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1. Optimal inspection of binary systems via value of information analysis

   https://doi.org/https://doi.org/10.1016/j.ress.2021.107944  

   Chaochao Lin, Junho Song (July 2021, Reliability engineering systems safety)

   null (Ed.)

   We develop computable metrics to assign priorities for information collection on binary systems composed of binary components. Components are worth inspecting because their condition states are uncertain, and system functioning depends on them. The Value of Information (VoI) enables assessment of the impact of information in decision making under uncertainty, including the component’s reliability and role in the system, the precision of the observation, the available maintenance actions and the expected economic loss. We introduce the VoI-based metrics for system-level (“global”) and component-level (“local”) maintenance actions, analyze the properties of these metrics, and apply them to series and parallel systems. We discuss their computational complexity in applications to general network systems and, to tame the complexity for the local metric assessment, we present a heuristic and assess its performance on some case studies. 

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2. Component-wise Markov decision process for solving condition-based maintenance of large multi-component systems with economic dependence

   https://doi.org/10.1080/24725854.2023.2295376  

   Bansal, Vipul; Chen, Yong; Zhou, Shiyu (February 2024, IISE Transactions)

   Condition-based maintenance of multi-component systems is a prevalent engineering problem due to its effectiveness in reducing the operational and maintenance costs of the system. However, developing the exact optimal maintenance decisions for the large multi-component system is computationally challenging, even not feasible, due to the exponential growth in system state and action space size with the number of components in the system. To address the scalability issue in CBM of large multi-component systems, we propose a Component-Wise Markov Decision Process(CW-MDP) and an Adjusted Component-Wise Markov Decision Process (ACW-MDP) to obtain an approximation of the optimal system-level CBM decision policy for large systems with heterogeneous components. We propose using an extended single-component action space to model the impact of system-level setup cost on a component-level solution. The theoretical gap between the proposed approach and system-level optima is also derived. Additionally, theoretical convergence and the relationship between ACW-MDP and CW-MDP are derived. The study further shows extensive numerical studies to demonstrate the effectiveness of component-wise solutions for solving large multi-component systems. 

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3. Optimal adaptive inspection and maintenance for redundant systems

   https://doi.org/10.1177/1748006X211020151  

   Lin, Chaochao; Pozzi, Matteo (May 2021, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability)

   Optimal exploration of engineering systems can be guided by the principle of Value of Information (VoI), which accounts for the topological important of components, their reliability and the management costs. For series systems, in most cases higher inspection priority should be given to unreliable components. For redundant systems such as parallel systems, analysis of one-shot decision problems shows that higher inspection priority should be given to more reliable components. This paper investigates the optimal exploration of redundant systems in long-term decision making with sequential inspection and repairing. When the expected, cumulated, discounted cost is considered, it may become more efficient to give higher inspection priority to less reliable components, in order to preserve system redundancy. To investigate this problem, we develop a Partially Observable Markov Decision Process (POMDP) framework for sequential inspection and maintenance of redundant systems, where the VoI analysis is embedded in the optimal selection of exploratory actions. We investigate the use of alternative approximate POMDP solvers for parallel and more general systems, compare their computation complexities and performance, and show how the inspection priorities depend on the economic discount factor, the degradation rate, the inspection precision, and the repair cost. 

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4. “Optimized Asset Management in Distribution Systems Based on Predictive Risk Analysis

   T. Dokic, M. Kezunovic (January 2018, Energies)

   The paper introduces an optimal maintenance scheduler based on predictive assessment of risk of outage and equipment failure in distribution networks. The variety of severe weather conditions are observed and their impact on the network components is quantified. The equipment deterioration and failure rates are observed continuously across the space and time using heterogeneous data. The risk of weather-related outages for each component is generated in real-time, and can be extracted at multiple temporal and spatial scales depending on the application of interest. The optimal maintenance scheduling that minimizes the system risk while maintaining the economic investment limits is developed. The benefits of the framework are presented using a distribution network asset management example. 

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5. Understanding Architectural Complexity, Maintenance Burden, and Developer Sentiment--A Large-Scale Study

   Cai, Yuanfang; He, Lanting He; Qian, Jun Qian; Kochinski, Yony; Zhang, Nan; Jaspan, Ciera; Bianco, Antonio (April 2025, IEEE)

   Abstract—Intuitively, the more complex a software system is, the harder it is to maintain. Statistically, it is not clear which complexity metrics correlate with maintenance effort; in fact, it is not even clear how to objectively measure maintenance burden, so that developers’ sentiment and intuition can be supported by numbers. Without effective complexity and maintenance metrics, it remains difficult to objectively monitor maintenance, control complexity, or justify refactoring. In this paper, we report a large-scale study of 1252 projects written in C++ and Java from Google LLC. We collected three categories of metrics: (1) architectural complexity, measured using propagation cost (PC), decoupling level (DL), and structural anti-patterns; (2) maintenance activity, measured using the number of changes, lines of code (LOC) written, and active coding time (ACT) spent on feature-addition vs. bug-fixing, and (3) developer sentiment on complexity and productivity, collected from 7200 survey responses. We statistically analyzed the correlations among these metrics and obtained significant evidence of the following findings: 1) the more complex the architecture is (higher propagation cost, more instances of anti-patterns), the more LOC is spent on bug-fixing, rather than adding new features; 2) developers who commit more changes for features, spend more lines of code on features, or spend more time on features also feel that they are less hindered by technical debt and complexity. To the best of our knowledge, this is the first large-scale empirical study establishing the statistical correlation among architectural complexity, maintenance activity, and developer sentiment. The implication is that, instead of solely relying upon developer sentiment and intuition to detect degraded structure or increased burden to evolve, it is possible to objectively and continuously measure and monitor architectural complexity and maintenance difficulty, increasing feature delivery efficiency by reducing architectural complexity and anti-patterns. 

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