Getting input for predictive maintenance 1

In last week’s blog post, we worked on setting up our accelerometer to input data to our jetson nano. We can now access the x, y, and z data of the BMA220 accelerometer. This week, we will cover our plan to implement the BMA220 for use with predictive maintenance.

The configuration:

The main components in this project are the BMA220 accelerometer, the jetson nano, an arduino, and an ac/dc motor. They will be configured as shown below.

The jetson nano will process the input from the BMA220, which will be fixed on to an ac/dc motor. The motor’s speed will be controlled by the arduino. In this project, it is important that all the components are fixed to a board. Any slight play in their stationary positions may throw off the data collection.

Collecting data:

To do any kind of machine learning, we first need a large set of data. In this case, when we are trying to predict failure, we will need ‘normal’ data, and ‘failure’ data. This is data taken when the machine (in this case a motor) is running fine, and when it is in a failed state or abnormal state. 

We will run the motor 24/7 for a week or so to collect a lot of normal data. Then, we will inject problems that cause the data to become abnormal. This can include things like messing with the bearing, bumps and shakes, etc. We will separate the datasets, train the model to recognize any abnormal data, and to notify us before something costly could happen.

Why is this useful:

Having a model like this is useful for many reasons. The main one is interchangeability. A model like the one we are making, that is trained to spot any abnormal data, can be implemented on various pieces of machinery. All that is needed is a large set of data, and since most machinery records its data anyway, this is easy to get. 

While you could theoretically hard-code all the rules to recognize failure for a certain machine, this is a lengthy process that will require a lot of human eyes looking over huge datasets. It quite literally is like searching for a needle in a haystack to find the triggers that signal the machine’s failure. With machine learning, you can easily be notified of any common failures, and even new failures that have never occurred before.

In next week’s blog post, we will have our motor and will be able to create our project for data collection. 

Other useful resources:

  • Machine learning techniques

There are various ways to implement machine learning in predictive maintenance. The one we are using here is to flag anomalous behavior. This article goes over the other ways ML can be used and how the steps differ.

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