This paper provides a comprehensive survey on the state-of-the-art condition monitoring and fault diagnostic technologies for wind turbines. The Part I of this survey briefly reviews the existing literature surveys on the subject, discusses the common failure modes in the major wind turbine components and subsystems, briefly reviews the condition monitoring and fault diagnostic techniques for these components and subsystems, and specifically discusses the issues of condition monitoring and fault diagnosis for offshore wind turbines.
This paper provides a comprehensive survey on the state-of-the-art condition monitoring and fault diagnostic technologies for wind turbines. The Part II of this survey focuses on the signals and signal processing methods used for wind turbine condition monitoring and fault diagnosis. ). respectively. A. VibrationMany WT faults induce vibrations of the corresponding WT subsystems, which can be detected by using the signals acquired from vibration sensors. Vibration monitoring is the dominant technique used in almost all commercially available WT condition monitoring systems (CMSs), in which the vibration sensors are usually installed on the casing of the gearbox, generator, main shaft and bearing, and blade surface.The major types of vibration sensors include accelerometers, velocity sensors, and displacement sensors. Accelerometers have the widest working frequency range from 1Hz to 30 kHz, In contrast, velocity sensors have a working frequency range from 10 Hz to 1 kHz and displacement sensors have a working frequency range of 1-100 Hz. Accelerometers are the most widely used vibration sensors in CMFD of WTs for their wide working frequency range. The signals acquired from accelerometers contain the information of accelerations of WT components caused by faults [1]. Displacement sensors have also been used in WT CMFD systems for diagnosing the faults leading to lowfrequency vibrations of WT components. Vibration monitoring has been used for CMFD of WT gearbox [2], [3], bearing [4], rotor and blade [5], generator, tower, main shaft, etc. The amplitude of the vibration signal can indicate the severity of a fault [6]. For example, the amplitude of the 1P frequency components in vibration signals provides a measure of rotor asymmetries [5].Through years of applications, the vibration-based CMFD technologies have been mature and standardized by ISO10816. However, this approach requires the installation of the capital cost and wiring complexity of the WT system. The vibration sensors are usually mounted on the surface or are buried in the body of WT components, making them difficult to access during WT operation. Moreover, the sensors and data acquisition devices are also inevitably subject to failure. Sensor failure may further cause the failure of WT control, mechanical, and electrical subsystems. These could cause additional problems with system reliability and additional operation and maintenance (O&M) costs. In addition, vibration signals usually have a low signal-to-noise ratio (SNR) when used to diagnose an incipient fault. B. AEMaterials that are subjected to stress or strain may emit sound waves, which is called AE [7]. The sources of AE can be located to detect possible defects of a structure using one or more AE sensors. The AE monitoring technology has been used for CMFD of WT blades [1], gearboxes [8], [9], and bearings [10]. As a blade is usually made of various materials and components, damages often grow over critical areas and the interfaces of different internal components inside the blade. Therefore...
Abstract-Gearbox faults constitute a significant portion of all faults and downtime in wind turbines (WTs). Current-based gearbox fault diagnosis has significant advantages over traditional vibration-based techniques in terms of cost, implementation, and reliability. This paper derives a mathematical model for a WT drive train consisting of a twostage gearbox and a permanent magnet (PM) generator, from which the characteristic frequencies of gear tooth breaks in generator stator current frequency spectra are clearly identified. A adaptive signal resampling algorithm is proposed to convert the variable fault characteristic frequencies to constant values for WTs running at variable speeds. A fault detector is proposed for diagnosis of gear tooth breaks using statistical analysis on the fault signatures extracted from the stator current spectra. Experimental results on a real gearbox are provided to show the effectiveness of the proposed model and method for diagnosis of gear tooth breaks.
Abstract-Drivetrain gearboxes play an important role in many modern industrial applications. This paper presents a novel method consisting of adaptive feature extraction and support vector machine (SVM)-based classification for condition monitoring and fault diagnosis of drivetrain gearboxes operating in variable-speed conditions. An adaptive signal resampling algorithm, a frequency tracker, and a feature generation algorithm are integrated in the proposed method for effective extraction of the features of gearbox faults from the stator current signal of the AC electric machine connected to the gearbox. A radial basis function kernel-SVM classifier is designed to identify the fault in the gearbox according to the fault features extracted. Experimental studies are performed for a drivetrain gearbox with a gear crack fault connected with a permanent magnet synchronous machine. Results show that the fault can be effectively identified by the proposed method.
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