A prognostic algorithm for machine performance assessment and its application

Abstract

This paper explores a method to assess assets performance and predict the remaining useful life, which would lead to proactive maintenance processes to minimize downtime of machinery and production in various industries, thus increasing efficiency of operations and manufacturing. At first, a performance model is established by taking advantage of logistic regression analysis with maximum-likelihood technique. Two kinds of application situations, with or without enough historical data, are discussed in detail. Then, real-time performance is evaluated by inputting features of online data to the logistic model. Finally, the remaining life is estimated using an ARMA model based on machine performance history; degradation predictions are also upgraded dynamically. The results such as current machine running condition and the remaining useful life, are output to the maintenance decision module to determine a window of appropriate maintenance before the machine fails. An application of the method on an elevator door motion system is demonstrated.

Keywords:

• Logistic regression
• performance assessment
• remaining life prediction
• elevator door system

Acknowledgments

JIHONG YAN is a postdoctoral research associate at the Center for IMS, Department of Industrial and Manufacturing Engineering, University of Wisconsin-Milwaukee, USA. Her research interests include intelligent methods for predictive maintenance, optimization scheduling methods, concurrent engineering, systems modelling, simulation and intelligent algorithms.

MUAMMER KOÇ, is an assistant research scientist in the Department of Mechanical Engineering at the University of Michigan, Ann Arbor, USA. He has been conducting research at the National Science Foundation Engineering Research Center for Reconfigurable Manufacturing Systems, the National Science Foundation Industry/University Cooperative Research Center for Intelligent Maintenance Systems, and the S.M. Wu Manufacturing Center. His research focus has been on advanced manufacturing processes, systems, forming of lightweight materials, micro-meso scale forming, e-Manufacturing and predictive modelling.

JAY LEE is Wisconsin Distinguished Professor and Rockwell Automation Professor at the University of Wisconsin-Milwaukee, director of the Center for IMS. His current research is in the areas of intelligent maintenance and self-maintenance systems. He has pioneered Watchdog Agent™ embedded prognostics technologies and web-enabled Device-to-Business (D2B)™ platform for predictive machine degradation assessment, remote monitoring and prognostics. He is a Fellow of SME and also a Fellow of ASME.