In order to solve the problem of low prediction accuracy when only vibration or oil signal is used to predict the remaining life of gear wear,a gear wear life feature fusion prediction method based on temporal pattern...In order to solve the problem of low prediction accuracy when only vibration or oil signal is used to predict the remaining life of gear wear,a gear wear life feature fusion prediction method based on temporal pattern attention mechanism is proposed.Firstly,deep residual shrinkage network(DRSN)is used to extract the features of the original vibration time series signals with low signal-tonoise ratio,and the vibration features associated with gear wear evolution are obtained.Secondly,the extracted vibration features and the oil monitoring data that can intuitively reflect the wear process information are jointly input into the bi-directional long short-term memory neural network based on temporal pattern attention mechanism(TPA-BiLSTM),the complex nonlinear relationship between vibration features,oil features and gear wear process evolution is further explored to improve the prediction accuracy.The gear life cycle dynamic response and wear process signals are obtained based on the gear numerical simulation model,and the feasibility of the proposed method is verified.Finally,the proposed method is applied to the residual life prediction of gear on a test bench,and the comparison between different methods proved the validity of the proposed method.展开更多
A research concerning the coupling conditions of gas leakage through suction valves and capacity regulation is performed in an industrial reciprocating compressor.Both internal flow and thermodynamic characteristic ar...A research concerning the coupling conditions of gas leakage through suction valves and capacity regulation is performed in an industrial reciprocating compressor.Both internal flow and thermodynamic characteristic are discussed in detail.The results show that the capacity of compressor can be regulated steplessly by controlling suction valve closure moment.And then the quantitative relationship between the capacity load and the closing angle of suction valve is revealed.The capacity load and valve leakage rate show obvious different features in P-V diagrams,which makes it easier to define appropriate features for detecting cracked or broken reciprocating compressor valves under varying load conditions.A set of curves of compression work and discharge gas mass are obtained and a method for rating thermal performance of a compressor is presented using these curves.展开更多
As the differences of sensor's precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machin...As the differences of sensor's precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn't require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor's own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.展开更多
基金Supported by the National Natural Science Foundation of China(No.52101343)the Aeronautical Science Foundation(ASFC)(No.201834S9002)Chongqing Technology Innovation and Application Development Special General Project(No.cstc2020jscx-msxm0411).
文摘In order to solve the problem of low prediction accuracy when only vibration or oil signal is used to predict the remaining life of gear wear,a gear wear life feature fusion prediction method based on temporal pattern attention mechanism is proposed.Firstly,deep residual shrinkage network(DRSN)is used to extract the features of the original vibration time series signals with low signal-tonoise ratio,and the vibration features associated with gear wear evolution are obtained.Secondly,the extracted vibration features and the oil monitoring data that can intuitively reflect the wear process information are jointly input into the bi-directional long short-term memory neural network based on temporal pattern attention mechanism(TPA-BiLSTM),the complex nonlinear relationship between vibration features,oil features and gear wear process evolution is further explored to improve the prediction accuracy.The gear life cycle dynamic response and wear process signals are obtained based on the gear numerical simulation model,and the feasibility of the proposed method is verified.Finally,the proposed method is applied to the residual life prediction of gear on a test bench,and the comparison between different methods proved the validity of the proposed method.
基金the National Natural Science Foundation of China(No.52101343)State Key Laboratory of Compressor Technology(An Hui Laboratory of Compressor Technology)(No.SKL-YSJ201808/SKL-YSJ201911)。
文摘A research concerning the coupling conditions of gas leakage through suction valves and capacity regulation is performed in an industrial reciprocating compressor.Both internal flow and thermodynamic characteristic are discussed in detail.The results show that the capacity of compressor can be regulated steplessly by controlling suction valve closure moment.And then the quantitative relationship between the capacity load and the closing angle of suction valve is revealed.The capacity load and valve leakage rate show obvious different features in P-V diagrams,which makes it easier to define appropriate features for detecting cracked or broken reciprocating compressor valves under varying load conditions.A set of curves of compression work and discharge gas mass are obtained and a method for rating thermal performance of a compressor is presented using these curves.
基金supported by National Hi-tech Research and Development Program of China (863 Program, Grant No. 2007AA04Z433)Hunan Provincial Natural Science Foundation of China (Grant No. 09JJ8005)Scientific Research Foundation of Graduate School of Beijing University of Chemical and Technology,China (Grant No. 10Me002)
文摘As the differences of sensor's precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn't require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor's own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.
