Trend 2: AI is enhancing mechanical and electrical equipment monitoring
In many industrial settings, a common method of identifying mechanical faults is to listen to the sounds they make. An experienced electrical inspector, for example, can ‘hear’ abnormal sounds coming from a transformer. Just by listening to the sound, they can determine whether it is running with an overload or experiencing a poor internal contact.
There are obvious drawbacks to human ear detection, however. To start with, it is clearly impossible for humans to focus on fault detection 24/7. Moreover, the presence – or absence – of experience can greatly affect the success of fault detection. Additionally, the human ear struggles to capture short and abrupt sounds for detailed analysis; it requires listening to sounds for a longer period to pinpoint a problem.
AI-equipped algorithm systems, on the other hand, can easily overcome all these challenges. AI-driven audio analysis enables real-time sounds to be monitored which, in turn, can be used to determine equipment status and identify abnormal sounds. This makes it possible to create automated quality inspection solutions. AI audio detection can also identify potential risks in the electricity sector, such as anomalies in substations and power grid monitoring.