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.
Use this Guidance to automate the detection of potential equipment failures, and provide recommended actions to take. It is easy to deploy and includes an example dataset, but you can modify the code to work with any dataset.
Predictive Maintenance Using Machine Learning allows you to run 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 build using the example code on GitHub.
Predictive Maintenance Using Machine Learning architecture
The code 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).
Predictive Maintenance Using Machine Learning 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 Guidance 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 code is configured to predict RUL from the example dataset. To use your own dataset, you must modify the code.
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