The electrical generation industry is looking for techniques to precisely determine the proper maintenance policy and schedule of their assets. Reliability-centered maintenance (RCM) is a methodology for choosing what maintenance activities have to be performed to keep the asset working within its designed function. Current developments in RCM models are struggling to solve the drawbacks of traditional RCM with regards to optimization and strategy selection; for instance, traditional RCM handles each failure mode individually with a simple yes or no safety question in which question has the possibility of major error and missing the effect of a combinational failure mode. Hence, in the present study, a hybrid RCM model was proposed to fill these gaps and find the optimal maintenance policies and scheduling by a combination of hybrid linguistic-failure mode and effect analysis (HL-FMEA), the co-evolutionary multi-objective particle swarm optimization (CMPSO) algorithm, an analytic network process (ANP), and developed maintenance decision tree (DMDT). To demonstrate the effectiveness and efficiencies of the proposed RCM model, a case study on the maintenance of an electrical generator was conducted at a Yemeni oil and gas processing plant. The results confirm that, compared with previous studies, the proposed model gave the optimal maintenance policies and scheduling for the electrical generator in a well-structured plan, economically and effectively.

The oil and gas industry is looking for ways to accurately identify and prioritize the failure modes (FMs) of the equipment. Failure mode and effect analysis (FMEA) is the most important tool used in the maintenance approach for the prevention of malfunctioning of the equipment. Current developments in the FMEA technique are mainly focused on addressing the drawbacks of the conventional risk priority number calculations, but the group effects and interrelationships of FMs on other measurements are neglected. In the present study, a hybrid distribution risk assessment framework was proposed to fill these gaps based on the combination of modified linguistic FMEA (LFMEA), Analytic Network Process (ANP), and Decision Making Trial and Evaluation Laboratory (DEMATEL) techniques. The hybrid framework of FMEA was conducted in a hazardous environment at a power generation unit in an oil and gas plant located in Yemen. The results show that mechanical and gas leakage FM in electrical generators posed a greater risk, which critically affects other FMs within the plant. It was observed that the suggested framework produced a precise ranking of FMs, with a clear relationship among FMs. Also, the comparisons of the proposed framework with previous studies demonstrated the multidisciplinary applications of the present framework.

Hybrid reinforcement’s novel composite (Al-Fe₃O₄-SiC) via powder metallurgy method was successfully fabricated. In this study, the aim was to define the influence of SiC-Fe₃O₄ nanoparticles on microstructure, mechanical, tribology, and corrosion properties of the composite. Various researchers confirmed that aluminum matrix composite (AMC) is an excellent multifunctional lightweight material with remarkable properties. However, to improve the wear resistance in high-performance tribological application, hardening and developing corrosion resistance was needed; thus, an optimized hybrid reinforcement of particulates (SiC-Fe₃O₄) into an aluminum matrix was explored. Based on obtained results, the density and hardness were 2.69 g/cm³, 91 HV for Al-30Fe₃O₄-20SiC, after the sintering process. Coefficient of friction (COF) was decreased after adding Fe₃O₄ and SiC hybrid composite in tribology behaviors, and the lowest COF was 0.412 for Al-30Fe₃O₄-20SiC. The corrosion protection efficiency increased from 88.07%, 90.91%, and 99.83% for Al-30Fe₃O₄, Al-15Fe₃O₄-30SiC, and Al-30Fe₃O₄-20SiC samples, respectively. Hence, the addition of this reinforcement (Al-Fe₃O₄-SiC) to the composite shows a positive outcome toward corrosion resistance (lower corrosion rate), in order to increase the durability and life span of material during operation. The accomplished results indicated that, by increasing the weight percentage of SiC-Fe₃O₄, it had improved the mechanical properties, tribology, and corrosion resistance in aluminum matrix. After comparing all samples, we then selected Al-30Fe₃O₄-20SiC as an optimized composite.

Underwater cables or pipelines are commonly utilized elements in ocean research, marine engineering, power transmission, and communication-based activities. Their performance necessitates regularly conducted inspection for maintenance purposes. A vision system is commonly used by autonomous underwater vehicles (AUVs) to track and search for underwater cable. Its traditional methods are characteristically applicable in AUVs, wherein they are equipped with handcrafted features and shallow trainable architectures. However, such methods are subpar or even incapable of tracking underwater cable in fast-changing and complex underwater conditions. In contrast to this, the deep learning method is linked with the capacity to learn semantic, high-level, and deeper features, thus rendering it recommended for performing underwater cable tracking. In this study, several deep Convolutional Neural Network (CNN) models were proposed to classify underwater cable images obtained from a set of underwater images, whereby transfer learning and data augmentation were applied to enhance the classification accuracy. Following a comparison and discussion regarding the performance of these models, MobileNetV2 outperformed among other models and yielded lower computational time and the highest accuracy for classifying underwater cable images at 93.5%. Hence, the main contribution of this study is geared toward developing a deep learning method for underwater cable image classification.

The purpose of this study is to contribute to the existing knowledge about the value of post-implementation Enterprise Resource Planning (ERP) system at the firm level. A questionnaire-based survey was conducted to collect data from 217 Malaysian firms that successfully implemented ERP system. Data analysis was conducted with partial least squares-structural equation modeling and partial least squares multi-group analysis techniques. Higher ERP spending and greater ERP scope in the post-implementation stage were associated with higher performance gains. Among other limitations, relying on a small sample size and cross-sectional data of this study and lack of generalizability of findings tend to have certain limitations. An interesting direction for future research would be to extend this study by conducting a multi-level analysis to understand how ERP spending and scope would affect the micro-level performance. Non-financial performance gain is another valuable outcome of ERP implementation. The choice between in-house and off-the-shelf ERP systems will have dramatic impacts on the future profitability of firms. ERP risk management team and related practices during implementation phase result in a significantly higher financial gain in the post-implementation phase. This study assesses the business value of ERP at the post-implementation phase while accounting for key contextual and managerial issues, a topic that has received little attention to date.