Prediction of remaining useful life (RUL) of Komatsu excavator under reliability analysis in the Weibull-frailty model

Abstract

It is an essential task to estimate the remaining useful life (RUL) of machinery in the mining sector aimed at ensuring the production and the customer’s satisfaction. In this study, a conceptual framework was used to determine the RUL under the reliability analysis in a frailty model. The proposed framework was implemented on a Komatsu PC-1250 excavator from the Sungun copper mine. Also, the Weibull-frailty model was selected to describe the failure behavior and compare it with the classical-exponential model. The frailty model was also used to evaluate the impact of unobserved environmental conditions on the RUL values. Both applied models were fitted to the obtained data from 80 operational hours of the Komatsu PC-1250 excavator. Plotting the results from the reliability analysis of two models demonstrated that the mine system with the frailty model performs better than the classical model before reaching the reliability of 80%. Besides, the frailty model shows a coherent with the operational time of the excavator, while the classical model demonstrates a sinusoid variation. The obtained results may be used for the development of maintenance, preventive repairs planning, and the spare parts replacement intervals.

Prediction of Remaining Useful Life for Components in SSC of RSG-Gas Based on Reliability Analysis

“…After entering the data, the relevant software is selected in the first step of the appropriate statistical approach, followed by the selection of an appropriate function or model of one of the main functions, for example, f(t) with the Weibull function. Furthermore, the cumulative distribution function F(t), reliability function R(t), the level of risk h(t), and then the remaining life of the component m(t) can be calculated using the available functions [6]. Data Processing Diagram on reliability analysis is shown in Figure 2 [6].…”

“…The hazard level is considered as the rate at which failure occurs over a certain time (t1,t2). This level is defined as the probability of occurrence of failure per unit time interval (t1,t2) so that failure has not occurred before t1 (initial interval) [6].…”

“…Once an indication of failure has been detected, it is necessary to estimate the accuracy of the RUL to make timely maintenance decisions to avoid failure. Likewise, its reliability and estimation accuracy tends to result in accurate determination of the optimal inspection interval, thereby minimizing the overall cost of the system [5,6].…”

Resilience analysis: A formulation to model risk factors on complex system resilience

“…By using the selected model in the previous section, the general form of the covariate-based reliability functions can be written as follow (Zaki et al 2019 ; Rod et al 2020 ; Ghomghaleh et al 2020 ; Barabadi et al 2021 ): …”

“…Accordingly, the covariate-based maintainability functions is given by (Zaki et al 2019 ; Rod et al 2020 ; Ghomghaleh et al 2020 ; Barabadi et al 2021 ): where and refer to the reliability and maintainability functions, which are based on the observed and unobserved covariates. The subscript θ is used to emphasize the dependence on the frailty variance θ .…”