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Adaptive Change Detection for Long-Term machinery monitoring Using Incremental Sliding-Window

Chinese Journal of Mechanical Engineering 2017年第6期30卷 1338-1346页

作者： Teng Wang Guo-Liang Lu Jie Liu Peng Yan Key Laboratory of High-Efficiency and Clean Mechanical Manufacture of MOE School of Mechanical EngineeringShandong University Department of Mechanical and Aerospace Engineering Carleton University

Detection of structural changes from an opera- tional process is a major goal in machine condition moni- toring. Existing methods for this purpose are mainly based on retrospective analysis, resulting in a large detection delay that limits their usages in real applications. This paper presents a new adaptive real-time change detection algorithm, an extension of the recent research by combin- ing with an incremental sliding-window strategy, to handle the multi-change detection in long-term monitoring of machine operations. In particular, in the framework, Hil- bert space embedding of distribution is used to map the original data into the Re-producing Kernel Hilbert Space （RK_HS） for change detection; then, a new adaptive threshold strategy can be developed when making change decision, in which a global factor （used to control the coarse-to-fine level of detection） is introduced to replace the fixed value of threshold. Through experiments on a range of real testing data which was collected from an experimental rotating machinery system, the excellent detection performances of the algorithm for engineering applications were demonstrated. Compared with state-of- the-art methods, the proposed algorithm can be more suitable for long-term machinery condition monitoring without any manual re-calibration, thus is promising in modern industries.

关键词： machine monitoring Change detection Long-term monitoring Adaptive threshold

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