International Journal of Reliability and Safety (8 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.
  
  
• Employing vision transformers for crack detection and health monitoring of concrete structures  
  by Hessam Kaveh, Reda Alhajj 
  Abstract: The safety and security of concrete structures is essential and should be regularly monitored by timely identifying deficiencies to avoid collapses which may lead to causalities and economic losses. The advancement in technology has enabled more automated flexible and smooth monitoring of concrete structures, including buildings, bridges, etc. Specialized cameras could captures images which could be analyzed for effective knowledge discovery. The work described in this paper addresses this serious issue by presenting a novel application of Vision Transformers (ViTs), a deep learning technique originally developed for image classification, to the task of crack detection in concrete structures. The main target is to improve crack and deficiency identification by utilizing a thoroughly trained ViTs model using public and proprietary datasets. Cracks and damages in concrete structures are to be identified and classified with high accuracy. This has been illustrated by conducting extensive experiments which reported promising evaluation metrics values.
  Keywords: crack detection; vision transformers; deep learning; structural health monitoring; civil infrastructure; machine learning.
  DOI: 10.1504/IJRS.2025.10073548
   
  
• Study on erosion performance evaluation and law of high-pressure liquid-solid two-phase flow throttle valve  
  by Qiandeng Li, Yuqiang Xu, Fuxiang Li, Zhichuan Guan, Chaobin Fan 
  Abstract: Throttling valves are critical for effective bottomhole pressure control in high-pressure gas wells. This study evaluates the erosion resistance of three common valve types cylindrical, wedge, and orifice plate through numerical simulations using the discrete phase model and field experiments. Results reveal that cylindrical valves offer superior erosion resistance. Key factors such as flow velocity, fluid viscosity, particle diameter, and sand content significantly influence erosion rates. A PSO-SVM-based prediction model was developed, achieving over 90% accuracy. The findings suggest cylindrical valves are optimal as primary throttling devices, with wedge valves as auxiliary options, while orifice plate valves are less suitable for 105 MPa choke manifolds in high-pressure wells. Increased flow velocity, sand content, and particle size were identified as the main contributors to erosion rate escalation. These insights support valve selection strategies that enhance well control reliability and erosion resistance in harsh operating environments.
  Keywords: throttle valve; numerical simulation; prediction of erosion rate; erosion rate; erosion resistance; field experiment.
  DOI: 10.1504/IJRS.2024.10073636
   
  
• A comparative analysis of neural network and ensemble learning models for automated root cause classification in fatal mine accident reports  
  by Kumar Arra, Suprakash Gupta 
  Abstract: Mining is one of the highest-risk industries due to the hazardous nature of coal extraction, a primary power generation source. This study uses 1305 mining accident reports, recorded from 1995 to 2015 in India, to determine the root causes of accidents and devise effective safety measures. The methodology follows a structured approach combining expert domain knowledge with advanced natural language processing techniques. We employed a novel hybrid vectorisation strategy combining N-grams, TF-IDF, and word embeddings to analyse accident reports. We stratified a 10-fold cross-validation used to address the imbalanced data distribution. This study compared three machine learning models: CatBoost, XGBoost and neural networks. CatBoost demonstrated superior performance with a 0.91 F1-score and 0.98 PR-AUC, outperforming both XGBoost (0.88, 0.96) and neural networks (0.84, 0.93). The developed system predicts accident root causes with 91% accuracy, providing a robust framework for improved decision-making and enhanced industry safety standards.
  Keywords: automated classification; accident prevention; ensemble learning; machine learning; mine accidents; neural networks; root cause analysis; text mining.
  DOI: 10.1504/IJRS.2025.10074662
   
  
• Automatic track monitoring and fault detection using vibration sensor data in railway transport system  
  by Si Chen 
  Abstract: The development of a real-time automatic track monitoring system can detect the condition of the track without interfering normal traffic. The objective of this study is to classify railway tracks as healthy or defective with the help of vibration readings and environmental factors such as temperature and humidity. The proposed fault detection system investigates the efficiency of several ML algorithms, which are combined with the Subtraction-Average-Based Optimizer for hyperparameter tuning to improve their performance in classifying railway tracks. According to results, the SABO-RF model provides a strong and reliable approach to real-time fault detection with 99.86% accuracy and 99.84% precision, which contributes toward preventing accidents and minimizing operational disruption in railway systems. The sensitivity of the SABO-RF model outlines the influence of vibration in dimension y with a strong positive contribution of +4.24 and a strong negative contribution of -3.87 in classifying railway tracks by model.
  Keywords: railway transportation system; railway track; fault detection; vibration sensors data; data-driven approach; hyperparameter tuning.
  DOI: 10.1504/IJRS.2025.10075056
   
  
• A systematic review for lifejackets  
  by Ruiliang Yang, Zhiwei Zhang, Libin Yang 
  Abstract: Lifejackets are effective devices for preventing drowning deaths; however, their wearing rate remains alarmingly low. This systematic review conducts a thorough analysis of lifejackets, providing essential foundational information regarding the factors contributing to their low usage and effective strategies for their research and development. Several major English databases were meticulously examined to identify studies related to lifejackets. A total of 96 studies were included in the analytical framework. The findings indicate that the wearing rate of lifejackets is indeed very low, but it can be significantly improved through legislative measures or educational initiatives. Currently, many lifejackets available on the market do not meet relevant safety standards, posing significant challenges to effective drowning prevention. To address the issue of low wearing rates and improve search and rescue efficiency, this paper presents three recommendations.
  Keywords: lifejacket; drowning; wearing rate; positioning device.
  DOI: 10.1504/IJRS.2025.10075113
   
  
• Rolling bearing fault vibration characteristic analysis software system accounting waviness and local defects  
  by Wujiu Pan, Yuanbin Chen, Conghui Han, Junyi Wang, Jianwen Bao 
  Abstract: As the core component of rotating machinery, the health status of rolling bearing directly affects the reliability and safety of equipment operation. Aiming at the vibration characteristics caused by bearing waviness and local defects, a dynamic model considering the waviness parameters of inner and outer races is established, and a time-varying displacement excitation local defect model based on piecewise function is proposed. The displacement, velocity, acceleration and frequency domain response characteristics of the bearing are accurately analyzed through nonlinear dynamic equations. On this basis, a software system integrating waviness analysis, single fault and composite fault simulation is developed based on MATLAB App Designer. The results show that the proposed model can effectively identify the waviness characteristic frequency (such as ball passing frequency and its harmonics) and local defect impact signals, and provide theoretical support and practical tools for bearing fault diagnosis and health monitoring in complex industrial scenes.
  Keywords: Rolling bearing;Waviness;Local defects;Composite fault diagnosis Software system development.
  
  
• Importance of component states under reliability correlation of multi-state systems based on multi-valued structure function  
  by Emad K. Mutar, Zahir Abdul Haddi Hassan 
  Abstract: The structure function is crucial for multi-state systems (MSSs), but its complexity rises as the number of components and states increases, making management difficult. It is important to address these challenges and formulate techniques to ensure the reliability of MSSs. This paper presents a new technique for assessing MSS reliability using multi-valued structure function and multi-valued decision diagram (MDD). It assumes component states follow a binomial distribution and explores the effects of the correlation coefficient. The paper evaluates Birnbaum Importance (BI), Risk Achievement Worth (RAW), and Risk Reduction Worth (RRW) for each component in the MSS across various performance levels. It simplifies computations by using subsystems, the chain rule, and conditional probability. The methods are demonstrated with numerical examples and an application related to a spacecraft system, showing how these measures improve reliability and confirm the effectiveness of the techniques in assessing reliability and component importance.
  Keywords: structure function; multi-state system; system performance; multi-valued decision diagram; component importance.
  DOI: 10.1504/IJRS.2025.10075196