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Separating Different Vibration Sources in Complex Fault Detection
Abstract and Figures
Any attempt to detect different types of machine faults reliably at an early stage requires advanced signal processing methods. It is well known that vibration measurements provide a good basis for condition monitoring and fault detection. The complexity of signal processing techniques depends on the type of fault. In many cases root mean square and peak values are useful features for fault detection. Unbalance, misalignment, bent shaft, mechanical looseness and some electrical faults, for example, can be detected using features of displacement and velocity. Advanced filter settings and higher order derivatives provide additional possibilities if faults cause high frequency vibrations or impacts. This paper presents results from investigations about various faults, e.g. unbalance, misalignment, resonance, cage fault, absence of lubrication in a ball bearing and their combinations. To detect the faults, also higher order derivatives, lp norms and dimensionless measurement index MIT are utilised.
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