ABSTRACT Objective Long‐running software systems suffer from aging, characterized by rising performance degradation, resource depletion, and elevated failure rates. Conventional rejuvenation techniques, which often depend on predetermined restart intervals, model the system as simply healthy, deteriorated, or failed. However, practical evidence from a variety of fields indicates that wear and defect buildup frequently accelerate nonlinearly, necessitating more adaptable mathematical explanations. Method Compared to simpler two‐parameter or linear models, an extended three‐parameter power‐law model which consists of a scaling factor, exponent, and offset is employed here to more accurately depict this behavior. The proposed framework enables adaptive rejuvenation policies triggered by observed conditions rather than rigid schedules by continuously monitoring a degradation metric tuned to this model. Renewal theory with rewards can be used to enhance rejuvenation timing and analytically evaluate steady‐state unavailability. Results By accurately simulating real‐world dynamics, numerical results show that these degradation‐aware, threshold‐based policies outperform fixed interval approaches by accurately modelling real‐world dynamics, particularly in scenarios with increasing aging. Conclusions Together, the extended degradation model and the proposed rejuvenation policies provide a unified analytical framework that improves system availability and reduces long‐run operational costs. This study shows that alert‐based strategies consistently outperform risk‐based policies because they allow for early, data‐driven maintenance across various software aging conditions.

The control room functions as the core nervous system of a nuclear power plant (NPP), emphasizing the crucial need for real-time monitoring of all activities inside to guarantee comprehensive safety. The maintenance of a high level of reliability in the real-time monitoring system within the control room of an NPP is of utmost importance in order to effectively mitigate any potential failures that may occur during the monitoring process. The software and hardware problems can both cause unplanned outages in a large-scale distributed monitoring system. To address the challenge of NPP control room monitoring, a particle filtering-based people tracking system for NPP control room monitoring is introduced to ensure the safety, security, and reliability of the NPP control room. In addition to tracking people for the monitoring of the NPP control room, the suggested technique also provides a reliability study of the large-distributed monitoring system.

Instrumentation and control systems are nervous systems of nuclear power plant (NPP). These systems interact with several safety-critical components of the NPP, such as actuators, transformers, control valves, sensors, circuit breakers, signal processing units, controllers, and heat exchangers. Therefore, the failure of these systems could result in significant financial loss, harm to human resources, or environmental damage. As a result, these systems need to be highly reliable and accurate. In this article, we suggest a framework, based on the batch deterministic and stochastic Petri nets (BDSPNs) to measure the reliability and performance of safety-critical system. The framework consists of three phases. In the first phase, the system is modeled using the BDSPN to derive the transition rate among the system states. In phase 2, the transition rate matrix is utilized to compute the steady-state probability values, which help to evaluate the response time of the system using Little's law. The third phase uses a transition probability matrix to assess the reliability of the system. The technique is illustrated on shutdown system of NPP and is validated on the operational profile data. The obtained accuracy of 99.9905% in measurement of reliability validates the approach.

Safety-critical systems (SCSs) mitigate the risk of catastrophic loss of assets and hence do have high dependability targets. Performance and reliability are the critical dependability attributes, particularly in control and safety systems, and hence essential to measure to ensure the dependability. Traditional methods either are not capable to capture the system dynamics or encounter state explosion problem. Also, the methods are not able to measure all critical performance attributes. This article proposes a novel approach to measure the performance and reliability of SCSs. Such systems contain multiple interconnecting processing nodes, the functional requirements of which are modeled using Petri net (PN). A set of ordinary differential equations (ODEs) is derived from the PN model that represents the state of the system. The ODE solution can be used to measure the critical performance attributes, such as latency time and throughput of the system. The proposed method can avoid the state explosion problem and also introduces new metrics of performance, along with their measurement: deadlock, liveness, stability, boundedness, and steady state. The proposed technique is applied to a case study of nuclear power plant. We obtained 99.887% and 99.939% accuracy of performance and reliability measurement, respectively, which proves the effectiveness of our approach.

Mechatronics systems of nuclear power plants (NPP) are safety-critical systems and, hence, accurate estimates of failures of such systems are essentially required. Failures of such systems depends on the correct functionality of their basic components. Component failure modes are identified by developing the models that can lead to failure of systems. Such failure modes are represented as “basic event” in the system models. For accuracy in measurement of failures, it is important to consider the epistemic correlation, in which, the state of the knowledge about failure parameters of the identical components is the same. The epistemic correlation is also termed as state of knowledge correlation. In this article, we provide methods to accommodate the epistemic correlation in the failure estimates for improved accuracy. The experimental results are shown on a case study of NPP mechatronics system.

Control and instrumentation (C&I) systems are used to measure the critical parameters to take decisions and, hence, such systems do have high reliability target. Therefore, it is essential to measure the reliability of such systems with high accuracy. Petri net (PN) is a mathematical tool that can model the stochastic behavior of the systems. The research work is devoted to propose a framework to measure the reliability of these systems using PN. The proposed framework contains three phases: 1) state-space identification, 2) probability of a system being in each state, and 3) reliability measurement. The proposed approach has been applied to 21 C&I systems of nuclear power plant (NPP) and is demonstrated on a case study of NPP to prove its effectiveness.

Safety-critical systems (SCSs) of nuclear power plants (NPPs) are being designed and developed to meet high dependability requirements. Fault tree analysis (FTA) is widely used for risk and reliability analysis of NPPs. However, fault trees (FTs) are static and have only limited capability to represent dynamic systems. FTA is also not capable of modeling non-binary logic and or modelling the system's evolution in time. Dynamic reliability methods are being developed to deal with such limitations. Time series Markov chains and dynamic flowgraph methodology are the dynamic reliability methods alternate to traditional FTA, which can be used for the system performance analysis. In this article, Time series Markov chains and DFM methods, for the system reliability predictions for the SCS of NPP are compared. The benefits of the proposed method are brought out to the traditional methods. The approach is applied on passive residual heat removal system of pressurized heavy water reactor under the station blackout scenario.

Reliability and safety have always been the main focus while developing safety critical systems (SCS). In this article, we have conducted a rigorous study and discussions with experienced practitioners worldwide the strategy for the development of SCS to investigate the several aspects related to reliability and safety analysis for SCS. We discussed with 21 practitioners from ten different organizations with the intention of knowing their approach they use day-to-day for the development of such systems. The aim of this research is to obtain an in-depth understanding of how reliability and safety engineering are carried out in the industries that develop SCS.