Online Condition Monitoring of Electric Machines

电机在线状态监测

基本信息

  • 批准号:
    RGPIN-2016-06311
  • 负责人:
  • 金额:
    $ 2.77万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2018
  • 资助国家:
    加拿大
  • 起止时间:
    2018-01-01 至 2019-12-31
  • 项目状态:
    已结题

项目摘要

Electric machines (EMs) (i.e., motors and generators) are commonly used in various domestic and industrial applications such as electric vehicles, machine tools, and wind turbines. Improving the performance and efficiency of EMs is of utmost significance to a wide array of industries. Although enormous effort has been undertaken over the decades to develop techniques and expert systems for automatic EM diagnosis, unfortunately, most of these systems cannot be used properly for industrial monitoring application due to poor reliability, with the occurrence of missed alarms (i.e., existing faults that are not identified) and false alarms (i.e., alarms triggered for reasons other than real faults). The objective of this research program is to develop new technologies and tools for intelligent diagnostics and prognostics (IDP) of EMs. The goal is to recognize the occurrence of an EM defect at its earliest stage, so as to prevent EM performance degradation, malfunction, or even catastrophic failure of the related facilities. When a potential problem arises, the IDP monitor can pinpoint the faulty components and estimate the remaining useful life of the faulty EM. In addition to improving production rates, the prognostic information can help to reduce operational costs because maintenance can be scheduled properly to avoid unexpected equipment shutdowns. Since the most commonly used EMs are induction motors (IMs) that also consume more than 50% of the electrical energy in the world each year, the proposed research will focus on IMs. The first research theme is to develop new signal processing techniques for more efficient denoising and IM fault detection. The second objective is to comprehensively assess the robustness of all available IM fault detection techniques corresponding to different operating and motor conditions. The third objective is to develop a new regulated predictor for multi-step-ahead forecasting of high-dimensional systems. Another objective of this innovative research program is to develop an IDP platform to integrate both diagnostic and prognostic information for a more positive assessment of IM conditions in real time. New strategies for system training will be proposed to improve the adaptive capability of the IDP to accommodate different IM conditions. This multidisciplinary research program will provide unique and leading-edge opportunities to train HQP in these related areas. In addition, the developed technologies and intelligent tools will benefit Canadian companies seeking to enhance their competitiveness in the global market by improving their production rates and quality and reducing costs.
电机(EM)(即电机和发电机)通常用于各种家庭和工业应用,如电动汽车、机床和风力涡轮机。提高新兴市场的性能和效率对各行各业都具有极其重要的意义。尽管几十年来为开发用于自动EM诊断的技术和专家系统付出了巨大的努力,但不幸的是,由于可靠性差,这些系统中的大多数不能正确地用于工业监测应用,出现漏报(即,现有故障未被识别)和误报(即,由真实故障以外的原因触发的报警)。这项研究计划的目标是开发新的技术和工具,用于EMS的智能诊断和预测(IDP)。目标是在最早阶段识别电磁缺陷的发生,以防止电磁性能退化、故障,甚至相关设施的灾难性故障。当潜在问题出现时,IDP监视器可以精确定位故障部件,并估计故障EM的剩余使用寿命。除了提高生产率,预测信息还有助于降低运营成本,因为可以适当地安排维护,以避免意外的设备停机。由于最常用的电机是感应电机(IMS),每年也消耗世界上50%以上的电能,因此拟议的研究将集中在IMS上。第一个研究主题是开发新的信号处理技术,以实现更有效的去噪和IM故障检测。第二个目标是全面评估对应于不同运行和电机条件的所有可用的IM故障检测技术的稳健性。第三个目标是开发一种新的可调节预报器,用于高维系统的多步超前预测。这一创新研究计划的另一个目标是开发一个IDP平台,以集成诊断和预后信息,以便实时更积极地评估IM状况。将提出新的系统培训战略,以提高IDP适应不同IM条件的能力。这一多学科的研究计划将提供在这些相关领域培训HQP的独特和前沿机会。此外,开发的技术和智能工具将使加拿大公司受益,这些公司寻求通过提高生产率和质量并降低成本来增强其在全球市场的竞争力。

项目成果

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会议论文数量(0)
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Wang, Wilson其他文献

An Adaptive Particle Filter Technique for System State Estimation and Prognosis
A multi-step predictor with a variable input pattern for system state forecasting
  • DOI:
    10.1016/j.ymssp.2008.09.006
  • 发表时间:
    2009-07-01
  • 期刊:
  • 影响因子:
    8.4
  • 作者:
    Liu, Jie;Wang, Wilson;Golnaraghi, Farid
  • 通讯作者:
    Golnaraghi, Farid
Pandemics and Their Impact on Medical Training: Lessons From Singapore
  • DOI:
    10.1097/acm.0000000000003441
  • 发表时间:
    2020-09-01
  • 期刊:
  • 影响因子:
    7.4
  • 作者:
    Liang, Zhen Chang;Ooi, Shirley Beng Suat;Wang, Wilson
  • 通讯作者:
    Wang, Wilson
Total knee arthroplasty in a patient with a fused ipsilateral hip
An enhanced Hilbert-Huang transform technique for bearing condition monitoring
  • DOI:
    10.1088/0957-0233/24/8/085004
  • 发表时间:
    2013-08-01
  • 期刊:
  • 影响因子:
    2.4
  • 作者:
    Osman, Shazali;Wang, Wilson
  • 通讯作者:
    Wang, Wilson

Wang, Wilson的其他文献

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{{ truncateString('Wang, Wilson', 18)}}的其他基金

Intelligent Diagnostics and Prognostics of Electric Vehicle Powertrains
电动汽车动力系统的智能诊断和预测
  • 批准号:
    RGPIN-2021-04272
  • 财政年份:
    2022
  • 资助金额:
    $ 2.77万
  • 项目类别:
    Discovery Grants Program - Individual
Intelligent Diagnostics and Prognostics of Electric Vehicle Powertrains
电动汽车动力系统的智能诊断和预测
  • 批准号:
    RGPIN-2021-04272
  • 财政年份:
    2021
  • 资助金额:
    $ 2.77万
  • 项目类别:
    Discovery Grants Program - Individual
Online Condition Monitoring of Electric Machines
电机在线状态监测
  • 批准号:
    RGPIN-2016-06311
  • 财政年份:
    2020
  • 资助金额:
    $ 2.77万
  • 项目类别:
    Discovery Grants Program - Individual
Online Condition Monitoring of Electric Machines
电机在线状态监测
  • 批准号:
    RGPIN-2016-06311
  • 财政年份:
    2019
  • 资助金额:
    $ 2.77万
  • 项目类别:
    Discovery Grants Program - Individual
Remote health condition monitoring of water pump systems
水泵系统的远程健康状况监测
  • 批准号:
    537683-2018
  • 财政年份:
    2019
  • 资助金额:
    $ 2.77万
  • 项目类别:
    Collaborative Research and Development Grants
Online Condition Monitoring of Electric Machines
电机在线状态监测
  • 批准号:
    RGPIN-2016-06311
  • 财政年份:
    2017
  • 资助金额:
    $ 2.77万
  • 项目类别:
    Discovery Grants Program - Individual
Online Condition Monitoring of Electric Machines
电机在线状态监测
  • 批准号:
    RGPIN-2016-06311
  • 财政年份:
    2016
  • 资助金额:
    $ 2.77万
  • 项目类别:
    Discovery Grants Program - Individual

相似海外基金

Next generation predictive maintenance for wind turbine blade/hub/rotor through novel online condition monitoring/root cause analysis: MONTURWIND
通过新颖的在线状态监测/根本原因分析对风力涡轮机叶片/轮毂/转子进行下一代预测性维护:MONTUWIND
  • 批准号:
    10041137
  • 财政年份:
    2023
  • 资助金额:
    $ 2.77万
  • 项目类别:
    Collaborative R&D
Online Electromagnetic Condition Monitoring Techniques for High Voltage Systems
高压系统在线电磁状态监测技术
  • 批准号:
    RGPIN-2017-05488
  • 财政年份:
    2022
  • 资助金额:
    $ 2.77万
  • 项目类别:
    Discovery Grants Program - Individual
Online Electromagnetic Condition Monitoring Techniques for High Voltage Systems
高压系统在线电磁状态监测技术
  • 批准号:
    RGPIN-2017-05488
  • 财政年份:
    2021
  • 资助金额:
    $ 2.77万
  • 项目类别:
    Discovery Grants Program - Individual
Online Electromagnetic Condition Monitoring Techniques for High Voltage Systems
高压系统在线电磁状态监测技术
  • 批准号:
    RGPIN-2017-05488
  • 财政年份:
    2020
  • 资助金额:
    $ 2.77万
  • 项目类别:
    Discovery Grants Program - Individual
Online Condition Monitoring of Electric Machines
电机在线状态监测
  • 批准号:
    RGPIN-2016-06311
  • 财政年份:
    2020
  • 资助金额:
    $ 2.77万
  • 项目类别:
    Discovery Grants Program - Individual
Online Condition Monitoring of Electric Machines
电机在线状态监测
  • 批准号:
    RGPIN-2016-06311
  • 财政年份:
    2019
  • 资助金额:
    $ 2.77万
  • 项目类别:
    Discovery Grants Program - Individual
Online Electromagnetic Condition Monitoring Techniques for High Voltage Systems
高压系统在线电磁状态监测技术
  • 批准号:
    507968-2017
  • 财政年份:
    2019
  • 资助金额:
    $ 2.77万
  • 项目类别:
    Discovery Grants Program - Accelerator Supplements
Online Electromagnetic Condition Monitoring Techniques for High Voltage Systems
高压系统在线电磁状态监测技术
  • 批准号:
    RGPIN-2017-05488
  • 财政年份:
    2019
  • 资助金额:
    $ 2.77万
  • 项目类别:
    Discovery Grants Program - Individual
Online Electromagnetic Condition Monitoring Techniques for High Voltage Systems
高压系统在线电磁状态监测技术
  • 批准号:
    507968-2017
  • 财政年份:
    2018
  • 资助金额:
    $ 2.77万
  • 项目类别:
    Discovery Grants Program - Accelerator Supplements
Online Electromagnetic Condition Monitoring Techniques for High Voltage Systems
高压系统在线电磁状态监测技术
  • 批准号:
    RGPIN-2017-05488
  • 财政年份:
    2018
  • 资助金额:
    $ 2.77万
  • 项目类别:
    Discovery Grants Program - Individual
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