|
Title
|
Blind source separation methods and their mechanical applications
|
| Author(s) |
Liu, Xianhua, Mechanical & Manufacturing Engineering, Faculty of Engineering, UNSW
|
| Resource Type |
Thesis
PhD Doctorate
|
| Keyword(s) |
Signal processing -- Digital techniques
Algorithms
|
| Date |
2006 |
| School/Centre |
University of New South Wales. School of Mechanical and Manufacturing Engineering |
| Description/Abstract |
Blind Source Separation is a modern signal processing technique which recovers both
the unknown sources and unknown mixing systems from only measured mixtures of
signals. It has application in diverse fields such as communication, image processing,
geological exploration and biomedical signal processing etc. This project studies the
BSS problem, develop separation methods and reveal the potential for mechanical
engineering applications.
There are two models for blind source separation corresponding to the two ways that
the sources are mixed, the instantaneous mixing model and the convolved mixing
model. The author carried out a theoretical study of the first model by proposing an
idea called Redundant Data Elimination which leads to geometric interpretation of the
model, explains that circular distribution property is the reason why Gaussian signal
mixtures can not be separated, and showed that this idea can improve separation
accuracy for unsymmetrically distributed sources. This new idea enabled evaluation
and comparison of two well-known algorithms and proposal of a simplified algorithm
based on Joint Approximate Diagonalization of fourth order cumulant matrices, which
is further developed by determining an optimized parameter value for separation
convergence. Also based on the understanding from the RDE, an outlier spherical
projection method is proposed to improve separation accuracy against outlier errors.
Mechanical vibration or acoustic problems belong to the second model. After some
theoretical study of the problem and the model, a novel application of the Blind Least
Mean Square algorithm using Gray's variable norm as cost function is applied to
engine vibration data to separate piston slap, fuel injection noise and cylinder pressure
effects. Further, the algorithm is combined with a deflation algorithm for successive
subtraction of recovered source responses from the measured mixture to enable the
recovery of more sources. The algorithms are verified to be successful by simulation,
and the separated engine sources are proved reasonable by analysing the engine
operation and physical properties of the sources.
The author also studied the relationship between these two models, the problems of
different approaches for solving the model such as the frequency domain approach and
the Bussgang approach, and sets out future research interests. |
| Language |
EN |
| Rights |
Please click here to view the rights |
| Citation Link |
Please use this identifier to cite or link to this item: http://handle.unsw.edu.au/1959.4/24961 |
| Full Text | 
|
| Total Attachment(s) | 2 |
List Of Titles
