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
Motor current signature analysis provides good results in laboratory environment. In real life situation, electrical machines usually share voltage and current from common terminals and would easily influence each oth...Motor current signature analysis provides good results in laboratory environment. In real life situation, electrical machines usually share voltage and current from common terminals and would easily influence each other. This will result in considerable amount of interferences among motors and doubt in identity of fault signals. Therefore, estimating the mutual influence of motors will help identifying the original signal from the environmental noise. This research aims at modelling the propagation of signals that are caused by faults of induction motors in power networks. Estimating the propagation pattern of fault signal leads to a method to discriminate and identify the original source of major events in industrial networks. Simulation results show that source of fault could be identified using this approach with a higher certainty than anticipated output coming of any individual diagnosis.展开更多
基于电机电流特征分析(Motor Current Signature Analysis,MCSA)的齿轮故障诊断技术,以电机定子电流为切入点,可以检测机器以及电机中的机械故障和电气异常,对系统不会造成任何干扰。同时,基于单片机构进行故障检测设计,结合频段和功率...基于电机电流特征分析(Motor Current Signature Analysis,MCSA)的齿轮故障诊断技术,以电机定子电流为切入点,可以检测机器以及电机中的机械故障和电气异常,对系统不会造成任何干扰。同时,基于单片机构进行故障检测设计,结合频段和功率谱密度(Power Spectral Density,PSD)数据判断齿轮故障。展开更多
The purpose of this paper is to analyze influence of design data on a component （1 - 2s）~ in stator currents of induction motors, mainly used for cage fault diagnosis. This paper shows that such an approach does not...The purpose of this paper is to analyze influence of design data on a component （1 - 2s）~ in stator currents of induction motors, mainly used for cage fault diagnosis. This paper shows that such an approach does not always lead to a correct outcome. The considerations are based on a ＂classical＂ model of induction motors extended to cage asymmetry by introducing cage asymmetry factors ko~ and ka. It has been found that in order to estimate the level of the component （1 - 2s）7~, it is enough to know the pole-pair number ＂p＂ and the number of rotor slots ＂N＂. The main objective of the paper is to provide engineers with simple qualitative prediction of effects due to cage faults for various motors when information on design data is very limited.展开更多
机床类设备传动链复杂,结构紧凑且封闭,对传动系统状态监测一直没有有效的分析办法,即使在噪声增大等恶劣情况下,常规的振动诊断方法也难以奏效。本文尝试用电流信号分析法,简称MCSA(Motor Current Signal Analysis),对机床状态进行测...机床类设备传动链复杂,结构紧凑且封闭,对传动系统状态监测一直没有有效的分析办法,即使在噪声增大等恶劣情况下,常规的振动诊断方法也难以奏效。本文尝试用电流信号分析法,简称MCSA(Motor Current Signal Analysis),对机床状态进行测试和分析。通过电机电流信号的传动链齿轮谱特征分析,对其做出了正确评估,表明定子电流能很好地反映机床传动系统部件的运行状况。同时进行了多种运行工况的测试与试验,研究结论对机床类设备的生产制造和现场运行状态的分析提供了有效依据。展开更多
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.
文摘Motor current signature analysis provides good results in laboratory environment. In real life situation, electrical machines usually share voltage and current from common terminals and would easily influence each other. This will result in considerable amount of interferences among motors and doubt in identity of fault signals. Therefore, estimating the mutual influence of motors will help identifying the original signal from the environmental noise. This research aims at modelling the propagation of signals that are caused by faults of induction motors in power networks. Estimating the propagation pattern of fault signal leads to a method to discriminate and identify the original source of major events in industrial networks. Simulation results show that source of fault could be identified using this approach with a higher certainty than anticipated output coming of any individual diagnosis.
文摘The purpose of this paper is to analyze influence of design data on a component （1 - 2s）~ in stator currents of induction motors, mainly used for cage fault diagnosis. This paper shows that such an approach does not always lead to a correct outcome. The considerations are based on a ＂classical＂ model of induction motors extended to cage asymmetry by introducing cage asymmetry factors ko~ and ka. It has been found that in order to estimate the level of the component （1 - 2s）7~, it is enough to know the pole-pair number ＂p＂ and the number of rotor slots ＂N＂. The main objective of the paper is to provide engineers with simple qualitative prediction of effects due to cage faults for various motors when information on design data is very limited.
文摘机床类设备传动链复杂,结构紧凑且封闭,对传动系统状态监测一直没有有效的分析办法,即使在噪声增大等恶劣情况下,常规的振动诊断方法也难以奏效。本文尝试用电流信号分析法,简称MCSA(Motor Current Signal Analysis),对机床状态进行测试和分析。通过电机电流信号的传动链齿轮谱特征分析,对其做出了正确评估,表明定子电流能很好地反映机床传动系统部件的运行状况。同时进行了多种运行工况的测试与试验,研究结论对机床类设备的生产制造和现场运行状态的分析提供了有效依据。
