What does this AWS Solutions Implementation do?
Predictive Maintenance Using Machine Learning deploys a machine learning (ML) model and an example dataset of turbofan degradation simulation data to train the model to recognize potential equipment failures.
You can use this solution to automate the detection of potential equipment failures, and provide recommended actions to take. The solution is easy to deploy and includes an example dataset but you can modify the solution to work with any dataset.
AWS Solutions Implementation overview
Predictive Maintenance Using Machine Learning enables you to execute automated data processing on an example dataset or your own dataset. The included ML model detects potential equipment failures and provides recommended actions to take. The diagram below presents the architecture you can automatically deploy using the solution’s implementation guide and accompanying AWS CloudFormation template.
Predictive Maintenance Using Machine Learning architecture
This solution includes an AWS CloudFormation template that deploys an example dataset of a turbofan degradation simulation contained in an Amazon Simple Storage Service (Amazon S3) bucket and an Amazon SageMaker endpoint with an ML model that will be trained on the dataset to predict remaining useful life (RUL).
The solution uses a SageMaker notebook instance to orchestrate the model, and a SageMaker training instance to perform the training. The training code and trained model are stored in the solution's Amazon S3 bucket.
The solution also deploys an Amazon CloudWatch Events rule that is configured to run once per day. The rule is configured to trigger an AWS Lambda function that creates an Amazon SageMaker batch transform job that uses the trained model to predict RUL from the example dataset.
By default, the solution is configured to predict RUL from the example dataset. To use your own dataset, you must modify the solution. For more information, see the deployment guide.
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