This dissertation presents a methodology for an efficient, non-destructive, and online
incipient fault diagnosis system (IFDS) to detect underground cable incipient faults before they
become catastrophic. The system provides vital information to help the operator with the
decision-making process regarding the condition assessment of the underground cable. It
incorporates advanced digital signal processing and pattern recognition methods to classify
recorded data into designated classes. Additionally, the IFDS utilizes novel detection
methodologies to detect when the cable is near failure.
The classification functionality is achieved through employing an ensemble of rule-based
and supervised classifiers. The Support Vector Machines, designed and used as a supervised
classifier, was found to perform superior. In addition to the normalized energy features
computed from wavelet packet analysis, two new features, namely Horizontal Severity Index,
and Vertical Severity Index are defined and used in the classification problem.
The detection functionality of the IFDS is achieved through incorporating a temporal
severity measure and a detection method. The novel severity measure is based on the temporal
analysis of arrival times of incipient abnormalities, which gives rise to a numeric index called the
Global Severity Index (GSI). This index portrays the progressive degradation path of
underground cable as catastrophic failure time approaches. The detection approach utilizes the
numerical modeling capabilities of SOM as well as statistical change detection techniques. The
natural logarithm of the chronologically ordered minimum modeling errors, computed from
exposing feature vectors to a trained SOM, is used as the detection index. Three modified change
detection algorithms, namely Cumulative Sum, Exponentially Weighted Moving Averages, and
Generalized Likelihood Ratio, are introduced and applied to this application. These algorithms
determine the change point or near failure time of cable from the instantaneous values of the
detection index.
Performance studies using field recorded data were conducted at three warning levels to
assess the capability of the IFDS in predicting the faults that actually occurred in the monitored underground cable. The IFDS presents a high classification rate and satisfactory detection
capability at each warning level. Specifically, it demonstrates that at least one detection
technique successfully provides an early warning that a fault is imminent.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/4675 |
| Date | 25 April 2007 |
| Creators | Jaafari Mousavi, Mir Rasoul |
| Contributors | Butler-Purry, Karen |
| Publisher | Texas A&M University |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | 8944986 bytes, electronic, application/pdf, born digital |
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