Muscat: In hopes of promoting the culture of research and innovation in the Sultanate of Oman, the ‘Block Funding Programme’ of the Ministry of Higher Education, Research and Innovation has funded several research projects by different target groups. ‘Intelligent Diagnostic Maintenance System for Process Machines’ by principial investigator Dr. Moammer bin Ali Saud Al Tubi is one such project.
Dr. Moammer is the Assistant Professor and Deputy Head of Mechanical & Industrial Engineering Department, College of Engineering, National University of Science & Technology. His project is one of the leading research projects in the engineering fields.
Discussing his research project, Dr. Moammer Al Tubi stated that, “In today’s business environment, manufacturers are facing the challenge of growing production demands with existing machinery and equipment, while continuing to reduce operational and maintenance costs. The most insidious cost that drags down productivity improvements is unplanned equipment and manufacturing process downtime. Industrial maintenance of production and operational machinery is undergoing rapid and important changes. The need for effective maintenance monitoring and management technique, therefore, has been recognised as very important and critical, especially when the equipment increases in size and complexity”.
Dr. Moammer mentioned that the diagnostic monitoring data can be effectively used to predict and estimate the remaining useful life for enabling the plant manager to plan maintenance in advance. The integration of modern signal processing systems and automatic fault detection using machine learning is considered to be an emerging trend in machine prognostics.
For this research project, Dr. Moammer and his team aimed to provide a novel approach to fault diagnosis and failure estimation of defective machine components, and the study presents an investigation for a number of mechanical conditions (healthy, imbalance, misalignment, gear fault, bearing fault). The vibration conditions are simulated and based on real-time vibration data that are acquired from a Machinery Fault Simulator (MFS).
According to Dr. Moammer, further investigations with more advanced methods like optimisation methods and feature selection methods are planned for this study in order to enhance and approach the best machinery diagnosis procedures.
This research project was published in International Journal of Engineering Trends and Technology. The research team included Dr. Moammer Al Tubi, Professor K.P. Ramachandran, Saleh Salim Al Araimi, Rene Pacturan, Dr. Amuthakannan, and Dr. Geetha Achuthan.