An improved data fusion technique for faults diagnosis in rotating machines

The composite spectrum (CS) data fusion technique has been shown to simplify rotating machines faults diagnosis by earlier studies. Faults diagnosis with the earlier CS relied solely on the amplitudes of several harmonics of the machine speed, owing to the loss of phase information leading to its computation. The proposed improved CS applies the concept of cross power spectrum density for computing a poly-Coherent Composite Spectrum (pCCS) of a machine, which retains amplitude and phase information at all measurement locations. The present study compares the proposed pCCS method with the earlier CS method for faults diagnosis in rotating machines, using experimental data from a rotating rig. Results and observations show that the proposed pCCS offered a much better representation of the machines dynamics when compared to the earlier CS method and hence better fault diagnosis.