An Investigation of the Effects of Measurement Noise in the Use of Instantaneous Angular Speed for Machine Diagnosis

In order to discriminate small changes for early fault diagnosis of rotating machines, condition
monitoring demands that the measurement of instantaneous angular speed (IAS) of the machines be as
accurate as possible. This paper develops the theoretical basis and practical implementation of IAS data
acquisition and IAS estimation when noise influence is included. IAS data is modelled as a frequency
modulated signal of which the signal-to-noise ratio can be improved by using a high-resolution encoder.
From this signal model and analysis, optimal configurations for IAS data collection are addressed for high
accuracy IAS measurement. Simultaneously, a method based on analytic signal concept and fast Fourier
transform is also developed for efficient and accurate estimation of IAS. Finally, a fault diagnosis is carried
out on an electric induction motor driving system using IAS measurement. The diagnosis results show that
using a high-resolution encoder and a long data stream can achieve noise reduction by more than 10 dB in
the frequency range of interest, validating the model and algorithm developed. Moreover, the results
demonstrate that IAS measurement outperforms conventional vibration in diagnosis of incipient faults of
motor rotor bar defects and shaft misalignment.