Abstract
Timely maintenance and accurate fault prediction of rotating machinery are essential for ensuring system availability, minimizing downtime, and contributing to sustainable production. This paper proposes a novel approach based on long-range dependence (LRD) and particle filter (PF) for degradation trend prediction of rotating machinery, taking the rolling bearing as an example. In this work, the degradation prediction is evaluated based on two health indicators time series; i.e., equivalent vibration severity (EVI) time series and kurtosis time series. Specifically, the degradation trend prediction issues here addressed have the following two distinctive features: (i) EVI time series with weak LRD property and (ii) kurtosis time series with sharp transition points (STPs) in the forecasted region. The core idea is that the parameters distribution of the LRD model can be updated recursively by the particle filter algorithm; i.e., the parameters degradation of the LRD model are restrained, and thus the prognostic results could be generated real-time, wherein the initial LRD model is designed randomly. The prediction results demonstrate that the significant improvements in prediction accuracy are obtained with the proposed method compared to some state-of-the-art approaches such as the autoregressive–moving-average (ARMA) model and the fractional order characteristic (FOC) model, etc.
Highlights
The prognostics and health management (PHM) in mechanical equipment and systems has become extremely important in many industrial applications including aero-engines, metallurgy machinery, wind turbine, etc.; owing to the fact that the PHM is able to minimize downtime and maintenance costs, maximize machinery utilization, and ensure system availability [1,2]
Prognostics is a key issue in PHM, which aims at utilizing real monitoring data to facilitate relevant degradation indicators and trends (DITs) that depict the current system health status and evaluate of remaining useful life (RUL) of a device
The particle filter frame (PFF) is a sequential series simulation-based algorithm based on a recursive Bayesian filter (RBF), which utilizes the Monte Carlo (MC) algorithm to constantly adjust the weight and position of the samples and revise the original experience conditional distribution according to the adjusted particle information [25,26,27]
Summary
The prognostics and health management (PHM) in mechanical equipment and systems has become extremely important in many industrial applications including aero-engines, metallurgy machinery, wind turbine, etc.; owing to the fact that the PHM is able to minimize downtime and maintenance costs, maximize machinery utilization, and ensure system availability [1,2]. If some sharp transition points (STPs) are contained in the health indicator time series (HITS), especially the time series at the incipient/severe fault phase, the traditional f -ARIMA and FOC approaches treat all time series values which ignore the fact that the STPs value should be preserved at a larger weight, limiting their effectiveness in practical application To overcome these issues and improve the prediction accuracy, this paper proposes a new approach for degradation prediction based on the long-range dependence (LRD) and particle filter (PF) algorithm, using EVI and kurtosis health indicators of a rolling bearing as the tested time series.
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