Actuator and Sensor Fault Classification for Wind Turbine Systems Based on Fast Fourier Transform and Uncorrelated Multi-Linear Principal Component Analysis Techniques

In response to the high demand of the operation reliability and predictive maintenance, health monitoring and fault diagnosis and classification have been paramount for complex industrial systems (e.g., wind turbine energy systems). In this study, data-driven fault diagnosis and fault classification strategies are addressed for wind turbine energy systems under various faulty scenarios. A novel algorithm is addressed by integrating fast Fourier transform and uncorrelated multi-linear principal component analysis techniques in order to achieve effective three-dimensional space visualization for fault diagnosis and classification under a variety of actuator and sensor faulty scenarios in 4.8 MW wind turbine benchmark systems. Moreover, comparison studies are implemented by using multi-linear principal component analysis with and without fast Fourier transform, and uncorrelated multi-linear principal component analysis with and without fast Fourier transformation data pre-processing, respectively. The effectiveness of the proposed algorithm is demonstrated and validated via the wind turbine benchmark

Εκφράζοντας τις πολυγραμμικές μεθόδους PCA και LDA ως προβλήματα ελαχίστων τετραγώνων.

Αν και η πρώτη ερευνητική δραστηριότητα σχετικά με την Ανάλυση Συνιστωσών (Component Analysis - CA) εμφανίστηκε αρκετές δεκαετίες πριν, ο τομέας αυτός είναι ακόμη αρκετά ενεργός. Δοσμένου ενός συνόλου δεδομένων, μία μέθοδος CA υπολογίζει μια απεικόνιση (mapping) των αρχικών δεδομένων, στην οποία τα χαρακτηριστικά κάθε δείγματος θα εξυπηρετούν καλύτερα τα διαθέσιμα εργαλεία και τον εκάστοτε σκοπό. Συνήθως, η προκύπτουσα προβολή έχει λιγότερα χαρακτηριστικά από το σύνολο εισόδου και συνεπώς η προσέγγιση αυτή ειναι γνωστή και ως Μείωση Διαστάσεων (Dimensionality Reduction). Παρόλο που αυτοί οι μέθοδοι ήταν αρχικά σχεδιασμένοι για διανυσματικά δεδομένα, η ανάγκη για ανάλυση πολυδιάστατων δεδομένων αποτέλεσε όχημα για την επέκταση τους σε τανυστές. Σε αυτήν την διπλωματική εργασία, θα εστιάσουμε σε δύο τέτοιες επεκτάσεις: την Πολυγραμμική Ανάλυση Κύριων Συνιστωσών (Multilinear Principal Component Analysis – MPCA) και την Ανάλυση Διάκρισης με Αναπαράσταση Τανυστή (Discriminant Analysis with Tensor Representation – DATER) και θα παρουσιάσουμε πώς διατυπώνονται ως προβλήματα εύρεσης ιδιοτιμών και ιδιοδιανυσμάτων. Μια τέτοια διατύπωση, ωστόσο, εμπεριέχει τα εξής προβλήματα: (1) δεν απαγορεύει την επίλυση προβλημάτων εύρεσης ιδιοτιμών και ιδιοδιανυσμάτων σε πίνακες κακής κατάστασης (ill-conditioned matrices), πράγμα που ισχύει αρκετά συχνά σε δεδομένα τανυστών [1] και (2) οι εμπλεκόμενοι πίνακες έχουν μεγάλες διαστάσεις και η επίλυση τέτοιων προβλημάτων απαιτεί αρκετό χρόνο. Για το σκοπό αυτό, προτείνουμε έναν τρόπο διατύπωσης των MPCA και DATER ως προβλήματα Παλινδρόμησης Τανυστών, έτσι ώστε να μπορούν να εφαρμοστούν περισσότερο αριθμητικά ευσταθείς και υπολογιστικά απλούστερες προσεγγίσεις (π.χ. Gradient Descent). Κατόπιν, εξετάζουμε την ποιότητα της πρότασης μας σε πραγματικά δεδομένα με πείραματα Αφαίρεσης Θορύβου (Image Denoising) και Αναγνώρισης Προσώπου (Face Recognition).Although the first works relevant to Component Analysis (CA) date many decades ago, it still remains a very active research area. Given a dataset, CA methods aim to find a mapping of it, the features of which are ideal for the available tools or the assigned task. Typically, the produced mapping has fewer features than the original data, therefore this approach is also known as Dimensionality Reduction. While these methods were designed to work on vectors, the need to analyze multidimensional datasets with an abundance of features, fueled their extension to tensors. In this thesis, we will investigate two such extensions, Multilinear Principal Component Analysis (MPCA) and Discriminant Analysis with Tensor Representation (DATER) and present how they are formulated as generalized eigenproblems. Such formulation, however, conceals several drawbacks: (1) it may require solving eigenproblems on ill-conditioned matrices, which is more than often the case when it comes to tensor data [1] and (2) the matrices involved are commonly highly dimensional and solving for their eigenvalues requires significant computation time. To this end, we will propose a Least Squares (LS) Tensor Regression formulation for MPCA and DATER, which makes applicable more numerically stable and computationally simpler approaches (e.g., Gradient Descent) and evaluate it in practice with an Image Denoising and Face Recognition task

Sensor Fusion and Process Monitoring for Ultrasonic Welding of Lithium-ion Batteries.

Ultrasonic metal welding is used for joining lithium-ion batteries of electric vehicles. The quality of the joints is essential to the performance of the entire battery pack. Hence, the ultrasonic welding process that creates the joints must be equipped with online sensing and real-time process monitoring systems. This would help ensure the process to be operated under the normal condition and quickly address quality-related issues. For this purpose, this dissertation develops methods in process monitoring and fault diagnosis using online sensing signals for ultrasonic metal welding. The first part of this dissertation develops a monitoring algorithm that targets near-zero misdetection by integrating univariate control charts and a multivariate control chart using the Mahalanobis distance. The proposed algorithm is capable of monitoring non-normal multivariate observations with adjustable control limits to achieve a near-zero misdetection rate while keeping a low false alarm rate. The proposed algorithm proves to be effective in achieving near-zero misdetection in process monitoring in ultrasonic welding processes. The second part of the dissertation develops a wavelet-based profile monitoring method that is capable of making decisions within a welding cycle and guiding real-time process adjustments. The proposed within-cycle monitoring technique integrates real-time monitoring and within-cycle control opportunity for defect prevention. The optimal decision point for achieving the most benefit in defect prevention is determined through the formulation of an optimization problem. The effectiveness of the proposed method is validated and demonstrated by simulations and case studies. The third part of this dissertation develops a method for effective monitoring and diagnosis of multi-sensor heterogeneous profile data based on multilinear discriminant analysis. The proposed method operates directly on the multi-stream profiles and then extracts uncorrelated discriminative features through tensor-to-vector projection, and thus preserving the interrelationship of different sensors. The extracted features are then fed into classifiers to detect faulty operations and recognize fault types. The research presented in this dissertation can be applied to general discrete cyclic manufacturing processes that have online sensing and control capabilities. The results of this dissertation are also applicable or expandable to mission-critical applications when improving product quality and preventing defects are of high interests.PhDIndustrial and Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113405/1/graceguo_1.pd

Biometric face recognition using multilinear projection and artificial intelligence

PhD ThesisNumerous problems of automatic facial recognition in the linear and multilinear subspace learning have been addressed; nevertheless, many difficulties remain. This work focuses on two key problems for automatic facial recognition and feature extraction: object representation and high dimensionality. To address these problems, a bidirectional two-dimensional neighborhood preserving projection (B2DNPP) approach for human facial recognition has been developed. Compared with 2DNPP, the proposed method operates on 2-D facial images and performs reductions on the directions of both rows and columns of images. Furthermore, it has the ability to reveal variations between these directions. To further improve the performance of the B2DNPP method, a new B2DNPP based on the curvelet decomposition of human facial images is introduced. The curvelet multi- resolution tool enhances the edges representation and other singularities along curves, and thus improves directional features. In this method, an extreme learning machine (ELM) classifier is used which significantly improves classification rate. The proposed C-B2DNPP method decreases error rate from 5.9% to 3.5%, from 3.7% to 2.0% and from 19.7% to 14.2% using ORL, AR, and FERET databases compared with 2DNPP. Therefore, it achieves decreases in error rate more than 40%, 45%, and 27% respectively with the ORL, AR, and FERET databases. Facial images have particular natural structures in the form of two-, three-, or even higher-order tensors. Therefore, a novel method of supervised and unsupervised multilinear neighborhood preserving projection (MNPP) is proposed for face recognition. This allows the natural representation of multidimensional images 2-D, 3-D or higher-order tensors and extracts useful information directly from tensotial data rather than from matrices or vectors. As opposed to a B2DNPP which derives only two subspaces, in the MNPP method multiple interrelated subspaces are obtained over different tensor directions, so that the subspaces are learned iteratively by unfolding the tensor along the different directions. The performance of the MNPP has performed in terms of the two modes of facial recognition biometrics systems of identification and verification. The proposed supervised MNPP method achieved decrease over 50.8%, 75.6%, and 44.6% in error rate using ORL, AR, and FERET databases respectively, compared with 2DNPP. Therefore, the results demonstrate that the MNPP approach obtains the best overall performance in various learning scenarios