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Abstract
Methods based on vibration analysis are currently regarded as the most conclusive means for fault diagnosis and health prognostics in rotary machinery. However, changing working conditions mean that the vibration signals originating from rotary machinery exhibit different levels of complexity. This complexity leads to increased difficulty in constructing health indicators (HIs). In this paper, we propose a multiscale Tsallis permutation entropy (MTPE) to construct the HIs of rotary machinery under different working conditions. MTPE values are a function of an entropy index and scale, which have the universality for handling the complexity of a permutated time series. The health condition of the rotary machinery was effectively represented by the MTPEs in conditional monitoring; the initial point of the unhealthy stage was found using the 3 σ interval. This was set as the alarm threshold according to the varying HI trend. Once this was established, dividing the stages into two-stage health stages (HS) was straightforward. Using a rolling bearing, a run-to-failure experiment was conducted and results suggested that the proposed method effectively assessed the status of the rotary machinery. Taken together, this study provided a novel complexity measure based on a methodology for constructing the HIs of rotary machinery and enriches conditional monitoring theory.
Highlights
Condition-based maintenance (CBM) is effective in reducing unnecessary maintenance operations and improving the reliability of rotary machinery [1]
Extensive research has been done into data-driven methods for CBM; from this work, vibration analysis has emerged as the most conclusive method for fault diagnosis and health prognostics in rotary machinery [2,3,4,5,6]. e prognostic program generally consists of four technical processes: data acquisition, health indicator (HI) construction, health stage (HS) division, and remaining useful life (RUL) prediction [1]
We proposed a multiscale Tsallis permutation entropy (MTPE) analysis for conditional monitoring in the degradation process of rotary machinery. e MTPE values are a function of both the entropy index and scale, which have the universality for dealing with the complexity of a permutated time series
Summary
Condition-based maintenance (CBM) is effective in reducing unnecessary maintenance operations and improving the reliability of rotary machinery [1]. One of the major tasks in CBM is health prognostics, which aims to predict the remaining useful life (RUL) of rotary machinery based on its historical and ongoing degradation trends. Along with the measured vibration signals, both time- [10] and frequency-domain features [11] are widely used to analyze the status of the rotary machinery. Conventional indicators, such as kurtosis [12] and skewness, fluctuate drastically and affect the assessment of the machinery running status [9]. With the development of the FFT algorithm, frequency-domain features are utilized as criteria to identify the health condition of the rotary machinery. An additional disadvantage is that the construction of frequencydomain features requires significant expertise
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