Tool wear can cause dimensional accuracy and poor surface quality in milling process. During the ... more Tool wear can cause dimensional accuracy and poor surface quality in milling process. During the operation of tool wear, it can also cause breakage and damage of the workpieces. To prevent these conditions, it's important that the tool wear is monitored and the remaining useful life (RUL) is predicted in real time. In this paper, time domain and frequency domain statistical features are firstly extracted using multi-sensory fusion method, including the cutting force, vibration and acoustic emission sensor. Seven eigenvectors are selected as the input of the prediction model based on the distance correlation coefficient between 140 feature vectors and the wear value, which provide the most sensitive features to wear faults. The paper establishes a nonlinear relationship between high-dimensional feature vectors and tools wear based on the evolving connectionist system (ECoS), which uses the incremental learning algorithm to realize real-time prediction of the tools wear. Finally, using the wear value predicted by ECoS as hidden state sequence of Hidden Semi-Markov Model (HSMM), the RUL prediction of the tool based on HMM is established. The 2010 PHM challenge data were used to train the model. The experimental result shows that in comparison with artificial neural network, the ECoS model has higher prediction accuracy, and its mean RMSE error for three tools is 14.8. In comparison with the RUL prediction of HMM model, Probability-based RUL prediction of HSMM is more stable. INDEX TERMS Multi-sensor fusion, evolving connectionist system, incremental learning algorithm, HSMM, remaining useful life prediction. ABBREVIATIONS RUL Remaining useful life. HMM Hidden markov model. ECoS Evolving connectionist system. PHM Prognostic and health management. HSMM Hidden semi-markov model. RMSE Root mean square error. The associate editor coordinating the review of this manuscript and approving it for publication was Zhaojun Steven Li .
With the rapid development of Internet and information technology, the network has been deeply in... more With the rapid development of Internet and information technology, the network has been deeply integrated into our lives. However, the rich services and applications of the Internet bring more security problems. Technology of network security assessment is a strategy to deal with the problems of network security at present. The basic concepts and research significance are shed light on. This paper described the architecture of network security assessment, and analyzed the research status mainly focusing on the method based on mathematical model, the method based on knowledge reasoning and the method based on pattern recognition. Then the advantages and disadvantages were pointed out respectively. Finally, some future research directions were given at the end.
Forecasting non-stationary stochastic time series represents a rather complex problem. The reason... more Forecasting non-stationary stochastic time series represents a rather complex problem. The reason is that such temporal series are not only self-similar but also exhibit a Long-Range Dependence (LRD). As it is known, the Fractional Brown Motion (FBM) can generate a non-stationary stochastic time series with self-similarity and LRD. In this study we investigate the properties of the LRD for identification of selfsimilarity and the LRD of non-stationary stochastic series by Hurst exponent. Parameter estimation is proposed for Stochastic differential Equation (SDE) of FBM based on Maximum Likelihood Estimation (MLE), and proves the convergence of MLE. The SDE is discretized.The difference equation constructed is the prediction model of the iterative format based on FBM. Monte Carlo simulation is applied to check the validity and accuracy of parameter estimation. We also give a practical example to demonstrate the appropriateness of the predictive model.
In quantum key distribution experiments, ground motion is usually used to simulate satellite-base... more In quantum key distribution experiments, ground motion is usually used to simulate satellite-based motion. The posture fluctuation of the platform affects the normal operation of the acquisition, tracking, and pointing (abbreviated as ATP) system seriously. To achieve the verification of the ground motion platform, the ATP parameters of the ground simulation motion system cannot be designed only according to the satellite-based ATP parameters. To solve this problem, a set of initial pointing system and inertial stabilization system is added to the simulation ATP system. This provides a technical solution for the ground simulation ATP system similar to the satellite-based motion platform. In the meanwhile, a tracking control strategy based on the identification method is proposed by establishing identification symbols. Compared with traditional proportion, integral, and differential (abbreviated as PID) control, this method overcomes the shortcoming of tentative modification of the c...
The safety and reliability of the mechanical system in the industrial process determines the qual... more The safety and reliability of the mechanical system in the industrial process determines the quality of products. Whether the fault can be identified and classified in time is the key to ensure the safe operation of the system and arrange the appropriate maintenance plan to restrain the deterioration of the fault. However, with the rapid development of manufacturing digitization, how to process large amounts of data quickly and accurately is faced with many problems. In this paper, a pattern recognition method of cyclic GMM-FCM (CGF) based on joint time-domain features is proposed. Firstly, the concept of joint time-domain features based on Vold-Kalman filter (VKF) is proposed. It retains the integrity of the signal components and avoids the problem of dimension disaster caused by anomaly detection, which laid a foundation for the accurate classification of sensitive feature sets. Secondly, a pattern recognition method of cyclic GMM-FCM is proposed. It can eliminate global and local...
The paper is aimed to analyze and get a further understanding of the dynamic characteristics of t... more The paper is aimed to analyze and get a further understanding of the dynamic characteristics of the tire by means of computing modal analysis. A tire modal testing system is built on the basis of the previous studies. We also attempt to study how the amplitude of exciting force and loading changes affect the tire modal test, which lays a foundation for applying the tire modal test into practical use. Besides, the relations of experimental modal and computing modal will also be analyzed. The analysis and application of the tire modal lay a solid foundation for medium-high frequency tire mechanical model which is being established by our research group. And it takes advantage of HYPERMESH and ANSYS to set up the finite element model of the tire and to analyze the tire structural dynamics, laying a foundation for setting, recognizing the correctness of experimental modal parameter as well as studying the relationship between calculating modal and experimental modal. And it also studies the law of tire's dynamic characteristics and its reason. The third part makes a summary of the full text.
In this paper, the alternating direction method of multipliers (ADMM) algorithm is applied to the... more In this paper, the alternating direction method of multipliers (ADMM) algorithm is applied to the compressed sensing theory to realize the sparse optimization of vibration signal. Solving the basis pursuit problem for minimizing theL1norm minimization under the equality constraints, the sparse matrix obtained by the ADMM algorithm can be reconstructed by inverse sparse orthogonal matrix inversion. This paper analyzes common sparse orthogonal basis on the reconstruction results, that is, discrete Fourier orthogonal basis, discrete cosine orthogonal basis, and discrete wavelet orthogonal basis. In particular, we will show that, from the point of view of central tendency, the discrete cosine orthogonal basis is more suitable, for instance, at the vibration signal data because its error is close to zero. Moreover, using the discrete wavelet transform in signal reconstruction there still are some outliers but the error is unstable. We also use the time complex degree and validity, for th...
The European physical journal. E, Soft matter, 2017
Light transmittance of a short-pitch deformed-helix ferroelectric liquid crystal cell was numeric... more Light transmittance of a short-pitch deformed-helix ferroelectric liquid crystal cell was numerically studied when low and high voltage frequencies ([Formula: see text] kHz and [Formula: see text] kHz, respectively) are applied to the cell. The reported finding in the decay of light transmittance at increasing frequency is obtained due to reasonable simplifications of the temporal dependence of director's azimuthal angle. By taking experimentally known data, we numerically demonstrate that the increase in temperature of ferroelectric liquid crystals yields an insignificant change of light transmittance within all visible spectra.
Gradual degradation of the bearing vibration signal is usually studied as a nonstationary stochas... more Gradual degradation of the bearing vibration signal is usually studied as a nonstationary stochastic time series. Roller bearings are working at high speed in a heavy load environment so that the combination of bearing faults gradually degraded during the rotation might lead to unpredicted catastrophic accidents. The degradation process has the property of long-range dependence (LRD), so that the fractional Brownian motion (fBm) is taken into account for a prediction model. Because of the dramatic changes in the bearing degradation process, the Hurst exponent that describes the fBm will change during the degradation process. A priori Hurst value of the conventional fBm in the prediction is fixed, thus inducing a minor accuracy of the prediction. To avoid this problem, we propose an improved prediction method. Based on the following steps, at the initial data processing, a skip-over factor is selected as the characteristics parameter of the bearing degradation process. A multifractio...
International Journal of Mechanical Engineering and Applications, 2015
Cutting tool wear is a very complex process. Various factors have a direct or indirect effect on ... more Cutting tool wear is a very complex process. Various factors have a direct or indirect effect on cutting tool wear, resulting in uncertainty, so it is difficult for experimental data and result to have good stability. However, Vibration analysis is a very important means for condition monitoring and fault diagnosis. This paper aims to study the methods of tool vibration signal processing, pattern recognition and trend prediction. Collected on tool vibration signal at different times, wavelet noise reduction is used to pretreat the vibration signals. Then, for the self-similar vibration signals, we propose the fractional Brownian motion (FBM) theory with long-range dependence (LRD). Combined with Wigner-Ville spectrum, characteristic parameter can be extracted, so the cutting tool wear state can be determined according to fractal dimension and average slope of the fitting curve of the logarithm power spectrum. Finally, we use FBM model to predict the trend of tool vibration signals. Experiments show that the methods have a good effect on tool wear state recognition and trend prediction.
The basic theories of load forecasting on the power system are summarized. Fractal theory, which ... more The basic theories of load forecasting on the power system are summarized. Fractal theory, which is a new algorithm applied to load forecasting, is introduced. Based on the fractal dimension and fractal interpolation function theories, the correlation algorithms are applied to the model of short-term load forecasting. According to the process of load forecasting, the steps of every process are designed, including load data preprocessing, similar day selecting, short-term load forecasting, and load curve drawing. The attractor is obtained using an improved deterministic algorithm based on the fractal interpolation function, a day's load is predicted by three days' historical loads, the maximum relative error is within 3.7%, and the average relative error is within 1.6%. The experimental result shows the accuracy of this prediction method, which has a certain application reference value in the field of short-term load prediction.
Tool wear can cause dimensional accuracy and poor surface quality in milling process. During the ... more Tool wear can cause dimensional accuracy and poor surface quality in milling process. During the operation of tool wear, it can also cause breakage and damage of the workpieces. To prevent these conditions, it's important that the tool wear is monitored and the remaining useful life (RUL) is predicted in real time. In this paper, time domain and frequency domain statistical features are firstly extracted using multi-sensory fusion method, including the cutting force, vibration and acoustic emission sensor. Seven eigenvectors are selected as the input of the prediction model based on the distance correlation coefficient between 140 feature vectors and the wear value, which provide the most sensitive features to wear faults. The paper establishes a nonlinear relationship between high-dimensional feature vectors and tools wear based on the evolving connectionist system (ECoS), which uses the incremental learning algorithm to realize real-time prediction of the tools wear. Finally, using the wear value predicted by ECoS as hidden state sequence of Hidden Semi-Markov Model (HSMM), the RUL prediction of the tool based on HMM is established. The 2010 PHM challenge data were used to train the model. The experimental result shows that in comparison with artificial neural network, the ECoS model has higher prediction accuracy, and its mean RMSE error for three tools is 14.8. In comparison with the RUL prediction of HMM model, Probability-based RUL prediction of HSMM is more stable. INDEX TERMS Multi-sensor fusion, evolving connectionist system, incremental learning algorithm, HSMM, remaining useful life prediction. ABBREVIATIONS RUL Remaining useful life. HMM Hidden markov model. ECoS Evolving connectionist system. PHM Prognostic and health management. HSMM Hidden semi-markov model. RMSE Root mean square error. The associate editor coordinating the review of this manuscript and approving it for publication was Zhaojun Steven Li .
With the rapid development of Internet and information technology, the network has been deeply in... more With the rapid development of Internet and information technology, the network has been deeply integrated into our lives. However, the rich services and applications of the Internet bring more security problems. Technology of network security assessment is a strategy to deal with the problems of network security at present. The basic concepts and research significance are shed light on. This paper described the architecture of network security assessment, and analyzed the research status mainly focusing on the method based on mathematical model, the method based on knowledge reasoning and the method based on pattern recognition. Then the advantages and disadvantages were pointed out respectively. Finally, some future research directions were given at the end.
Forecasting non-stationary stochastic time series represents a rather complex problem. The reason... more Forecasting non-stationary stochastic time series represents a rather complex problem. The reason is that such temporal series are not only self-similar but also exhibit a Long-Range Dependence (LRD). As it is known, the Fractional Brown Motion (FBM) can generate a non-stationary stochastic time series with self-similarity and LRD. In this study we investigate the properties of the LRD for identification of selfsimilarity and the LRD of non-stationary stochastic series by Hurst exponent. Parameter estimation is proposed for Stochastic differential Equation (SDE) of FBM based on Maximum Likelihood Estimation (MLE), and proves the convergence of MLE. The SDE is discretized.The difference equation constructed is the prediction model of the iterative format based on FBM. Monte Carlo simulation is applied to check the validity and accuracy of parameter estimation. We also give a practical example to demonstrate the appropriateness of the predictive model.
In quantum key distribution experiments, ground motion is usually used to simulate satellite-base... more In quantum key distribution experiments, ground motion is usually used to simulate satellite-based motion. The posture fluctuation of the platform affects the normal operation of the acquisition, tracking, and pointing (abbreviated as ATP) system seriously. To achieve the verification of the ground motion platform, the ATP parameters of the ground simulation motion system cannot be designed only according to the satellite-based ATP parameters. To solve this problem, a set of initial pointing system and inertial stabilization system is added to the simulation ATP system. This provides a technical solution for the ground simulation ATP system similar to the satellite-based motion platform. In the meanwhile, a tracking control strategy based on the identification method is proposed by establishing identification symbols. Compared with traditional proportion, integral, and differential (abbreviated as PID) control, this method overcomes the shortcoming of tentative modification of the c...
The safety and reliability of the mechanical system in the industrial process determines the qual... more The safety and reliability of the mechanical system in the industrial process determines the quality of products. Whether the fault can be identified and classified in time is the key to ensure the safe operation of the system and arrange the appropriate maintenance plan to restrain the deterioration of the fault. However, with the rapid development of manufacturing digitization, how to process large amounts of data quickly and accurately is faced with many problems. In this paper, a pattern recognition method of cyclic GMM-FCM (CGF) based on joint time-domain features is proposed. Firstly, the concept of joint time-domain features based on Vold-Kalman filter (VKF) is proposed. It retains the integrity of the signal components and avoids the problem of dimension disaster caused by anomaly detection, which laid a foundation for the accurate classification of sensitive feature sets. Secondly, a pattern recognition method of cyclic GMM-FCM is proposed. It can eliminate global and local...
The paper is aimed to analyze and get a further understanding of the dynamic characteristics of t... more The paper is aimed to analyze and get a further understanding of the dynamic characteristics of the tire by means of computing modal analysis. A tire modal testing system is built on the basis of the previous studies. We also attempt to study how the amplitude of exciting force and loading changes affect the tire modal test, which lays a foundation for applying the tire modal test into practical use. Besides, the relations of experimental modal and computing modal will also be analyzed. The analysis and application of the tire modal lay a solid foundation for medium-high frequency tire mechanical model which is being established by our research group. And it takes advantage of HYPERMESH and ANSYS to set up the finite element model of the tire and to analyze the tire structural dynamics, laying a foundation for setting, recognizing the correctness of experimental modal parameter as well as studying the relationship between calculating modal and experimental modal. And it also studies the law of tire's dynamic characteristics and its reason. The third part makes a summary of the full text.
In this paper, the alternating direction method of multipliers (ADMM) algorithm is applied to the... more In this paper, the alternating direction method of multipliers (ADMM) algorithm is applied to the compressed sensing theory to realize the sparse optimization of vibration signal. Solving the basis pursuit problem for minimizing theL1norm minimization under the equality constraints, the sparse matrix obtained by the ADMM algorithm can be reconstructed by inverse sparse orthogonal matrix inversion. This paper analyzes common sparse orthogonal basis on the reconstruction results, that is, discrete Fourier orthogonal basis, discrete cosine orthogonal basis, and discrete wavelet orthogonal basis. In particular, we will show that, from the point of view of central tendency, the discrete cosine orthogonal basis is more suitable, for instance, at the vibration signal data because its error is close to zero. Moreover, using the discrete wavelet transform in signal reconstruction there still are some outliers but the error is unstable. We also use the time complex degree and validity, for th...
The European physical journal. E, Soft matter, 2017
Light transmittance of a short-pitch deformed-helix ferroelectric liquid crystal cell was numeric... more Light transmittance of a short-pitch deformed-helix ferroelectric liquid crystal cell was numerically studied when low and high voltage frequencies ([Formula: see text] kHz and [Formula: see text] kHz, respectively) are applied to the cell. The reported finding in the decay of light transmittance at increasing frequency is obtained due to reasonable simplifications of the temporal dependence of director's azimuthal angle. By taking experimentally known data, we numerically demonstrate that the increase in temperature of ferroelectric liquid crystals yields an insignificant change of light transmittance within all visible spectra.
Gradual degradation of the bearing vibration signal is usually studied as a nonstationary stochas... more Gradual degradation of the bearing vibration signal is usually studied as a nonstationary stochastic time series. Roller bearings are working at high speed in a heavy load environment so that the combination of bearing faults gradually degraded during the rotation might lead to unpredicted catastrophic accidents. The degradation process has the property of long-range dependence (LRD), so that the fractional Brownian motion (fBm) is taken into account for a prediction model. Because of the dramatic changes in the bearing degradation process, the Hurst exponent that describes the fBm will change during the degradation process. A priori Hurst value of the conventional fBm in the prediction is fixed, thus inducing a minor accuracy of the prediction. To avoid this problem, we propose an improved prediction method. Based on the following steps, at the initial data processing, a skip-over factor is selected as the characteristics parameter of the bearing degradation process. A multifractio...
International Journal of Mechanical Engineering and Applications, 2015
Cutting tool wear is a very complex process. Various factors have a direct or indirect effect on ... more Cutting tool wear is a very complex process. Various factors have a direct or indirect effect on cutting tool wear, resulting in uncertainty, so it is difficult for experimental data and result to have good stability. However, Vibration analysis is a very important means for condition monitoring and fault diagnosis. This paper aims to study the methods of tool vibration signal processing, pattern recognition and trend prediction. Collected on tool vibration signal at different times, wavelet noise reduction is used to pretreat the vibration signals. Then, for the self-similar vibration signals, we propose the fractional Brownian motion (FBM) theory with long-range dependence (LRD). Combined with Wigner-Ville spectrum, characteristic parameter can be extracted, so the cutting tool wear state can be determined according to fractal dimension and average slope of the fitting curve of the logarithm power spectrum. Finally, we use FBM model to predict the trend of tool vibration signals. Experiments show that the methods have a good effect on tool wear state recognition and trend prediction.
The basic theories of load forecasting on the power system are summarized. Fractal theory, which ... more The basic theories of load forecasting on the power system are summarized. Fractal theory, which is a new algorithm applied to load forecasting, is introduced. Based on the fractal dimension and fractal interpolation function theories, the correlation algorithms are applied to the model of short-term load forecasting. According to the process of load forecasting, the steps of every process are designed, including load data preprocessing, similar day selecting, short-term load forecasting, and load curve drawing. The attractor is obtained using an improved deterministic algorithm based on the fractal interpolation function, a day's load is predicted by three days' historical loads, the maximum relative error is within 3.7%, and the average relative error is within 1.6%. The experimental result shows the accuracy of this prediction method, which has a certain application reference value in the field of short-term load prediction.
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