Fault diagnosis of rotating machinery is of considerable significance to ensure high reliability
and safety in industrial machinery. The key to fault diagnosis consists in detecting potential
incipient fault presence, recognizing fault patterns, and identifying degrees of failures in
machinery. The process of data-driven fault diagnosis method often requires extracting
useful feature representations from measurements to make diagnostic decision-making.
Entropy measures, as suitable non-linear complexity indicators, estimate dynamic changes
in measurements directly, which are challenging to be quantified by conventional statistical
indicators. Compared to single-scale entropy measures, multiple-scale entropy measures
have been increasingly applied to time series complexity analysis by quantifying entropy
values over a range of temporal scales. However, there exist a number of challenges in
traditional multiple-scale entropy measures in analyzing bearing signals for bearing fault
detection. Specifically, a large majority of multiple-scale entropy methods neglect high�frequency information in bearing vibration signal analysis. Moreover, the data length of
transformed multiple signals is greatly reduced as scale factor increases, which can introduce
incoherence and bias in entropy values. Lastly, non-linear and non-stationary behaviors of
vibration signals due to interference and noise may reduce the diagnostic performance of
traditional entropy methods in bearing health identification, especially in complex industrial
settings.
This dissertation proposes a novel multiple-scale entropy measure, named Adaptive
Multiscale Weighted Permutation Entropy (AMWPE), for extracting fault features associated
with complexity change in bearing vibration analysis. A new scale-extraction mechanism -
adaptive Fine-to-Coarse (F2C) procedure - is presented to generate multiple-scale time series
from the original signal. It has advantages of extracting low- and high-frequency information
from measurements and generating improved multiple-scale time series with a hierarchical
structure. Numerical evaluation is carried out to study the performance of the AMWPE
measure in analyzing the complexity change of synthetic signals. Results demonstrated that
the AMWPE algorithm could provide high consistency and stable entropy values in entropy
estimation. It also presents high robustness against noise in analyzing noisy bearing signals in
comparison with traditional entropy methods. Additionally, a new bearing diagnosis method
is put forth, where the AMWPE method is applied for entropy analysis and a multi-class
support vector machine classifier is used for identifying bearing fault patterns, respectively.
Three experimental case studies are carried out to investigate the effectiveness of the
proposed diagnosis method for bearing diagnosis. Comparative studies are presented to
compare the diagnostic performance of the proposed entropy method and traditional entropy
methods in terms of computational time of entropy estimation, feature representation, and
diagnosis accuracy rate. Further, noisy bearing signals with different signal-to-noise ratios
are analyzed using various entropy measures to study their robustness against noise in
bearing diagnosis. Additionally, the developed adaptive F2C procedure can be extended to a
variety of entropy algorithms based on improved single-scale entropy method used in entropy
estimation. In the combination of artificial intelligence techniques, the improved entropy
algorithms are expected to apply to machine health conditions and intelligent fault diagnosis
in complex industrial machinery. Besides, they are suitable to evaluate the complexity
and irregularity of other non-stationary signals measured from non-linear systems, such as
acoustic emission signals and physiological signals
