An Intelligent Condition-based Key Machinery Assets Maintenance Management Platform for the Textile Industry

In this project, we propose to design and develop an intelligent condition-based key machinery assets maintenance management platform for the textile industry using artificial intelligence and smart sensor techniques. This is done to obtain data, such as real-time asset condition status from different types of sensor devices (e.g., infrared temperature, accelerometer, and optical sensors). Based on the captured and processed data, we monitor the current and/or predict future states of an asset and provide recommendations for maintenance actions and operational decisions.

Effective machinery assets maintenance management is very important for textile enterprises as it improves product quality and production output, optimizes manufacturing scheduling, and reduces the probability that a sudden asset break-down may occur. The project will directly benefit the local textile industry, especially companies that have their own assets in the manufacturing plants. At present, there is a great need for continuous tracking of the asset condition status. The proposed technology, when fully implemented, will enable companies to take remedial measures for optimizing manufacturing and maintenance schedules, detect anomalies, report potential problems, recommend maintenance and operational procedures, provide accurate assets condition status reports, and, ultimately, improve operation efficiency.