The vibration signals of multi-fault rolling bearings under nonstationary conditions are characterized by intricate modulation features,making it difficult to identify the fault characteristic frequency.To remove the ...The vibration signals of multi-fault rolling bearings under nonstationary conditions are characterized by intricate modulation features,making it difficult to identify the fault characteristic frequency.To remove the time-varying behavior caused by speed fluctuation,the phase function of target component is necessary.However,the frequency components induced by different faults interfere with each other.More importantly,the complex sideband clusters around the characteristic frequency further hinder the spectrum interpretation.As such,we propose a demodulation spectrum analysis method for multi-fault bearing detection via chirplet path pursuit.First,the envelope signal is obtained by applying Hilbert transform to the raw signal.Second,the characteristic frequency is extracted via chirplet path pursuit,and the other underlying components are calculated by the characteristic coefficient.Then,the energy factors of all components are determined according to the time-varying behavior of instantaneous frequency.Next,the final demodulated signal is obtained by iteratively applying generalized demodulation with tunable E-factor and then the band pass filter is designed to separate the demodulated component.Finally,the fault pattern can be identified by matching the prominent peaks in the demodulation spectrum with the theoretical characteristic frequencies.The method is validated by simulated and experimental signals.展开更多
With the development of multi-signal monitoring technology,the research on multiple signal analysis and processing has become a hot subject.Mechanical equipment often works under variable working conditions,and the ac...With the development of multi-signal monitoring technology,the research on multiple signal analysis and processing has become a hot subject.Mechanical equipment often works under variable working conditions,and the acquired vibration signals are often non-stationary and nonlinear,which are difficult to be processed by traditional analysis methods.In order to solve the noise reduction problem of multiple signals under variable speed,a COT-DCS method combining the Computed Order Tracking(COT)based on Chirplet Path Pursuit(CPP)and Distributed Compressed Sensing(DCS)is proposed.Firstly,the instantaneous frequency(IF)is extracted by CPP,and the speed is obtained by fitting.Then,the speed is used for equal angle sampling of time-domain signals,and angle-domain signals are obtained by COT without a tachometer to eliminate the nonstationarity,and the angledomain signals are compressed and reconstructed by DCS to achieve noise reduction of multiple signals.The accuracy of the CPP method is verified by simulated,experimental signals and compared with some existing IF extraction methods.The COT method also shows good signal stabilization ability through simulation and experiment.Finally,combined with the comparative test of the other two algorithms and four noise reduction effect indicators,the COT-DCS based on the CPP method combines the advantages of the two algorithms and has better noise reduction effect and stability.It is shown that this method is an effective multi-signal noise reduction method.展开更多
Path planning for space vehicles is still a challenging problem although considerable progress has been made over the past decades.The major difficulties are that most of existing methods only adapt to static environm...Path planning for space vehicles is still a challenging problem although considerable progress has been made over the past decades.The major difficulties are that most of existing methods only adapt to static environment instead of dynamic one,and also can not solve the inherent constraints arising from the robot body and the exterior environment.To address these difficulties,this research aims to provide a feasible trajectory based on quadratic programming（QP） for path planning in three-dimensional space where an autonomous vehicle is requested to pursue a target while avoiding static or dynamic obstacles.First,the objective function is derived from the pursuit task which is defined in terms of the relative distance to the target,as well as the angle between the velocity and the position in the relative velocity coordinates（RVCs）.The optimization is in quadratic polynomial form according to QP formulation.Then,the avoidance task is modeled with linear constraints in RVCs.Some other constraints,such as kinematics,dynamics,and sensor range,are included.Last,simulations with typical multiple obstacles are carried out,including in static and dynamic environments and one of human-in-the-loop.The results indicate that the optimal trajectories of the autonomous robot in three-dimensional space satisfy the required performances.Therefore,the QP model proposed in this paper not only adapts to dynamic environment with uncertainty,but also can satisfy all kinds of constraints,and it provides an efficient approach to solve the problems of path planning in three-dimensional space.展开更多
传统纯跟踪控制器在面向实际应用时往往难以较好地处理传感器信号延时、执行器响应滞后等因素带来的控制滞后问题以及欠缺内外部扰动干预下的自主抗干扰能力;同时在跟踪临近终点的目标点时,因前视距离作为控制律的分母且逐渐减小,导致...传统纯跟踪控制器在面向实际应用时往往难以较好地处理传感器信号延时、执行器响应滞后等因素带来的控制滞后问题以及欠缺内外部扰动干预下的自主抗干扰能力;同时在跟踪临近终点的目标点时,因前视距离作为控制律的分母且逐渐减小,导致输入转角值发生突变且伴随方向盘出现不稳定地晃动现象,影响行驶稳定性及人员乘坐体验感。针对此,提出了一种基于改进纯跟踪的路径跟踪控制器。首先,构建了纯跟踪控制器的基础模型并为改善纯跟踪控制的滞后影响,建立了一种结合规划路径和规划速度信息的道路预瞄模型。然后考虑系统因内部及外部环境影响产生的未知干扰并设计非线性扩张状态观测器(nonlinear expanded state observer,NESO)进行扰动估计及实时补偿,以提升纯跟踪控制器的抗干扰能力。并进一步通过转向稳定调节模型以改善纯跟踪控制器临近终点时的转角突变问题。最终,基于软件在环动力学仿真平台以不同初始航向及存在初始偏差工况下测试所提控制器的有效性,并进一步在实车平台进行闭环测试验证,实验结果表明改进的纯跟踪算法具有良好的跟踪精度和转向平顺性。展开更多
基金Project(2018YJS137)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(51275030)supported by the National Natural Science Foundation of China
文摘The vibration signals of multi-fault rolling bearings under nonstationary conditions are characterized by intricate modulation features,making it difficult to identify the fault characteristic frequency.To remove the time-varying behavior caused by speed fluctuation,the phase function of target component is necessary.However,the frequency components induced by different faults interfere with each other.More importantly,the complex sideband clusters around the characteristic frequency further hinder the spectrum interpretation.As such,we propose a demodulation spectrum analysis method for multi-fault bearing detection via chirplet path pursuit.First,the envelope signal is obtained by applying Hilbert transform to the raw signal.Second,the characteristic frequency is extracted via chirplet path pursuit,and the other underlying components are calculated by the characteristic coefficient.Then,the energy factors of all components are determined according to the time-varying behavior of instantaneous frequency.Next,the final demodulated signal is obtained by iteratively applying generalized demodulation with tunable E-factor and then the band pass filter is designed to separate the demodulated component.Finally,the fault pattern can be identified by matching the prominent peaks in the demodulation spectrum with the theoretical characteristic frequencies.The method is validated by simulated and experimental signals.
基金the financial support of this work by the National Natural Science Foundation of Hebei Province China under Grant E2020208052.
文摘With the development of multi-signal monitoring technology,the research on multiple signal analysis and processing has become a hot subject.Mechanical equipment often works under variable working conditions,and the acquired vibration signals are often non-stationary and nonlinear,which are difficult to be processed by traditional analysis methods.In order to solve the noise reduction problem of multiple signals under variable speed,a COT-DCS method combining the Computed Order Tracking(COT)based on Chirplet Path Pursuit(CPP)and Distributed Compressed Sensing(DCS)is proposed.Firstly,the instantaneous frequency(IF)is extracted by CPP,and the speed is obtained by fitting.Then,the speed is used for equal angle sampling of time-domain signals,and angle-domain signals are obtained by COT without a tachometer to eliminate the nonstationarity,and the angledomain signals are compressed and reconstructed by DCS to achieve noise reduction of multiple signals.The accuracy of the CPP method is verified by simulated,experimental signals and compared with some existing IF extraction methods.The COT method also shows good signal stabilization ability through simulation and experiment.Finally,combined with the comparative test of the other two algorithms and four noise reduction effect indicators,the COT-DCS based on the CPP method combines the advantages of the two algorithms and has better noise reduction effect and stability.It is shown that this method is an effective multi-signal noise reduction method.
基金supported by National Natural Science Foundation of China (Grant Nos. 61035005,61075087)Hubei Provincial Natural Science Foundation of China (Grant No. 2010CDA005)Hubei Provincial Education Department Foundation of China (Grant No.Q20111105)
文摘Path planning for space vehicles is still a challenging problem although considerable progress has been made over the past decades.The major difficulties are that most of existing methods only adapt to static environment instead of dynamic one,and also can not solve the inherent constraints arising from the robot body and the exterior environment.To address these difficulties,this research aims to provide a feasible trajectory based on quadratic programming（QP） for path planning in three-dimensional space where an autonomous vehicle is requested to pursue a target while avoiding static or dynamic obstacles.First,the objective function is derived from the pursuit task which is defined in terms of the relative distance to the target,as well as the angle between the velocity and the position in the relative velocity coordinates（RVCs）.The optimization is in quadratic polynomial form according to QP formulation.Then,the avoidance task is modeled with linear constraints in RVCs.Some other constraints,such as kinematics,dynamics,and sensor range,are included.Last,simulations with typical multiple obstacles are carried out,including in static and dynamic environments and one of human-in-the-loop.The results indicate that the optimal trajectories of the autonomous robot in three-dimensional space satisfy the required performances.Therefore,the QP model proposed in this paper not only adapts to dynamic environment with uncertainty,but also can satisfy all kinds of constraints,and it provides an efficient approach to solve the problems of path planning in three-dimensional space.
文摘传统纯跟踪控制器在面向实际应用时往往难以较好地处理传感器信号延时、执行器响应滞后等因素带来的控制滞后问题以及欠缺内外部扰动干预下的自主抗干扰能力;同时在跟踪临近终点的目标点时,因前视距离作为控制律的分母且逐渐减小,导致输入转角值发生突变且伴随方向盘出现不稳定地晃动现象,影响行驶稳定性及人员乘坐体验感。针对此,提出了一种基于改进纯跟踪的路径跟踪控制器。首先,构建了纯跟踪控制器的基础模型并为改善纯跟踪控制的滞后影响,建立了一种结合规划路径和规划速度信息的道路预瞄模型。然后考虑系统因内部及外部环境影响产生的未知干扰并设计非线性扩张状态观测器(nonlinear expanded state observer,NESO)进行扰动估计及实时补偿,以提升纯跟踪控制器的抗干扰能力。并进一步通过转向稳定调节模型以改善纯跟踪控制器临近终点时的转角突变问题。最终,基于软件在环动力学仿真平台以不同初始航向及存在初始偏差工况下测试所提控制器的有效性,并进一步在实车平台进行闭环测试验证,实验结果表明改进的纯跟踪算法具有良好的跟踪精度和转向平顺性。
