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Predictive Maintenance Through Machine IQ

Self-monitoring is a built-in test (BIT) mechanism that allows machines to perform self-tests to discover whether it needs maintenance or repair. Typical tests are for temperature, current, voltage, (motor) torque, or communication quality. For example, the decreased torque output on a robot arm or overheating/vibration on a CNC motor. However, even after identifying potential issues, technician accessibility could be limited and checking problems onsite during operational hours could be costly. Additionally, managers on the enterprise-end of the business need systems online so they can monitor the operational performance of assets and their availability.

Collecting machine data and connecting systems to the cloud enables remote real-time monitoring and remote maintenance of your factory and machines. With full transparency to machine performance, system status, and more importantly hard data, managers are able to keep track of current machine performance. Operators can even see historical data on similar machines to predict performance variation outside the acceptable boundaries. With this abundance of data, software engineers are able to build a mathematic model based on per-alarm occurrences to predict machine failure so the operators can proactively schedule maintenance, improving the Mean Time To Repair (MTTR) and saving significant profit loss from downtime and repair.

Combining this real-time information with remotely connected maintenance software also allows operators to service machines remotely before costly issues can occur and with this knowledge they can have the technician scheduled to arrive outside peak operating hours to avoid downtime.



Here are two different use cases for CNC machine status monitoring:

The customer can use the Moxa OPC UA suite to collect production and machine data to acquire OEE (Overall Equipment Effectiveness). Temperature, current, oil level, and pressure data can be gathered and pushed to their private cloud over VPN gateway. On the other hand, the customer can use an IIoT gateway to collect the machine data and push it to a public cloud such as Microsoft Azure and perform data analysis through an Azure provided, or user-developed algorithm to find abnormal patterns. Through this approach, the machine maintenance engineer can complete predictive maintenance based on the results. As a result unexpected machine shutdown can be avoided to increase machine availability.



Remote maintenance can also be achieved by connecting equipment to IIoT-ready remote access gateways. The data is sent to MES and SCADA/Cloud Asset Performance Management software for analysis and will notify you when there are operation anomalies that affect your production quality. This can include abnormal vibration of the CNC machine, malfunction of the conveyer linear motor, or if the temperature for a reflow machine in a SMT production line drops too low. Being able to detect and take action ahead of time saves cost and time boosting production efficiency.

 

If you are planning to implement predictive maintenance, Moxa can help. We offer connectivity and networking devices that bring the data from your production equipment to SCADA/MES systems. To speak to someone about how you can connect your devices for remote monitoring and maintenance, reach out to one of our industrial networking professionals.

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