Researchers have developed a system for efficient, low-cost monitoring for machine health including overall quality, condition, and operation status.
The innovation uses audio-based artificial intelligence technology to monitor the overall conditions of machines in factories, hospitals, and other locations. The system uses a stethoscope-like system as a sensor and analyzes the data with a neural network-based framework.
The solution uses the concept of doctors listening to a body to assess the initial condition or experts listening to the machine sounds to know what is happening. Artificial intelligence is used to train a wide range of sounds from the machine and determine many things about the machine or process autonomously.
The system can detect anomalies without being fed a training set and is easier and more cost-effective than accelerometers or acoustic emission sensors. The technology is designed to use internal sounds from a machine to determine the machine status, assess process conditions, diagnose machine condition, and predict machine failures.
Since only sound is used, it can be used for a number of different applications. 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.
For more information, contact Chris Adam at