FUTURE MANUFACTURING
Anomaly detection reduces rejects and downtime
CLAUDIO GUSMINI
Anomaly detection solutions based on artificial intelligence identify deviations before they develop into production errors , thus reducing rejects and unplanned downtime from the outset . Automated anomaly detection is universally applicable , allowing a seamless integration into existing solutions .
Source : shutterstock
AI-based defect detection can detect complex anomalies .
An essential part of production optimization is the capability to prevent irregularities before they occur . Anomaly detection systems monitor processes and report anomalies preemptively and in real-time . This is accomplished by systems powered by artificial intelligence ( AI ). With the help of artificial intelligence , users can constantly monitor important parameters in production processes and identify sensor signals that deviate from typical values . These include measurements such as pressure , temperature , and engine speed . Thanks to AIassisted anomaly detection , qualified personnel can evaluate these abnormalities more quickly and decide whether they are negligible or ought to be corrected . Such inputs are usually made via a user interface that allows production workers to perform analyses easily .
Pre-trained learning model accelerates error detection
Industrial anomaly detection is usually built on an artificial intelligence learning model that can be optimally pre-trained for the respective production environment . Hereby defect detection can distinguish between tolerable and critical conditions . Through continuous assessment during production , these solutions are getting better and better at identifying faults early on in the production process and in notifying operators before they occur .
In addition , defect detection which is based on artificial intelligence also recognizes complex anomalies based on a combination of multiple deviations . This is a significant advantage for manufacturing companies , as defects and failures often occur due to a summation or concatenation of several individual faults . Moreover , through continuous assessment , self-learning anomaly detection identifies more and more such multifactorial deviations .
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