Artificial Intelligence for Predictive Maintenance in Industry 4.0
Artificial Intelligence for Predictive Maintenance in Industry 4.0
Manufacturing processes are sets of structured operations to transform raw material or semi-finished product parts into further completed products. To ensure high productivity, availability, and efficiency of manufacturing processes, the detection of harmful tendencies and conditions of production lines is a crucial issue for manufacturers. In general, anomaly detection on production lines is performed by analysing data collected by sensors, which are located on machine components and also in production environments. The collected data records real-time situations and reflects the correctness of mechanical system conditions.
Within manufacturing processes, the detection of anomalies such as mechanical faults and failures enables the launching of predictive maintenance tasks, which aim to predict future faults, errors, and failures and also enable maintenance actions. Normally, a predictive maintenance task relies on the monitoring of a measurable system diagnostic parameter, which identifies the state of a system [1]. In this way, maintenance decisions, such as calling the intervention of a machine operator, are proposed based on the severity of anomalies, to prevent the halt of the production lines, and to minimize economic loss.
The following figure shows a typical predictive maintenance task. By collecting machine historical data, we may identify possible machine degradation trends over time. This allows us to plan and trigger maintenance actions (replacement of machine tool, cooling down the machine, clean the dust on machine surface…) to recover the machines from abnormal conditions to normal ones. These maintenance actions can reduce the downtime of production lines and improve productivity.
Several artificial intelligence techniques have been used to detect wear and tear in mechanical units and to predict future machinery conditions, such as machine learning, data mining, statistics, and information theory [2]. The use of these advanced techniques is transforming the traditional manufacturing industry into the new generation, where data is analysed and processed in a more efficient and intelligent way.
References:
[1] Grall, A., Dieulle, L., Bérenguer, C. and Roussignol, M., 2002. Continuous-time predictive-maintenance scheduling for a deteriorating system. IEEE transactions on reliability, 51(2), pp.141-150.
[2] Chandola, V., Banerjee, A. and Kumar, V., 2009. Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), pp.1-58.
