International Journal of Reliability and Safety

Li, Qiandeng; Xu, Yuqiang; Li, Fuxiang; Guan, Zhichuan; Fan, Chaobin

doi: 10.1504/ijrs.2026.153138pmid: N/A

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.

Yang, Ruiliang; Zhang, Zhiwei; Yang, Libin

doi: 10.1504/ijrs.2026.153140pmid: N/A

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. This paper encompasses studies on lifejackets that address drowning prevention published after 2009. In contrast, literature pertaining to lifejackets that is not focused on drowning prevention, as well as non-research and non-review publications, is excluded from the scope of this study. A total of 93 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.

Sihmar, Parveen; Modgil, Vikas

doi: 10.1504/ijrs.2026.153143pmid: N/A

The sugar industry is increasingly challenged to increase efficiency, reduce waste and ensure continuous production despite complex system dynamics and frequent component failures. This research objective is to identify optimal operating parameters and maintenance strategies to maximise system availability by integrating Stochastic Petri Nets (SPN) with Particle Swarm Optimisation (PSO). This research presents a comprehensive examination of a sugar industry feeding system, employing SPN to model its components and evaluate their performance. Maintenance tasks are prioritised according to the possibility and severity of potential problems by assessing the influence of varying failure and repair rates. The key contribution of this study is the identification of the heat generating system as the most critical subsystem, where optimisation efforts increased availability to 85.57%. The analysis offers practical implications for improving reliability, reducing downtime and lowering operational costs for sugar manufacturers worldwide. Limitations include assumptions of constant failure and repair rates and restricted modelling of human operational errors. The results from this analysis offer practical guidelines for enhancing production system performance and reducing operational costs, thereby benefiting sugar manufacturers globally.

Kaveh, Hessam; Alhajj, Reda

doi: 10.1504/ijrs.2026.153137pmid: N/A

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. Specialised cameras capture images which can be analysed 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 utilising a thoroughly trained ViTs model using public and proprietary data sets. Cracks and damages in concrete structures are identified and classified with high accuracy. This has been illustrated by conducting extensive experiments which reported promising evaluation metrics values.

Uddin, Md. Mahfuz; Islam, Samiul; Salam, Md. A.; Shraboni, Tamanna Rahman; Ahmed, Tofayel; Karim, Md. Rezaul

doi: 10.1504/ijrs.2026.153142pmid: N/A

Model-based clustering and K-means clustering are widely used in various data clustering fields. This paper compares the performance of model-based clustering and K-means clustering in reliability analysis using automobile component warranty claims data. The Weibull and lognormal mixture models are applied to model the usage rate variable. The Expectation-Maximisation (EM) algorithm is employed to obtain the maximum likelihood estimates of the parameters for the mixture models. It also obtains the nonparametric estimates of the reliability function of the usage rate. The 5-fold Weibull mixture model achieves 79.2% accuracy, outperforming K-means clustering (67.6% accuracy) and the 5-fold lognormal mixture model (54.7% accuracy). Simulation studies confirm the applicability of the method and the superiority of model-based clustering, particularly the Weibull mixture model. The findings will have managerial implications for accurately assessing and predicting the component's reliability, and offering a flexible two-dimensional warranty policy, which can enhance customer satisfaction and the company's reputation.

Gopal, Nand; Panchal, Dilbagh

doi: 10.1504/ijrs.2026.153141pmid: N/A

The proposed integrated framework aims to detect the failure causes based on criticality of Milk Processing Unit (MPU). Fuzzy Multi Criteria Decision Making (MCDM) approach has been integrated with Failure Mode Effect Analysis (FMEA) approach. The FMEA approach was utilised to pinpoint potential failure causes within the MPU. Moreover, fuzzy ratings were collected pertaining to three distinct risk factors. The Fuzzy Combinative Distance-based Assessment (FCODAS) approach was integrated into a fuzzy FMEA to prioritise failure causes. The rankings derived from this analysis were compared with the results obtained from the decision-making approaches Fuzzy-Complex Proportional Assessment (FCOPRAS) and Fuzzy-Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS). To check the consistency of the result, sensitivity analysis was performed to assess the robustness of the integrated framework. The plant's maintenance manager was provided the analysis-based ranking results, and he stated his consensus with the attained outcomes.