Enabling precise forecasting of the remaining useful life (RUL) for machines can reduce maintenance cost, increase availability, and prevent catastrophic consequences. Data-driven RUL prediction methods have already achieved acclaimed performance. However, they usually assume that training testing data are collected from same condition (same distribution or domain), which is generally not valid...

Remaining useful life prediction can assess the time to failure of degradation systems. Currently, numerous neural network-based methods have been proposed by researchers. However, most work contains an implicit prerequisite: network training and testing data same operating conditions. To solve this problem, adversarial discriminative domain adaption method based on is improve accuracy cross-do...

With continuous improvements in integration, totalization and automation, remaining useful life predictions of mechanical equipment have become a key feature technology core element prediction health management. The traditional method based on degradation mechanisms is not fully capable predicting life, especially for special power plants that use industrial transmissions, barrel launchers, etc...

In today’s competitive market, production costs, lead time and optimal machine utilization are crucial values for companies. Since machine or process breakdowns severely limit their effectiveness, methods are needed to predict products’ life expectancy. Furthermore, continuous assessment and prediction of product’s performance could also enable a collaborative product life-cycle management in w...

The condition monitoring data of gears is asymmetric distributed, moreover, sampling is usually conducted discontinuously in practice. Thus makes it difficult to predict gear remaining useful life accurately considering the two reasons above. In this paper, a fusion method is proposed using Elman Neural Network to modify residual series of grey model since Elman Neural Network performs better o...

This paper discusses the significance and interpretation of uncertainty in the remaining useful life (RUL) prediction of components used in several types of engineering applications, and answers certain fundamental questions such as “Why is the RUL prediction uncertain?”, “How to interpret the uncertainty in the RUL prediction?”, and “How to compute the uncertainty in the RUL prediction?”. Prog...

The estimation of remaining useful life is significant in the context of prognostics and health monitoring, and the prediction of remaining useful life is essential for online operations and decision-making. However, it is challenging to accurately predict the remaining useful life in practical aerospace applications due to the presence of various uncertainties that affect prognostic calculatio...

This paper presents a methodology that improves fatigue-performance evaluations using model-based data interpretation. The accuracy of stress-range values is essential for quantifying fatigue damage. These values are usually predicted using physics-based models such as those used within finite element analyses. In the modelling process, simplifications are inevitable, thus causing systematic er...

The estimation of remaining useful life (RUL) of a faulty component is at the center of system prognostics and health management. It gives operators a potent tool in decision making by quantifying how much time is left until functionality is lost. This is especially true for aerospace systems, where unanticipated subsystem downtime may lead to catastrophic failures. RUL prediction needs to cont...

Reliability is a key parameter for the development of safe and effective military vehicles with a reasonable life cycle cost. One innovative technology that is being promoted in the Department of Defense is the use of Health and Usage Monitoring Systems and remaining life prognostics to improve reliability and availability. The feasibility of using data collected from a limited set of existing ...