Identifying unusual behaviors or patterns in machine components using sensor data can prevent small glitches from creating major operational problems. In cases where large numbers of sensor feeds are involved, challenges emerge due to the sheer volume and velocity of data streaming off the equipment. In addition, meaningful analysis from the data is a nontrivial task, since slowing or shutting down production in order to examine a machine carefully should only be done when truly necessary. For these reasons, simple threshold-based alerting is normally unsuitable as it will generate too many false positives. More advanced analytics methods can, however, be easily implemented and will flag potentially serious issues without reducing overall equipment effectiveness (OEE).
This solutions flyer explains how advanced data analytics tools can help manufacturing operations improve productivity and quality by spotting outliers, trends, and clusters in sensor data streaming off production equipment and apply machine learning to understand where and when likely anomalies may occur.