Sound Signal Based Fault Classification System in Motorcycles Using Hybrid Feature Sets and Extreme Learning Machine Classifiers

作     者：T.Jayasree R.Prem Ananth 

作者机构：department of electronics&communication engineeringgovernment college of engineeringtirunelvelitamil naduindia department of electronics&communication engineeringfrancis xavier engineering collegetirunelvelitamil naduindia 

出 版 物：《Sound & Vibration》 (声音与振动（英文）)

年 卷 期：2020年第54卷第1期

页      面：57-74页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

主　　题：Extreme learning machine wavelet transform feature audio 

摘      要：Vehicles generate dissimilar sound patterns under different working *** generated sound patterns signify the condition of the engines,which in turn is used for diagnosing various *** this paper,the sound signals produced by motorcycles are analyzed to locate various *** important attributes are extracted from the generated sound signals based on time,frequency and wavelet domains which clearly describe the statistical behavior of the ***,various types of faults are classified using the Extreme Learning Machine(ELM)classifier from the extracted ***,the improved classification performance is obtained by the combination of feature sets in different *** simulation results clearly demonstrate that the proposed hybrid feature set together with the ELM classifier gives more promising results with higher classification accuracy when compared with the other conventional methods.