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The purpose of this paper is to develop an approach for estimating the remaining useful life (RUL) of metropolitan train wheels considering measurement error.

The paper proposes a wear model of a metropolitan train wheel based on a discrete state space model; the model considers the wheel’s stochastic degradation and measurement error simultaneously. The paper estimates the RUL on the basis of the estimated degradation state. Finally, it presents a case study to verify the proposed approach. The results indicate that the proposed method is superior to methods that do not consider measurement error and can improve the accuracy of the estimated RUL.

RUL estimation is a key issue in condition-based maintenance and prognostics and health management. With the rapid development of advanced sensor technologies and data acquisition facilities for the maintenance of metropolitan train wheels, condition monitoring (CM) is becoming more accurate and more affordable, creating the possibility of estimating the RUL of wheels using CM data. However, the measurements of the wheels, especially the wayside measurements, are not yet precise enough. On the other hand, few existing studies of the RUL estimation of train wheels consider measurement error.

The approach described in this paper will make the RUL estimation of metropolitan train wheels easier and more precise.

Hundreds of million yuan are wasted every year due to over re-profiling of rail wheels in China. The ability to precisely estimate RUL will reduce the number of re-profiling activities and achieve significant economic benefits. More generally, the paper could enrich the body of knowledge of RUL estimation for a slowly degrading system considering measurement error.

The purpose of this paper is to determine optimal replacement threshold and optimal inspection interval for an item subjected to condition-based maintenance (CBM). The primarily assumption is that the item's failure replacement cost depends on the item's degradation state at which failure occurs and/or the time the item fails. The cost of inspection is also taken into account.

The control limit replacement policy framework, already reported by some research referred to in this paper, is first extended to include the non-decreasing failure replacement cost assumption. Then, for alternative inspection intervals, replacement thresholds together with their associated total cost including the inspection cost are computed. By comparing the total costs, the optimal inspection interval and its corresponding optimal replacement threshold are simultaneously identified.

The mathematical formulation required for the determination of optimal replacement threshold and optimal inspection interval for an item subjected to CBM under the assumption of non-decreasing failure cost is provided.

In some practical situations where CBM is implemented, the failure replacement cost may depend on the time the failure happens and/or may depend on the system's degradation state. In addition, inspections often incur cost. Under such circumstances, findings of this paper can be utilized for the determination of optimal replacement threshold and optimal inspection interval for the underlying system.

Using the approach proposed in this paper, one could obtain the optimal replacement threshold and the optimal inspection interval for a system subjected to CBM.