首页|Shanghai Maritime University Researchers Update Current Data on Support Vector M achines (Rolling bearing fault diagnosis based on RQA with STD and WOA-SVM)
Shanghai Maritime University Researchers Update Current Data on Support Vector M achines (Rolling bearing fault diagnosis based on RQA with STD and WOA-SVM)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Fresh data on are presented in a new r eport. According to news reporting from Shanghai, People's Republic of China, by NewsRx journalists, research stated, "A rolling bearing fault diagnosis method based on Recursive Quantitative Analysis (RQA) combined with time domain feature extraction and Whale Optimization Algorithm Support Vector Machine (WOA-SVM) is proposed." Our news editors obtained a quote from the research from Shanghai Maritime Unive rsity: "Firstly, the recurrence graph of the vibration signal is drawn, and the nonlinear feature parameters in the recurrence graph combined with Standard Devi ation (STD) are extracted by recursive quantitative analysis method to generate feature vectors; after that, in order to construct the optimal support vector ma chine model, the Whale Optimization Algorithm is used to optimize the c and g pa rameters. Finally, both Recursive Quantitative Analysis and standard deviation a re combined with the WOA-SVM model to perform fault diagnosis of rolling bearing s. The rolling bearing datasets from Case Western Reserve University and Jiangna n University were used for example analysis, and the fault identification accura cy reached 100 % and 95.00%, respectively. Compared to other methods, the method proposed in this paper has higher diagnostic accuracy and wide practical applicability, and the risk of accidents can be reduced thro ugh accurate fault diagnosis, which is also important for safety and environment al policies." According to the news editors, the research concluded: "This research originated in the field of mechanical fault diagnosis to solve the problem of fault diagno sis of rolling bearings in industrial production, it builds on previous research and explores new methods and techniques to fill some gaps in the field of mecha nical fault diagnosis."
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