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Rolling element bearing faults of a laboratory scale wind turbine gearbox operating under nonstationary loads have been diagnosed using condition monitoring (CM) techniques such as vibration analysis, acoustic analysis, and lubrication oil analysis. Two local bearing faults, namely, bearing inner race fault and bearing outer fault are seeded in the gearbox. The raw data from these techniques are decomposed and wavelet approximation coefficients of level four (a4) are extracted using discrete wavelet transform (DWT). A plethora of statistical features is computed from the wavelet approximation coefficients and the most significant features are being identified by implementing the decision tree algorithm. The classification efficiencies of each of these CM techniques are compared by using the support-vector machine algorithm. Furthermore, an integrated CM scheme is developed by combining the individual CM techniques and the fault diagnosing ability of the integrated CM scheme is compared with the individual CM techniques. A principal component analysis-based approach is used as a feature classification algorithm and an input feature matrix is formed by combining the significant features extracted from vibration, acoustic, and lubrication oil analysis. It has been observed that the integrated CM scheme has provided better classification interpretations than the single CM techniques and it can be extended for real time fault diagnosis of a wind turbine gearbox.

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Wind energy is an emerging, clean and renewable source of energy. It is estimated that by year 2035, wind energy will be generating more than 25% of the world's electricity according to International Energy Agency (IEA). With the increase in demand for wind energy, its maintenance issues are becoming more prominent .The scheduled maintenance is more economical than unscheduled repair resulting from failure. So a continuous condition monitoring of various critical components like bearings, gearbox, and shafts of wind turbine is essential in order to enable predictive maintenance. 10% of the total failure is contributed by the bearings, shaft and gear box failures, but the downtime is more than 50% of the total downtime. This paper discusses the development of a bench-top test rig which is designed to mimic the operating condition of an actual wind turbine and use it for monitoring its condition so as to diagnose the incipient faults in its critical components using latest machine learning algorithms such as Artificial Neural Network (ANN).

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Plates with periodic cavities show excellent vibration attenuation characteristics. This behavior can be attributed to the presence of frequency bandgaps on account of interference between the incident wave and the reflected wave from the cavities. The present work investigates the vibration attenuation/bandgap characteristics of plates with varying shapes of periodic cavities, such as square, circular, vertical rectangle, and horizontal rectangle, through experiments and simulation. Vibration responses of different periodic plates have been studied by carrying out frequency sweep on a vibration shaker. The investigation has been restricted to flexural vibrations of the plates, which are the predominant mode of vibration in many practical vibration scenarios. The frequency bandgaps, observed through the experiment, have been compared with the numerical simulation by harmonic analysis and by carrying out dispersion analysis on a unit cell of the periodic structure using Floquet–Bloch theory. Dispersion curves of the periodic plates yielded bandgaps, which were observed to be in agreement with the bandgaps from the experiment. The effect of variation in the aspect ratio of the cavities, that is length-to-width ratio, on the bandgaps has also been examined. It has been demonstrated that by suitable selection of the shape/size of the periodic cavity, desired vibration attenuation bandgaps can be realized for a 2-dimensional structure.

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