I have been mainly focused on the intersection of optimization, artificial intelligence, and industry 4.0. My research projects have explored the application of advanced clustering techniques to improve maintenance strategies, examined the complex relationships between network topology and outage causes, and investigated into the role of IoT technologies in predictive maintenance, highlighting the potential for real-time monitoring and proactive maintenance. These articles collectively demonstrate my interest in developing data-driven approaches to optimize equipment performance, minimize downtime, and enhance overall system resilience. More details on these topics and their implications will be discussed further.
Since I am open to collaborate with others on interdisciplinary projects, you can reach me at me@arminmokhtari.com.
In my thesis, I had to overcome some unique challenges, including decision-making in the case of numerous alternatives. I combined different Machine Learning, Fuzzy Concept, and Decision-Making approaches. Although those concepts have been used in maintenance, they have not been combined as a novel approach to ease decision-making in maintenance. Thus, I decided to publish a paper and present my method that will be beneficial in the field of decision-making.
Data can enhance the equipment maintenance and asset management by providing predictive insights and minimizing downtime. Implementing data gathering and predictive maintenance systems is essential for improving reliability and cost efficiency. However, addressing challenges such as high implementation costs, data integration issues, and the need for skilled personnel is crucial for maximizing their benefits. Maintenance managers at a steel holding company in Iran as a case study aimed to implement predictive maintenance but faced high costs for full implementation. Selecting a subset of equipment parts posed a complex decision-making problem, as eligibility needed to be based on maintenance criteria rather than traditional factors like price and location. To address this, we proposed a framework using machine learning to cluster equipment parts based on maintenance-related criteria. While clustering simplifies decision-making, it introduces uncertainty. To mitigate this, we represent each cluster with a trapezoidal fuzzy number. The Silhouette method is employed to determine the optimal number of clusters, followed by the K-means++ method for clustering. Our approach successfully grouped 201 equipment parts into seven clusters based on criteria such as importance, maintenance period, and daily working hours. Fuzzy logic is used to interpret the clusters, reducing uncertainty and ensuring that no equipment is overlooked.
Moosavirad, S. H., Shahi Moridi, S., Mirhosseini, M., Nikpour, H., & Mokhtari, A. (2022). Prioritizing power outages causes in different scenarios of the global business network matrix. Decision Making: Applications in Management and Engineering, SE-Regular articles. https://doi.org/10.31181/dmame0301072022m
Related to Mr. Shahi Moridi's research project on Kerman power outages, I contributed to his article at my supervisor's lab. Thanks to my supervisor Dr. Mousavi, this project flourished my skills in Multi-Criteria Decision Making. More details are provided below.
Power outage is one of the significant problems for electricity distribution companies. Power outages cause customer dissatisfaction and reduce distribution companies' profits and revenues. Therefore, the electricity distribution companies are trying to moderate the leading causes of the outage. However, the dynamics of environmental conditions create uncertainties that require prioritizing the solutions of outages causes in different situations. Therefore, this study presents a scenario-based approach to prioritize power outage causes. Four case studies have been conducted in four cities of Kerman province in Iran. First, the prioritization criteria and causes of the outage were identified using literature and interviews with experts in this field. Then, the Global Business Network matrix was used to create four possible scenarios. Then, the Best-Worst method and TOPSIS method were applied to weight the prioritizing criteria and prioritize the causes of the outages in different scenarios. The results showed that working in the power network limit zone, as one of the causes of outage in Sirjan and Jiroft cities, has the most priority. Also, the collision of external objects, birds, and annoying trees should be considered by managers as the leading causes of outages in Bam and Kahnuj cities.
Presented at The 5th international conference on Internet of Things and its applications, this article was the first article prepared by me. I had to broaden my horizons in IoT since my MSc thesis topic was the Evaluation of IoT applications in maintenance using MCDM methods. Hence, I decided to review many international articles in IoT, which led to this article. The abstract of the article is presented underneath.
Predictive maintenance is the most common example given for IIoT offers on the market and is attracting industry funds and research attention. This article's primary purpose is a systematic review of the maintenance literature focusing on the Internet of Things to understand the field better and find research gaps. In this study, recent articles in the field of IoT applications in predictive maintenance were extracted, and by classifying them, three outstanding results were obtained, and the research gaps were recognized. First, utilizing the Internet of Things is significantly essential in predictive maintenance. Second, despite the use of IoT technologies and machine learning algorithms in recent research, it is necessary to evaluate the performance of these systems and identify the factors that affect their performance. Third, it is possible to identify the best technology and algorithm from the wide range of options available in future research.