Real-Time Detection of Faults in Rotating Blades Using Frequency Response Function Analysis

Turbo machines develop faults in the rotating blades during operation in undesirable conditions. Such faults in the rotating blades are fatigue cracks, mechanical looseness, imbalance, misalignment, etc. Therefore, it is crucial that the blade faults should be detected and diagnosed in order to minimize the severe damage of such machines. In this paper, vibration analysis of the rotating blades is conducted using an experimental laboratory setup in order to develop a methodology to detect faults in the rotating blades. The faults considered for the study include cracks and mechanical looseness for which dynamic responses are recorded using a laser vibrometer. Analysis has been carried out by comparing the frequency response function spectrums of the fault blade with those of the healthy blade related to the resonance frequency. The Internet of Things and wireless sensor networks are implemented to transmit the measured data to the cloud platform. A support vector machine algorithm is used for preparing the learning model in order to extract and classify the faults of the rotating blades. It can be clearly seen from the results that there is variation in the frequency response function spectrums of healthy and faulty conditions of the rotating blades.