Predictive Maintenance for Smart Manufacturing: An AI and IoT-Based Approach

Abstract

In this era of AI, Edge AI is getting popular day by day. The main advantage of Edge AI over conventional AI is that it requires very small processing power to perform its task. And this can be easily deployed on a microcontroller and mobile processor. On the other hand, permanent damage of an industrial machine increases maintenance cost and reduces productivity. This paper presents a new predictive maintenance system for smart manufacturing, using AI and IoT technologies. The developed device uses the inertia measurement unit (IMU) sensors and deep learning to detect machinery faults. We use six channel of accelerometer data to analysis the horizontal and vertical vibration of machine. The proposed system analyzes the data locally to reduce latency and transmits the results to the cloud server for further processing. So during the development, a special dataset was prepared with the possible fault conditions of machines to train the neural model. Deep learning-based algorithms identify any kind of anomaly in the machine and alert the user to an impending failure, thus improving timeliness in their intervention. This will reduce downtime and chances of major machine damage. This approach will be very useful in the proactive maintenance domain, with higher performance of machinery, to help the user schedule a timely maintenance visit before the permanent damage of the machine.