AI to Revolutionise the Analysis of Acoustic Data
Sounds Good
AI to Revolutionise the Analysis of Acoustic Data
By Dr Phong Nguyen , Chief AI Officer at FPT Software
We have all seen the antiques expert flick a fingernail against a piece of porcelain or glass to see if it ‘ rings true ’ or returns a flat note indicating a hairline crack somewhere in the item being examined . Sound is an important tool in detecting faults or problems from machines to engines , weld integrity to ball-bearing races and a wide range of products . Whereas visual inspection has substantially been taken over by sensors , sound has remained the province of the human ear in most cases . The problem has been that the frequency of equipment failure in real environments is low and the number of ways in which equipment can fail is large ; an expert can hear the difference , but the ear can be distracted by other noises or overwhelmed by a noisy environment . An obvious answer is to use an electronic listening device to pick out anomalies but if anomalies are few and far between the training of such a device would be very slow , very expensive and specific to individual applications .
In Manufacturing , valuable assets are often expensive , difficult to replace and costly in terms of associated lost production . It is crucial for business operation to ensure the maximum lifespan and optimal condition of machinery , equipment , and production lines . Previously , the interpretation of sounds in industrial settings was the confine of a few experienced maintenance and operations personnel . The advent of AI has revolutionised the analysis of acoustic data , providing a new source of information that can be utilized to solve real-world problems .
The challenge to create a solution that would identify anomalies indicating faults in the sound produced by a wide range of
FPT Software ’ s acoustic anomaly detection product , SoundAI , is a platform that can easily be deployed to any edge devices with low-code requirements from the customer .
machines , equipment and products was put to the artificial intelligence ( AI ) team at FPT Software . Conventional approaches to the problems had failed to produce acceptably high levels of detection . So , they looked for a new way of solving the problem . The solution they developed uses a mixture of different sound representations , a Mixed Feature , and a new auto-encoder architecture , a deeplearning method called a Fully Connected U-net ( FCUN ) to increase the resolution of the output . AI can automatically analyse , quantify , tag and identify the source of sounds at a scale and level of precision that was previously unimaginable .
AI , coupled to machine learning ( ML ), is then used to learn the representation of normal data and detects abnormal data when discrepancies are detected between normal data and new data . Trained with normal data it identifies potential issues , before they result in downtime or production delays , with an accuracy of 95 %. A single machine-learning model for different types of sound helps to simplify installation and increase predictive latency in real time . Deployment in many applications , to start achieving working results , is often as quick as two weeks . The result is a machine learning operations platform ( MLOps ) that uses acoustic anomaly detection to automate product and machine inspection and health monitoring as well as providing predictive maintenance data for equipment .
Sound can play a vital role in the predictive maintenance of manufacturing equipment , as unusual or unexpected sounds from the equipment can be an early predictable indicator of potential failures , which may not be detected by visual inspection due to the complexity of an assembled machine . Sound sensors can be deployed without physical contact , which makes them suitable for applications where contact-based sensors may cause damage or be impractical .
FPT Software ’ s acoustic anomaly detection product , SoundAI , is a platform that can easily be deployed to any edge devices with low-code requirements from the customer . It supports machine health monitoring , even with no abnormal data and it improves accuracy continuously with a detection rate of above 95 %.
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