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18. A Review on Vibration-Based Condition Monitoring ofRotating Machinery:
In the literature review, the central idea revolves around the application of machine learning techniques for fault diagnosis in rotating machinery based on vibration analysis. Various algorithms and models such as Artificial Neural Networks (ANNs), Deep Neural Networks (DNNs), Support Vector Machines (SVMs), Decision Trees (DTs), and K-Nearest Neighbor (KNN) have been explored. SVMs have shown the best classification accuracy, with high precision in fault diagnosis. However, supervised machine learning methods require labeled data, which can be time-consuming and prone to errors. Additionally, the threshold-based methods may face challenges in setting appropriate alarm triggers, leading to false alarms or missed detections.
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