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Fault diagnosis of harmonic reducer based on multiple feature spaces adaptive network
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Renxiang CHEN1, Xiao ZHANG1, Jialin LI1, Baojun YANG2, Xu ZHANG1
Journal of Vibration Engineering | 2025, 38(2) : 432 - 440
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Journal of Vibration Engineering | 2025, 38(2): 432-440
Fault diagnosis of harmonic reducer based on multiple feature spaces adaptive network
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Renxiang CHEN1, Xiao ZHANG1, Jialin LI1, Baojun YANG2, Xu ZHANG1
Affiliations
  • 1.Chongqing Engineering Laboratory for Transportation Engineering Application Robot, Chongqing Jiaotong University, Chongqing 400074, China
  • 2.Chongqing Robotics Institute, Chongqing 400714, China
Published: 2025-02-10 doi: 10.16385/j.cnki.issn.1004-4523.2025.02.022
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Due to the differences in data distribution caused by different locations of multiple measuring points, the fault diagnosis of the harmonic reducer is often ineffective. A fault diagnosis method for the harmonic reducer, based on a multiple feature spaces adaptation network (MFSAN), is proposed. Firstly, the vibration signal of the harmonic reducer is transformed using continuous wavelet transform to construct a time-frequency diagram that characterizes its operational state. Secondly, the data measured by sensors at different positions are divided into multiple source domain and target domain data, which are mapped to different feature spaces to obtain feature representations for each measuring point position. Then, the adaptive network is used to automatically transfer the knowledge learned from the source domain to the target domain features and automatically align the feature distribution of a specific domain to learn multiple domain-invariant representations. Finally, a domain-specific decision boundary is used to align the output of the classifier, effectively solving the data distribution differences caused by sensor location. Experimental results of harmonic reducer diagnosis of an industrial robot show that the identification accuracy of this method is 99.72%, which is higher than that of other comparison methods. The effectiveness and feasibility of this method are thus verified.

fault diagnosis  /  harmonic reducer  /  continuous wavelet transform  /  multiple feature spaces adaptation
Renxiang CHEN, Xiao ZHANG, Jialin LI, Baojun YANG, Xu ZHANG. Fault diagnosis of harmonic reducer based on multiple feature spaces adaptive network[J]. Journal of Vibration Engineering, 2025 , 38 (2) : 432 -440 . DOI: 10.16385/j.cnki.issn.1004-4523.2025.02.022
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Year 2025 volume 38 Issue 2
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Article Info
doi: 10.16385/j.cnki.issn.1004-4523.2025.02.022
  • Receive Date：2023-01-25
  • Online Date：2026-02-11
  • Published：2025-02-10
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History
  • Received：2023-01-25
  • Revised：2023-05-17
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Affiliations
    1.Chongqing Engineering Laboratory for Transportation Engineering Application Robot, Chongqing Jiaotong University, Chongqing 400074, China
    2.Chongqing Robotics Institute, Chongqing 400714, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
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Genus		 种数
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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