Recommender System
Assembly Line
A recommender system for human operators in industrial automation
This paper presents a recommender system to assist human operators in industrial automation applications, which suggests proper actions to take during process operation and those to avoid. The proposed recommender system is built based on a three-stage computational procedure. In the first stage, historical process data segments containing timestamped events are clustered using a k-medoids-based algorithm, where instead of distance metrics, structural similarity of the segments is used. This is achieved by using a Smith–Waterman-based algorithm to consider all events and their timestamps in the segments. In the second stage, a modified collaborative filtering technique applicable to time sequences is proposed, where not only operator ratings, but also the structural similarity of the segments are taken into account. In the third stage, possible actions are analyzed and accordingly, proper and improper actions are determined and presented to the human operators. The effectiveness of the proposed recommender system is examined through a power system operation example.
Smart operation recommender system digitalizing OT knowledge to improve productivity
To contribute to resilient manufacturing systems through digitalization, it will be important to connect physical phenomena, OT knowledge and sensor data in parallel with developing AI to analyze and learn from the data. My colleagues and I developed an operation recommender system that can discriminate factors contributing to defects and recommend appropriate countermeasures. We will continue developing the technology to resolve issues in manufacturing by considering different manufacturing processes.