No abstract

No abstract

No abstract

Prognostics and Health Management (PHM), including monitoring, diagnosis, prognosis, and health management, occupies an increasingly important position in reducing costly breakdowns and avoiding catastrophic accidents in modern industry. With the development of artificial intelligence (AI), especially deep learning (DL) approaches, the application of AI-enabled methods to monitor, diagnose and predict potential equipment malfunctions has gone through tremendous progress with verified success in both academia and industry. However, there is still a gap to cover monitoring, diagnosis, and prognosis based on AI-enabled methods, simultaneously, and the importance of an open source community, including open source datasets and codes, has not been fully emphasized. To fill this gap, this paper provides a systematic overview of the current development, common technologies, open source datasets, codes, and challenges of AI-enabled PHM methods from three aspects of monitoring, diagnosis, and prognosis.

Remaining useful life (RUL) prediction plays a vital role in prognostics and health management (PHM) for improving the reliability and reducing the cycle cost of numerous mechanical systems. Deep learning (DL) models, especially deep convolutional neural networks (DCNNs), are becoming increasingly popular for RUL prediction, whereby state-of-the-art results have been achieved in recent studies. Most DL models only provide a point estimation of the target RUL, but it is highly desirable to have associated confidence intervals for any RUL estimate. To improve on existing methods, we construct a probabilistic RUL prediction framework to estimate the probability density of target outputs based on parametric and non-parametric approaches. The model output is an estimate of the probability density of the target RUL, rather than just a single point estimation. The main advantage of the proposed method is that the method can naturally provide a confidence interval (aleatoric uncertainty) of the target prediction. We verify the effectiveness of our constructed framework via a simple DCNN model on a publicly available degradation simulation dataset of turbine engines. The source codes will be released at https://github.com/ZhaoZhibin/Probabilistic_RUL_Prediction.