Kick start machine stability with data science services

Kick start machine stability with data science services


Omron Automation is offering Data Science Services to help machine builders and users improve productivity using collected machine data. For example, a Sysmac AI controller collects signals from sensors and actuators and logs them at high frequency. But then what?

Valuable information needs to be extracted from the data and presented to the right audience, at the right time and in the right way. Omron Data Science Services provide the option of working in close collaboration with their data scientists, who understand how to convert this data to information.

What are Data Science Services?

OMRON Data Science Services help to kick-start machine stability and provide a basis for anomaly detection and predictive maintenance modelling. Using this service provides machine performance optimisation for the user. Moreover, it can detect abnormal situations before costly faults occur and impact on the whole production line. By alerting engineering teams with the root cause of problems ensures faster intervention and reduced downtime.

OMRON Sysmac AI-Controller

The OMRON Sysmac AI-Controller also helps improve up-time in other ways, such as with proof of concepts. When combined with inputs from the machine operators and engineers provides a detailed insight into the machine. It highlights deviations and inconsistencies, such as sensor misalignment, misconfiguration and worn-out parts.

Over time, the monitoring helps to detect process and quality anomalies and provides drift trends for predictive maintenance. This helps to reduce micro stoppages in a machine or identify the root cause of an intermittent or indistinct problem.

Anomaly Detection Monitoring Service

Continuous monitoring  of signals helps manufacturers to achieve machine stability, detect anomalies, and predict failures to increase Overall Equipment Efficiency (OEE).

Anomaly detection identifies unusual patterns like network intrusion, fault detection, system health monitoring and event detection in sensor networks. It is a critical technique for identifying hard to find and rare events that influence a process.

Supporting Technologies

The Sysmac AI-Controller checks the condition of the machines to ensure equipment availability. It also provides tools used in the data management process of:

– Collection: collecting raw high-fidelity data from machines in real time.

– Analysis: storing data on the machine and creating a model.

– Utilisation: tracking machine features and acting in real-time based on that model.

The data analysis function of the AI-Controller enables machine learning, modelling and data analysis. The embedded OMRON machine learning engine uses the Isolation Forest algorithm to identify abnormal cases. To put all the power of AI close to the machine, the solution executes completely at the Edge.

The AI-Controller provides data acquisition in real-time (1/ms) for fast and reliable results. Other platforms also connect to the Controller for streaming data to a suitable analytical tool like Python. This supports further data analysis, machine learning modelling, and building operational and analytical dashboards.

In their white paper, Omron considers an example of Anomaly Detection Service for a Pin Stitcher Machine in an assembly line at their Netherlands plant. The Sysmac AI-Controller in the Pin Stitcher operates as the machine controller whilst capturing alarms and machine signals in real-time.









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