Remaining useful life estimation - A review on the statistical data driven approaches

Degradation modeling plays an important role in system health diagnosis and remaining useful life (RUL) prediction. Recently, a class of Wiener process models with adaptive drift was proposed for degradation-based RUL prediction, which has been proven flexible and effective. However, the existing studies use an autoregressive model of order 1 for the adaptive drift, which can result in difficulties in both model estimation and RUL prediction. This paper proposes a new adaptive Wiener process model that utilizes a Brownian motion for the adaptive drift. The new model shares the flexibility of the existing models, but avoids the difficulties in model estimation and RUL prediction. A model estimation procedure based on maximum likelihood estimation is developed, and the RUL prediction based on the proposed model is formulated. The effectiveness of the model in RUL prediction is validated using simulation and through an application to the lithium-ion battery degradation data.

RUL Prediction of Deteriorating Products Using an Adaptive Wiener Process Model

https://hal.archives-ouvertes.fr/hal-01050920/document

Proton exchange membrane fuel cell degradation prediction based on Adaptive Neuro-Fuzzy Inference Systems .

Due to the recent development of cyber-physical systems, big data, cloud computing, and industrial wireless networks, a new era of industrial big data is introduced. Deep learning, which brought a revolutionary change in computer vision, natural language processing, and a variety of other applications, has significant potential for solutions providing in sophisticated industrial applications. In this paper, a concept of device electrocardiogram (DECG) is presented, and an algorithm based on deep denoising autoencoder (DDA) and regression operation is proposed for the prediction of the remaining useful life of industrial equipment. First, the concept of electrocardiogram is explained. Then, a problem statement based on manufacturing scenario is presented. Subsequently, the architecture of the proposed algorithm called integrated DDA and the algorithm workflow are provided. Moreover, DECG is compared with traditional factory information system, and the feasibility and effectiveness of the proposed algorithm are validated experimentally. The proposed concept and algorithm combine typical industrial scenario and advance artificial intelligence, which has great potential to accelerate the implementation of industry 4.0.

Industrial Big Data Analytics for Prediction of Remaining Useful Life Based on Deep Learning

Remaining useful lifetime estimation and noisy gamma deterioration process

In this paper, prognostic tools are developed to detect the onset of electrical failures in an aircraft power generator, and to predict the generator's remaining useful life (RUL). Focus is on the rotor circuit since failure mode, effects, and criticality analysis (FMECA) studies indicate that it is a high priority candidate for condition monitoring. A signature feature is developed and tested by seeded fault experiments to verify that the initial stages of rotor faults are observable under diverse generator load conditions. A tracking filter is used to assess the damage state and predict generator RUL. This information helps to avoid unexpected failures while reducing the overall life-cycle cost of the system.

Prognostic Health Management of Aircraft Power Generators

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Prognostic Health Management of Aircraft Power Generators