A fleet of self-service systems, such as electric vehicle charging piles (EVCPs), is usually installed in a specific location. During operation, these systems are subject to random failures. However, they are usually operated without on-site staff. It is quite common that a customer may switch to other unoccupied systems for service when the initially selected system is found to have failed or fails during service. This is called failure-induced demand switching (FDS). With continuous customer arrivals and system failures, such FDS events occur repeatedly and interact dynamically, making modeling and enhancing service levels quite difficult. The challenge becomes even greater when a unique two-dimensional inspection and maintenance (IM) policy is adopted to handle the maintenance needs of self-service systems in hopes of retaining their service level with respect to long-run demand satisfaction. In this paper, we investigate the long-term service reliability of a fleet of self-service systems subject to FDS and a two-dimensional IM policy. First, we model the fleet state transition process and characterize its analytical properties. Next, we measure the fleet’s long-term service reliability and obtain the analytical expressions for crucial service level metrics, such as the expected number of failed systems, the expected length of an operation cycle, and service reliability loss due to imperfect monitoring. The managerial implications regarding the selections of EVCPs and IM policy are proposed based on a numerical study of two fleets of EVCPs in Hong Kong. These implications are expected to assist the operators in ensuring fleet service levels in the long run at a minimal operation and maintenance cost. Note to Practitioners—This paper models the service reliability of a fleet of self-service systems (e.g., EVCPs) over time. Service reliability, reflecting the fleet’s service level, is defined as the proportion of demands being fulfilled and is of the utmost concern of system operators. However, under repeated FDS due to continuous customer arrivals and system failures, it is difficult to assess the fleet’s service reliability using existing methodologies. The task becomes more challenging when failures of such systems are not perfectly detected in practice. This paper develops mathematical models and a novel two-dimensional inspection and maintenance policy to overcome the technical barriers. The models enable assessing the service reliability of various self-service systems experiencing non-constant service rates. Two case studies of fleets of AC and DC EVCPs in Hong Kong are provided to demonstrate the practical applicability of the proposed models. Indeed, the core findings of this work assist practitioners in: (i) finding the optimal maintenance policy that maximizes the fleet’s service reliability, (ii) assessing the service reliability loss due to imperfect failure detection, and (iii) evaluating the sensitivity of the optimal service reliability with respect to the customer arrival rate, system failure rate, and customer behavior in reporting system failures. We also show that the proposed models provide the exact solutions to the above-mentioned metrics when the maintenance duration is longer than the service duration.

Rolling bearings are critical components in modern mechanical systems and have been extensively equipped in various rotating machinery. However, their operating conditions are becoming increasingly complex due to diverse working requirements, dramatically increasing their failure risks. Worse still, the interference of strong background noises and the modulation of varying speed conditions make intelligent fault diagnosis very challenging for conventional methods with limited feature extraction capability. To this end, this study proposes a periodic convolutional neural network (PeriodNet), which is an intelligent end-to-end framework for bearing fault diagnosis. The proposed PeriodNet is constructed by inserting a periodic convolutional module (PeriodConv) before a backbone network. PeriodConv is developed based on the generalized short-time noise resist correlation (GeSTNRC) method, which can effectively capture features from noisy vibration signals collected under varying speed conditions. In PeriodConv, GeSTNRC is extended to the weighted version through deep learning (DL) techniques, whose parameters can be optimized during training. Two open-source datasets collected under constant and varying speed conditions are adopted to assess the proposed method. Case studies demonstrate that PeriodNet has excellent generalizability and is effective under varying speed conditions. Experiments adding noise interference further reveal that PeriodNet is highly robust in noisy environments.

Critical systems, such as telecommunication networks, power grids, transportation networks, and supply chains, have been dramatically expanded over the past decades. To avoid significant interruptions of their services, failure-prevention technologies and strategies have been explored extensively. However, in addition to inherent faults and expected failures, such systems are subject to natural and man-made hazards. The frequent occurrences of these hazards result in an increase in the systems’ operational uncertainty as well as significant disruptions of their services. Unfortunately, the traditional reliability metrics do not adequately describe a system's performance under such hazards. There is a need for assessing the resilience of a system, which characterizes the system's performance deterioration and restoration under hazards. To date, substantial effort has been devoted to describing and quantifying system resilience from different perspectives. However, conceptual understanding and visionary transition from traditional reliability to resilience are more than just taking one step further. In this article, we briefly review approaches that qualitatively and quantitatively assess system resilience and discuss their applicable scenarios and limitations. Challenges and opportunities in system resilience modeling and enhancement, such as multihazard resilience modeling and restoration sequence optimization, are also presented so that more reliability researchers and practitioners may dive into and contribute to this important area.

The outpatient services in large-scale hospitals consist of multiple specialty departments, servers, and multiple stages. Single-server and small clinics have been extensively studied, but little attention has been paid to the modeling and evaluation of large-scale outpatient care systems. In this study, complex patient routings in large-scale outpatient systems are specified, and three patient flow variability factors (e.g., return rate, first-lab-visit rate, and second-lab-visit rate) are discussed. The measures, namely, patient satisfaction, resource utilization, and system efficiency, are based on the ratios of “effective time” over “holistic time.” The purpose of this study is to find the effect of patient flow variability and service time variability on the efficiency of a multistage outpatient system. A discrete-event simulation (DES) model is built, and a real case is presented. A novel method to generate the transition matrix based on patient flow variability is proposed. Our results show that the patient flow variability has a negative impact on patient satisfaction and a positive effect on resource utilization and system efficiency. Service time variability has a negative impact on resource utilization but has a combined effect on patient satisfaction and system efficiency with patient flow variability. The interactions between the two factors are thoroughly discussed.

Identifying critical department(s) to enhance the supply in large-scale systems is beneficial to support the system efficiency. Large-scale outpatient systems (LSOSs) are usually faced with crowdedness and, hence, require critical departmental improvement to maintain patient satisfaction under a limited budget. Besides, when demand surges (DSs) or physicians are absent [supply loss (SL)], critical department identification becomes one of the key steps to resilient clinical management. To improve the clinical services and mitigate the risk of disruptions efficiently, we conduct critical department analysis under three scenarios: one clinical improvement scenario [supply enhancement (SE)] and two clinical disruption scenarios (DS and SL). These scenarios can happen in different departments in varying time sessions (e.g., am or pm). We define the criticality of a department as the change of patient satisfaction with respect to the change of departmental supply and demand. We accordingly propose a simulation-based ranking method and implement a case study in an LSOS. The simulation results show that the criticality of the department highly depends on the time session. Surprisingly, SE may reduce patient satisfaction when the supply increases in several specific departments. Key findings and managerial insights are further discussed.

Sequential strategies and integrated design and control (IDC) strategies have been developed to optimize engineering systems. Nevertheless, neither of them considers the impact of uncertainty when optimizing system performance. To improve the robustness of the system performance and ensure the systematic optimality, this article proposes an integrated robust design and robust control (IRDRC) strategy for a general engineering system using the genetic algorithm. The proposed IRDRC strategy is applied to a rocket flight attitude control system to test its effectiveness. The results show that the IRDRC strategy outperforms existing sequential strategies and IDC strategies, concerning system-level optimization and system robustness.

In this paper, we investigate a system composed of mixtures of one-shot units with nonhomogeneous and time-dependent characteristics. We propose analytical expressions to predict system reliability metrics and consider a physics-statistics-based lifetime model to demonstrate the units' failure mechanism as well as its failure process uncertainty. We design a sequence of optimum accelerated non-destructive testing (NDT) plans to predict the reliability metrics of the system and show that a well-designed sequential accelerated NDTs is an effective approach to shorten the test duration with negligible consequence on system reliability metrics.

Traditional reliability analysis and prediction are performed by utilizing observed failure or degradation data of test units or field observations. Reliability testing is usually performed to predict reliability or performed as acceptance testing or reliability demonstration test. Moreover, in many cases, reliability tests are performed repeatedly during the entire life of the system by testing different samples with different characteristics in the system. At the end of each test, available data only show the number and combination of failed units which are then used for reliability estimation and prediction. This paper investigates several effective approaches to obtain expressions for system reliability metrics under different scenarios, considering the mixture characteristics of the units. The proposed approaches apply to general cases when the population size, as well as the mixture of units, increase over time. A simulation model is utilized to validate the proposed models.