An Improved Recursive ARIMA Method with Recurrent Process for Remaining Useful Life Estimation of Bearings

Abstract

A typical way to predict the remaining useful life (RUL) of bearings is to predict certain health indicators (HIs) according to the historical HI series and forecast the end of life (EOL). The autoregressive neural network (ARNN) is an early idea to combine the artificial neural network (ANN) and the autoregressive (AR) model for forecasting, but the model is limited to linear terms. To overcome the limitation, this paper proposes an improved autoregressive integrated moving average with the recurrent process (ARIMA-R) method. The proposed method adds moving average (MA) components to the framework of ARNN, adding the long-range dependence and nonlinear factors. To deal with the recursive characteristics of the MA term, a process of MA component estimating is constructed based on the expectation-maximum method. In the concrete realization of the method, the rotation tree (RTF) is introduced in place of ANN to improve the prediction performance. The experiment on FEMTO datasets reveals that the proposed ARIMA-R method outperforms the ARNN method in terms of predictive performance evaluation indicators.

Remaining useful life prediction for rolling bearings based on RVM-Hausdorff distance

“…The advantage of the data-driven approach is that RUL can be achieved only from the signals collected by the sensors, through data processing and analysis, and modeling of the data characteristics themselves, without the user needing to know the exact failure mechanism [14]. Luo et al proposed an improved recursive ARIMA method with a cyclic process for the estimation of remaining bearing life [15]. Chen et al proposed a novel prognostic model based on relative characteristics and multivariate support vector machine (SVM) to solve the RUL prediction problem of rolling bearings with small samples [16].…”

Forecasting the Dynamic Response of Rotating Machinery under Sudden Load Changes

“…Several works discuss the application of ARIMA to vibration data. Al-Bugharbee et al [18], Gao et al [19], and Luo et al [20] explored the application of time series analysis techniques, including ARIMA models, for vibration data analysis in condition monitoring. These papers discuss the process of modeling vibration signals using ARIMA models and demonstrate the approach's effectiveness in detecting faults and predicting future vibration levels.…”