Large steam turbines used for electrical power generation demand governing systems of very high integrity (safety) and availability. The latest generation of electronic governors uses microprocessors in a distributed, two level architecture to achieve the required integrity and availability and in addition provides greater configuration flexibilities and wider facilities than earlier governors. Rolling element bearings are one of the major machinery components used in industries like power plants, chemical plants and automotive industries that require precise and efficient performance. Vibration monitoring and analysis is useful tool in the field of predictive maintenance in small hydro electric power plants. Health of rolling element bearings can be easily identified using vibration monitoring because vibration signature reveals important information about the fault development within them. Numbers of vibration analysis techniques are being used to diagnosis of rolling element bearings faults. This paper proposes a new signal feature extraction and fault diagnosis method for fault diagnosis of low-speed machinery. Initially, the proposed work explores the Continuous Wavelet Transform (CWT) to adaptively remove the exact noises from vibration analysis and then feature extraction is performed by exploiting the noise removed pre-processed data. Statistic filter (SF) and Hilbert transform (HT) are combined with moving-peak-hold method (M-PH) to extract features of a fault signal, and Special bearing diagnostic symptom parameters (SSPs) in a frequency domain that are sensitive to bearing fault diagnosis are defined to recognize fault types. The SF is first used to adaptively cancel noises, and then fault detection is performed by exploiting the optimum symptom parameters in a time domain to identify a normal or fault state. For precise diagnosis, the SSPs are calculated after the signals are processed by M-PH and HT.

用于发电的大型蒸汽轮机要求控制系统具有很高的完整性（安全性）和可用性。最新一代的电子调速器在分布式两级体系结构中使用微处理器，以实现所需的完整性和可用性，并且与早期的调速器相比，还提供了更大的配置灵活性和更广泛的功能。滚动轴承是发电厂，化工厂和汽车行业等要求精确和高效性能的行业中使用的主要机械部件之一。振动监测和分析是小型水力发电厂的预测性维护领域中的有用工具。滚动轴承的健康状况可以通过振动监测轻松识别，因为振动信号显示了有关轴承内部故障发展的重要信息。许多振动分析技术被用于诊断滚动轴承故障。提出了一种用于低速机械故障诊断的信号特征提取与故障诊断新方法。最初，提出的工作探索了连续小波变换（CWT）以从振动分析中自适应地去除精确的噪声，然后通过利用去除噪声的预处​​理数据来进行特征提取。统计滤波器（SF）和希尔伯特变换（HT）与移动峰值保持方法（M-PH）相结合，以提取故障信号的特征，定义对轴承故障诊断敏感的频域中的特殊轴承诊断症状参数（SSP），以识别故障类型。SF首先用于自适应消除噪声，然后通过在时域中利用最佳症状参数来识别正常或故障状态来执行故障检测。为了精确诊断，在通过M-PH和HT处理信号后计算SSP。