predictive maintenance
Comparative measurements between a digital acquisition system and an analog acquisition system, namely National Instruments CompactRIO.
The dataset includes the measurements of a 10-days accelerated test performed on a tapered bearing (12 rolling elements) mounted on a motor test bench, from the brand-new condition until failure. The test bench was working at 2400 revolution per minute (RPM), and the bearing was preloaded with 4000 N.
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This paper focuses on advancements in predictive maintenance driven by artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT). It explores applications in the predictive maintenance in industries, aiming to provide a comprehensive understanding of current methodologies and future prospects. The discussion focuses on predictive maintenance methodologies, highlighting strengths, limitations, challenges, and opportunities.
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The data set contains electrical and mechanical signals from experiments on three-phase induction motors. The experimental tests were carried out for different mechanical loads on the induction motor axis and different severities of broken bar defects in the motor rotor, including data regarding the rotor without defects. Ten repetitions were performed for each experimental condition.
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