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Sensors 2019, 19(2), 412; https://doi.org/10.3390/s19020412

Differential Equation-Based Prediction Model for Early Change Detection in Transient Running Status

1
Key Laboratory of High-efficiency and Clean Mechanical Manufacture of MOE, National Demonstration Center for Experimental Mechanical Engineering Education, School of Mechanical Engineering, Shandong University, Jinan 250061, China
2
School of Mechatronics Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
*
Author to whom correspondence should be addressed.
This paper is an extended version of paper published in Wen, X.; Lu, G.; Yan, P. Improving Structural Change Detection using a Differential Equation-based Prediction Model for Condition Monitoring of Rotating Machines. In Proceedings of the 2018 IEEE International Conference on Prognostics and Health Management (ICPHM), Seattle, WA, USA, 11–13 June 2018.
Received: 23 December 2018 / Revised: 16 January 2019 / Accepted: 18 January 2019 / Published: 20 January 2019
(This article belongs to the Special Issue Sensors for Prognostics and Health Management)
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Abstract

Early detection of changes in transient running status from sensor signals attracts increasing attention in modern industries. To achieve this end, this paper presents a new differential equation-based prediction model that can realize one-step-ahead prediction of machine status. Together with this model, an analysis of continuous monitoring of condition signal by means of a null hypothesis testing is presented to inspect/diagnose whether an abnormal status change occurs or not during successive machine operations. The detection operation is executed periodically and continuously, such that the machine running status can be monitored with an online and real-time manner. The effectiveness of the proposed method is demonstrated using three representative real-engineering applications: external loading status monitoring, bearing health status monitoring and speed condition monitoring. The method is also compared with those benchmark methods reported in the literature. From the results, the proposed method demonstrates significant improvements over others, which suggests its superiority and great potentials in real applications. View Full-Text
Keywords: prediction model; early change detection; differential equation; machine running status prediction model; early change detection; differential equation; machine running status
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Wen, X.; Chen, G.; Lu, G.; Liu, Z.; Yan, P. Differential Equation-Based Prediction Model for Early Change Detection in Transient Running Status. Sensors 2019, 19, 412.

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