The Experts below are selected from a list of 13395 Experts worldwide ranked by ideXlab platform

Jong-myon Kim - One of the best experts on this subject based on the ideXlab platform.

  • A Massively Parallel Approach to Real-Time Bearing Fault Detection Using Sub-Band Analysis on an FPGA-Based Multicore System
    IEEE Transactions on Industrial Electronics, 2016
    Co-Authors: Myeongsu Kang, Jaeyoung Kim, In-kyu Jeong, Jong-myon Kim, Michael Pecht
    Abstract:

    The fact that rolling element bearing faults have an amplitude-modulating effect on their characteristic frequencies calls for sub-band analysis to determine an optimal sub-band signal that contains intrinsic information about bearing faults. In this regard, it is significant to accurately assess the presence of a bearing's abnormal symptoms. Hence, a bearing abnormality index (BAI) that properly quantifies how much information a sub-band signal contains about bearing faults is presented. Additionally, to facilitate real-time sub-band analysis based on the BAI, a massively parallel approach is introduced, where the approach involves the use of the Multicore System. Likewise, the Multicore System supports high-performance computing by exploiting 128 processing elements operating at 200 MHz in a Xilinx Virtex-7 field-programmable gate array (FPGA) device.

  • an fpga based Multicore System for real time bearing fault diagnosis using ultrasampling rate ae signals
    IEEE Transactions on Industrial Electronics, 2015
    Co-Authors: Myeongsu Kang, Jaeyoung Kim, Jong-myon Kim
    Abstract:

    The demand for online fault diagnosis has recently increased in order to prevent severe unexpected failures in machinery. To address this issue, this paper first proposes a comprehensive bearing fault diagnosis algorithm, which consists of fault signature extraction through time–frequency analysis and one-against-all multiclass support vector machines in order to make reliable decisions. In addition, acoustic emission (AE) signals sampled at 1 MHz are used for the early identification of bearing failures. Despite the fact that the proposed fault diagnosis methodology shows satisfactory classification accuracy, its computation complexity limits its use in real-time applications. Therefore, this paper also presents a high-performance Multicore architecture, including 64 processing elements operating at 50 MHz in a Xilinx Virtex-7 field-programmable gate array device to support online fault diagnosis. The experimental results indicate that the Multicore approach executes 1339.3x and 1293.1x faster than the high-performance Texas Instrument (TI) TMS320C6713 and TMS320C6748 digital signal processors (DSPs), respectively, by exploiting the massive parallelism inherent in the bearing fault diagnosis algorithm. In addition, the Multicore approach outperforms the equivalent sequential approach that runs on the TI DSPs by substantially reducing the energy consumption.

Myeongsu Kang - One of the best experts on this subject based on the ideXlab platform.

  • A Massively Parallel Approach to Real-Time Bearing Fault Detection Using Sub-Band Analysis on an FPGA-Based Multicore System
    IEEE Transactions on Industrial Electronics, 2016
    Co-Authors: Myeongsu Kang, Jaeyoung Kim, In-kyu Jeong, Jong-myon Kim, Michael Pecht
    Abstract:

    The fact that rolling element bearing faults have an amplitude-modulating effect on their characteristic frequencies calls for sub-band analysis to determine an optimal sub-band signal that contains intrinsic information about bearing faults. In this regard, it is significant to accurately assess the presence of a bearing's abnormal symptoms. Hence, a bearing abnormality index (BAI) that properly quantifies how much information a sub-band signal contains about bearing faults is presented. Additionally, to facilitate real-time sub-band analysis based on the BAI, a massively parallel approach is introduced, where the approach involves the use of the Multicore System. Likewise, the Multicore System supports high-performance computing by exploiting 128 processing elements operating at 200 MHz in a Xilinx Virtex-7 field-programmable gate array (FPGA) device.

  • an fpga based Multicore System for real time bearing fault diagnosis using ultrasampling rate ae signals
    IEEE Transactions on Industrial Electronics, 2015
    Co-Authors: Myeongsu Kang, Jaeyoung Kim, Jong-myon Kim
    Abstract:

    The demand for online fault diagnosis has recently increased in order to prevent severe unexpected failures in machinery. To address this issue, this paper first proposes a comprehensive bearing fault diagnosis algorithm, which consists of fault signature extraction through time–frequency analysis and one-against-all multiclass support vector machines in order to make reliable decisions. In addition, acoustic emission (AE) signals sampled at 1 MHz are used for the early identification of bearing failures. Despite the fact that the proposed fault diagnosis methodology shows satisfactory classification accuracy, its computation complexity limits its use in real-time applications. Therefore, this paper also presents a high-performance Multicore architecture, including 64 processing elements operating at 50 MHz in a Xilinx Virtex-7 field-programmable gate array device to support online fault diagnosis. The experimental results indicate that the Multicore approach executes 1339.3x and 1293.1x faster than the high-performance Texas Instrument (TI) TMS320C6713 and TMS320C6748 digital signal processors (DSPs), respectively, by exploiting the massive parallelism inherent in the bearing fault diagnosis algorithm. In addition, the Multicore approach outperforms the equivalent sequential approach that runs on the TI DSPs by substantially reducing the energy consumption.

Michael Pecht - One of the best experts on this subject based on the ideXlab platform.

  • A Massively Parallel Approach to Real-Time Bearing Fault Detection Using Sub-Band Analysis on an FPGA-Based Multicore System
    IEEE Transactions on Industrial Electronics, 2016
    Co-Authors: Myeongsu Kang, Jaeyoung Kim, In-kyu Jeong, Jong-myon Kim, Michael Pecht
    Abstract:

    The fact that rolling element bearing faults have an amplitude-modulating effect on their characteristic frequencies calls for sub-band analysis to determine an optimal sub-band signal that contains intrinsic information about bearing faults. In this regard, it is significant to accurately assess the presence of a bearing's abnormal symptoms. Hence, a bearing abnormality index (BAI) that properly quantifies how much information a sub-band signal contains about bearing faults is presented. Additionally, to facilitate real-time sub-band analysis based on the BAI, a massively parallel approach is introduced, where the approach involves the use of the Multicore System. Likewise, the Multicore System supports high-performance computing by exploiting 128 processing elements operating at 200 MHz in a Xilinx Virtex-7 field-programmable gate array (FPGA) device.

Jaeyoung Kim - One of the best experts on this subject based on the ideXlab platform.

  • A Massively Parallel Approach to Real-Time Bearing Fault Detection Using Sub-Band Analysis on an FPGA-Based Multicore System
    IEEE Transactions on Industrial Electronics, 2016
    Co-Authors: Myeongsu Kang, Jaeyoung Kim, In-kyu Jeong, Jong-myon Kim, Michael Pecht
    Abstract:

    The fact that rolling element bearing faults have an amplitude-modulating effect on their characteristic frequencies calls for sub-band analysis to determine an optimal sub-band signal that contains intrinsic information about bearing faults. In this regard, it is significant to accurately assess the presence of a bearing's abnormal symptoms. Hence, a bearing abnormality index (BAI) that properly quantifies how much information a sub-band signal contains about bearing faults is presented. Additionally, to facilitate real-time sub-band analysis based on the BAI, a massively parallel approach is introduced, where the approach involves the use of the Multicore System. Likewise, the Multicore System supports high-performance computing by exploiting 128 processing elements operating at 200 MHz in a Xilinx Virtex-7 field-programmable gate array (FPGA) device.

  • an fpga based Multicore System for real time bearing fault diagnosis using ultrasampling rate ae signals
    IEEE Transactions on Industrial Electronics, 2015
    Co-Authors: Myeongsu Kang, Jaeyoung Kim, Jong-myon Kim
    Abstract:

    The demand for online fault diagnosis has recently increased in order to prevent severe unexpected failures in machinery. To address this issue, this paper first proposes a comprehensive bearing fault diagnosis algorithm, which consists of fault signature extraction through time–frequency analysis and one-against-all multiclass support vector machines in order to make reliable decisions. In addition, acoustic emission (AE) signals sampled at 1 MHz are used for the early identification of bearing failures. Despite the fact that the proposed fault diagnosis methodology shows satisfactory classification accuracy, its computation complexity limits its use in real-time applications. Therefore, this paper also presents a high-performance Multicore architecture, including 64 processing elements operating at 50 MHz in a Xilinx Virtex-7 field-programmable gate array device to support online fault diagnosis. The experimental results indicate that the Multicore approach executes 1339.3x and 1293.1x faster than the high-performance Texas Instrument (TI) TMS320C6713 and TMS320C6748 digital signal processors (DSPs), respectively, by exploiting the massive parallelism inherent in the bearing fault diagnosis algorithm. In addition, the Multicore approach outperforms the equivalent sequential approach that runs on the TI DSPs by substantially reducing the energy consumption.

Tinghao Tsai - One of the best experts on this subject based on the ideXlab platform.

  • online real time task scheduling in heterogeneous Multicore System on a chip
    IEEE Transactions on Parallel and Distributed Systems, 2013
    Co-Authors: Yashu Chen, Han Chiang Liao, Tinghao Tsai
    Abstract:

    Online task scheduling in heterogeneous Multicore System-on-a-chip is a challenging problem due to precedence constraints and nonpreemptive task execution in the synergistic processor core. This study first proposes an online heterogeneous dual-core scheduling framework for dynamic workloads with real-time constraints. The general purpose processor core and the synergistic processor core are dedicated to separate schedulers with different scheduling policies, and precedence constraints among tasks are dealt with through interaction between the two schedulers. This framework is also configurable for low priority inversion and high System utilization. We then extend this framework to heterogeneous Multicore Systems with well-known dispatcher schemas. This paper presents a real case study to show the practicability of the proposed methodology, and presents a series of extensive simulations to obtain comparison studies using different workloads and scheduling algorithms.