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GEAR FAULT DIAGNOSIS METHOD BASED ON INTRINSIC SCALE COMPONENTS SYMBOL ENTROPY AND ANNC
In order to improve fault diagnosis accuracy of gear,a fault extraction method of gear based on symbol entropy of LCD was proposed. The vibration signal was decomposed adaptively with local characteristic-scale decomposition（ LCD） to obtain the components in different scales of the original signal. Considering the ability of the symbol entropy in distinguishing the complexity of different signals effectively,the symbol entropy of intrinsic scale components（ ISCs） by LCD was calculated. Thus the complexity metric in different scales of the original signal was gained,which was consequently taken as the feature parameter to describe different gear states. The feature parameters were then put into ANNC for diagnosing the gear faults. Experiment results of gear show that the proposed method can classify typical fault of gear exactly and has certain superiority when compared with some other methods.
GEAR FAULT DIAGNOSIS METHOD BASED ON INTRINSIC SCALE COMPONENTS SYMBOL ENTROPY AND ANNC
In order to improve fault diagnosis accuracy of gear,a fault extraction method of gear based on symbol entropy of LCD was proposed. The vibration signal was decomposed adaptively with local characteristic-scale decomposition（ LCD） to obtain the components in different scales of the original signal. Considering the ability of the symbol entropy in distinguishing the complexity of different signals effectively,the symbol entropy of intrinsic scale components（ ISCs） by LCD was calculated. Thus the complexity metric in different scales of the original signal was gained,which was consequently taken as the feature parameter to describe different gear states. The feature parameters were then put into ANNC for diagnosing the gear faults. Experiment results of gear show that the proposed method can classify typical fault of gear exactly and has certain superiority when compared with some other methods.
GEAR FAULT DIAGNOSIS METHOD BASED ON INTRINSIC SCALE COMPONENTS SYMBOL ENTROPY AND ANNC
QUAN ZhenYa (author) / SHEN LiHong (author) / ZHAO FuQiang (author)
2020
Article (Journal)
Electronic Resource
Unknown
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