Quality assurance of electrical components of cars such as alternators and starters is
an important consideration due to both commercial and safety reasons. The focus of
this research is to develop a complete Fault Detection and Diagnosis (FDD) solution
for alternators and starters for their implementation in test cells. The FDD would
enable more reliable testing of production line parts without compromising high production
throughput. Our proposed solution includes three elements: (1) background
noise elimination; (2) fault detection and analysis; and (3) fault classi cation for fault
type identi cation.
Noise gating, Extended Multi-Scale Principle Component Analysis (EMSPCA),
and Logistic Discriminant classi er were used to perform these three elements. The
FDD strategy detects and extracts fault signatures from multiple sensors (which are
vibration and sound measurements in this research). Included in this strategy is
ltering of the background noise in sound measurements to enable operation and
maintain FDD performance in noisy conditions. The EMSPCA is the core of the
FDD strategy. EMSPCA breaks the fault into time-frequency scales using wavelets
and applies Principle Component Analysis (PCA) on each scale. This reveals the
signature of the fault. The fault signature is then examined by a classi er to match it
with the correct type of faults. The total FDD strategy is automated and no operator
intervention is required.
The advantages of the proposed FDD strategy are: (1) high accuracy in detection
and diagnosis; (2) robustness in noisy industrial conditions; and (3) no need for operators'
intervention. These advantages make the proposed FDD strategy a promising
candidate for mass industrial applications. / Thesis / Master of Applied Science (MASc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/16793 |
| Date | 06 1900 |
| Creators | Ismail, Mahmoud |
| Contributors | Habibi, Saeid, Ziada, Samir, Mechanical Engineering |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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