首页|Research on Machine Learning Reported by Researchers at Alexandria University (Machine learning and IoT - Based predictive maintenance approach for industrial applications)

Research on Machine Learning Reported by Researchers at Alexandria University (Machine learning and IoT - Based predictive maintenance approach for industrial applications)

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Investigators discuss new findings in artificial intelligence. According to news reporting out of Alexandria University by NewsRx editors, research stated, “Unplanned outage in industry due to machine failures can lead to significant production losses and increased maintenance costs.” Financial supporters for this research include Stdf. Our news reporters obtained a quote from the research from Alexandria University: “Predictive maintenance methods use the data collected from IoT-enabled devices installed in working machines to detect incipient faults and prevent major failures. In this study, a predictive maintenance system based on machine learning algorithms, specifically AdaBoost, is presented to classify different types of machines stops in real-time with application in knitting machines. The data collected from the machines include machine speeds and steps, which were pre-processed and fed into the machine learning model to classify six types of machines stops: gate stop, feeder stop, needle stop, completed roll stop, idle stop, and lycra stop. The model is trained and optimized using a combination of hyperparameter tuning and cross-validation techniques to achieve an accuracy of 92% on the test set.”

Alexandria UniversityCyborgsEmerging TechnologiesMachine Learning

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Feb.1)
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