An adaptive prognostics method for fusing CDBN and diffusion process: Application to bearing data

Abstract In the coming era of big data, many advances and attempts have been witnessed in deep learning based remaining useful life (RUL) prediction methods which can construct the mapping relation between the massive information and the RUL. Existing studies leverage advanced deep networks for RUL prediction mainly generating the point estimates for the RUL while the prognostic uncertainty quantification is often difficult. However, it is well admitted that such prognostic uncertainty quantification is important and cannot be neglected for health management of degrading products. The purpose of this paper is to develop an adaptive prognostic method towards both the massive data and prognostics uncertainty by leveraging the advantages of deep learning methods in processing massive data and stochastic methods in the uncertainty representation. To do so, a continuous deep belief network (CDBN) is first utilized to extract the deep hidden features behind the massive information, and then, we determine the health index via the self-organizing map (SOM) neural network based on the extracted features. Next, the diffusion process is applied to construct the health index evolving model. The parameters in the diffusion process are estimated online by combining Bayesian method and Expectation Maximization (EM) algorithm. Consequently, the probability density function (PDF) of the RUL can be obtained and updated adaptively. Finally, a practical case study for bearings is provided to substantiate the effectiveness and superiority of the proposed method. Experimental results indicate that the proposed method can provide more accurate RUL predictions.