Reliability Modeling of Infrastructure Load-Sharing Systems With Workload Adjustment

Abstract

Motivated by the need to support effective asset management of infrastructure systems, this paper presents a novel reliability model for a load-sharing system where the operator can adjust component work load to balance system degradation. The operator intervention effect, combined with other system complexities, makes modeling reliability interesting and challenging. We first develop cost modeling for a load-sharing system that has experienced operational service at the time of analysis. The system replacement process is modeled as a delayed renewal process for which the expected operational cost of the system is derived. A numerical algorithm is proposed to compute the cost, and the error bound is shown to be of order O(n −1). Next, we extend modeling to consider multiple heterogeneous systems located at different sites within the infrastructure network. Heterogeneities here refer to possible cross-site differences in the operating environments and the operators' actions. When the heterogeneities are observable, we model as covariates; otherwise, we model as random effects. Statistical inference methods are developed for the proposed models. An example using real data from a water utility illustrates the logical model behavior given parameter choices as well as showing how analysis might inform asset management.

A performance-based warranty for products subject to competing hard and soft failures

“…One result noteworthy is that Sun et al (2019) prove that under some mild conditions, the approximation error, M I (t n ; r) − M I (t n ; r), is O(n −1 ).…”

Robust condition‐based production and maintenance planning for degradation management

“…(2020), we assume the system is not correctively replaced until T , with a corrective replacement cost $$c_{\text{r}}+c_{\text{f}}$$, where $$c_{\rm f}>0$$ is a failure penalty cost. A fixed replacement time is often desired in practice because it facilitates budget allocations of decision‐makers (Sun et al., 2019). The time T is optimized in Section 3.2.…”

“…Instead of passively waiting for the degradation to hit the threshold, the failure risk can also be mitigated if we actively adjust the workload to the system to manage the system degradation before a prefixed replacement epoch. This strategy is attractive by virtue of its fixed replacement time and is inherently feasible for production systems because we can dynamically control the production or manufacturing rate, for example, adjusting the speed of a stamping machine (Hao et al., 2015) and the filtration rate of a rapid gravity filter in a waterworks (Sun et al., 2019). Recent developments of the Internet‐of‐Things (IoT) technology further facilitate a remote adjustment of the production rate (uit het Broek et al., 2020).…”

“…Sun et al. (2019) analyze a degradation dataset from a waterworks, where workloads of rapid gravity filters are dynamically adjusted. Basciftci et al.…”

Joint optimization of condition‐based maintenance and condition‐based reallocation for a system with multiple degrading components

“…Zhao et al 26 studied the joint optimization of maintenance and performance-control policies for mission-critical systems where degradation can be controlled by adjusting the performance levels. Sun et al 27 presented a reliability model for a load-sharing system that the operator can adjust component workload to balance system degradation. But this kind of method mentioned above cannot be used for systems that the workload of each component is unmanageable.…”