Video and Webinar Series

Predictive Maintenance

Predictive maintenance lets you estimate the optimum time to do maintenance by predicting time to failure of a machine. This way, you can minimize downtime and maximize equipment lifetime. In this series, you’ll learn how predictive maintenance works and how it is different from other strategies such as reactive and preventive maintenance. The videos will also walk you through a workflow that will help you develop a predictive maintenance algorithm. You’ll learn about condition indicators and how you can extract them from your data to discriminate between healthy and faulty states. Machine learning models are trained using the extracted condition indicators to classify different types of faults. The videos will also help you understand different estimator models, such as survival, similarity, and degradation, that are used to estimate the remaining useful life of a machine.

はじめに | 予知保全、パート 1 さまざまな保全戦略と予知保全のワークフローについて説明します。予知保全によって、故障までの時間を予測して最適なメンテナンス実施時期を見つけます。

Feature Extraction for Identifying Condition Indicators Watch this video to learn how you can extract condition indicators from your data. Condition indicators help you distinguish between healthy and faulty states of a machine.

Remaining Useful Life Estimation Predictive maintenance lets you estimate the remaining useful life (RUL) of your machine. Explore three common models to estimate RUL: similarity, survival, and degradation.

How to Use Diagnostic Feature Designer for Feature Extraction Learn how you can extract time-domain and spectral features using Diagnostic Feature Designer for developing your predictive maintenance algorithm.

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