Blade Tip Timing (BTT) methods are being implemented that have led to a non-intrusive technique being deployed in certain sectors of Industry. Data sets produced during the development cycle are now providing upfront information that is being used to develop monitoring capability supporting in-service health monitoring. Recent years have witnessed a growing interest in blade health monitoring and its potential to detect the occurrence of both transient and permanent foreign object damage (FOD) and estimate the severity of damage to blades. FOD damage detection is beneficial to both the fan and first stage compressors and the ability to detect it leads to a reduction in the number of inspection that recurrently scheduled.
The expected behaviour under transient FOD condition is a ‘ringing’ signal which is a damped exponential signal. The lack of real FOD data collected requires that a signal is simulated and used to develop and validate detection systems.
Blade tip timing is an effective implementation of non-intrusive technology by circumferentially arranged sensors to obtain the time of arrival (TOA) of blades. However, due to the high degree of undersampling inherent in the data the detection of short-lived events poses a problem.
In this paper the use of a method called ‘Damping Averaging Built-in Matrix’ (DABM), which use the combination of several revolutions data and OPR (once per revolution) data to enhance the sample rate while eliminating the damping effect. After solving the matrix we are able to obtain the frequency and damping of the blade when transient FOD occurs. The FEM (finite element model) of the blade is also built to infer the stress of blade at different levels of FOD. The method is applied to both the simulated data and experimental data to verify its effectiveness. By developing this method further we can provide a capability that could reduce the operation and maintenance cost and increase the security of the engine whilst in operation.