The application of ultrasound techniques to monitor the condition of structures is becoming more prominent because these techniques can detect the early symptoms of defects such as cracks and other defects.The early d...The application of ultrasound techniques to monitor the condition of structures is becoming more prominent because these techniques can detect the early symptoms of defects such as cracks and other defects.The early detection of defects is of vital importance to avoid major failures with catastrophic consequences.An assessment of an ultrasound technique was used to investigate fatigue damage behaviour.Fatigue tests were performed according to the ASTM E466-96 standard with the attachment of an ultrasound sensor to the test specimen.AISI 1045 carbon steel was used due to its wide application in the automotive industry.A fatigue test was performed under constant loading stress at a sampling frequency of 8 Hz.Two sets of data acquisition systems were used to collect the fatigue strain signals and ultrasound signals.All of the signals were edited and analysed using a signal processing approach.Two methods were used to evaluate the signals,the integrated Kurtosis-based algorithm for z-filter technique(I-kaz) and the short-time Fourier transform(STFT).The fatigue damage behaviour was observed from the initial stage until the last stage of the fatigue test.The results of the I-kaz coefficient and the STFT spectrum were used to explain and describe the behaviour of the fatigue damage.I-kaz coefficients were ranged from 60 to 61 for strain signals and ranged from 5 to 76 for ultrasound signals.I-kaz values tend to be high at failure point due to high amplitude of respective signals.STFT spectrogram displays the colour intensity which represents the damage severity of the strain signals.I-kaz technique is found very useful and capable in assessing both stationary and non-stationary signals while STFT technique is suitable only for non-stationary signals by displaying its spectrogram.展开更多
Modern agricultural mechanization has put forward higher requirements for the intelligent defect diagnosis.However,the fault features are usually learned and classified under all speeds without considering the effects...Modern agricultural mechanization has put forward higher requirements for the intelligent defect diagnosis.However,the fault features are usually learned and classified under all speeds without considering the effects of speed fluctuation.To overcome this deficiency,a novel intelligent defect detection framework based on time-frequency transformation is presented in this work.In the framework,the samples under one speed are employed for training sparse filtering model,and the remaining samples under different speeds are adopted for testing the effectiveness.Our proposed approach contains two stages:1)the time-frequency domain signals are acquired from the mechanical raw vibration data by the short time Fourier transform algorithm,and then the defect features are extracted from time-frequency domain signals by sparse filtering algorithm;2)different defect types are classified by the softmax regression using the defect features.The proposed approach can be employed to mine available fault characteristics adaptively and is an effective intelligent method for fault detection of agricultural equipment.The fault detection performances confirm that our approach not only owns strong ability for fault classification under different speeds,but also obtains higher identification accuracy than the other methods.展开更多
The quality of the micro-mechanical machining outcome depends significantly on the tracking performance of the miniaturized linear motor drive precision stage. The tracking behavior of a direct drive design is prone t...The quality of the micro-mechanical machining outcome depends significantly on the tracking performance of the miniaturized linear motor drive precision stage. The tracking behavior of a direct drive design is prone to uncertainties such as model parameter variations and disturbances. Robust optimal tracking controller design for this kind of precision stages with mass and damping ratio uncertainties was researched. The mass and damping ratio uncertainties were modeled as the structured parametric uncertainty model. An identification method for obtaining the parametric uncertainties was developed by using unbiased least square technique. The instantaneous frequency bandwidth of the external disturbance signals was analyzed by using short time Fourier transform technique. A two loop tracking control strategy that combines the p-synthesis and the disturbance observer （DOB） techniques was proposed. The p-synthesis technique was used to design robust optimal controllers based on structured uncertainty models. By complementing the/z controller, the DOB was applied to further improving the disturbance rejection performance. To evaluate the positioning performance of the proposed control strategy, the comparative experiments were conducted on a prototype micro milling machine among four control schemes： the proposed two-loop tracking control, the single loop μ control, the PID control and the PID with DOB control. The disturbance rejection performances, the root mean square （RMS） tracking errors and the performance robustness of different control schemes were studied. The results reveal that the proposed control scheme has the best positioning performance. It reduces the maximal errors caused by disturbance forces such as friction force by 60% and the RMS errors by 63.4% compared with the PID control. Compared to PID with DOB control, it reduces the RMS errors by 29.6%.展开更多
In order to investigate the effect of sampling frequency and time on pressure fluctuations, the three-dimensional unsteady numerical simulations were conducted in a circulating water pump. Through comparison of turbul...In order to investigate the effect of sampling frequency and time on pressure fluctuations, the three-dimensional unsteady numerical simulations were conducted in a circulating water pump. Through comparison of turbulence models with hydraulic performance experiment, SST k-co model was confirmed to study the rational determination of sampling frequency and time better. The Fast Fourier Transform （FFT） technology was then adopted to process those fluctuating pressure signals obtained. On these bases, the characteristics of pressure fluctuations acting on the tongue were discussed. It is found that aliasing errors decrease at higher sampling frequency of 17 640 Hz, but not at a lower sampling frequency of 1 764 Hz. Correspondingly, an output frequency range ten-times wider is obtained at 17 640 Hz. Compared with 8R, when the sampling time is shorter, the amplitudes may be overvalued, and the frequencies and amplitudes of low-frequency fluctuations can not be well predicted. The frequencies at the tongue are in good agreement with the values calculated by formula and the frequency compositions less than the blade passing frequency are accurately predicted.展开更多
Designing optimal time and spatial difference step size is the key technology for quantum-random filtering(QSF)to realize time-varying frequency periodic signal filtering.In this paper,it was proposed to use the short...Designing optimal time and spatial difference step size is the key technology for quantum-random filtering(QSF)to realize time-varying frequency periodic signal filtering.In this paper,it was proposed to use the short-time Fourier transform(STFT)to dynamically estimate the signal to noise ratio(SNR)and relative frequency of the input time-varying frequency periodic signal.Then the model of time and space difference step size and signal to noise ratio(SNR)and relative frequency of quantum random filter is established by least square method.Finally,the parameters of the quantum filter can be determined step by step by analyzing the characteristics of the actual signal.The simulation results of single-frequency signal and frequency time-varying signal show that the proposed method can quickly and accurately design the optimal filter parameters based on the characteristics of the input signal,and achieve significant filtering effects.展开更多
基金Projects(UKM-KK-03-FRGS0118-2010,UKM-OUP-NBT-28-135/2011)supported by FRGS Universiti Kebangsaan Malaysia,Malaysia
文摘The application of ultrasound techniques to monitor the condition of structures is becoming more prominent because these techniques can detect the early symptoms of defects such as cracks and other defects.The early detection of defects is of vital importance to avoid major failures with catastrophic consequences.An assessment of an ultrasound technique was used to investigate fatigue damage behaviour.Fatigue tests were performed according to the ASTM E466-96 standard with the attachment of an ultrasound sensor to the test specimen.AISI 1045 carbon steel was used due to its wide application in the automotive industry.A fatigue test was performed under constant loading stress at a sampling frequency of 8 Hz.Two sets of data acquisition systems were used to collect the fatigue strain signals and ultrasound signals.All of the signals were edited and analysed using a signal processing approach.Two methods were used to evaluate the signals,the integrated Kurtosis-based algorithm for z-filter technique(I-kaz) and the short-time Fourier transform(STFT).The fatigue damage behaviour was observed from the initial stage until the last stage of the fatigue test.The results of the I-kaz coefficient and the STFT spectrum were used to explain and describe the behaviour of the fatigue damage.I-kaz coefficients were ranged from 60 to 61 for strain signals and ranged from 5 to 76 for ultrasound signals.I-kaz values tend to be high at failure point due to high amplitude of respective signals.STFT spectrogram displays the colour intensity which represents the damage severity of the strain signals.I-kaz technique is found very useful and capable in assessing both stationary and non-stationary signals while STFT technique is suitable only for non-stationary signals by displaying its spectrogram.
基金Project(51675262)supported by the National Natural Science Foundation of ChinaProject(2016YFD0700800)supported by the National Key Research and Development Program of China+2 种基金Project(6140210020102)supported by the Advance Research Field Fund Project of ChinaProject(NP2018304)supported by the Fundamental Research Funds for the Central Universities,ChinaProject(2017-IV-0008-0045)supported by the National Science and Technology Major Project
文摘Modern agricultural mechanization has put forward higher requirements for the intelligent defect diagnosis.However,the fault features are usually learned and classified under all speeds without considering the effects of speed fluctuation.To overcome this deficiency,a novel intelligent defect detection framework based on time-frequency transformation is presented in this work.In the framework,the samples under one speed are employed for training sparse filtering model,and the remaining samples under different speeds are adopted for testing the effectiveness.Our proposed approach contains two stages:1)the time-frequency domain signals are acquired from the mechanical raw vibration data by the short time Fourier transform algorithm,and then the defect features are extracted from time-frequency domain signals by sparse filtering algorithm;2)different defect types are classified by the softmax regression using the defect features.The proposed approach can be employed to mine available fault characteristics adaptively and is an effective intelligent method for fault detection of agricultural equipment.The fault detection performances confirm that our approach not only owns strong ability for fault classification under different speeds,but also obtains higher identification accuracy than the other methods.
基金Project(50875257) supported by the National Natural Science Foundation of China
文摘The quality of the micro-mechanical machining outcome depends significantly on the tracking performance of the miniaturized linear motor drive precision stage. The tracking behavior of a direct drive design is prone to uncertainties such as model parameter variations and disturbances. Robust optimal tracking controller design for this kind of precision stages with mass and damping ratio uncertainties was researched. The mass and damping ratio uncertainties were modeled as the structured parametric uncertainty model. An identification method for obtaining the parametric uncertainties was developed by using unbiased least square technique. The instantaneous frequency bandwidth of the external disturbance signals was analyzed by using short time Fourier transform technique. A two loop tracking control strategy that combines the p-synthesis and the disturbance observer （DOB） techniques was proposed. The p-synthesis technique was used to design robust optimal controllers based on structured uncertainty models. By complementing the/z controller, the DOB was applied to further improving the disturbance rejection performance. To evaluate the positioning performance of the proposed control strategy, the comparative experiments were conducted on a prototype micro milling machine among four control schemes： the proposed two-loop tracking control, the single loop μ control, the PID control and the PID with DOB control. The disturbance rejection performances, the root mean square （RMS） tracking errors and the performance robustness of different control schemes were studied. The results reveal that the proposed control scheme has the best positioning performance. It reduces the maximal errors caused by disturbance forces such as friction force by 60% and the RMS errors by 63.4% compared with the PID control. Compared to PID with DOB control, it reduces the RMS errors by 29.6%.
基金Project supported by the Priority Academic Development Program of Jiangsu Higher Education Institutions, ChinaProject(CXZZ12_0680) supported by Postgraduate Innovation Foundation of Jiangsu Province, ChinaProject(12JDG082) supported by the Advanced Talent Foundation of Jiangsu University, China
文摘In order to investigate the effect of sampling frequency and time on pressure fluctuations, the three-dimensional unsteady numerical simulations were conducted in a circulating water pump. Through comparison of turbulence models with hydraulic performance experiment, SST k-co model was confirmed to study the rational determination of sampling frequency and time better. The Fast Fourier Transform （FFT） technology was then adopted to process those fluctuating pressure signals obtained. On these bases, the characteristics of pressure fluctuations acting on the tongue were discussed. It is found that aliasing errors decrease at higher sampling frequency of 17 640 Hz, but not at a lower sampling frequency of 1 764 Hz. Correspondingly, an output frequency range ten-times wider is obtained at 17 640 Hz. Compared with 8R, when the sampling time is shorter, the amplitudes may be overvalued, and the frequencies and amplitudes of low-frequency fluctuations can not be well predicted. The frequencies at the tongue are in good agreement with the values calculated by formula and the frequency compositions less than the blade passing frequency are accurately predicted.
基金Projects(2017H0022,2016H6015)supported by Fujian Science and Technology Key Project,China
文摘Designing optimal time and spatial difference step size is the key technology for quantum-random filtering(QSF)to realize time-varying frequency periodic signal filtering.In this paper,it was proposed to use the short-time Fourier transform(STFT)to dynamically estimate the signal to noise ratio(SNR)and relative frequency of the input time-varying frequency periodic signal.Then the model of time and space difference step size and signal to noise ratio(SNR)and relative frequency of quantum random filter is established by least square method.Finally,the parameters of the quantum filter can be determined step by step by analyzing the characteristics of the actual signal.The simulation results of single-frequency signal and frequency time-varying signal show that the proposed method can quickly and accurately design the optimal filter parameters based on the characteristics of the input signal,and achieve significant filtering effects.
