Signal processing plays a crucial role in addressing failures in electrical machines. Experimental data are never perfect due to the intrusion of undesirable fluctuations unrelated to the investigated phenomenon, namely so-called noise. Noise has disturbing effects on the measurement data and, in the same way, could diminish or mask the fault patterns in feature extraction using different signal processors. This article introduces various types of noise occurring in an industrial environment. Several measurements are performed in the laboratory and power plants to identify the dominant type of noise. Fault detection in a custom-made 100-kVA synchronous generator under an interturn short-circuit fault is also studied using measurements of the air-gap magnetic field. Signal processing tools such as fast Fourier transform, short-time Fourier transform (STFT), discrete wavelet transform, continuous wavelet transform (CWT), and time-series data mining are used to diagnose the faults, with a central focus on additive noise impacts on processed data. Two novel patterns are introduced based on STFT and CWT for interturn short-circuit fault detection of synchronous generators that do not need a priori knowledge of a healthy machine. Useful methods are presented for hardware noise rejection.

中文翻译：

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用于噪声环境下运行的水轮发电机故障检测的信号处理工具的性能评估


信号处理在解决电机故障方面发挥着至关重要的作用。由于与所研究现象无关的不良波动（即所谓的噪声）的侵入，实验数据永远不会完美。噪声会对测量数据产生干扰影响，并且以同样的方式，可能会减少或掩盖使用不同信号处理器进行特征提取时的故障模式。本文介绍了工业环境中出现的各种类型的噪声。在实验室和发电厂进行了多次测量，以确定噪声的主要类型。还利用气隙磁场测量研究了定制 100 kVA 同步发电机在匝间短路故障下的故障检测。使用快速傅里叶变换、短时傅里叶变换（STFT）、离散小波变换、连续小波变换（CWT）和时间序列数据挖掘等信号处理工具来诊断故障，重点关注加性噪声影响关于处理后的数据。基于 STFT 和 CWT 引入了两种新颖的模式，用于同步发电机匝间短路故障检测，不需要健康机器的先验知识。提出了用于硬件噪声抑制的有用方法。