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FUTURE CONTROL TECHNOLOGIES IN MOTOR DIAGNOSTICS AND SYSTEM WELLNESS

机译:电机诊断和系统健康中的未来控制技术

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Over the past few years, industrial manufacturing disciplines have evolved from a strategy of routine scheduled maintenance of electrical equipment to Condition Based Maintenance (CBM). In the CBM approach, equipment maintenance based on a routine schedule can be replaced with an approach based on system wellness diagnostics. This approach might rely on non-invasive monitoring of three-phase induction motors to report equipment condition and enable maintenance intervention before a failure occurs. Recent research conducted at the University of Sussex in the United Kingdom and at Georgia Institute of Technology has been conducted developing algorithms of motor current signature analysis (CSA) and power signature analysis (PSA) resulting in a reliable model to predict motor and driven load failure. Pump cavitation, rotational unbalance and mechanical alignment are some of the areas where a mathematical model has been developed using Fast Fourier transform (FFT) analysis enabling online diagnostics during operation. An extensive testing program to validate and refine the mathematical model was conducted both in the test lab and in field process applications. A Motor Wellness Relay is under development in a mechanical package designed to replace a conventional thermal-overload relay of a NEMA or IEC rated motor starter. With this approach, the on-line Motor Wellness Relay enables CBM via a control device that is already required for the control circuit. This paper will discuss the mathematical model and field tests to validate the model and introduce a Motor Wellness Relay that could be used to perform on-line diagnostics. Alternative system communication architectures to support a wellness platform will also be reviewed and discussed.
机译:在过去几年中,工业制造学科已经从常规预定维护电气设备的策略演变为基于条件的维护(CBM)。在CBM方法中,基于常规时间表的设备维护可以用基于系统健康诊断的方法替换。这种方法可能依赖于对三相感应电机的非侵入性监测报告设备条件并在发生故障之前启用维护干预。最近在英国和佐治亚州理工学院进行了最近进行的研究,已经开展了电机电流签名分析(CSA)和功率签名分析(PSA)的算法,导致可靠的模型来预测电动机和驱动负载失败。泵空化,旋转不平衡和机械对准是使用快速傅里叶变换(FFT)分析开发数学模型的一些区域,在运行期间启用在线诊断。用于验证和优化数学模型的广泛测试程序是在测试实验室和现场过程应用程序中进行的。在机械封装中,电机健康继电器正在开发中,旨在更换NEMA或IEC额定电机启动器的传统热过载继电器。利用这种方法,在线电机健康继电器通过控制电路已经需要的控制装置实现CBM。本文将讨论数学模型和现场测试以验证模型,并引入可用于执行在线诊断的电机健康继电器。还将审查和讨论支持健康平台的替代系统通信架构。

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