Practical Process History-Based Fault Detection and Diagnosis Algorithms Using Multivariate Statistical Process Control, Frequency, and Time-Frequency Approaches Addressing Variability in Datasets
Bibliographic record
Abstract
This thesis addresses the development of innovative techniques for fault detection and diagnosis for rotary machines, explicitly focusing on Belt Starter Generators (BSGs) and Internal Combustion Engines (ICEs). The primary objective is to create methodologies that enhance the accuracy and reliability of fault detection systems by utilizing advanced signal processing and machine learning approaches. Three key contributions are made in this research. First is the integration of Short-Time Fourier Transform (STFT) with Principal Component Analysis (PCA)-based Multivariate Statistical Process Control (MSPC). This novel approach addresses the challenges posed by data variability in industrial environments. It significantly improves the robustness of fault detection algorithms. Second, the research develops the Adapted Local Binary Pattern (ALBP) method for bearing fault detection in BSG systems. This method combines signal processing techniques with computer vision algorithms to achieve superior accuracy without increasing computational complexity. Additionally, innovative strategies for handling domain shifts, such as multi-sensory multiblock analysis and domain adaptation techniques, are proposed to ensure consistent fault detection performance across different operating conditions. Third, the practical applicability of the proposed methodologies is thoroughly evaluated through comprehensive validation experiments using real-world industrial data. The results demonstrate the effectiveness and reliability of these methods, highlighting their potential for real-world implementation. These contributions enhance the state-of-the-art in fault detection and diagnosis, significantly improving operational reliability and efficiency for industrial machinery. This thesis presents substantial advancements in fault detection and diagnosis, providing robust solutions to address variability and domain shift challenges in industrial data. The findings contribute to safer and more efficient industrial operations, reinforcing the importance of advanced analytical techniques in the automotive industry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".