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By Sharath Kumar | Published November 1, 2018 - Updated November 1, 2018
IoT equipment failure prediction involves collecting sensor values and running algorithms to anticipate impending failures. Core building blocks include identifying the features or factors contributing to equipment failures. Then you configure a predictive model to train the model, followed by scoring the test data to check the reliability of the predictive model. Python 2.0 software is used, with sample sensor data loaded into the IBM Watson Studio cloud.
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