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.