Figure 1. The OtoSense system.
Anyone familiar with the necessity of maintaining a mechanical machine knows how important the sounds and vibrations it makes are. Proper machine health monitoring through sound and vibrations can cut maintenance costs in half and double the lifetime. Implementing live acoustic data and analysis is another important approach for condition-based monitoring (CbM) systems.
We can learn what the normal sound of a machine is. When the sound changes, we identify it as abnormal. Then we may learn what the problem is so that we can associate that sound with a specific issue. Identifying anomalies takes a few minutes of training, but connecting sounds, vibrations, and their causes to perform diagnostics can take a lifetime. There are experienced technicians and engineers with this knowledge, but they are a scarce resource. Instinctively recognizing a problem from sound alone can be difficult, even with recordings, descriptive frameworks, or in-person training with experts.
Because of this, our team at Analog Devices has spent the last 20 years on understanding how humans make sense of sounds and vibrations. Our objective was to build a system able to learn sounds and vibrations from a machine and decipher their meaning to detect abnormal behavior and to perform diagnostics. This article details the architecture of OtoSense, a machine health monitoring system that enables what we call computer hearing, which allows a computer to make sense of the leading indicators of a machine’s behavior: sound and vibration.
This system applies to any machine and works in real time with no network connection needed. It has been adapted for industrial applications and it enables a scalable, efficient machine health monitoring system.
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