Researchers at Purdue University have announced a system for efficient, low-cost monitoring for machine health, including overall quality, condition, and operation status.
The Purdue team’s innovation uses audio-based artificial-intelligence technology to monitor the overall conditions of machines in factories, hospitals, and other locations. The Purdue system uses a stethoscope-like system as a sensor and analyses the data with a neural network-based framework.
“Our solution is to use the concept of doctors listening to a body to assess the initial condition or experts listening to the machine sounds to know what is going on,” said Martin Jun, a Purdue innovator and associate professor of mechanical engineering. “We are using artificial intelligence to train a wide range of sounds from the machine and determine many things about the machine or process autonomously.”
Jun said this system can detect anomalies without being fed a training set and is easier and more cost-effective than accelerometers or acoustic-emission sensors.
The Purdue technology is designed to use internal sounds from a machine to determine the machine status, assess process conditions, diagnose machine conditions, and predict machine failures.“
Since only sound is used, it can be used for a number of different applications,” Jun said. “Having one low-cost sensor for many different purposes can address the current challenges in the area where most of the solutions are quite customized to specific problems.”