International Journal of Reliability and Safety (14 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.
  
  
• Coupling of risk factors in emergency processes for oil storage system fires based on the Bayesian network and N-K model  
  by Changfeng Yuan, Xing Sun, Lulu Niu, Yating Tong, Qing Zhang 
  Abstract: Frequent secondary accidents caused by emergency treatment of oil storage system fires (OSSF) show that risk factors and their interactions in emergency processes can lead to recurrence of accidents. To quantitatively evaluate coupling effects of risk factors on accident development, a novel risk coupling effect analysis (RCEA) method based on the Bayesian network (BN) and N-K model is proposed. Based on a statistical analysis of 252 typical accidents, risk coupling types caused by different factors are defined. Risk coupling value is calculated using the N-K model. A RCAE model based on the BN and N-K model is constructed. The model is used to analyze the coupling effect of risk factors, including: coupling degree variation characteristics, sensitivity and joint adjustment measures of risk factors. Some risk management suggestions are proposed. This study presents a new research idea and measurement method for evaluating risk coupling effect in emergency processes for OSSF.
  Keywords: oil storage system fire; risk factor; Bayesian network; N-K model; coupling effect.
  DOI: 10.1504/IJRS.2025.10072752
   
  
• Construction fatigue prediction model based on improved random forest algorithm  
  by Fuhai Wu 
  Abstract: Construction workers are prone to construction fatigue in high-intensity working environments, and failure to receive effective rest may result in casualties and property damage. The study uses deep learning algorithms to construct an intelligent fatigue prediction model aimed at accurately assessing the fatigue status of construction workers. The study takes smartphones to collect basic data and inputs it into an improved random forest algorithm for fatigue feature recognition. Then, an intelligent construction fatigue recognition model is established based on the improved random forest algorithm. The research model had an accuracy rate of 94.7% in recognizing different human movements, and an accuracy rate of 91% in predicting construction fatigue. The designed method accurately predicts the complete exhaustion, fatigue, concentration, and excitement states of workers, and its predictive ability is superior to other prediction models. The research model can effectively assist construction managers in accurately detecting workers' fatigue status and taking timely intervention measures to reduce safety accidents.
  Keywords: RF; PCA; PSO; fatigue; construction.
  DOI: 10.1504/IJRS.2025.10072988
   
  
• Power grid fault diagnosis technology based on improved deep Q-network model  
  by Qiang Wang, Hongyan Song 
  Abstract: As the increasing complexity of the power system, the difficulty of fault diagnosis in the power grid is also increasing. In response to the issue of continuously decreasing fault diagnosis accuracy, a power grid fault diagnosis model based on an improved deep Q-network model is raised. This model enhances its information integration capability by constructing a fault parameter recognition model and introducing alarm information for text processing. By identifying power grid fault parameters and processing alarm information, the efficiency and accuracy of fault diagnosis can be improved. The experimental results show that the model shows significant performance improvement in multiple state dimensions, and is significantly better than the traditional algorithm in single fault diagnosis and multi-fault diagnosis scenarios. The results show that the proposed method has significant advantages in the accuracy of fault prediction, processing efficiency and antinoise ability, which verifies the validity and practicability of applying this model in complex power systems. It also emphasises the importance of combining reinforcement learning with unstructured data to further promote the development of smart grid technology.
  Keywords: DQN; alarm information; power grid; reinforcement learning; fault diagnosis.
  DOI: 10.1504/IJRS.2025.10072989
   
  
• Patient safety culture: a narrative literature review by characteristics of the safety attitude questionnaire dimensions during the COVID-19  
  by Muhammad Alshyyab, Yousef Alasheh, Rania Albsoul 
  Abstract: Patient safety culture is defined as the perceptions of staff toward patient safety in their healthcare institutions. The COVID-19 pandemic has placed excessive pressure on frontline healthcare workers. The aim of this narrative review is to explore the status of patient safety culture in healthcare organizations based on the findings of the Safety Attitude Questionnaire (SAQ) during COVID-19. The review was carried out in four databases in 2022 using the search terms; safety culture, patient safety culture, safety climate, COVID-19, and safety attitude questionnaire. The search was limited to English-language articles published in peer-reviewed journals. The review identified that the Job satisfaction dimension of patient safety culture was strong among all of the included studies with a range from 75% to 88%. The findings of this review may inform decision-makers to identify areas of weaknesses and strengths for patient safety culture in healthcare organizations, particularly during pandemics.
  Keywords: patient safety; patient safety culture; patient safety climate; COVID-19; SAQ; safety attitudes questionnaire; literature review.
  DOI: 10.1504/IJRS.2025.10073534
   
  
• 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
   
  
• Research on PCB defect detection in intelligent ships based on hybrid CN-YoLov5  
  by Yuchen Cui 
  Abstract: With the progress of intelligent ship engineering, Printed Circuit Boards (PCBs) have become indispensable in ship control systems. However, during PCB manufacturing and subsequent operation, various defects frequently occur, undermining their reliability and threatening the stable operation of ship control systems. Accurately identifying microscopic targets, balancing detection efficiency and precision and detecting small-sized and complex defects are significant challenges. To address these issues, this paper presents Hybrid CN YoLov5, a novel PCB defect detection technique based on an improved YOLOv5s framework. It enhances the models target feature capturing ability by replacing the C3 module with the C3_SAC module and the Conv module with SAConv, and improves small object recognition accuracy through the integration of the NWD loss function. The incorporation of the CBAM attention mechanism strengthens the models feature extraction and overall recognition and classification performance. Experimental results show that compared with the original YOLOv5s, the mean Average Precision (mAP) of Hybrid CN YoLov5 reaches 95.80% (an improvement of 2.50%) and the precision reaches 100% (an increase of 6.79%), indicating its effectiveness for PCB defect detection and great potential in the quality inspection and fault diagnosis of ship-based PCB systems.
  Keywords: PCB defect detection; Yolov5; SAConv; global attention mechanism; NWD loss function.
  DOI: 10.1504/IJRS.2025.10073934
   
  
• Seismic safety evaluation of dams using a 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 utilised 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 the normal range, showcasing the effectiveness of the three indicators in assessing dam safety.
  Keywords: cloud model; dam seismic safety assessment; Koyna dams; a correlation coefficient method; risk assessment.
  DOI: 10.1504/IJRS.2025.10069925
   
  
• Estimation of conditional stress strength reliability using ranked set sampling: exponential case  
  by M. Architha, Parameshwar V. Pandit 
  Abstract: This study focuses on estimating conditional stress strength reliability of a system using ranked set sampling, when stress and strength variables follow independent exponential distributions. Two estimation methods are used, namely Maximum Likelihood Estimation (MLE) and bootstrap estimation. The asymptotic confidence interval is constructed based on a maximum likelihood estimator and the Boot-P confidence interval is constructed. A simulation study is carried out to determine the Mean Square Error (MSE) and length of the confidence interval. This study uses MSE and the length of the confidence interval to compare the estimator based on ranked set sampling to that based on simple random sampling in the context of exponential distribution.
  Keywords: exponential distribution; simple random sampling; ranked set sampling; stress-strength model; conditional stress-strength model; maximum likelihood estimator; bootstrap estimation; confidence interval.
  DOI: 10.1504/IJRS.2024.10072886
   
  
• Improving the accuracy of drowning detection based on improved YOLOv5  
  by Kaikai Wang, Ruiliang Yang, Libin Yang 
  Abstract: Drowning stands as a primary cause of unintentional deaths globally. This paper presents an improved YOLOv5 algorithm tailored for drowning detection, aiming to effectively mitigate drowning incidents. The improved YOLOv5 incorporates the Ghost-CBAM-C3 (GCC) module, which comprises Ghost-bottleneck modules and the CBAM module, and the learning rate decay of Cosine Annealing. To gauge the algorithm's efficacy, four self-made data sets were curated utilising a DJI mini3pro drone over both swimming pools and natural water bodies. Experimental findings underscore the heightened performance of the improved YOLOv5 over the original YOLOv5s. This enhancement manifests in a precision boost from 92.8 to 97.1%, and the values for mean average precision (mAP@0.5), weights and the Frames-Per-Second (FPS) are 93.2, 14.1 and 23.70, respectively, affirming its applicability in real-time scenarios. Furthermore, results indicate superior performance of the swimming pool data set compared to those from natural water bodies.
  Keywords: drowning detection; improved YOLOv5; self-made data sets; CBAM; safety; drone; labelImg software; k-means; SPPF; Ghost module.
  DOI: 10.1504/IJRS.2024.10072350
   
  
• 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) are 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
   
  
• Fatigue in the Indonesian palm oil industry: a critical review  
  by Taufiq Ihsan, Vioni Derosya 
  Abstract: The palm oil industry in Indonesia is a major contributor to global oil and fat production, employing millions of workers. Despite its vast workforce, there is a significant lack of information regarding worker fatigue. This review highlights critical fatigue-related issues in Indonesian palm oil plantations. We conducted a comprehensive literature review, gathering publications addressing fatigue risk factors, short-term and long-term health and safety consequences, and various fatigue mitigation strategies. Working in oil palm plantations exposes individuals to multiple fatigue-inducing factors. These factors not only lead to immediate effects like reduced cognitive function and accidents but also contribute to chronic illnesses through autonomic, immunological and metabolic pathways. Given the frequency and severity of worker fatigue, it is crucial to evaluate the effectiveness of existing legislation and industry practices while optimising working, living and sleeping conditions. Considering the current workplace conditions, a thorough assessment of potential preventive measures, including fatigue prediction tools and personalised fatigue management systems, is recommended.
  Keywords: mitigation strategies; palm oil; risk factors; worker fatigue.
  DOI: 10.1504/IJRS.2025.10072246
   
  
• Examining the antecedents of deep safety compliance and surface safety compliance: an expanding of technology acceptance model  
  by Ho Y. Hiep, Nguyen Ngoc Hien 
  Abstract: This study examines the antecedents of deep safety compliance and surface safety compliance among garment and footwear workers in Vietnam. The study expands on the technology acceptance model by incorporating social cognitive theory to investigate the influence of participative management and co-worker support on perceived usefulness, perceived ease of use and self-efficacy, ultimately impacting deep safety compliance and surface safety compliance. Data from a survey of 549 workers in five garment and footwear enterprises in Vietnam was analysed using partial least squares structural equation modelling. Findings revealed that both participative management and co-worker support significantly enhance perceived usefulness, perceived ease of use of safety procedures and worker self-efficacy. These perceptions, in turn, positively influence deep safety compliance and negatively impact surface safety compliance. This research adds a novel finding to the technology acceptance model by demonstrating the significant influence of participative management and co-worker support on safety compliance, expanding its applicability in the safety domain. Other literature contributions and practical implications for enhancing workplace safety are also discussed.
  Keywords: deep safety compliance; surface safety compliance; self-efficacy; participative management; co-worker support.
  DOI: 10.1504/IJRS.2025.10072657