Rotating machines are widely used in industries, manufacturing, and oil and gas plants as a critical component for process availability. The inadvertent failure of these rotating machines causes significant process downtime and incurs higher repair costs and loss of revenue. These failures can belong to electrical, thermal, or mechanical fault categories. Early detection of all these faults is very much critical for avoiding complete failure of these machines and requires continuous 24/7 online monitoring using sensors or intelligent electronic devices (IEDs). However, an affordable low-cost and efficient online monitoring is desired to practice specifically for medium-voltage (MV) machines which have a larger install base. Electrical signature analysis (ESA) technology offers such flexibility and requires measuring just current and/or voltage at a motor control panel for machine health diagnosis, unlike vibration analysis (VA) requiring installation of sensors and its wiring on the machine. Furthermore, an intelligent, self-reliable, and autonomous ESA procedure is required for monitoring the machines due to the lack of ESA standards in practice unlike for VA. This article proposes a new autonomous electrical signature analysis (AESA)-based measurement technique implemented in IED, that is, Protection Relay, offering 24/7 online monitoring. The proposed technique implemented in Relay not only avoids dependence on standalone ESA hardware but also provides earlier detection of failures using peak and energy magnitudes computation approach at fault frequencies. To validate the proposed method, various tests were performed on the actual 1000- and 300-HP motors with/without mechanical faults in a machine repair shop and the results are discussed. The performance of the proposed method is also compared with the commercially available third-party ESA device results proving the efficacy.

中文翻译：

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基于自主电气签名分析的工业电机故障监测方法

旋转机被广泛用于工业，制造以及石油和天然气工厂，是过程可用性的关键组成部分。这些旋转机器的意外故障会导致大量的过程停机时间，并导致更高的维修成本和收入损失。这些故障可能属于电气，热或机械故障类别。为了避免这些机器完全失效，尽早发现所有这些故障非常重要，并且需要使用传感器或智能电子设备（IED）进行连续24/7在线监测。但是，需要一种价格合理的低成本且有效的在线监视来专门针对具有较大安装基础的中压（MV）机器进行操作。电气特征分析（ESA）技术提供了这样的灵活性，并且仅需要在电动机控制面板上测量电流和/或电压即可进行机器健康诊断，而振动分析（VA）则需要在机器上安装传感器及其接线。此外，由于实际上缺少与VA不同的ESA标准，因此需要智能，自可靠和自治的ESA程序来监视机器。本文提出了一种新的基于自动电子签名分析（AESA）的测量技术，该技术在IED中实现，即保护继电器，可提供24/7在线监测。在Relay中实施的拟议技术不仅避免了对独立ESA硬件的依赖，而且还使用故障频率处的峰值和能量幅度计算方法提供了更早的故障检测。为了验证所提出的方法，在修理厂对实际的1000-HP和300-HP电动机进行了各种测试，发现有无机械故障，并讨论了结果。还将该提议方法的性能与可商购的第三方ESA设备结果进行了比较，证明了其有效性。