首页|Newcastle University Researcher Describes New Findings in Machine Learning (An Embedded Machine Learning Fault Detection System for Electric Fan Drive)
Newcastle University Researcher Describes New Findings in Machine Learning (An Embedded Machine Learning Fault Detection System for Electric Fan Drive)
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Current study results on artificial intelligence have been published. According to news reporting originating from Callaghan, Australia, by NewsRx correspondents, research stated, “Industrial fans are critical components in industrial production, where unexpected damage of important fans can cause serious disruptions and economic costs. One trending market segment in this area is where companies are trying to add value to their products to detect faults and prevent breakdowns, hence saving repair costs before the main product is damaged.” The news correspondents obtained a quote from the research from Newcastle University: “This research developed a methodology for early fault detection in a fan system utilizing machine learning techniques to monitor the operational states of the fan. The proposed system monitors the vibration of the fan using an accelerometer and utilizes a machine learning model to assess anomalies. Several of the most widely used algorithms for fault detection were evaluated and their results benchmarked for the vibration monitoring data. It was found that a simple Convolutional Neural Network (CNN) model demonstrated notable accuracy without the need for feature extraction, unlike conventional machine learning (ML)-based models. Additionally, the CNN model achieved optimal accuracy within 30 epochs, demonstrating its efficiency. Evaluating the CNN model performance on a validation dataset, the hyperparameters were updated until the optimal result was achieved. The trained model was then deployed on an embedded system to make real-time predictions. The deployed model demonstrated accuracy rates of 99.8%, 99.9% and 100.0% for Fan-Fault state, Fan-Off state, and Fan-On state, respectively, on the validation data set.”
Newcastle UniversityCallaghanAustraliaAustralia and New ZealandCyborgsEmbedded SystemsEmerging TechnologiesMachine Learning
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