Abstract
Remaining useful life (RUL) prediction can provide early warnings of failure and has become a key component in the prognostics and health management of systems. Among the existing methods for RUL prediction, the Wiener-process-based method has attracted great attention owing to its favorable properties and flexibility in degradation modeling. However, shortcomings exist in methods of this type; for example, the degradation indicator and the first predicting time (FPT) are selected subjectively, which reduces the prediction accuracy. Toward this end, this paper proposes a new approach for predicting the RUL of rotating machinery based on an optimal degradation indictor. First, a genetic programming algorithm is proposed to construct an optimal degradation indicator using the concept of FPT. Then, a Wiener model based on the obtained optimal degradation indicator is proposed, in which the sensitivities of the dimensionless parameters are utilized to determine the FPT. Finally, the expectation of the predicted RUL is calculated based on the proposed model, and the estimated mean degradation path is explicitly derived. To demonstrate the validity of this model, several experiments on RUL prediction are conducted on rotating machinery. The experimental results indicate that the method can effectively improve the accuracy of RUL prediction.
Highlights
Rotating machinery are the most widely used mechanical equipment in industry and generally consist of motor, shaft, gear box, bearing, and load [1, 2]
Based on the above discussions, an integrated remaining useful life (RUL) prediction method was proposed based on genetic programming (GP) and the concepts of first predicting time (FPT) and Wiener process degradation modeling, which aimed to solve the problem that the degradation progression might be nonmonotonic
In order to improve the prediction accuracy of rotating machinery, an integrated RUL prediction method based on GP and Wiener process degradation modeling was proposed in this paper
Summary
Rotating machinery are the most widely used mechanical equipment in industry and generally consist of motor, shaft, gear box, bearing, and load [1, 2]. There is little research concerning the construction of an optimal degradation indicator and the selection of first predicting time (FPT) based on the Wiener process degradation model. To derive optimal prognostic features, Liao employed the Paris model, combined with a genetic programming (GP) method, to predict the RUL of bearings in [15] In this literature, the FPT was not considered while using GP to generate the optimal prognostic features. Little reported literature can be found on constructing an optimal degradation indicator based on the FPT and addressing the Wiener degradation model for rotating machinery. Based on the above discussions, an integrated RUL prediction method was proposed based on GP and the concepts of FPT and Wiener process degradation modeling, which aimed to solve the problem that the degradation progression might be nonmonotonic. The RUL prediction and the comparison of the proposed method with other prediction methods are presented in Section 7, and Section 8 concludes this paper
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