On August 27, 2016, Southwest Airlines flight 3472 from New Orleans to Orlando had to perform an emergency landing when a fan blade separated from the engine hub and destroyed the cowling and punctured the fuselage. Initial findings from the metallurgical examination conducted in the National Transpiration Safety Board Materials Laboratory found that the fracture surface of the missing blade showed curving crack arrest lines consistent with fatigue crack growth. Fatigue is often cause by resonate vibrations. Modal analysis is a method that can model the natural frequencies and bending modes of turbomachinery blades. When performing modal analysis with finite element solvers like Mechanical ANSYS, images are often generated to help an engineer identify mode shapes created by nodal displacements. Manually identifying mode shapes from these generated images is an expensive task. This research proposes an automated process to identify mode shapes from gray-scale images of turbomachinery blades within a jet-engine. This work introduces mode shape identification using principal component analysis (PCA), similar to approaches in facial and other recognition tasks in computer vision. This technique calculates the projected values of potentially linearly correlated values onto P-linearly orthogonal axes, where P is the number of principal axes that define a subset space. Classification was performed using support vector machines (SVM). Using the PCA and SVM algorithm, approximately 5300 training images, representative of 16 different modes, were used to create a classifier. A test set was created with approximately 2000 unknown mode images. The classifier achieved on average 98.4% accuracy on the test set when using the bilinear Eigenface method. The accuracy was 98.6% when using the triangle interpolate Eigenface method. In addition, The results suggest that using digital images to perform mode shape identification can be achieved with better accuracy and computation performance compared to previous work. Potential generalization of this method could be applied to other engineering design and analysis applications.

On August 27, 2016, Southwest Airlines flight 3472 from New Orleans to Orlando had to perform an emergency landing when a fan blade separated from the engine hub and destroyed the cowling and punctured the fuselage. Initial findings from the metallurgical examination conducted in the National Transpiration Safety Board Materials Laboratory found that the fracture surface of the missing blade showed curving crack arrest lines consistent with fatigue crack growth. Fatigue is often cause by resonate vibrations. Modal analysis is a method that can model the natural frequencies and bending modes of turbomachinery blades. When performing modal analysis with finite element solvers like Mechanical ANSYS, images are often generated to help an engineer identify mode shapes created by nodal displacements. Manually identifying mode shapes from these generated images is an expensive task. This research proposes an automated process to identify mode shapes from gray-scale images of turbomachinery blades within a jet-engine. This work introduces mode shape identification using principal component analysis (PCA), similar to approaches in facial and other recognition tasks in computer vision. This technique calculates the projected values of potentially linearly correlated values onto P-linearly orthogonal axes, where P is the number of principal axes that define a subset space. Classification was performed using support vector machines (SVM). Using the PCA and SVM algorithm, approximately 5300 training images, representative of 16 different modes, were used to create a classifier. A test set was created with approximately 2000 unknown mode images. The classifier achieved on average 98.4% accuracy on the test set when using the bilinear Eigenface method. The accuracy was 98.6% when using the triangle interpolate Eigenface method. In addition, The results suggest that using digital images to perform mode shape identification can be achieved with better accuracy and computation performance compared to previous work. Potential generalization of this method could be applied to other engineering design and analysis applications.

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)

Chromatography units, used in the production of pharmaceuticals, degrade with use and need to be changed or repackaged. This study investigates the effectiveness of two statistical methods, principal component analysis and simple and one-point multiparameter technique, for determining degradation in the Fibro chromatography unit. The methods have been shown to be effective on resin chromatography columns but not before tested on the relatively new Fibro chromatography unit. The statistical methods are implemented in an unreleased version of the monitoring and control software Unicorn. This implementation aims to be a proof of concept for including more complex methods for monitoring runs directly in the software, easing the workflow of operators by removing the need to export measurements to a third-party program. The methods were tested on measurements of absorbance, conductivity, and pressure from two series of chromatograms performed on two Fibro chromatography units. One of the units was defective and broke down halfway through the series. Principle component analysis could clearly visualize a difference between early and late runs on the defective unit. The same could only be achieved for the non-defective unit by excluding measurements of pressure. Simple and one-point multiparameter technique visualized trends from early to late in the series which were much clearer for the defective unit. Both methods showed signs of predicting degradation in a Fibro chromatograpy unit but require validation on chromatogram series with more direct measurements of performance and a wider range of failure causes.

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