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A review of the literature on helicopter rotor system health monitoring is conducted in this paper. An introduction is provided to the work on rotor track and balance and commercial health and usage monitoring systems. Research on the modelling of typical rotor system faults using aeroelastic analysis is discussed and the use of damage detection algorithms based on neural network, fuzzy logic, and system identification is pointed out. The use of non-destructive testing (NDT) approaches, such as modal methods, acoustic emission, and wave-based approaches for rotor health monitoring is discussed. Finally, work on the health monitoring of composite helicopter rotors is discussed and inverse problem solution and life prediction issues are addressed. Future research needs in the area are pointed out.

Since thin-walled composite structures are widely used in structural engineering, damage in such structures is an important issue of research. Matrix cracking is a principal cause of failure in composites. In the present study, a composite matrix cracking model is implemented in a thin-walled hollow circular cantilever beam using an effective stiffness approach. Such structures are used to model connecting shafts and helicopter tail boom, for example, because of their high stiffness-to-weight ratios and excellent crashworthiness characteristics. The effect of variation in crack density on the fundamental frequency, for various combinations of ½AE m =90 n s composite is studied. Using these change in frequencies due to matrix cracking, a genetic fuzzy system for crack density and crack location detection is generated. The genetic fuzzy system combines the uncertainty representation characteristics of fuzzy logic with the learning ability of genetic algorithm. It is observed that the success rate of the genetic fuzzy system in the presence of noise is dependent on crack density (level of damage), number of 90 plies, angle of constraining layer (), and noise level. It is found that the genetic fuzzy system shows excellent damage detection and isolation performance, and is robust to presence of noise in data.

This study investigates the effect of damage on beams with fixed boundary conditions using Fourier analysis of mode shapes in the spatial domain. A finite element model is used to obtain the mode shapes of a damaged fixed—fixed beam, and the damaged mode shapes are expanded using a spatial Fourier series and the effect of damage on the harmonics is investigated. This approach contrasts with the typical time domain application of Fourier analysis for vibration problems. It is found that damage causes considerable change in the Fourier coefficients of the mode shapes, which are found to be sensitive to both damage size and location. Therefore, a damage index in the form of a vector of Fourier coefficients is formulated. A neural network is trained to detect the damage location and size using Fourier coefficients as input. Numerical studies show that damage detection using Fourier coefficients and neural networks has the capability to detect the location and damage size accurately. Finally, the performance of the method in the presence of noise is studied and it is found that the method performs satisfactorily in the presence of some noise in the data.

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