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While manufacturing are rapidly becoming more advanced, the world faces a shrinking labor force and shortage of skilled engineers. Omron will realize a factory of the future where people and machines grow together by leveraging AI and IoT technologies at the machine level and converting tacit knowledge, such as intuition and experience of experts, into explicit knowledge.

The artificial intelligence machine automation controller (AI controller) integrates unique AI functionality into control, allowing you to leverage information at the machine level in real time. The AI controller can very quickly and accurately detect momentary irregularity of equipment and feed back to control in real time. As well as enabling trend monitoring at the machine level, this also prevents quality defects that occur on high-speed production lines within a very short time.

In addition, significant patterns which data scientists usually discover by mining data are provided as software functional components : Sysmac Library for AI controllers. The AI Predictive Maintenance Library to realize non-stop equipment is now available, and other libraries to realize equipment maximizing performance and zero defect equipment will also be available soon.

Strange behavior is monitored using machine data in real time, which allows you to carry out maintenance based on machine status when it is really necessary.

Skilled engineers perform maintenance based on their intuition and experience regularly or after failure has occurred (time-based maintenance).

AI monitors machine status using machine data. Predictive maintenance is performed based on machine status when it is necessary (status-based maintenance).

1. Minimized downtime reduces production losses

2. Just-in-time maintenance reduces costs

3. Replacing components when necessary reduces stock of components

4. Error locations can be identified without analysis

5. Maintenance work can be standardized without special knowledge and skills

A learning model including a threshold value is generated from current machine data. (Usual behavior is learned.)

The machine is monitored based on the learning model.
If the machine status exceeds the threshold value, a notification is issued.

The machine status is checked.
If no error is found, a new threshold value is set.

An error occurs while threshold value setting and monitoring are repeated.
Components are replaced.

A new learning model including the threshold value is generated based on the previous error level after components are replaced. Repeating these steps makes status-based maintenance more reliable.

The unique data utilization functionality to provide ultimate edge control makes previously invisible machine status visible, which enables the AI controller to detect strange behavior of machines at the microsecond level.

Collection and storage of time-series data are fully synchronized with the control cycle.
The periodically sampled data is used to understand machine behavior, enabling creation of accurate learning models and judgment. Moreover, the host connection functionality allows the linkage of AI between the host and machine levels, which helps optimize the introduction of IoT to factories.

The AI engine provides both speed and accuracy—Omron has developed an AI engine based on the machine learning engine Isolation Forest that is ideal for real-time processing and tuned it to increase detection accuracy. The algorithm applicable to multimodal data can be used for high-mix production lines where two or more operating modes are required.

To resolve issues, manufacturers need to utilize data collected from machines in various scenes.
However, performing tasks for data utilization is sometimes time consuming and even difficult because it requires data science skills for data analysis and know-how of manufacturing machines for improvement.
The data mining software incorporating Omron’s unique automatic analysis technology automates data science tasks, enabling even on-site engineers to easily analyze data.

This software automatically extracts feature data to detect irregularity from machine data that is difficult to distinguish between normal and abnormal, and generates a machine learning model.

The AI Predictive Maintenance Library, a collection of software components, calculates optimal future values to judge behavior from data of operating mechanisms.
You can now start to do predictive maintenance.

Time elapses and ambient temperature changes throughout the day and year after the machine is started.
Omron has developed its own feature values that minimize the effects of environmental changes, helping you stabilize your predictive maintenance activities.

Omron helps you perform predictive maintenance using AI.