A Novel Method for Remaining Useful Life Prediction of Roller Bearings Involving the Discrepancy and Similarity of Degradation Trajectories

Abstract

Accurate remaining useful life (RUL) prediction of bearings is the key to effective decision-making for predictive maintenance (PdM) of rotating machinery. However, the individual heterogeneity and different working conditions of bearings make the degradation trajectories of bearings different, resulting in the mismatch between the RUL prediction model established by the full-life training bearing and the testing bearings. To address this challenge, this paper proposes a novel RUL prediction method for roller bearings that considers the difference and similarity of degradation trajectories. In this method, a feature extraction method based on continuous wavelet transform (CWT) and convolutional autoencoder (CAE) is proposed to extract the deep features associated with bearing performance degradation before the degradation indicator (DI) is obtained by applying the self-organizing maps (SOM) method. Next, a dynamic time warping (DTW) based method is applied to perform the similarity matching of degradation trajectories of the training and testing bearings. Driven by the historical DIs of the given bearing, the grey forecasting model with full-order time power terms (FOTP-GM) is applied to model the degradation trajectory using a parameter optimization method. Then, the failure threshold of the given testing bearing can be determined using a data-driven method without manual intervention. Finally, the RUL of the given testing bearing can be estimated using the preset failure threshold and the optimized degradation trajectory model of the given testing bearing. The experimental results show that the proposed method retains the individual differences of bearing degradation trend, realizes the independent and reasonable bearing failure threshold setting, and improves the prediction accuracy of RUL.

Research on bearing remaining useful life anti-noise prediction based on fusion of color-grayscale time-frequency features

“…The actual lifetimes of bearing1_1 and bearing1_2 are 28030s and 8710s, respectively. In this paper, the HI values corresponding to the end of life of the training set bearings are used as the fault thresholds for the same operating conditions [56]. The HI values corresponding to the end of life of the two training set bearings in figure 11 are 0.5.…”

Remaining useful life prediction method based on stacked autoencoder and generalized wiener process for degrading bearing

“…To illustrate the effectiveness of the suggested approach, a comparative analysis was conducted using methods from the following references to predict the RUL of the studied bearings. Specifically, the model-based method from [34] is referred to as M2, [35] is referred to as M3, [36] is referred to as M4, and [37] is referred to as M5. Additionally, the performance of the prediction methods was comprehensively evaluated using the scoring function from the IEEE PHM 2012 Challenge [38].…”

An Improved Convolutional-Neural-Network-Based Fault Diagnosis Method for the Rotor–Journal Bearings System

“…The traditional machine learning algorithms commonly applied in intelligent fault diagnosis of rotating machinery mainly contain support vector machines (SVM) [7,8] and artificial neural networks (ANN) [9,10]. However, the traditional intelligent diagnosis methods have inherent limitations [11]: (1) Variable working conditions and composite faults make it difficult to extract signal features effectively; (2) the extracted signal features must be selected with the advice of experienced engineering experts; (3) shallow machine learning algorithms are not able to adequately learn complex nonlinear relationships between the input data.…”