In this paper PREDICTIVE-based MAINTENANCE and condition-based MAINTENANCE have been studied for water pump stations. Measurements of vibrations in suitable places, analysis of the vibration trend and spectrums have been dealt with in details. Using the trend of vibrations, existence of a fault in machinery can be detected. The vibration spectrums can also be used in fault diagnosis. Furthermore, in this paper the practical results of using a condition-based monitoring system in one of Tehran's water pump stations has been presented. Using this method, the existing faults have been detected before the critical situations and the proper repair has been done on the machinery. Using vibration signals for condition monitoring and fault diagnosis started for the first time at Tehran Water and Wasteweter Company. Promising results of this method can be used for other water and wastewater companies.

An appropriate MAINTENANCE policy says that repairs should be done when needed. Although preventive repairs can reduce sudden and unexpected repairs, they still reduce availability and increase repair costs. Companies need to develop online and PREDICTIVE MAINTENANCE strategies that can anticipate that any failure could occur at any time, and recognize this need from the signs and symptoms of equipment. This is called PREDICTIVE MAINTENANCE or condition-based MAINTENANCE. in this paper we have tried to design a decision support model for PREDICTIVE MAINTENANCE based on conditions based on data mining techniques. This project has been carried out in one of the oil and gas exploitation companies in the south of the country and the selected equipment for this project is gas turbines, which is one of the most basic and critical equipments in oil processing factories. In this project, MPL neural network data mining technique has been used to predict the occurrence of failure in the equipment. Finally, suggestions such as the development of this model for other equipment, controlling the duration of viewing the equipment status and determining the optimal MAINTENANCE time for the future are presented.

Today, MAINTENANCE and repair have become very important in the manufacturing industry. An ecient solution to prevent downtime is to predict equipment failure. Therefore, accurate and correct prediction of breakdown events in the eld of PREDICTIVE MAINTENANCE can be very useful. In general, each prediction will be accompanied by a certain amount of error, which in various ways tries to control this error or limit it to a reasonable amount. In this thesis, a framework has been proposed that species when the system under review will need MAINTENANCE and repairs to prevent downtime as much as possible. Therefore, the main purpose of this thesis is to design and implement an ecient combination structure to accurately predict failure events using both standard statistical standard models and machine learning in PREDICTIVE MAINTENANCE. The literature review results indicate that the use of these methods in recent years has led to extensive advances in the eld of providing accurate forecasts and subsequently improved the level of decisions made by managers and decisionmakers. The proposed model is used to predict failure events in benchmark data related to the truck air pressure system. Finally, the performance of the proposed model is compared with other data-driven techniques individually and in combination, which includes logit models, support vector machines, and multilayer perceptron neural networks. According to the numerical values obtained from the nal analysis, the results indicate that the backup vector machine model has higher prediction accuracy than other single models, and also the results indicate the eciency and eectiveness of the proposed parallel combination structure compared to the use of models individually and in series combination in modeling and forecasting issues. The parallel hybrid model improved the accuracy of predictions by an average of 11% in test data and 7% in training data. Therefore, due to the greater accuracy in combining classical statistical models and machine learning in parallel, the use of this combined method to improve the accuracy of predictions in the eld of PREDICTIVE MAINTENANCE is recommended for future studies.

Purpose: This paper proposes several innovative approaches to model evaluation after obtaining the reinforcement learning model of scheduling with PREDICTIVE MAINTENANCE. To train this model, its reward and loss function must be determined according to the conditions of the workshop environment. Methodology: This learning model is examined in different modes of work entry into the workshop and the results obtained from other scheduling methods show better outputs. Findings: The PREDICTIVE MAINTENANCE model is evaluated by four learning methods and the quality of these models is examined. By selecting and adding the best machine failure model to the scheduling reinforcement learning model, the instant tasks entered into the workshop are assigned to the machines. By comparing the proposed method with the previous ones, the best performance is found and shown. Originality/Value: One of the innovations of this paper is to provide a definition of the reward function for the issue.

In this article, a robust model PREDICTIVE controller is designed for maintaining the satellite's low earth orbit in the presence of perturbations and uncertainties. The designed control system makes it possible for the satellite to automatically keep the orbital parameters within the allowed range without intervention of the ground station. For this purpose, Hall effect thrusters, which are among the electric thrusters with small size, have been used, and the output of the controller is implemented by continuously commanding these thrusters instead of the traditional impulsive thrust generation algorithms. Based on this, it is possible to modify all orbital parameters simultaneously. In the presented solution, the gain of the controller is calculated online by solving an optimization problem whose objective is to minimize the orbit MAINTENANCE error and also the fuel consumption. Also, the optimization problem based on linear matrix inequalities has been developed to ensure the stability of the closed loop system and to be robust against orbital perturbations and unknown parametric uncertainties. Finally, numerical simulations show the effectiveness of the proposed control scheme and the high efficiency of the closed loop system despite uncertainties and perturbations.

The objective of this study is to explore the influence of PREDICTIVE MAINTENANCE technologies on operational efficiency in manufacturing startups, focusing on implementation processes, operational impacts, and the challenges encountered. This qualitative study employed semi-structured interviews to gather data from key stakeholders in manufacturing startups, including founders, operations managers, and MAINTENANCE engineers. A total of 22 participants were interviewed, with the sample size determined by theoretical saturation. The interviews were transcribed verbatim and analyzed using NVivo software. Thematic analysis was conducted to identify and categorize key themes and subthemes related to the implementation and impact of PREDICTIVE MAINTENANCE technologies. The analysis revealed three main themes: Implementation Process, Operational Impact, and Challenges and Barriers. Within these themes, several categories and concepts emerged. The Implementation Process theme highlighted the importance of planning, technology selection, system integration, employee involvement, pilot testing, change management, and post-implementation review. The Operational Impact theme identified efficiency gains, PREDICTIVE analytics, MAINTENANCE scheduling, resource optimization, and quality improvement as significant outcomes. The Challenges and Barriers theme underscored technological challenges, financial constraints, organizational resistance, skill gaps, data management issues, and the necessity of vendor support. The findings indicate that PREDICTIVE MAINTENANCE technologies significantly enhance operational efficiency in manufacturing startups by reducing downtime, increasing productivity, and optimizing resource utilization.

Background and Objectives: In obese people, 5-10% reduction in initial weight can decrease the obesity induced risks. Therefore, it is important to identify the PREDICTIVE factors effective in weight loss MAINTENANCE. The present research was conducted to determine the role of important PREDICTIVE factors in weight loss MAINTENANCE. Methods: For this purpose, a total of 200 women with obesity, were selected using convenience sampling. After explaining the study aims and obtaining the permission to enter the study, they were asked to complete the research tools, including Multidimensional Self-Body Relationship Questionnaire, Three-Factor Eating Behavior Questionnaire, Treatment Outcome Expectations, Weight Lifestyle Self-Efficacy Questionnaire, Caring Environment Questionnaire, Rosenberg Self-Esteem Scale, Depression, Anxiety, and Stress Scale (DASS-21), Multidimensional Perceived Social Support Scale (MSPSS), and Dichotomous Thinking in Eating Disorders Scale (DTEDS). Data were analyzed using SPSS software and Pearson correlation and step by step regression statistical methods. Significance level was considered to be p < 0. 05. Results: The results of the research showed that variables of weight lifestyle self-efficacy, body image, self-esteem, dichotomous thinking, and perceived social support predict 39. 8% of variance of successful long-term weight loss MAINTENANCE in the participants. Conclusion: The psychological and behavioral factors play an important role in successful long-term weight loss MAINTENANCE in obese subjects. Consideration of these factors seems necessary in weight loss programs.

Buildings consume approximately one-third of the world's energy, with the commercial and housing sectors' Heating, Ventilation, and Air Conditioning (HVAC) systems being the largest contributors to energy. Energy wastage is significant as a result of system faults, which indicates the importance of efficient control of energy in HVAC in saving energy as well as providing comfort to the occupants. Techniques in Artificial Intelligence (AI), such as Machine Learning (ML) and Deep Learning (DL), are now used to optimize HVAC energy efficiency as well as facilitate PREDICTIVE MAINTENANCE, which reduces downtime as well as costs. Past research has underestimated qualitative faults analysis in HVAC systems or suffered from inaccurate identification using AI. This paper proposes an innovative AI-based framework to manage energy in buildings. The framework uses Fault Tree Analysis (FTA) initially to perform qualitative analysis regarding the effect of HVAC system faults in energy consumption. Next, it applies AI models, namely Long Short-Term Memory (LSTM) networks as well as Gated Recurrent Unit (GRU) networks, trained using experimental data from real-building environments. The models are designed to detect faults accurately as well as in time. The main goal is to save energy from wastage as well as ensure occupant comfort through timely MAINTENANCE as well as replacement of faulty equipment. Most notably, the GRU approach showed higher accuracy in the identification of faults compared to LSTM. The framework's accurate identification of the occurrence as well as the nature of the faults is an improvement in the efficiency of the building.

Background and Purpose: Vehicles are considered as one of the most demanded equipment in a defense organization, if they are not serviced in a timely and efficient manner, they are considered as a threat to that organization. On the other hand, due to the significant increase in data related to MAINTENANCE of these devices in the organization under study, in this study, data mining technique has been used to increase the data conversion into information rate and knowledge discovery in order to prevent cease of missions. The purpose of this paper is to present the architecture of classifying the data generated from the reference and breakdowns of passenger cars of the studied organization and to predict the distances between their breakdowns through data mining method in order to plan their MAINTENANCE and repairs. Methods: Based on this, the required data for a sample including 150 light vehicles were extracted. In this research, year of construction of the car, distance traveled and cause of failure as input variables and the intervals between failures as output variables were defined. Findings: Based on data mining principles and using SPSSModeler software, vehicles were grouped based on the distances between breakdowns (km) with the C&R tree algorithm. Conclusion: according to the outputs analysis, the MAINTENANCE and repair group of the studied organization should adjust the service and inspection plan of the vehicles based on the grouping done in the research and by considering the suggestions provided.