Digital Signal Processing Reference
In-Depth Information
plications to damages caused by corrosion in chemical process installations [86]. As follow
there is an explanation for some of the most examined in the scientific literature.
The application of wavelets transforms in wind turbines focuses on the implementation of
adaptive controllers for wind energy conversion systems. Wavelet transform is capable of
providing a good and quick approximation. The drivers studied under different noise levels
achieved higher performances [69]. Other works study the monitoring and diagnosis of
faults in induced generators with satisfactory results. In these cases a combination of DWTs,
accompanied by statistical data and energy is proposed. The use of decomposed signals
spectral components is other highly interesting technique of study. Its harmonic content has
suitable characteristics to be employed in fault diagnosis as an alternative to conventional
methods [3].
Rolling bearing plays an important role in rotating machines. The choice of a particular
wavelet family is crucial for the maintenance and fault diagnosis. The location of peaks on
the vibration spectrum can identify a particular fault. Wavelet decomposition trees are a
useful tool for this identification. The mean square error extracted from the terminal nodes
of a tree reports the failure and its size [17]. There are also studies focused on determining
what type of wavelet is suitable for bearing maintenance [79].
The wavelet transform is a good signal analysis method when a variation of time but not of
space exists. The analysis provides information about the frequency of the signal, being a
solution for the engine failure detection. There are detection algorithms that identify the
presence of a fault in working condition and are ahead of the shutdown of the system, re‐
ducing costs and downtimes [19] [20]. These algorithms are independent of the type of en‐
gine used. Other studies in this field, present methods to detect imbalances in the stator
voltage of a three phase induction motor. The wavelet transform of the stator current is ana‐
lysed. Computationally, these methods are less expensive than other existing and can detect
faults in an early stage. In the same vein, monitoring fatigue damage has been studied [65].
3. Condition Monitoring for engine-generator mechanism
A novel approach for Condition Monitoring based on wavelet transforms is introduced. A
system for a mechanism based on an engine and a generator will be shown. It has been de‐
signed to represent any similar mechanism located in a wind turbine, generally in the na‐
celle. These mechanisms are used in cooling devices (generators, gearboxes), electric motors
for service crane, yaw motors, pitch motors (depending on the configuration) or pumps (oil,
water) according to the sub systems configurations, ventilators, etc (Figure 4).
A set of faults are induced in different experiments: ski-slope faults, misalignment faults, an‐
gular misalignment faults, parallel misalignment faults, rotating looseness faults and exter‐
nal noise faults. Pattern recognition is obtained from the extraction of vibration and acoustic
signals. A Fault Detection and Diagnosis method is developed from the patterns of these
signals. In order to recognize the patterns, three basic steps have been followed [37]:
