AI Enhanced Alarm Presentation for Quality Monitoring

Abstract

This paper presents an AI based method for improving poorly performing quality prediction models. The method improves automatically the usability of the low quality alarm predictions in the web based quality monitoring tool that provides decision support for users. The tool enables the utilization of the models that suffer from the lack of information because of a long time gap to the predicted future. The reliability of the presented alarms in a monitoring tool will be improved by reducing the amount of false alarms.

Artificial intelligence and internet of things in manufacturing decision processes

“…ML techniques are categorized into several classes depending on how they are learned and what they are used for. UL, a branch of ML where a model learns patterns and structures in data without any explicit labeling or guidance from a predefined set of output labels, is considered in [27], [33], [46], [57], [59], [62], [78], [82]. UL in hardware/sensor networks and cloud/fog/edge architecture of IoT domain improves manufacturing analytics in operations management processes, especially to obtain better detection for condition monitoring of sensor-node [27], [57], machine anomalies [33], and alteration of transmitted manufacturing data [82].…”