为满足电机故障检测的需要,结合DSP精度高、数字信号处理能力强和ARM资源丰富、性能价格比高等特点,开发了一款基于TMS320F2812和S3C44B0X的双CPU结构的异步电动机故障检测装置。介绍了系统的主要功能并重点分析了系统采用的MCSA(Motor ...为满足电机故障检测的需要,结合DSP精度高、数字信号处理能力强和ARM资源丰富、性能价格比高等特点,开发了一款基于TMS320F2812和S3C44B0X的双CPU结构的异步电动机故障检测装置。介绍了系统的主要功能并重点分析了系统采用的MCSA(Motor Current Signature Analysis)技术、测量算法、快速傅里叶变换FFT和局部频谱的连续细化分析方法,给出了系统的硬件结构、软件框图和设计方案以及转子断条故障检测的实验结果。展开更多
The proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) to diagnosis the stator faults of Induction Motors. The performance of the proposed method deals with the emergin...The proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) to diagnosis the stator faults of Induction Motors. The performance of the proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) and the Zero-Sequence Voltage Component (ZSVC) to diagnose the stator faults of Induction Motors. The unalleviated study of the robustness of the industrial appliances is obligatory to verdict the fault of the machines at precipitate stages and thwart the machine from brutal damage. For all kinds of industry, a machine failure escorts to a diminution in production and cost increases. The Motor Current Signature Analysis (MCSA) is referred as the most predominant way to diagnose the faults of electrical machines. Since the detailed analysis of the current spectrum, the method will portray the typical fault state. This paper aims to present dissimilar stator faults which are classified under electrical faults using MCSA and the comparison of simulation and hardware results. The magnitude of these fault harmonics analyzes in detail by means of Finite-Element Method (FEM). The anticipated method can effectively perceive the trivial changes too during the operation of the motor and it shows in the results.展开更多
文摘为满足电机故障检测的需要,结合DSP精度高、数字信号处理能力强和ARM资源丰富、性能价格比高等特点,开发了一款基于TMS320F2812和S3C44B0X的双CPU结构的异步电动机故障检测装置。介绍了系统的主要功能并重点分析了系统采用的MCSA(Motor Current Signature Analysis)技术、测量算法、快速傅里叶变换FFT和局部频谱的连续细化分析方法,给出了系统的硬件结构、软件框图和设计方案以及转子断条故障检测的实验结果。
文摘The proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) to diagnosis the stator faults of Induction Motors. The performance of the proposed method deals with the emerging technique called as Motor Current Signature Analysis (MCSA) and the Zero-Sequence Voltage Component (ZSVC) to diagnose the stator faults of Induction Motors. The unalleviated study of the robustness of the industrial appliances is obligatory to verdict the fault of the machines at precipitate stages and thwart the machine from brutal damage. For all kinds of industry, a machine failure escorts to a diminution in production and cost increases. The Motor Current Signature Analysis (MCSA) is referred as the most predominant way to diagnose the faults of electrical machines. Since the detailed analysis of the current spectrum, the method will portray the typical fault state. This paper aims to present dissimilar stator faults which are classified under electrical faults using MCSA and the comparison of simulation and hardware results. The magnitude of these fault harmonics analyzes in detail by means of Finite-Element Method (FEM). The anticipated method can effectively perceive the trivial changes too during the operation of the motor and it shows in the results.
