A new algorithm was developed to correctly identify fault conditions and accurately monitor fault development in a mine hoist. The new method is based on the Wavelet Packet Transform (WPT) and kernel PCA (Kernel Princ...A new algorithm was developed to correctly identify fault conditions and accurately monitor fault development in a mine hoist. The new method is based on the Wavelet Packet Transform (WPT) and kernel PCA (Kernel Principal Compo- nent Analysis, KPCA). For non-linear monitoring systems the key to fault detection is the extracting of main features. The wavelet packet transform is a novel technique of signal processing that possesses excellent characteristics of time-frequency localization. It is suitable for analysing time-varying or transient signals. KPCA maps the original input features into a higher dimension feature space through a non-linear mapping. The principal components are then found in the higher dimen- sion feature space. The KPCA transformation was applied to extracting the main nonlinear features from experimental fault feature data after wavelet packet transformation. The results show that the proposed method affords credible fault detection and identification.展开更多
Multiple dominant gear meshing frequencies are present in the vibration signals collected from gearboxes and the conventional spiky features that represent initial gear fault conditions are usually difficult to detect...Multiple dominant gear meshing frequencies are present in the vibration signals collected from gearboxes and the conventional spiky features that represent initial gear fault conditions are usually difficult to detect. In order to solve this problem, we propose a new gearbox deterioration detection technique based on autoregressive modeling and hypothesis testing in this paper. A stationary autoregressive model was built by using a normal vibration signal from each shaft. The established autoregressive model was then applied to process fault signals from each shaft of a two-stage gearbox. What this paper investigated is a combined technique which unites a time-varying autoregressive model and a two sample Kolmogorov-Smimov goodness-of-fit test, to detect the deterioration of gearing system with simultaneously variable shaft speed and variable load. The time-varying autoregressive model residuals representing both healthy and faulty gear conditions were compared with the original healthy time-synchronous average signals. Compared with the traditional kurtosis statistic, this technique for gearbox deterioration detection has shown significant advantages in highlighting the presence of incipient gear fault in all different speed shafts involved in the meshing motion under variable conditions.展开更多
With the rapid development of high-speed-railway,environment around high voltage device on train roof becomes very complicated. Most train accidents happened due to occurrence of flashover on roof insulator,but the in...With the rapid development of high-speed-railway,environment around high voltage device on train roof becomes very complicated. Most train accidents happened due to occurrence of flashover on roof insulator,but the insulation condition estimation of insulator in such environment is much difficult. To ensure the insulation property of electric equipment,and guarantee the operation safety of high-speed-train,here established an instrument with high reliability which can on-line monitor insulation condition of roof insulator and give out advanced alarm before the incipient insulator flashover. The instrument consists of three parts,Data Acquisition & Sensor,Data Processing and Back Processing. Anti-interference and protection methods are processed to Rogowski coil sensor for better leakage current signal. To avoid the fluctuation from railway power supply,four modules are set to filter the power supply waveform. Through laboratory measurement,it is shown that the leakage current and the impedance angle can be detected by the instrument accurately. From the comparison of leakage current and impedance angle results under different moisture condition and the alarm operation when leakage current value reached threshold,this instrument can give out enough information for staff to understand the insulation condition of insulator.展开更多
Wind turbine blades are prone to failure due to high tip speed,rain,dust and so on.A surface condition detecting approach based on wind turbine blade aerodynamic noise is proposed.On the experimental measurement data,...Wind turbine blades are prone to failure due to high tip speed,rain,dust and so on.A surface condition detecting approach based on wind turbine blade aerodynamic noise is proposed.On the experimental measurement data,variational mode decomposition filtering and Mel spectrogram drawing are conducted first.The Mel spectrogram is divided into two halves based on frequency characteristics and then sent into the convolutional neural network.Gaussian white noise is superimposed on the original signal and the output results are assessed based on score coefficients,considering the complexity of the real environment.The surfaces of Wind turbine blades are classified into four types:standard,attachments,polishing,and serrated trailing edge.The proposed method is evaluated and the detection accuracy in complicated background conditions is found to be 99.59%.In addition to support the differentiation of trained models,utilizing proper score coefficients also permit the screening of unknown types.展开更多
基金Projects 50674086 supported by the National Natural Science Foundation of ChinaBS2006002 by the Society Development Science and Technology Planof Jiangsu Province20060290508 by the Doctoral Foundation of Ministry of Education of China
文摘A new algorithm was developed to correctly identify fault conditions and accurately monitor fault development in a mine hoist. The new method is based on the Wavelet Packet Transform (WPT) and kernel PCA (Kernel Principal Compo- nent Analysis, KPCA). For non-linear monitoring systems the key to fault detection is the extracting of main features. The wavelet packet transform is a novel technique of signal processing that possesses excellent characteristics of time-frequency localization. It is suitable for analysing time-varying or transient signals. KPCA maps the original input features into a higher dimension feature space through a non-linear mapping. The principal components are then found in the higher dimen- sion feature space. The KPCA transformation was applied to extracting the main nonlinear features from experimental fault feature data after wavelet packet transformation. The results show that the proposed method affords credible fault detection and identification.
基金supported by National Natural Science Foundation of China (Grant No. 50675232)Key Project of Ministry of Education of ChinaChongqing Municipal Natural Science Key Foundation of China (Grant No. 2007BA6021)
文摘Multiple dominant gear meshing frequencies are present in the vibration signals collected from gearboxes and the conventional spiky features that represent initial gear fault conditions are usually difficult to detect. In order to solve this problem, we propose a new gearbox deterioration detection technique based on autoregressive modeling and hypothesis testing in this paper. A stationary autoregressive model was built by using a normal vibration signal from each shaft. The established autoregressive model was then applied to process fault signals from each shaft of a two-stage gearbox. What this paper investigated is a combined technique which unites a time-varying autoregressive model and a two sample Kolmogorov-Smimov goodness-of-fit test, to detect the deterioration of gearing system with simultaneously variable shaft speed and variable load. The time-varying autoregressive model residuals representing both healthy and faulty gear conditions were compared with the original healthy time-synchronous average signals. Compared with the traditional kurtosis statistic, this technique for gearbox deterioration detection has shown significant advantages in highlighting the presence of incipient gear fault in all different speed shafts involved in the meshing motion under variable conditions.
基金supporting program of the National Science Foundation for Distinguished Young Scholars of China(Project No.51325704)the National Basic Research Program of China(973 Program,Project No.2011CB711105-4)。
文摘With the rapid development of high-speed-railway,environment around high voltage device on train roof becomes very complicated. Most train accidents happened due to occurrence of flashover on roof insulator,but the insulation condition estimation of insulator in such environment is much difficult. To ensure the insulation property of electric equipment,and guarantee the operation safety of high-speed-train,here established an instrument with high reliability which can on-line monitor insulation condition of roof insulator and give out advanced alarm before the incipient insulator flashover. The instrument consists of three parts,Data Acquisition & Sensor,Data Processing and Back Processing. Anti-interference and protection methods are processed to Rogowski coil sensor for better leakage current signal. To avoid the fluctuation from railway power supply,four modules are set to filter the power supply waveform. Through laboratory measurement,it is shown that the leakage current and the impedance angle can be detected by the instrument accurately. From the comparison of leakage current and impedance angle results under different moisture condition and the alarm operation when leakage current value reached threshold,this instrument can give out enough information for staff to understand the insulation condition of insulator.
基金funded by the National Nature Science Founda-tion of China(Grant Nos.51905469 and 11672261)the National key research and development program of China under grant number(Grant No.2019YFE0192600)。
文摘Wind turbine blades are prone to failure due to high tip speed,rain,dust and so on.A surface condition detecting approach based on wind turbine blade aerodynamic noise is proposed.On the experimental measurement data,variational mode decomposition filtering and Mel spectrogram drawing are conducted first.The Mel spectrogram is divided into two halves based on frequency characteristics and then sent into the convolutional neural network.Gaussian white noise is superimposed on the original signal and the output results are assessed based on score coefficients,considering the complexity of the real environment.The surfaces of Wind turbine blades are classified into four types:standard,attachments,polishing,and serrated trailing edge.The proposed method is evaluated and the detection accuracy in complicated background conditions is found to be 99.59%.In addition to support the differentiation of trained models,utilizing proper score coefficients also permit the screening of unknown types.
