The main maintenance policies are corrective or preventive. Corrective maintenance (also known as unscheduled or failure-based maintenance) is carried out when turbines break down and when faults are detected or failures occur in any of the components. Immediate refurbishment or replacement of parts may be necessary and unscheduled downtime will occur (Ben-Daya & Duffuaa, 2009). Corrective maintenance is therefore the most expensive of strategies, and wind farm operators hope to resort to it as little as possible. The stages of corrective maintenance are shown in Figure 11.8.

In the WT industry, the state-of-the-art way of deciding on a maintenance strategy is reliability-centred maintenance (RCM), which has been formally defined as ‘a process used to determine what must be done to ensure that any physical asset continues to do whatever its users want it to do in its present operating context’ (Moubray, 1997). It involves maintaining system functions, identifying failure modes, prioritizing functions, identifying preventive maintenance requirements and selecting the most appropriate maintenance tasks (Smith, 1993), with the objective of effectively managing risk of system failure (Garcia Marquez & Pedregal, 2007). Operational and maintenance policies are optimized such that the overall maintenance is reduced (Ben-Daya & Duffuaa, 2009). RCM has been recognized and accepted in many industrial fields, such as steel plants (Deshpande & Modak, 2002a), railway networks (Garcia Marquez, Schmid, & Collado, 2003a, 2003b), ship maintenance and other industries (Deshpande & Modak, 2002b). RCM in the WT industry is addressed by Andrawus, Watson, Kishk, and Adam (2009).

Vibration analysis continues to be ‘the most popular technology employed in WT, especially for rotating equipment’ (Hameed, Hong, Choa, Ahn, & Song, 2009) (see Figure 11.11). Different sensors are required for different frequencies: ‘position transducers are used for the low frequency range, velocity sensors in the middle frequency area, accelerometers in the high frequency range and spectral emitted energy sensors for very high frequencies’ (Verbruggen, 2003).

Rapid release of strain energy takes place and elastic waves are generated when the structure of a metal is altered, and this can be analysed by acoustic emissions (AEs). The primary sources of AEs in WTs are the generation and propagation of cracks (Yoshioka & Takeda, 1994), and the AE technique has been found to detect some faults earlier than other techniques such as vibration analysis (Tandon, Yadava, & Ramakrishna, 2007) (see Figure 11.12). The measurement and interpretation of AE parameters for fault detection in radially loaded ball bearings at different speed ranges has been demonstrated by Tandon and Nakra (1990). In addition, the application of AEs for the detection of bearing failures has been presented by Tan (1990). Acoustic monitoring has some similarities with vibration monitoring, but ‘vibration sensors are mounted on the component involved’ (Verbruggen, 2003), for example, to detect movement, acoustic sensors are attached with flexible glue with low attenuation and record sound directly. AE sensors have been used successfully in monitoring bearings and gearboxes and for detecting damage in blades of a WT (Wei & McCarty, 1993) (see Figure 11.13). Application of AEs is also possible in an in-service WT for a real-time rotating blade (Blanch & Dutton, 2003; Giebel, Oliver, Malcolm, & Kaj, 2006). Non-destructive testing techniques using acoustic waves to improve the safety of WT blades are presented by Jungert (2008) and techniques enabling the assessment of the damage criticality for blades of small WTs are based on AE data from publications by Anastassopoulos et al. (2002) and Joosse et al. (2002). The use of AEs for both CM of rotating WT components as well as blades is gradually growing.

Ultrasonic testing (UT) techniques are used extensively by the wind energy industry for the structural evaluation of WT towers and blades (see Figure 11.14). UT is generally used for the detection and qualitative assessment of surface and subsurface structural defects (Knezevic, 1993; Deshpande & Modak, 2002a). Ultrasonic wave propagation characteristics allow the location and type of defect detected to be estimated, thus providing a reliable method of determining the material properties of the principal turbine components. Signal-processing algorithms including time frequency techniques and wavelet transforms can be used to extract more information (Tsai, Hsieh, & Huang, 2006). An ultrasound technique to visualize the inner structure of WT blades is presented by Juengert and Grosse (2009); the deployment of UT techniques for inspecting the whole multi-layered structure of the WT blade and finding defects such as delaminations and lack of glue is illustrated by Raišutis, Jasiūnienė, and Žukauskas (2008); and the ultrasonic air-coupled technique has been used to research internal defects in wind turbine blades (Jasiūnienė, Raišutis, Šliteris, Voleišis, & Jakas, 2008). Ultrasonically obtained images make it possible to recognize the geometry of defects and to estimate their approximate dimensions. Jasiuniene et al. (2009) adapted an air-coupled ultrasonic technique for better identification of the shape and size of defects in a WT blade. A review of other methods has been provided by Raišutis, Jasiūnienė, Šliteris, and Vladišauskas (2008).

Whether for the ultimate purpose of guaranteeing oil quality or the condition of the various moving parts, ‘oil analysis is mostly executed off-line by taking samples’ (Hameed et al., 2009) despite online sensors for monitoring oil temperature, contamination and moisture (Yin, Wang, Yan, Xiao, & Wang, 2003) having (for years) been ‘available at an acceptable price level’ (Verbruggen, 2003) (see Figure 11.15). Little or no vibration may be evident while faults are developing, but analysis of the oil can provide early warnings; a case study of a WT gearbox is described by Leske and Kitaljevich (2006). In the ‘case of excessive filter pollution, oil contamination or change in component properties, characterization of the particulates can give an indication of excessive wear’ (Verbruggen, 2003). Such approaches are particularly effective in avoiding catastrophic failures and are cost-efficient (Toms, 1998). Online oil analysis is gradually becoming more important, with several ongoing pilot projects.

Strain measurement using strain gauges can be very useful for forecasting the lifetime of a component and protecting against high stress levels, especially in the blades (see Figure 11.16). An assessment of the interpretation of signals from strain gauge sensors installed on blades has been performed to adjust calibration practices and sensor selection (Morfiadakis, Papadopoulos, & Philippidis, 2000). Optical fibre sensors are still very expensive (Hameed et al., 2009), but cost-effective systems based on fibre optics are being developed. Giebel et al. (2006) illustrate how load monitoring can be performed using strain sensors in the rotor blades. Strain measurement can be expected to grow in importance as an input to CM.

CM of electrical equipment such as motors, generators and accumulators is typically performed using voltage and current analysis (see Figure 11.17). Discharge measurements are used for medium- and high-voltage grids. A spectral analysis of the stator current (Schoen, Lin, Habetler, Schlag, & Farag, 1995) in the generator can be used for detecting isolation faults in the cabling without influencing WT operation.

The shock pulse method has been used as a quantitative method for CM of bearings and works by detecting the mechanical shocks that are generated ‘when a ball or roller in a bearing comes in contact with a damaged area of raceway or with debris’ (Butler, 1973) (see Figure 11.18). Signals are picked up by transducers, and analysis (e.g. using a normalized shock value (Zhen, Zhengjia, Yanyang, & Xuefeng, 2008)) gives an indication of system condition (Tandon & Nakra, 1992b). Low-frequency signals of vibration collected in the nacelle and caused by other sources can easily be filtered electronically. A case study of the shock pulse method with a piezoelectric transducer is described by Morando (1988). The method is occasionally used by the industry to support vibration measurements.

Maintenance based on process parameters and the detection of signals exceeding predefined control limits is common practice in WTs; control systems are becoming increasingly sophisticated and diagnostic capabilities ever better. Transient and oscillatory stability were analysed with different wind scenarios for electricity generation process by Müller, Pöller, Basteck, Tilscher, and Pfister (2006). Zaher and McArthur (2007) provide an explanation of the use of signals and trending for fault detection based on parameter estimation.

The relationship between parameters such as power, wind speed, blade angle and rotor speed can also be used to assess WT condition and to detect faults early (Sorensen et al., 2002). Previous work includes power and voltage flicker analysis with variable wind speed and turbulence (Müller et al., 2006). ‘Similar to estimation of process parameter(s), more sophisticated methods, including trending, are not often used’ (Verbruggen, 2003). The torque and power generated with wind time series taken in the field as measured by an anemometer is considered by Dale, Dolan, and Lehn (2005).

Radiographic imaging of critical structural turbine components using X-rays is only rarely used, although it does provide useful information regarding the structural condition of the component being inspected. Radiographic imaging depends ‘on the different level of absorption of X-ray photons as they pass through a material’. ‘To detect tight delaminations or cracks, having gaps less than 50 μm, the backscatter X-ray imaging technique’ (Raišutis, Jasiūnienė, Šliteris, et al., 2008) is used. X-ray imaging is useful to locate the internal defects of the WT, and the main advantage of X-ray inspection is its accuracy (Peters, 1998). A transportable radiographic system for WT blades has been recently demonstrated as ‘a solution to find defects and reduce the cost of inspection’ (Fantidis, Potolias, & Bandekas, 2011). Figure 11.19 shows the X-ray inspections of a blade.

Thermography often is used for monitoring electronic and electric components and identifying failure (Smith, 1978). The technique is only applied off-line and often involves visual interpretation of hot spots that arise because of bad contact or a system fault. At present, the technique is not particularly well established for online CM, but cameras and diagnostic software that are suitable for online process monitoring are starting to become available. Infrared cameras have been used to visualize variations in blade surface temperature (Rumsey & Musial, 2001) and can ‘effectively indicate cracks as well as places threatened by damage’ (Doliński & Krawczuk, 2009). In the longer term, this might be applicable to WT generators and power electronics as well (see Figure 11.20).