Abstract
The remaining useful life (RUL) prediction of rolling bearings has recently gained increasing interest. Many models have been established to catch the degradation performance of bearings. However, there are two shortcomings existing in those models: (1) the health indicator (HI) that used for the first predicting time (FPT) selection is insensitive to incipient faults; (2) the parameter estimation must be based on the historical data, which are not available for some applications due to expensive experiment cost. To overcome the first shortcoming, this paper firstly adopts the mean absolute value of extremums (MAVE) of signals to feature signal energy. Then, the root mean square of the MAVE values (RMS-MAVE) is developed as a new HI to embody signal changes. After that, based on RMS-MAVE values, an adaptive FPT selection approach is proposed by the $3\sigma $ approach. For the second shortcoming, through coupling acquired measurement data with the exponential model, a dynamic exponential regression (DER) model based on RMS-MAVE values is proposed to predict the RUL of bearings. The comparison study indicates that RMS-MAVE is superior to the existed ones in FPT selection for distinguishing different health state of bearings, and the DER model performs better than the existed ones in RUL prediction.
Highlights
Rolling bearings are widely used in rotating machinery, and their failure is one of the foremost causes of the failures in both industry and domestic appliances [1]
To model the degradation process of bearings and predict its remaining useful life (RUL) without historical data, noticed that the exponential model has been shown effective in modeling the degradation processes when the cumulative damage has a particular effect on the rate of degradation [9]–[11], [26], a dynamic exponential regression (DER) model is proposed based on the most recent smoothed root mean square (RMS)-mean absolute value of extremums (MAVE) values, where the parameters of the model are updated in real-time to ensure the RUL prediction accuracy
This paper proposes an RUL prediction method for rolling bearings based on RMS-MAVE and DER model
Summary
Rolling bearings are widely used in rotating machinery, and their failure is one of the foremost causes of the failures in both industry and domestic appliances [1]. Yang: Remaining Useful Life Prediction of Rolling Bearings Based on RMS-MAVE and DER Model the model parameters are accurately estimated Their applications will be restricted when the mechanical systems are too complex to understand the physics of damage. To model the degradation process of bearings and predict its RUL without historical data, noticed that the exponential model has been shown effective in modeling the degradation processes when the cumulative damage has a particular effect on the rate of degradation [9]–[11], [26], a dynamic exponential regression (DER) model is proposed based on the most recent smoothed RMS-MAVE values, where the parameters of the model are updated in real-time to ensure the RUL prediction accuracy. This paper proposes an RUL prediction method for rolling bearings based on RMS-MAVE and DER model.
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