The ability to monitor machine internals provides an important aspect for predictive maintenance and troubleshooting that protects the long-term investments (the machines themselves). In this study, we instantiated the Blocmount Data Analytics Pipeline (DAP) using a developed Fanuc Focas-enabled application coupled with Amazon Web Services (AWS) to acquire, analyze and present live machine data through various dashboards.

Fanuc Focas is a library that allows applications to connect to Fanuc Focas-enabled controllers and read/write data directly from/to the machine. Based on the type of the machine and type of controllers, different functions in the Application Programming Interfaces (API) are used. The application can connect to the machine directly through its ethernet port or through a switch (to allow the same computer to access multiple machines).

In this study, our BM DAP acquired various machine-level data. This included the following (partial list):

• State of the machine
• Positions of the axes (absolute, relative, machine, distance-to-go)
• Load on the servo motors
• Speed of the spindles

The application polls such data every given interval that is supplied as a parameter (e.g., 10 seconds) and it uses the AWS SDK to send this data through a Lambda function to be stored in the cloud in a time-series Influx database. The Influx database provides an efficient and scalable platform to process, store, and query data in real-time.

In this study we developed various dashboards using Grafana. These dashboards are hosted on AWS and they securely connect to the Influx database. They display live machine data as well as the results of the analytics (e.g., predictions, alerts, etc.). They are accessible with proper credentials through any web-browser. Our BM DAP supports notifications via email, text messages, or Teams messages.