Predictive Maintenance

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If you think about all the machines you may use in a day; from the coffee machine at the crack of dawn to the airplane you’re taking to hang with the in-laws for the weekend. What impact would it have on you if one of both of these machines were to fail? We are surrounded by machines and are becoming more and more dependent on them, and the quality of the machines comes down to how useful, efficient and reliable they are, therefore predictive maintenance is invaluable. 

The sweet spot

Predictive maintenance is an umbrella term that incorporates a range of use-cases like predictive fault detection, time-to-failure models, anomaly detection, fault classification etc.

Predictive maintenance is a strategy driven by predictive analytics. When a high probability of imminent failure is detected in a system/machine, solutions are deployed by machine learning algorithms, just as they have been programmed to do so. The success of predictive maintenance models depends on three main components: availability to precise historical data, framing the problem appropriately, and thoroughly evaluating the predictions.

Predictive maintenance tries to find the sweet spot between preventive maintenance and actual device failure. The main intention is to monitor devices and predict future failure and time-to-failure as failures of critical equipment or devices in production leads to downtime and revenue loss.

Key benefits

There has been a surge in predictive maintenance use-cases after the advent of Internet of Things (IoT).

There are high cost savings amongst other benefits associated with using predictive maintenance as a strategy, such as:

How can we help

AWS has a stack of services lined-up for predictive maintenance tasks such as AWS IoT Core – managed cloud devices and process messages; AWS GreenGrass – local compute, device messaging, ML model inference, etc.; Amazon SageMaker – build, train and deploy models (also supports PyTorch or TensorFlow/Keras frameworks for running deep learning predictive maintenance models); AWS IoT Analytics – store and process device time series data for anomaly detection etc.

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"Invention is not the product of logical thought, even though the final product is tied to a logical structure."
- Albert Einstein