The concept of ‘Predictive Maintenance’ has become widespread in the manufacturing industry as manufacturers begin to digitalize their production lines for increased productivity and competitiveness. By monitoring the function and health of machines based on data received through device logs and sensors, predictive maintenance analytics can forecast machine failures and eventually trigger some alarms so technicians can take counter-measures in time, such as servicing or replacing the affected machine.
In order for any machine abnormality to be detected throughout a production line, diverse amounts of data gathered from multiple sensors are first transmitted over a wireless network to a central computer server for processing and analysis. But as the number of sensors increases in the future, the wireless communication technology for Wireless Sensor Networks (WSNs) may be facing bandwidth constraints and be unable to transmit the increasingly large sensor data to the computer server.
Powered by the Internet of Things (IoT), AI is becoming a key enabler for predictive maintenance and performance improvement, because of its cognitive abilities such as learning, reasoning and problem-solving. Rohm and IME aim to develop an AI chip that is capable of processing and analyzing data at the source, drastically reduces the amount of sensor data to be transmitted wirelessly to a central computer server for it to be further processed and analyzed.