International Journal of Reliability and Safety (7 papers in press)

Regular Issues

• Pre-chamber spark ignition: a reliability analysis of pre-chamber valve functions  
  by Faraz Akbar, Sarah Zaki 
  Abstract: A pre-chamber ignition allows spark-ignition engines to operate in lean air-fuel settings. It improves fuel efficiency and reduces emissions. In this study, a reliability analysis of a single GE Jenbacher J620 natural gas engine was done. It was operational on continuous load in the power generation sector in Karachi, Pakistan. A bathtub curve of the GE J620 pre-chamber gas valve (PCV) was generated. The three-year industrial data comprised PCV failures that occurred between two overhauls. During infant mortality, the curve revealed 7 failures during 1000 hours. This decreased to a failure for the next two cycles of thousand hours each. There was a 40% decrease in reliability after 1500 hours. Exponential distribution revealed that the mean time-to-failure (MTTF) was 545.5 hours. This study was the first of its kind in the facility. Previously, much time was lost in breakdown maintenance. Thus, it helped to increase the systems reliability.
  Keywords: bathtub curve; exponential distribution; failure rate; fuel injection; gas engine; pre-chamber combustion; pre-chamber spark ignition; pre-chamber valve; probability density function; reliability.
  
  
• Seismic safety evaluation of dam using cloud model  
  by Alabhya Sharma, Shiv Dayal Bharti, Mahendra Kumar Shrimali, Tushar Kanti Datta 
  Abstract: For the preliminary estimate of the seismic safety of the dam, expert opinions are often relied upon. However, expert opinions, when expressed linguistically, are associated with uncertainty and fuzziness. To address this inadequacy, cloud models have been utilized in numerous studies. In the present investigation, a cloud model is employed to predict the seismic safety of a concrete gravity dam. Experts evaluate seismic safety factors of dams, focusing on seismic damage potential, hazard, and structural strength. Each factor has key sub-indicators rated on a five-point scale. Through qualitative-to-quantitative conversion, cloud points are generated for analysis. The coefficient of variation method identifies sub-indicator influences on each factor. Comparing these cloud models to standard ones visually depicts dam safety. Illustrated with Koyna dam, this approach reveals its seismic safety below normal range, showcasing the effectiveness of the three indicators in assessing dam safety.
  Keywords: cloud model; dam seismic safety assessment; Koyna dam; correlation coefficient method; risk assessment.
  DOI: 10.1504/IJRS.2025.10069925
   
  
• Testing scenario generation and selection for autonomous vehicles using an integrated approach based on real-world accident data  
  by Guozheng Song, Xiaopeng Li 
  Abstract: The safety and reliability of Autonomous Vehicles (AVs) is a core concern, which should be validated before application. The critical testing scenarios extracted from historical accidents of AVs can help achieve the efficient safety and reliability testing of AVs. This paper presents an integrated approach that combines a data-driven method with a Bayesian Network (BN). The information including states, states' occurrence likelihoods and quantitative relationships of variables related to scenarios are learned from an AV accident database of California Department of Motor Vehicles (DMV), which is applied to establish a BN. Then the scenarios are generated and assessed with the BN and a severity matrix. The testing scenarios are selected based on their weighted consequence severity and risk. In this way, this work achieved critical testing scenarios for the automated driving systems (ADSs) and perception systems (PSs) of AVs based on the AV accident database.
  Keywords: autonomous vehicle; Bayesian network; testing scenario generation and selection.
  DOI: 10.1504/IJRS.2024.10070893
   
  
• Modelling and availability optimisation of zinc plating process by using particle swarm optimisation  
  by Ajay Kumar, Devender Singh Punia 
  Abstract: The main aim of this paper is to increase the productivity and reduce the maintenance cost of systems in process industries. Markov modelling is used for mathematical modelling, and steady state as well as transition state availability of various subsystems is analysed for finding the critical subsystem in a production process. The zinc plating process within the screw manufacturing industry is considered as a case study for optimising the availability of the system. The considered system comprises repairable subsystems organised in mixed configurations, assuming their performance parameters like failure and repair rates follow exponential distributions. To solve these equations, a numerical method, specifically the Runge-Kutta fourth-order method, is employed and MATLAB software is utilised for numerical computations. To optimise the results, a Particle Swarm Optimisation technique is used for optimising the availability of the system for various performance parameters and the results are helpful for planning the maintenance policy of the plant.
  Keywords: availability; SSA; steady state analysis; particle swarm optimisation; reliability; TSA; transient state analysis; MTBF; mean time between failure; FRR; failure and repair rates.
  DOI: 10.1504/IJRS.2024.10070101
   
  
• Adaptive navigation of mobile robots: synergising attractor dynamics and DDPG reinforcement learning for safe dynamic obstacle avoidance  
  by Walid Jebrane, Nabil El Akchioui 
  Abstract: Robot navigation in complex and dynamic environments remains a challenging problem, requiring methods that can efficiently adapt to unforeseen obstacles and goal-oriented tasks. This paper presents a novel approach that combines the biologically-inspired Attractor Dynamics Approach with the Deep Deterministic Policy Gradient (DDPG) algorithm to enable a mobile robot, specifically the e-puck robot, to navigate through cluttered spaces while avoiding collisions with moving obstacles effectively. The Attractor Dynamics Approach utilises attractors as goals and repulsive forces to avoid obstacles, offering robust and goal-oriented navigation even with very low-level sensory information. In parallel, the DDPG-based reinforcement learning component fine-tunes the robot's motion controls based on range sensor readings, ensuring precise and adaptive obstacle avoidance. The integration of these two techniques empowers the robot to autonomously explore its environment, dynamically adjust its trajectory and reach predefined targets successfully and safely.
  Keywords: deep deterministic policy gradient; attractor dynamics approach; robot navigation; obstacle avoidance; deep reinforcement learning.
  DOI: 10.1504/IJRS.2024.10068880
   
  
• Recital appraisal based on fuzzy reliability for maintenance scheduling of transportation system  
  by Sushma Kamlu, V. Laxmi 
  Abstract: Maintenance is a routine and recurring process of observing a meticulous system in its customary service condition to deliver its predictable performance without any time delay. It also encompasses restraining the downtime of certain equipment as the catastrophe of a component causes service failure. The reliability of a transportation system can be improved by a better maintenance approach, consequently increasing its availability. The physical need for any system can be described through an upgraded information system. It encompasses the failure history; hours spent on maintenance and operating performance of the vehicle, which are monitored over time. In this work, the fuzzy model has been designed to obtain the fuzzy mean time to failure/repair values. The entire process has been carried out on the basis of expert knowledge for optimisation of safety and reliability assessments. This information can be used to make an in-time decision, prolong the life of the equipment, or amend inclusive vehicle reliability. The overall reliability of the transportation system has been appraised based on the reliability of individual vehicles.
  Keywords: fuzzy reliability; MMTF; maintenance mean time failure; MTTR; mean time to repair; recital appraisal; transportation system.
  DOI: 10.1504/IJRS.2024.10068946
   
  
• Risk assessment model for the pre-construction phase of redevelopment housing projects  
  by Salman Khursheed, Sanika Upasani, Virendra Kumar Paul 
  Abstract: This research presents a comprehensive framework to address the multifaceted risks associated with government-led housing redevelopment projects in Delhi, India. By employing a rigorous methodology involving literature review, stakeholder engagement and statistical analysis, the study identifies and prioritises critical pre-construction risks such as regulatory hurdles, community displacement and financial constraints using both the Risk Priority Number (RPN) and Analytic Hierarchy Process (AHP) methodologies. The developed risk assessment model integrates case study findings and stakeholder preferences to provide a holistic understanding of project vulnerabilities. The research proposes effective mitigation strategies informed by best practices and expert consultations, with a focus on preventative measures and rapid response capabilities. The study's findings offer valuable insights for policymakers, urban planners and project managers, contributing to the successful implementation of resilient and sustainable redevelopment projects in Delhi. Key recommendations include early and continuous stakeholder engagement, rigorous adherence to legal and regulatory requirements, flexible planning approaches and comprehensive mitigation plans for identified risks.
  Keywords: public housing redevelopment projects; risk identification and quantification; risk analysis; risk assessment model; risk matrix; AHP; analytic hierarchical process.
  DOI: 10.1504/IJRS.2024.10069100