Advances in machine condition monitoring technologies are driven by the rise in complexity of modern
machines and the increased demand for product reliability. Condition monitoring research tends to
focus on the development of signal processing algorithms that are sensitive to machine faults, robust
under time-varying operating conditions, and informative regarding the nature and extent of machine
faults. A significant challenge remains for monitoring the condition of machines that are subject to
time-varying operating conditions. The here presented work is concerned with the development of
cost effective condition monitoring algorithms. It is investigated how empirical models (including
probability density distributions and regression functions) may be used to extract diagnostic information
from machine response signals that have been generated under fluctuating operating conditions.
The proposed methodology is investigated on a number of case studies, including gearboxes, alternator
end windings, and haul roads. It is shown how empirical models for machine condition monitoring
may generally be implemented according to one of two basic approaches. The two approaches
are referred to as discrepancy analysis and waveform reconstruction.
Discrepancy analysis is concerned with the comparison of a novel signal to a reference model. The
reference model is sufficiently expressive to represent vibration response as measured on a healthy
machine over a range of operating conditions. The novel signal is compared to the reference model
in such a manner that a discrepancy signal transform is obtained. A discrepancy signal is sensitive to
faults, robust to time-varying operating conditions, and inherently simple. As such it may further beWaveform reconstruction implements a regression function to model machine response as a function
of different state space variables. The regression function may subsequently be exploited to extract
diagnostic information. The machine response may for instance be reconstructed at a specified steady
state operating condition. This renders the signal wide-sense stationary so that Fourier analysis may
analysed in order to extract periodicities and magnitudes as diagnostic markers.