Asset Performance Demo with Robotic Arm
In this video, we will discuss a use case for Asset Performance Management (APM) using MicroAI™. It introduces a simple concept that can be applied to any machine. At first, we will need to define our use case and end goal. We will then list all the components needed to achieve that goal. Later, we can implement this and run test cases. Let us start out by defining our use case.
The APM Objectives and Requirements
We need to monitor a robot arm. We need alerts to be generated when there is a collision, abnormal increase in temperature, and abnormal sound patterns. All these parameters will be used as building blocks for our use case. This type of asset monitoring brings a lot of value to any business that depends on reliable asset performance.
Now, let’s list out things that we need. We need a prebuilt and configured robot arm that moves from left to right. We also need Raspberry Pi to run MicroAI(TM). We will need three different types of sensors: a sound sensor, a temperature sensor, and a gyro sensor.
We need to model the MicroAI™ before executing it. Before doing that, we need to mount the sensors, remembering that the position of the sensors does matter. This is especially true for the gyro and acceleration sensor. If you position it incorrectly you will get negative readings. In this case, we will mount the gyro and acceleration sensor on top of the robot arm tip. This way it will monitor the movement of the robot arm precisely. Sound and temperature sensors will be mounted on top of the robot’s motor housing. After mounting, make sure the sensor’s readings are accurate and make any necessary changes.

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