to estimate a bearing’s remaining useful life (RUL). The concept is based on the actual running conditions of the bearing combined with a new linear ARXLaguerre representation. The proposed model exploits the vibration and force measurements to reconstruct the Laguerre filter outputs; the dimensionality reduction of the model is subject to an optimal choice of Laguerre poles which is performed using GAs. The paper explains the test rig, data collection, approach, and results. So far and compared to classic methods, the proposed model is effective in tracking the evolution of the bearing’s health state and accurately estimates the bearing’s RUL. As long as the collected data are relevant to the real health state of the bearing, it is possible to estimate the bearing’s lifetime under different operating conditions. Keywords Vibration analysis . Condition monitoring . RUL . Rolling-element bearings . Through-life engineering . GAs . ARX-Laguerre model
1 Introduction In many industries, prognostics and health management (PHM) are key tools for condition-based maintenance. The bearing industry, like other industries, uses various approaches to extract health indicators that help in decisionmaking for maintenance. One common method to determine the remaining useful life is to use the dynamic load capacity and applied load, a method endorsed by standard ISO281:1977 and the modified ISO281:2007. However, in real operating conditions, the bearing can suffer from unexpected circumstances, and the actual operating life could be completely different [1]. Another method is to monitor the condition of the bearing to determine its health state and remaining useful life (RUL). This paper proposes a new model to track the evolution of bearing health using vibration data to more accurately estimate the bearing’s RUL. The concept is
* Taoufik Najeh [email protected] 1
Division of Operation and Maintenance, Luleå University of Technology, 97187 Luleå, Sweden
based on the actual running conditions of the bearing combined with a new linear ARX-Laguerre representation. By the end of the eighteenth century, bearing manufacturers and users started to focus on bearing selection and the life of bearings needed for a well-designed machine. In 1896, Stribeck [2] was the first to measure the mechanical fatigue of bearings. A decade later, Goodman [3] adopted the fatigue approach to determine load limits on cylindrical roller bearings. For quite some time, Palmgren’s work was the most important in the field of bearing life calculations. In 1947, Palmgren and Lundberg used Palmgren’s previous work and t
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