It includes feature extraction using predictive maintenance workflows and signal-based methods.
The important procedure in the development of predictive maintenance algorithms is the identification of situational indicators. These are characteristics of systems that change behavior in a predictable manner as the system deteriorates. Situational indicators help distinguish between failure and normal operation. The materials include an overview of situational indicators, as well as the predictive maintenance workflow. [Contents] ■ What are situational indicators ■ Visual exercises ■ Feature extraction using signal-based methods ■ Predictive maintenance workflow ■ Overview of good features and why they are important *For more details, please refer to the PDF materials or feel free to contact us.
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【Other Contents】 ■Data Acquisition ■Data Preprocessing ■Using Time Domain Features to Identify Situation Indicators ■Model Training *For more details, please refer to the PDF document or feel free to contact us.
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For more details, please refer to the PDF document or feel free to contact us.
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MathWorks is a leading company in numerical analysis software for engineers and researchers. Engineers and scientists around the world use MathWorks products as tools to accelerate discovery, innovation, and development. Please feel free to contact us if you have any inquiries.
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