en.Wedoany.com Reported - Omron has developed an anomaly detection solution for its oxygen sensor production line based on a special algorithm and real-time data fusion. This solution shortens the data acquisition cycle from 100 milliseconds to 2 milliseconds and introduces the Isolation Forest algorithm to replace traditional upper/lower limit algorithms, boosting anomaly detection accuracy to over 89%. The solution has been applied on a production line containing core equipment such as screw conveyors, servo presses, and cylinders, achieving an intelligent upgrade in equipment health management and fault early warning.

The oxygen sensor production line uses talcum powder, ceramic tubes, and wiring harnesses as raw materials, completing production through processes such as assembly, riveting, and inspection. Previously, equipment data was isolated, the anomaly miss rate was high, and the decentralized display of production line status led to delayed fault response. Maintenance also relied on manual inspections with inefficient task assignment. To address these pain points, Omron implemented high-frequency data acquisition via a PLC internal circular stack (every 2 milliseconds) and used the MQTT protocol to actively push stack arrays at a frequency of every 100 milliseconds. At the algorithm level, the solution iterated from a standard deviation (upper/lower limit) algorithm to an Isolation Forest algorithm model, which isolates data points by constructing multiple decision trees, enabling more precise anomaly detection.

In terms of system configuration, the solution integrates core products such as the NJ-series NJ501 CPU Unit from the Machine Automation Controller line, achieving a full-link closed loop from data acquisition and analysis to visualization dashboards. After implementation, the management level can promote full lifecycle health management of equipment, the business level has optimized maintenance strategies and reduced unplanned downtime, and the field level can accurately collect production data, intuitively display equipment status, and push maintenance tasks via email, with anomaly detection accuracy exceeding 89%.

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