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
Understand the Use-case under 5 minutes
Video (4 minutes)
Comparing Reactive Maintenance to AI-based Predictive Maintenance. Allows early prediction for expected failures, ordering needed parts, and recommended actions
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Video (2.5 minutes)
Demonstrates Smart Manufacturing specific use cases such as Reliability, Asset Energy Management, Operational Excellence. Predictive Maintenance, CBM, & EAM
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Get to know more Business and Technical details about the use-case (15-30 minutes)
More detailed introduction covering business and technical aspects
White Paper (16 minutes)
A high-level overview of the process involved in setting up a predictive maintenance program for any type of high-capital asset. Why ML, where to find data, expected challenges, steps to kickstart a PdM Project, & more
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Article (13 minutes)
Benefits and ROI of Predictive Maintenance, how it works, data sets needed, industries that could benefit from it, how to implement a PdM program, and vendor solutions landscape
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Video (41 minutes)
Session at the International Maintenance Conference. How does Machine Learning apply to PdM today, and how to effectively launch a ML-based PdM program. Digs into some technical aspects like feature engineering and types of modeling
WatchCase studies, Organizational Aspects, Return on Investment examples
Article + Video (17 minutes)
Steps for establishing a PdM program: Identify assets for PdM, Establish the presence of actionable data, choose condition monitoring techniques, and more. Expected Organizational Challenges and how to mitigate them
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White Paper (14 minutes)
Expands on the Value and ROI generated from PdM programs applied in Manufacturing. PdM Strategies and insights about each. Case Studies. Expected Challenges, & more
VisitMore details on the technical aspects of the use-case
Video (47 minutes)
By Google Cloud: Learn how to build advanced predictive maintenance solution. Learn what is predictive monitoring and new scenarios you can unlock for competitive advantage
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Video (39 minutes)
Live demo introducing Amazon Lookout for Equipment, which allows you to analyze the data from the sensors on your equipment to automatically train a ML models based on your equipment data
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Video (29 minutes)
Introduction + Demo for Amazon Monitron: an end-to-end system that uses machine learning to detect abnormal conditions in industrial equipment and enable predictive maintenance.
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Technical resources that will help you implement the use-case (notebooks, tutorials..)
Solution Template (Code + Data)
Open-source solution template showcases a complete Azure infrastructure capable of supporting Predictive Maintenance scenarios. Includes: notebooks, data generator, tech documentation, demo dashboard, & more (runnable in Azure)
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Solution Template (Code + Data)
This template demonstrates how to build and deploy predictive maintenance models to predict asset failures. Example of simulated aircraft engine run-to-failure events to demonstrate the predictive maintenance modeling process. (runnable in Azure ML Studio)
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Video Series (6 minutes)
Detailed explanation for best practices when building PdM workflows. 5 videos each expanding on an important concept like feature extraction, Remaining Useful Lifetime Prediction, using Diagnostic Features, and Digital Twins
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AI Service
This guide provides information about setting up sites within your project, placement options for gateways and sensors, and Amazon Monitron hardware specifications
VisitAI Service
An overview of the service, how it works, and an example use case. Github Repo here including code for data prep, model training & evaluation, and inferencing scheduling
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AI Service
Includes an AWS CloudFormation template that deploys an example dataset of a turbofan degradation simulation contained in S3 bucket and an Amazon SageMaker endpoint
VisitGithub Repo (Runnable in Colab)
LSTM approach for predictive maintenance both for classification and regression. Dataset: Simulated aircraft engine run-to-failure events
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Data Sets you can use to build Demos, POCs, or test Algorithms
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
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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