首页|Shijiazhuang University Researcher Provides New Study Findings on Machine Learni ng (An Intelligent Fault Diagnosis Algorithm for Vehicle Internal Combustion Eng ines Based on Instantaneous Speed for a Smart City)
Shijiazhuang University Researcher Provides New Study Findings on Machine Learni ng (An Intelligent Fault Diagnosis Algorithm for Vehicle Internal Combustion Eng ines Based on Instantaneous Speed for a Smart City)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Data detailed on artificial intelligence have bee n presented. According to news reporting out of Shijiazhuang University by NewsR x editors, research stated, "Affected by interference factors such as Gaussian n oise, the traditional methods have the problems of inaccurate diagnosis results of unsteady vibration signals, high uncertainty of fault diagnosis, and low over all fault diagnosis accuracy." Our news journalists obtained a quote from the research from Shijiazhuang Univer sity: "In this paper, a fault diagnosis algorithm of vehicle internal combustion engine based on instantaneous speed and machine learning is proposed. The insta ntaneous speed is measured by the hardware method. According to the processing r esults of instantaneous speed, the unsteady vibration signal of the vehicle inte rnal combustion engine is analyzed, and the principal components of unsteady vib ration are separated to suppress the interference of Gaussian strong noise. The running state of the vehicle internal combustion engine is identified by the wav elet transform method." According to the news reporters, the research concluded: "According to the ident ification results, the fault diagnosis of the vehicle internal combustion engine is realized by the twin support vector machine classification algorithm in mach ine learning. The experimental results show that the minimum uncertainty coeffic ient of fault diagnosis in this algorithm is 0.08, the accuracy of the unsteady vibration signal diagnosis is higher, and the overall accuracy of fault diagnosi s is lower."
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