Electric motors are the driving force behind modern industry, and their breakdown can have significant impacts on the operations of an industry, causing downtime, increase in repair and replacement costs, pose safety risks, and have environmental consequences.

Predictive maintenance is a critical aspect of ensuring the longevity and reliability of motors in various industries, such as manufacturing, transportation, and energy. By detecting potential problems before they become serious, businesses can avoid costly downtime and repairs. Additionally, they can schedule maintenance at more convenient times, reducing the impact on production schedules. Traditionally, predictive maintenance requires invasive techniques that often disrupt the normal operation of the motor, causing unwanted downtime and costly repairs. However, a new research project proposes a non-invasive approach to motor predictive maintenance. Non-invasive methods reduce the need for disassembly, which can be time-consuming and costly.

The project aims to develop a proof of concept for a novel technology that can predict motor failures without the need for invasive procedures. The research team at T4B Services LTD is developing and testing a new non-invasive monitoring technique that uses advanced signal processing algorithms to detect abnormal signals that are early indicators of motor failure. Before this technology can be implemented on a larger scale, the research team needs to determine its feasibility and effectiveness. The proof of concept research will involve testing the technology on a small scale experimental setup and analysing the results to determine its potential for broader applications. The project will also explore potential challenges, such as the limitations of non-invasive monitoring techniques and the need for accurate data analysis.

The proposed research has the potential to revolutionize the field of motor predictive maintenance. By eliminating the need for invasive procedures, this technology could significantly reduce downtime and maintenance costs, making it a more cost-effective and efficient option for industries that rely on motors. Additionally, the non-invasive approach could provide more accurate and timely predictions, allowing for proactive maintenance instead of reactive repairs.

Project EFM-DETECT REP-2022-025 financed by the Malta Council for Science & Technology, for and on behalf of the Foundation for Science and Technology, through the FUSION: R&I Research Excellence Programme.

Article Author: T4B Services Ltd