Critical Success Factors of the Reliability-Centred Maintenance Implementation in the Oil and Gas Industry

Reliability-Centred Maintenance (RCM) is a strategic process to improve the maintenance planning of companies which contributes to sustainable production. This method has been applied by numerous industries to achieve an efficient maintenance process, but many have not fully completed their goals. The reason for this failure is that RCM implementation is complex, and organisations need to have adequate preparations before they implement it. In the pre-implementation phase, it is necessary to know the number of Critical Success Factors (CSFs) as a critical measure for implementing the RCM method successfully. Therefore, it is important for practitioners to apply a symmetric mechanism involving fuzzy systems to achieve the desired RCM implementation. There are a limited number of studies that have observed these factors regarding the characteristics of oil and gas companies, especially in the pre-implementation phase. Addressing RCM pre-implementation issues is of high importance from the economic perspective of sustainability for oil and gas organisations. The objective of this study is to investigate significant items in RCM pre-implementation through a combination of quantitative and qualitative analyses. The Nominal Group Technique (NGT) method is applied by gaining the opinion of experts to determine the factors and prioritising them using mathematical modelling. A group of related experts from the oil and gas industry were initially interviewed and surveyed to determine the critical success factors. These identified factors were then analysed using quantitative analysis to identify the important degrees and scored using Fuzzy Analytic Network Process (FANP). Fifteen major factors affecting the criticality of successful RCM implementation have been identified and prioritised, based on their weights. The model proposed in this study could be used as a guideline for assessing CSFs in other countries. To apply the proposed model in different contexts, it needs to be modified according to the needs, policies, and perspectives of each country

An integrated data mining algorithms and meta-heuristic technique to predict the readmission risk of diabetic patients

Reducing hospital readmission rate is a significant challenge in the healthcare industry for managers and policymakers seeking to improve healthcare and lower costs. This study integrates data mining and meta-heuristic techniques to predict the early readmission probability of diabetic patients within 30 days of discharge. The research dataset was obtained from the UC Irvine Machine Learning Repository, including 101765 instances with 50 features representing patient and hospital outcomes, collected from 130 US hospitals. After data preprocessing, including cleansing, sampling, and normalization, a Chi-square analysis is done to confirm and rank the 20 identified factors affecting the readmission risk. As the algorithms' performance could vary based on the features’ characteristics, several classification algorithms, including a Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM), are employed. Moreover, the Genetic Algorithm (GA) is integrated into the SVM algorithm, called GA-SVM, for hyper-parameter tuning and increasing the prediction accuracy. The performance of the models was evaluated using accuracy, recall, precision, and f-measure metrics. The results indicate that the accuracy of RF, GA-SVM, SVM, and NN are calculated respectively as 74.04 %, 73.52 %, 72.40 %, and 70.44 %. Using GA to adjust c and gamma hyper-parameters led to a 1.12 % increase in SVM prediction accuracy. In response to increasing demand and considering poor hospital conditions, particularly during epidemics, these findings point out the potential benefits of a more tailored methodology in managing diabetic patients

Accuracy Improvement of Mood Disorders Prediction using a Combination of Data Mining and Meta-Heuristic Algorithms

Introduction: Since the delay or mistake in the diagnosis of mood disorders due to the similarity of their symptoms hinders effective treatment, this study aimed to accurately diagnose mood disorders including psychosis, autism, personality disorder, bipolar, depression, and schizophrenia, through modeling and analyzing patients' data. Method: Data collected in this applied developmental research included 996 records with 130 features obtained by interviewing and completing questionnaires in a mental hospital in the city of Sari, Iran in 2021. After preprocessing, the number of features was reduced to 91, and then through Principal Component Analysis (PCA) reduced to 35 factors.  Modeling was done in Python software with K-Nearest Neighbor (KNN), Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM) algorithms. The models were evaluated to select algorithms with higher accuracy. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were applied to determine the optimal parameters of the selected algorithms. Results: Among the machine learning algorithms, random forest with 91% accuracy and support vector machine with 90% accuracy showed better performance. The genetic algorithms did not make any notable increase in prediction accuracy. Whereas considering N=30, T=150, W=0.9, c1=2, and c2=2 in the particle swarm optimization algorithm increased the prediction accuracy up to 3.3 %. Conclusion: With less classification error compared to similar studies, the PSO-SVM model designed in this study can be used in patient data monitoring with acceptable accuracy and can be used in intelligent systems in psychiatric centers

Modeling and Predicting the Risk of Coronary Artery Disease Using Data Mining Algorithms

Introduction: Coronary artery disease (CAD) is one of the most common causes of death in adults while accurate and early diagnosis can lead to treatment and survival of patients to a great extent. Therefore, the objective of this study was to identify the effective factors leading to this disease and develop a data-driven model to assist physicians in predicting and diagnosing it. Method: This is an applied research, considering 2038 medical records, collected from Shahid Rajaei Heart Hospital in Tehran, during 5 years. A data preprocessing was carried out and random balanced sampling reduced the dataset into 1000 records, with 500 CAD and 500 Normal. Literature review, consultation with specialist physicians, and weighting using the Chi-square method led to the determination of important features. Support Vector Machine, Neural Network and Random Forest algorithms were applied in RapidMiner and Python. Results: Among the 35 identified variables, the most important features included VHD, Chest pain, LDL, RWMA, TG, Na, K, BP, and weight. The F-measure, precision, accuracy, and recall for random forest algorithm were calculated as 82.11%, 81.40%, 79.07%, and 85.40%, respectively, and the error rate was 18.6%. Conclusion: Random Forest predicted the risk of CAD with a reasonable precision. In comparison, due to the large number of input nodes, the error rate of the Neural Network model was relatively higher (23.6%)

System dynamics modeling and simulation of power plant maintenance process considering safety improvement

Multiple variables and subsystems increase the complexity of safety-based maintenance modeling in large systems, including power plants. This paper is modeling the power plant maintenance system by considering safety indicators. Then it simulates and analyzes the behavior of the designed model for a numerical sample. Regarding the literature review, a causal loop diagram of the maintenance system for power plant was designed and a safety subsystem was added to the model, based on the experts’ opinion, and finally the stock and flow diagram was formulated using AnyLogic software. The model was simulated in three scenarios including scenario one, training, scenario two, adding new equipment in the seventh year and scenario three, a combination of adding new equipment and training. The first simulation confirmed that increasing the training rate reduced the breakdown rate and equipment failure rate up to the sixth year, as well as reducing the accident rate and cost and increasing safety until the fifth year. Current costs increased. The second indicated that the addition of eight new equipment in the seventh year will improve the model up to 15 years later, and the amount of profit from the second year to the fifteenth year is more than scenario one and three. The combination of scenarios one and two, the optimal scenario, causes a greater amount of safety, a lower failure rate from the sixth year onward, a lower accident rate and cost, and a lower current cost from the fifth year onward