The Experts below are selected from a list of 45 Experts worldwide ranked by ideXlab platform

Tsuyoshi Inoue - One of the best experts on this subject based on the ideXlab platform.

  • visualization of gear motor shaft whirling feature based on time series analysis for rotary machine Component Condition Monitoring
    IEEE International Conference on Prognostics and Health Management, 2020
    Co-Authors: Kesaaki Minemura, Shota Yabui, Kohei Iwata, Tsuyoshi Inoue
    Abstract:

    A technique for visualization of a gear-motor shaft’s whirling feature is proposed based on time-series analysis for rotary machine Component Condition Monitoring. It is necessary to develop many technological elements, including machine Components, the Internet of Things (IoT), sensing, signal processing and modeling for machine Component Condition Monitoring. When a machine Component is connected to another device, the machine Component’s features change because of the connection. Specifically, this work considers the case of a machine Component where the shaft around the axis connecting the Component to another device does not form a circular orbit. It is assumed that the shaft does not have a circular orbit and it is thus necessary to visualize the shaft using a signal processing technique based on this assumption. In general methods, however, because a constant speed and circular orbit are assumed, some errors occur because of the noncircular orbit. In this paper, we consider visualization using a signal processing technique that focuses on the rotational axis, particularly for connections between rotary machine Components for Condition Monitoring. In the proposed method, a time waveform is converted into polar coordinates and expressed in terms of its amplitude and angular direction. By calculating the density distribution for each angle, the features are confirmed even if the shaft orbit does not become a circle. Furthermore, it aids in judging whether the feature change has followed a machine Component Condition change in the trajectory. Measurement data were obtained through verification experiments. It is confirmed that the density distribution’s relative standard deviation is less than approximately 0.05 and that the orbit is constant under normal Conditions. From the experimental results, it is confirmed that the proposed signal processing method is thus effective for machine Component Condition Monitoring.

  • ICPHM - Visualization of gear-motor shaft whirling feature based on time-series analysis for rotary machine Component Condition Monitoring
    2020 IEEE International Conference on Prognostics and Health Management (ICPHM), 2020
    Co-Authors: Kesaaki Minemura, Shota Yabui, Kohei Iwata, Tsuyoshi Inoue
    Abstract:

    A technique for visualization of a gear-motor shaft’s whirling feature is proposed based on time-series analysis for rotary machine Component Condition Monitoring. It is necessary to develop many technological elements, including machine Components, the Internet of Things (IoT), sensing, signal processing and modeling for machine Component Condition Monitoring. When a machine Component is connected to another device, the machine Component’s features change because of the connection. Specifically, this work considers the case of a machine Component where the shaft around the axis connecting the Component to another device does not form a circular orbit. It is assumed that the shaft does not have a circular orbit and it is thus necessary to visualize the shaft using a signal processing technique based on this assumption. In general methods, however, because a constant speed and circular orbit are assumed, some errors occur because of the noncircular orbit. In this paper, we consider visualization using a signal processing technique that focuses on the rotational axis, particularly for connections between rotary machine Components for Condition Monitoring. In the proposed method, a time waveform is converted into polar coordinates and expressed in terms of its amplitude and angular direction. By calculating the density distribution for each angle, the features are confirmed even if the shaft orbit does not become a circle. Furthermore, it aids in judging whether the feature change has followed a machine Component Condition change in the trajectory. Measurement data were obtained through verification experiments. It is confirmed that the density distribution’s relative standard deviation is less than approximately 0.05 and that the orbit is constant under normal Conditions. From the experimental results, it is confirmed that the proposed signal processing method is thus effective for machine Component Condition Monitoring.

Ralph P. Tatam - One of the best experts on this subject based on the ideXlab platform.

  • Railway track Component Condition Monitoring using optical fibre Bragg grating sensors
    Measurement Science and Technology, 2016
    Co-Authors: Stephen J. Buggy, Stephen W. James, Stephen E. Staines, R. Carroll, P. Kitson, D. Farrington, L. Drewett, J. Jaiswal, Ralph P. Tatam
    Abstract:

    The use of optical fibre Bragg grating (FBG) strain sensors to monitor the Condition of safety critical rail Components is investigated. Fishplates, switchblades and stretcher bars on the Stagecoach Supertram tramway in Sheffield in the UK have been instrumented with arrays of FBG sensors. The dynamic strain signatures induced by the passage of a tram over the instrumented Components have been analysed to identify features indicative of changes in the Condition of the Components.

  • Toward track Component Condition Monitoring using optical fibre Bragg grating sensors
    OFS2012 22nd International Conference on Optical Fiber Sensors, 2012
    Co-Authors: Stephen J. Buggy, Stephen W. James, Stephen E. Staines, R. Carroll, P. Kitson, D. Farrington, L. Drewett, J. Jaiswal, Ralph P. Tatam
    Abstract:

    Optical fibre Bragg grating sensors have been field-trialed for the Monitoring of dynamic loading of fish-plates, stretcher bars and switchblades on a tram network, with the aim of developing a Condition Monitoring system. This paper provides preliminary data showing the ability to identify changes in track/Component Condition.

Kesaaki Minemura - One of the best experts on this subject based on the ideXlab platform.

  • visualization of gear motor shaft whirling feature based on time series analysis for rotary machine Component Condition Monitoring
    IEEE International Conference on Prognostics and Health Management, 2020
    Co-Authors: Kesaaki Minemura, Shota Yabui, Kohei Iwata, Tsuyoshi Inoue
    Abstract:

    A technique for visualization of a gear-motor shaft’s whirling feature is proposed based on time-series analysis for rotary machine Component Condition Monitoring. It is necessary to develop many technological elements, including machine Components, the Internet of Things (IoT), sensing, signal processing and modeling for machine Component Condition Monitoring. When a machine Component is connected to another device, the machine Component’s features change because of the connection. Specifically, this work considers the case of a machine Component where the shaft around the axis connecting the Component to another device does not form a circular orbit. It is assumed that the shaft does not have a circular orbit and it is thus necessary to visualize the shaft using a signal processing technique based on this assumption. In general methods, however, because a constant speed and circular orbit are assumed, some errors occur because of the noncircular orbit. In this paper, we consider visualization using a signal processing technique that focuses on the rotational axis, particularly for connections between rotary machine Components for Condition Monitoring. In the proposed method, a time waveform is converted into polar coordinates and expressed in terms of its amplitude and angular direction. By calculating the density distribution for each angle, the features are confirmed even if the shaft orbit does not become a circle. Furthermore, it aids in judging whether the feature change has followed a machine Component Condition change in the trajectory. Measurement data were obtained through verification experiments. It is confirmed that the density distribution’s relative standard deviation is less than approximately 0.05 and that the orbit is constant under normal Conditions. From the experimental results, it is confirmed that the proposed signal processing method is thus effective for machine Component Condition Monitoring.

  • ICPHM - Visualization of gear-motor shaft whirling feature based on time-series analysis for rotary machine Component Condition Monitoring
    2020 IEEE International Conference on Prognostics and Health Management (ICPHM), 2020
    Co-Authors: Kesaaki Minemura, Shota Yabui, Kohei Iwata, Tsuyoshi Inoue
    Abstract:

    A technique for visualization of a gear-motor shaft’s whirling feature is proposed based on time-series analysis for rotary machine Component Condition Monitoring. It is necessary to develop many technological elements, including machine Components, the Internet of Things (IoT), sensing, signal processing and modeling for machine Component Condition Monitoring. When a machine Component is connected to another device, the machine Component’s features change because of the connection. Specifically, this work considers the case of a machine Component where the shaft around the axis connecting the Component to another device does not form a circular orbit. It is assumed that the shaft does not have a circular orbit and it is thus necessary to visualize the shaft using a signal processing technique based on this assumption. In general methods, however, because a constant speed and circular orbit are assumed, some errors occur because of the noncircular orbit. In this paper, we consider visualization using a signal processing technique that focuses on the rotational axis, particularly for connections between rotary machine Components for Condition Monitoring. In the proposed method, a time waveform is converted into polar coordinates and expressed in terms of its amplitude and angular direction. By calculating the density distribution for each angle, the features are confirmed even if the shaft orbit does not become a circle. Furthermore, it aids in judging whether the feature change has followed a machine Component Condition change in the trajectory. Measurement data were obtained through verification experiments. It is confirmed that the density distribution’s relative standard deviation is less than approximately 0.05 and that the orbit is constant under normal Conditions. From the experimental results, it is confirmed that the proposed signal processing method is thus effective for machine Component Condition Monitoring.

Stephen J. Buggy - One of the best experts on this subject based on the ideXlab platform.

  • Railway track Component Condition Monitoring using optical fibre Bragg grating sensors
    Measurement Science and Technology, 2016
    Co-Authors: Stephen J. Buggy, Stephen W. James, Stephen E. Staines, R. Carroll, P. Kitson, D. Farrington, L. Drewett, J. Jaiswal, Ralph P. Tatam
    Abstract:

    The use of optical fibre Bragg grating (FBG) strain sensors to monitor the Condition of safety critical rail Components is investigated. Fishplates, switchblades and stretcher bars on the Stagecoach Supertram tramway in Sheffield in the UK have been instrumented with arrays of FBG sensors. The dynamic strain signatures induced by the passage of a tram over the instrumented Components have been analysed to identify features indicative of changes in the Condition of the Components.

  • Toward track Component Condition Monitoring using optical fibre Bragg grating sensors
    OFS2012 22nd International Conference on Optical Fiber Sensors, 2012
    Co-Authors: Stephen J. Buggy, Stephen W. James, Stephen E. Staines, R. Carroll, P. Kitson, D. Farrington, L. Drewett, J. Jaiswal, Ralph P. Tatam
    Abstract:

    Optical fibre Bragg grating sensors have been field-trialed for the Monitoring of dynamic loading of fish-plates, stretcher bars and switchblades on a tram network, with the aim of developing a Condition Monitoring system. This paper provides preliminary data showing the ability to identify changes in track/Component Condition.

Shuangwen Sheng - One of the best experts on this subject based on the ideXlab platform.

  • Effective and accurate approaches for wind turbine gearbox Condition Monitoring
    Wind Energy, 2013
    Co-Authors: Huageng Luo, Charles T. Hatch, Matthew Kalb, Jesse Hanna, Adam Weiss, Shuangwen Sheng
    Abstract:

    This paper presents effective and accurate approaches in vibration-based wind turbine drivetrain Component Condition Monitoring. Detailed spectral analysis and acceleration enveloping techniques were used to effectively extract the gear and bearing damage features. Synchronous analysis was used to accurately detect specific damage features during constantly varying operational Conditions. A typical wind turbine gearbox amplifies shaft speed two orders of magnitude from the rotor to the generator. To account for all necessary vibration signatures, synchronous sampling must be carried out for multiple revolutions and at a relatively high rate. The synchronous sampling used in this paper was carried out in the digital domain after both the keyphasor and the vibration signals were digitized at high sampling rate and high sampling resolution analog-to-digital conversion. Sometimes, the shaft speed is provided in a speed time history format, as in the case of the National Renewable Energy Laboratory (NREL) Round Robin project. To carry out synchronous sampling using the speed time history, a unique synthesized synchronous sampling technique was adopted. The approach presented in this paper was realized using MATLAB (MathWorks, Natick, MA) codes and then validated with a wind turbine field case. It was also applied to the NREL wind turbine drivetrain Condition Monitoring Round Robin project. The identified damage results using the techniques discussed were compared with post-test inspection results with good correlation. Copyright © 2013 John Wiley & Sons, Ltd.