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Predictive Maintenance

Predicting machine failure before it happens to avoid downtime and reduce maintenance costs. Prediction happens based on historical and real-time sensor feeds, vibration, voltage, pressure, temperature, historical failure incidents. AI can also recommend optimal time for intervention and best actions to avoid failures

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1. Get Inspired

Understand the Use-case under 5 minutes

2. Know More

Get to know more Business and Technical details about the use-case (15-30 minutes)

Deeper Intro

More detailed introduction covering business and technical aspects

Business Focused

Case studies, Organizational Aspects, Return on Investment examples

Tech focused

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3. Do

Technical resources that will help you implement the use-case (notebooks, tutorials..)

Data Sets

Data Sets you can use to build Demos, POCs, or test Algorithms

Turbofan engine degradation simulation data set

Run-to-failure data: Engine degradation simulation was carried out using C-MAPSS tool. Four different sets were simulated under different combinations of operational conditions and fault modes

AI4I 2020 Predictive Maintenance Dataset (UCI)

Since real predictive maintenance datasets are generally difficult to obtain and in particular difficult to publish, we present and provide a synthetic dataset that reflects real predictive maintenance encountered in industry to the best of our knowledge


Off-the-Shelf Products using AI for Predictive Maintenance

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