The Experts below are selected from a list of 3423 Experts worldwide ranked by ideXlab platform
Haidong Shao - One of the best experts on this subject based on the ideXlab platform.
-
electric locomotive bearing fault diagnosis using a novel convolutional Deep Belief Network
IEEE Transactions on Industrial Electronics, 2018Co-Authors: Haidong Shao, Hongkai Jiang, Haizhou Zhang, Tianchen LiangAbstract:Bearing fault diagnosis is of significance to enhance the reliability and security of electric locomotive. In this paper, a novel convolutional Deep Belief Network (CDBN) is proposed for bearing fault diagnosis. First, an auto-encoder is used to compress data and reduce the dimension. Second, a novel CDBN is constructed with Gaussian visible units to learn the representative features. Third, exponential moving average is employed to improve the performance of the constructed Deep model. The proposed method is applied to analyze experimental signals collected from electric locomotive bearings. The results show that the proposed method is more effective than the traditional methods and standard Deep learning methods.
-
rolling bearing fault feature learning using improved convolutional Deep Belief Network with compressed sensing
Mechanical Systems and Signal Processing, 2018Co-Authors: Haidong Shao, Hongkai Jiang, Haizhou Zhang, Wenjing Duan, Tianchen LiangAbstract:Abstract The vibration signals collected from rolling bearing are usually complex and non-stationary with heavy background noise. Therefore, it is a great challenge to efficiently learn the representative fault features of the collected vibration signals. In this paper, a novel method called improved convolutional Deep Belief Network (CDBN) with compressed sensing (CS) is developed for feature learning and fault diagnosis of rolling bearing. Firstly, CS is adopted for reducing the vibration data amount to improve analysis efficiency. Secondly, a new CDBN model is constructed with Gaussian visible units to enhance the feature learning ability for the compressed data. Finally, exponential moving average (EMA) technique is employed to improve the generalization performance of the constructed Deep model. The developed method is applied to analyze the experimental rolling bearing vibration signals. The results confirm that the developed method is more effective than the traditional methods.
-
rolling bearing fault diagnosis using adaptive Deep Belief Network with dual tree complex wavelet packet
Isa Transactions, 2017Co-Authors: Haidong Shao, Hongkai Jiang, Fuan Wang, Yanan WangAbstract:Abstract Automatic and accurate identification of rolling bearing fault categories, especially for the fault severities and compound faults, is a challenge in rotating machinery fault diagnosis. For this purpose, a novel method called adaptive Deep Belief Network (DBN) with dual-tree complex wavelet packet (DTCWPT) is developed in this paper. DTCWPT is used to preprocess the vibration signals to refine the fault characteristics information, and an original feature set is designed from each frequency-band signal of DTCWPT. An adaptive DBN is constructed to improve the convergence rate and identification accuracy with multiple stacked adaptive restricted Boltzmann machines (RBMs). The proposed method is applied to the fault diagnosis of rolling bearings. The results confirm that the proposed method is more effective than the existing methods.
Tianchen Liang - One of the best experts on this subject based on the ideXlab platform.
-
electric locomotive bearing fault diagnosis using a novel convolutional Deep Belief Network
IEEE Transactions on Industrial Electronics, 2018Co-Authors: Haidong Shao, Hongkai Jiang, Haizhou Zhang, Tianchen LiangAbstract:Bearing fault diagnosis is of significance to enhance the reliability and security of electric locomotive. In this paper, a novel convolutional Deep Belief Network (CDBN) is proposed for bearing fault diagnosis. First, an auto-encoder is used to compress data and reduce the dimension. Second, a novel CDBN is constructed with Gaussian visible units to learn the representative features. Third, exponential moving average is employed to improve the performance of the constructed Deep model. The proposed method is applied to analyze experimental signals collected from electric locomotive bearings. The results show that the proposed method is more effective than the traditional methods and standard Deep learning methods.
-
rolling bearing fault feature learning using improved convolutional Deep Belief Network with compressed sensing
Mechanical Systems and Signal Processing, 2018Co-Authors: Haidong Shao, Hongkai Jiang, Haizhou Zhang, Wenjing Duan, Tianchen LiangAbstract:Abstract The vibration signals collected from rolling bearing are usually complex and non-stationary with heavy background noise. Therefore, it is a great challenge to efficiently learn the representative fault features of the collected vibration signals. In this paper, a novel method called improved convolutional Deep Belief Network (CDBN) with compressed sensing (CS) is developed for feature learning and fault diagnosis of rolling bearing. Firstly, CS is adopted for reducing the vibration data amount to improve analysis efficiency. Secondly, a new CDBN model is constructed with Gaussian visible units to enhance the feature learning ability for the compressed data. Finally, exponential moving average (EMA) technique is employed to improve the generalization performance of the constructed Deep model. The developed method is applied to analyze the experimental rolling bearing vibration signals. The results confirm that the developed method is more effective than the traditional methods.
Hongkai Jiang - One of the best experts on this subject based on the ideXlab platform.
-
electric locomotive bearing fault diagnosis using a novel convolutional Deep Belief Network
IEEE Transactions on Industrial Electronics, 2018Co-Authors: Haidong Shao, Hongkai Jiang, Haizhou Zhang, Tianchen LiangAbstract:Bearing fault diagnosis is of significance to enhance the reliability and security of electric locomotive. In this paper, a novel convolutional Deep Belief Network (CDBN) is proposed for bearing fault diagnosis. First, an auto-encoder is used to compress data and reduce the dimension. Second, a novel CDBN is constructed with Gaussian visible units to learn the representative features. Third, exponential moving average is employed to improve the performance of the constructed Deep model. The proposed method is applied to analyze experimental signals collected from electric locomotive bearings. The results show that the proposed method is more effective than the traditional methods and standard Deep learning methods.
-
rolling bearing fault feature learning using improved convolutional Deep Belief Network with compressed sensing
Mechanical Systems and Signal Processing, 2018Co-Authors: Haidong Shao, Hongkai Jiang, Haizhou Zhang, Wenjing Duan, Tianchen LiangAbstract:Abstract The vibration signals collected from rolling bearing are usually complex and non-stationary with heavy background noise. Therefore, it is a great challenge to efficiently learn the representative fault features of the collected vibration signals. In this paper, a novel method called improved convolutional Deep Belief Network (CDBN) with compressed sensing (CS) is developed for feature learning and fault diagnosis of rolling bearing. Firstly, CS is adopted for reducing the vibration data amount to improve analysis efficiency. Secondly, a new CDBN model is constructed with Gaussian visible units to enhance the feature learning ability for the compressed data. Finally, exponential moving average (EMA) technique is employed to improve the generalization performance of the constructed Deep model. The developed method is applied to analyze the experimental rolling bearing vibration signals. The results confirm that the developed method is more effective than the traditional methods.
-
rolling bearing fault diagnosis using adaptive Deep Belief Network with dual tree complex wavelet packet
Isa Transactions, 2017Co-Authors: Haidong Shao, Hongkai Jiang, Fuan Wang, Yanan WangAbstract:Abstract Automatic and accurate identification of rolling bearing fault categories, especially for the fault severities and compound faults, is a challenge in rotating machinery fault diagnosis. For this purpose, a novel method called adaptive Deep Belief Network (DBN) with dual-tree complex wavelet packet (DTCWPT) is developed in this paper. DTCWPT is used to preprocess the vibration signals to refine the fault characteristics information, and an original feature set is designed from each frequency-band signal of DTCWPT. An adaptive DBN is constructed to improve the convergence rate and identification accuracy with multiple stacked adaptive restricted Boltzmann machines (RBMs). The proposed method is applied to the fault diagnosis of rolling bearings. The results confirm that the proposed method is more effective than the existing methods.
Houbing Song - One of the best experts on this subject based on the ideXlab platform.
-
Network traffic prediction based on Deep Belief Network and spatiotemporal compressive sensing in wireless mesh backbone Networks
Wireless Communications and Mobile Computing, 2018Co-Authors: Laisen Nie, Houbing Song, Xiaojie Wang, Liangtian Wan, Dingde JiangAbstract:Wireless mesh Network is prevalent for providing a decentralized access for users and other intelligent devices. Meanwhile, it can be employed as the infrastructure of the last few miles connectivity for various Network applications, for example, Internet of Things (IoT) and mobile Networks. For a wireless mesh backbone Network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for Network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a Network traffic prediction method based on a Deep learning architecture and the Spatiotemporal Compressive Sensing method. The proposed method first adopts discrete wavelet transform to extract the low-pass component of Network traffic that describes the long-range dependence of itself. Then, a prediction model is built by learning a Deep architecture based on the Deep Belief Network from the extracted low-pass component. Otherwise, for the remaining high-pass component that expresses the gusty and irregular fluctuations of Network traffic, the Spatiotemporal Compressive Sensing method is adopted to predict it. Based on the predictors of two components, we can obtain a predictor of Network traffic. From the simulation, the proposed prediction method outperforms three existing methods.
-
Network traffic prediction based on Deep Belief Network in wireless mesh backbone Networks
IEEE Wireless Communications and Networking Conference WCNC, 2017Co-Authors: Laisen Nie, Shui Yu, Dingde Jiang, Houbing SongAbstract:Wireless mesh Network is prevalent for providing a decentralized access for users. For a wireless mesh backbone Network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for Network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a Network traffic prediction method based on a Deep Belief Network and a Gaussian model. The proposed method first adopts discrete wavelet transform to extract the low-pass component of Network traffic that describes the long-range dependence of itself. Then a prediction model is built by learning a Deep Belief Network from the extracted low-pass component. Otherwise, for the rest high-pass component that expresses the gusty and irregular fluctuations of Network traffic, a Gaussian model is used to model it. We estimate the parameters of the Gaussian model by the maximum likelihood method. Then we predict the high-pass component by the built model. Based on the predictors of two components, we can obtain a predictor of Network traffic. From the simulation, the proposed prediction method outperforms three existing methods.
Laisen Nie - One of the best experts on this subject based on the ideXlab platform.
-
Network traffic prediction based on Deep Belief Network and spatiotemporal compressive sensing in wireless mesh backbone Networks
Wireless Communications and Mobile Computing, 2018Co-Authors: Laisen Nie, Houbing Song, Xiaojie Wang, Liangtian Wan, Dingde JiangAbstract:Wireless mesh Network is prevalent for providing a decentralized access for users and other intelligent devices. Meanwhile, it can be employed as the infrastructure of the last few miles connectivity for various Network applications, for example, Internet of Things (IoT) and mobile Networks. For a wireless mesh backbone Network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for Network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a Network traffic prediction method based on a Deep learning architecture and the Spatiotemporal Compressive Sensing method. The proposed method first adopts discrete wavelet transform to extract the low-pass component of Network traffic that describes the long-range dependence of itself. Then, a prediction model is built by learning a Deep architecture based on the Deep Belief Network from the extracted low-pass component. Otherwise, for the remaining high-pass component that expresses the gusty and irregular fluctuations of Network traffic, the Spatiotemporal Compressive Sensing method is adopted to predict it. Based on the predictors of two components, we can obtain a predictor of Network traffic. From the simulation, the proposed prediction method outperforms three existing methods.
-
Network traffic prediction based on Deep Belief Network in wireless mesh backbone Networks
IEEE Wireless Communications and Networking Conference WCNC, 2017Co-Authors: Laisen Nie, Shui Yu, Dingde Jiang, Houbing SongAbstract:Wireless mesh Network is prevalent for providing a decentralized access for users. For a wireless mesh backbone Network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for Network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a Network traffic prediction method based on a Deep Belief Network and a Gaussian model. The proposed method first adopts discrete wavelet transform to extract the low-pass component of Network traffic that describes the long-range dependence of itself. Then a prediction model is built by learning a Deep Belief Network from the extracted low-pass component. Otherwise, for the rest high-pass component that expresses the gusty and irregular fluctuations of Network traffic, a Gaussian model is used to model it. We estimate the parameters of the Gaussian model by the maximum likelihood method. Then we predict the high-pass component by the built model. Based on the predictors of two components, we can obtain a predictor of Network traffic. From the simulation, the proposed prediction method outperforms three existing methods.
