As a type of reciprocating machine, the reciprocating compressor has a compact structure and many excitation sources.Once the small end bearing of the connecting rod is worn, it is easy to cause the sintering of the b...As a type of reciprocating machine, the reciprocating compressor has a compact structure and many excitation sources.Once the small end bearing of the connecting rod is worn, it is easy to cause the sintering of the bearing and the abnormal vibration of the body.Based on the characteristics of poor lubrication state and complex force of connecting rod small head bearing, a mixed lubrication model considering oil groove feed was established, and the dynamic simulation of the reciprocating compressor model with lubricated bearings was carried out;considering different speeds and gas load conditions, the law of the impact of the eigenvalues changing with working conditions was explored.The fault simulation experiment was carried out by selecting representative working conditions, which verified the correctness of the simulation method.The study found that two contact collisions between the pin and the bearing bush occurred in one cycle, the collision impact was more severe under the wear fault, and the existence of the gap made the dynamic response more sensitive to the change of working conditions.This research provides ideas for the location and feature extraction of fault symptom signal angular segments in the process of complex measured signal processing.展开更多
The change of working conditions not only makes the data distribution inconsistent,but also increases the diagnosis difficulty of fuzzy samples at the fault boundary.The traditional distance-based deep metric learning...The change of working conditions not only makes the data distribution inconsistent,but also increases the diagnosis difficulty of fuzzy samples at the fault boundary.The traditional distance-based deep metric learning cannot effectively classify the fuzzy samples at the fault boundary.In the traditional transfer learning models,the maximum mean discrepancy(MMD)and joint maximum mean discrepancy only increase the transferability of same-class samples,and neglect the discriminability of different-class samples across different domains.The discriminative joint probability MMD(DJP-MMD)increases the transferability of same-class samples and the discriminability of different-class samples across different domains,but it only considers the global transferability of all fault classes,ignoring the different transferability of each same fault class.Therefore,a Yu norm-based deep transfer metric learning based on weighted DJP-MMD is proposed to further improve the diagnosis accuracy of bearings under variable working conditions.The deep transfer metric learning model adopts the Yu norm-based similarity instead of the distance-based similarity to effectively classify the data samples,especially those at the fault boundary,and uses the weighted DJP-MMD to measure the data distribution discrepancy between the source and target domains to increase the transferability of each same-class samples and discriminability of different-class samples across different domains.Through the fault diagnosis analysis on bearings under variable working conditions,the diagnosis results demonstrate that the proposed deep transfer metric learning model can diagnose bearing faults with higher accuracy,stronger generalization and anti-noise capabilities compared with other fault diagnosis methods based on transfer learning.展开更多
基金Supported by the National Natural Science Foundation of China (No.52101343)。
文摘As a type of reciprocating machine, the reciprocating compressor has a compact structure and many excitation sources.Once the small end bearing of the connecting rod is worn, it is easy to cause the sintering of the bearing and the abnormal vibration of the body.Based on the characteristics of poor lubrication state and complex force of connecting rod small head bearing, a mixed lubrication model considering oil groove feed was established, and the dynamic simulation of the reciprocating compressor model with lubricated bearings was carried out;considering different speeds and gas load conditions, the law of the impact of the eigenvalues changing with working conditions was explored.The fault simulation experiment was carried out by selecting representative working conditions, which verified the correctness of the simulation method.The study found that two contact collisions between the pin and the bearing bush occurred in one cycle, the collision impact was more severe under the wear fault, and the existence of the gap made the dynamic response more sensitive to the change of working conditions.This research provides ideas for the location and feature extraction of fault symptom signal angular segments in the process of complex measured signal processing.
基金funded by the National Natural Science Foundation of China(Grant No.51775391).
文摘The change of working conditions not only makes the data distribution inconsistent,but also increases the diagnosis difficulty of fuzzy samples at the fault boundary.The traditional distance-based deep metric learning cannot effectively classify the fuzzy samples at the fault boundary.In the traditional transfer learning models,the maximum mean discrepancy(MMD)and joint maximum mean discrepancy only increase the transferability of same-class samples,and neglect the discriminability of different-class samples across different domains.The discriminative joint probability MMD(DJP-MMD)increases the transferability of same-class samples and the discriminability of different-class samples across different domains,but it only considers the global transferability of all fault classes,ignoring the different transferability of each same fault class.Therefore,a Yu norm-based deep transfer metric learning based on weighted DJP-MMD is proposed to further improve the diagnosis accuracy of bearings under variable working conditions.The deep transfer metric learning model adopts the Yu norm-based similarity instead of the distance-based similarity to effectively classify the data samples,especially those at the fault boundary,and uses the weighted DJP-MMD to measure the data distribution discrepancy between the source and target domains to increase the transferability of each same-class samples and discriminability of different-class samples across different domains.Through the fault diagnosis analysis on bearings under variable working conditions,the diagnosis results demonstrate that the proposed deep transfer metric learning model can diagnose bearing faults with higher accuracy,stronger generalization and anti-noise capabilities compared with other fault diagnosis methods based on transfer learning.
