The Experts below are selected from a list of 9441 Experts worldwide ranked by ideXlab platform

Ming Zhao - One of the best experts on this subject based on the ideXlab platform.

  • transient feature extraction of Encoder Signal for condition assessment of planetary gearboxes with variable rotational speed
    Measurement, 2020
    Co-Authors: Chuancang Ding, Ming Zhao, Jing Lin, Baoxiang Wang, Kaixuan Liang
    Abstract:

    Abstract This paper proposes a transient feature extraction algorithm of Encoder Signal for the condition assessment of planetary gearboxes under variable speed condition. In the proposed method, local polynomial fitting and sparsity based algorithm (LSA) is first constructed to extract time domain transient features from raw Encoder Signal and then order tracking (OT) technique is applied to convert non-stationary transient features in time domain into stationary ones in angular domain. Meanwhile, to solve the formulated local polynomial fitting and sparsity based optimization problem, a fast convergence algorithm is derived based on alternating direction method of multipliers and majorization-minimization. Additionally, an adaptive parameter selection strategy is introduced for automatically selecting appropriate parameters of the proposed method LSA-OT. With the proposed method LSA-OT, the transient features can be effectively extracted and the condition assessment of planetary gearboxes can be easily achieved. The effectiveness of LSA-OT is confirmed by simulation evaluation and case study.

  • from polynomial fitting to kernel ridge regression a generalized difference filter for Encoder Signal analysis
    IEEE Transactions on Instrumentation and Measurement, 2020
    Co-Authors: Ming Zhao
    Abstract:

    Encoder Signal analysis (ESA) provides a novel tool for condition monitoring of machinery. Nevertheless, how to enhance the incipient fault signatures using ESA, especially in noisy measurements, remains a challenging issue. In view of this limitation, a new method termed generalized difference filter (GDF) is proposed for weak feature enhancement of Encoder Signals. To better capture the fault transients, kernel ridge regression is first introduced for Signal approximation in high-dimensional feature space. A stochastic error minimization scheme is then proposed to improve approximation accuracy in a data-driven manner. Finally, a fast algorithm for GDF is developed based on the Gaussian kernel. With this method, the denoising and differencing of Encoder Signals can be treated in a unified framework. The effectiveness of the proposed method is validated by both simulated studies and experimental data.

  • a multivariate Encoder information based convolutional neural network for intelligent fault diagnosis of planetary gearboxes
    Knowledge Based Systems, 2018
    Co-Authors: Jinyang Jiao, Ming Zhao, Jing Lin, Jian Zhao
    Abstract:

    Abstract Rotary Encoder Signal, as the built-in position information, possesses a wide variety of advantages over vibration Signal and has aroused great interest in the field of health monitoring for rotating machinery. However, there are two major issues when attempting to detect and diagnose failures with Encoder information. First of all, a series of proper Signal processing methods need to be designed for fault feature extraction, which largely relies on the expert experience and domain knowledge. Furthermore, existing studies primarily concentrate on a single transform, such as instantaneous angular speed, which neglects the diversity of Encoder information. In view of above deficiencies, a multivariate Encoder information based convolutional neural network (MEI-CNN) is proposed for intelligent diagnosis in this paper. In this framework, three different types of dynamic Encoder information are firstly acquired by analyzing and processing the raw position sequence, after that multivariate Encoder information (MEI) data are constructed by data fusion. Finally, a concise and effective convolutional neural network is designed to extract discriminating features and provide diagnosis results. The proposed method not only overcomes drawbacks of traditional techniques based on vibration analysis, but also provides an intelligent way to achieve satisfactory diagnosis results. The effectiveness and superiority of MEI-CNN are validated by experimental data from a planetary gearbox test rig. The results also indicate that the proposed method may offer a promising tool for intelligent diagnosis of rotating machinery.

  • instantaneous speed jitter detection via Encoder Signal and its application for the diagnosis of planetary gearbox
    Mechanical Systems and Signal Processing, 2018
    Co-Authors: Ming Zhao, Jing Lin, Xiaodong Jia, Yaguo Lei, Jay Lee
    Abstract:

    Abstract In modern rotating machinery, rotary Encoders have been widely used for the purpose of positioning and dynamic control. The study in this paper indicates that, the Encoder Signal, after proper processing, can be also effectively used for the health monitoring of rotating machines. In this work, a Kurtosis-guided local polynomial differentiator (KLPD) is proposed to estimate the instantaneous angular speed (IAS) of rotating machines based on the Encoder Signal. Compared with the central difference method, the KLPD is more robust to noise and it is able to precisely capture the weak speed jitters introduced by mechanical defects. The fault diagnosis of planetary gearbox has proven to be a challenging issue in both industry and academia. Based on the proposed KLPD, a systematic method for the fault diagnosis of planetary gearbox is proposed. In this method, residual time synchronous time averaging (RTSA) is first employed to remove the operation-related IAS components that come from normal gear meshing and non-stationary load variations, KLPD is then utilized to detect and enhance the speed jitter from the IAS residual in a data-driven manner. The effectiveness of proposed method has been validated by both simulated data and experimental data. The results demonstrate that the proposed KLPD-RTSA could not only detect fault signatures but also identify defective components, thus providing a promising tool for the health monitoring of planetary gearbox.

Jay Lee - One of the best experts on this subject based on the ideXlab platform.

  • instantaneous speed jitter detection via Encoder Signal and its application for the diagnosis of planetary gearbox
    Mechanical Systems and Signal Processing, 2018
    Co-Authors: Ming Zhao, Jing Lin, Xiaodong Jia, Yaguo Lei, Jay Lee
    Abstract:

    Abstract In modern rotating machinery, rotary Encoders have been widely used for the purpose of positioning and dynamic control. The study in this paper indicates that, the Encoder Signal, after proper processing, can be also effectively used for the health monitoring of rotating machines. In this work, a Kurtosis-guided local polynomial differentiator (KLPD) is proposed to estimate the instantaneous angular speed (IAS) of rotating machines based on the Encoder Signal. Compared with the central difference method, the KLPD is more robust to noise and it is able to precisely capture the weak speed jitters introduced by mechanical defects. The fault diagnosis of planetary gearbox has proven to be a challenging issue in both industry and academia. Based on the proposed KLPD, a systematic method for the fault diagnosis of planetary gearbox is proposed. In this method, residual time synchronous time averaging (RTSA) is first employed to remove the operation-related IAS components that come from normal gear meshing and non-stationary load variations, KLPD is then utilized to detect and enhance the speed jitter from the IAS residual in a data-driven manner. The effectiveness of proposed method has been validated by both simulated data and experimental data. The results demonstrate that the proposed KLPD-RTSA could not only detect fault signatures but also identify defective components, thus providing a promising tool for the health monitoring of planetary gearbox.

Jing Lin - One of the best experts on this subject based on the ideXlab platform.

  • transient feature extraction of Encoder Signal for condition assessment of planetary gearboxes with variable rotational speed
    Measurement, 2020
    Co-Authors: Chuancang Ding, Ming Zhao, Jing Lin, Baoxiang Wang, Kaixuan Liang
    Abstract:

    Abstract This paper proposes a transient feature extraction algorithm of Encoder Signal for the condition assessment of planetary gearboxes under variable speed condition. In the proposed method, local polynomial fitting and sparsity based algorithm (LSA) is first constructed to extract time domain transient features from raw Encoder Signal and then order tracking (OT) technique is applied to convert non-stationary transient features in time domain into stationary ones in angular domain. Meanwhile, to solve the formulated local polynomial fitting and sparsity based optimization problem, a fast convergence algorithm is derived based on alternating direction method of multipliers and majorization-minimization. Additionally, an adaptive parameter selection strategy is introduced for automatically selecting appropriate parameters of the proposed method LSA-OT. With the proposed method LSA-OT, the transient features can be effectively extracted and the condition assessment of planetary gearboxes can be easily achieved. The effectiveness of LSA-OT is confirmed by simulation evaluation and case study.

  • a multivariate Encoder information based convolutional neural network for intelligent fault diagnosis of planetary gearboxes
    Knowledge Based Systems, 2018
    Co-Authors: Jinyang Jiao, Ming Zhao, Jing Lin, Jian Zhao
    Abstract:

    Abstract Rotary Encoder Signal, as the built-in position information, possesses a wide variety of advantages over vibration Signal and has aroused great interest in the field of health monitoring for rotating machinery. However, there are two major issues when attempting to detect and diagnose failures with Encoder information. First of all, a series of proper Signal processing methods need to be designed for fault feature extraction, which largely relies on the expert experience and domain knowledge. Furthermore, existing studies primarily concentrate on a single transform, such as instantaneous angular speed, which neglects the diversity of Encoder information. In view of above deficiencies, a multivariate Encoder information based convolutional neural network (MEI-CNN) is proposed for intelligent diagnosis in this paper. In this framework, three different types of dynamic Encoder information are firstly acquired by analyzing and processing the raw position sequence, after that multivariate Encoder information (MEI) data are constructed by data fusion. Finally, a concise and effective convolutional neural network is designed to extract discriminating features and provide diagnosis results. The proposed method not only overcomes drawbacks of traditional techniques based on vibration analysis, but also provides an intelligent way to achieve satisfactory diagnosis results. The effectiveness and superiority of MEI-CNN are validated by experimental data from a planetary gearbox test rig. The results also indicate that the proposed method may offer a promising tool for intelligent diagnosis of rotating machinery.

  • instantaneous speed jitter detection via Encoder Signal and its application for the diagnosis of planetary gearbox
    Mechanical Systems and Signal Processing, 2018
    Co-Authors: Ming Zhao, Jing Lin, Xiaodong Jia, Yaguo Lei, Jay Lee
    Abstract:

    Abstract In modern rotating machinery, rotary Encoders have been widely used for the purpose of positioning and dynamic control. The study in this paper indicates that, the Encoder Signal, after proper processing, can be also effectively used for the health monitoring of rotating machines. In this work, a Kurtosis-guided local polynomial differentiator (KLPD) is proposed to estimate the instantaneous angular speed (IAS) of rotating machines based on the Encoder Signal. Compared with the central difference method, the KLPD is more robust to noise and it is able to precisely capture the weak speed jitters introduced by mechanical defects. The fault diagnosis of planetary gearbox has proven to be a challenging issue in both industry and academia. Based on the proposed KLPD, a systematic method for the fault diagnosis of planetary gearbox is proposed. In this method, residual time synchronous time averaging (RTSA) is first employed to remove the operation-related IAS components that come from normal gear meshing and non-stationary load variations, KLPD is then utilized to detect and enhance the speed jitter from the IAS residual in a data-driven manner. The effectiveness of proposed method has been validated by both simulated data and experimental data. The results demonstrate that the proposed KLPD-RTSA could not only detect fault signatures but also identify defective components, thus providing a promising tool for the health monitoring of planetary gearbox.

Yaguo Lei - One of the best experts on this subject based on the ideXlab platform.

  • instantaneous speed jitter detection via Encoder Signal and its application for the diagnosis of planetary gearbox
    Mechanical Systems and Signal Processing, 2018
    Co-Authors: Ming Zhao, Jing Lin, Xiaodong Jia, Yaguo Lei, Jay Lee
    Abstract:

    Abstract In modern rotating machinery, rotary Encoders have been widely used for the purpose of positioning and dynamic control. The study in this paper indicates that, the Encoder Signal, after proper processing, can be also effectively used for the health monitoring of rotating machines. In this work, a Kurtosis-guided local polynomial differentiator (KLPD) is proposed to estimate the instantaneous angular speed (IAS) of rotating machines based on the Encoder Signal. Compared with the central difference method, the KLPD is more robust to noise and it is able to precisely capture the weak speed jitters introduced by mechanical defects. The fault diagnosis of planetary gearbox has proven to be a challenging issue in both industry and academia. Based on the proposed KLPD, a systematic method for the fault diagnosis of planetary gearbox is proposed. In this method, residual time synchronous time averaging (RTSA) is first employed to remove the operation-related IAS components that come from normal gear meshing and non-stationary load variations, KLPD is then utilized to detect and enhance the speed jitter from the IAS residual in a data-driven manner. The effectiveness of proposed method has been validated by both simulated data and experimental data. The results demonstrate that the proposed KLPD-RTSA could not only detect fault signatures but also identify defective components, thus providing a promising tool for the health monitoring of planetary gearbox.

Xiaodong Jia - One of the best experts on this subject based on the ideXlab platform.

  • instantaneous speed jitter detection via Encoder Signal and its application for the diagnosis of planetary gearbox
    Mechanical Systems and Signal Processing, 2018
    Co-Authors: Ming Zhao, Jing Lin, Xiaodong Jia, Yaguo Lei, Jay Lee
    Abstract:

    Abstract In modern rotating machinery, rotary Encoders have been widely used for the purpose of positioning and dynamic control. The study in this paper indicates that, the Encoder Signal, after proper processing, can be also effectively used for the health monitoring of rotating machines. In this work, a Kurtosis-guided local polynomial differentiator (KLPD) is proposed to estimate the instantaneous angular speed (IAS) of rotating machines based on the Encoder Signal. Compared with the central difference method, the KLPD is more robust to noise and it is able to precisely capture the weak speed jitters introduced by mechanical defects. The fault diagnosis of planetary gearbox has proven to be a challenging issue in both industry and academia. Based on the proposed KLPD, a systematic method for the fault diagnosis of planetary gearbox is proposed. In this method, residual time synchronous time averaging (RTSA) is first employed to remove the operation-related IAS components that come from normal gear meshing and non-stationary load variations, KLPD is then utilized to detect and enhance the speed jitter from the IAS residual in a data-driven manner. The effectiveness of proposed method has been validated by both simulated data and experimental data. The results demonstrate that the proposed KLPD-RTSA could not only detect fault signatures but also identify defective components, thus providing a promising tool for the health monitoring of planetary gearbox.