This paper describes the failure detection system of an electro-hydraulic actuator with dual modular redundancy based on a hybrid twin TM concept. Hybrid twin TM is a combination of virtual twin that operates in parallel with the actuator and represents its ideal behaviour, and a digital twin that identifies possible failures using the sensor readings residuals. Simulation-based system reliability analysis helps to generate a dataset for training the digital twin using machine learning algorithms. A systematic failure detection approach based on decision trees and the process of analysing the quality of the result is described.

Detecting failures in tricone drill bits is crucial in the mining industry due to their potential consequences, including operational losses, safety hazards, and delays in drilling operations. Timely identification of failures allows for proactive maintenance and necessary measures to ensure smooth drilling processes and minimize associated risks. Accurate failure detection helps mining operations avoid financial losses by preventing unplanned breakdowns, costly repairs, and extended downtime. Moreover, it optimizes operational efficiency by enabling timely maintenance interventions, extending the lifespan of drill bits, and minimizing disruptions. Failure detection also plays a critical role in ensuring the safety of personnel and equipment involved in drilling operations. Traditionally, failure detection in tricone drill bits relies on manual inspection, which can be time-consuming and labor-intensive. Incorporating artificial intelligence-based approaches can significantly enhance efficiency and accuracy. This thesis uses machine learning methods for failure inference in tricone drill bits. A classic Convolutional Neural Network (CNN) classification method was initially explored, but its performance was insufficient due to the small dataset size and imbalanced data. The problem was reformulated as an object detection task to overcome these limitations, and a post-processing operation was incorporated. Data augmentation techniques enhanced the training and evaluation datasets, improving failure detection accuracy. Experimental results highlighted the need for revising the initial CNN classification method, given the limitations of the small and imbalanced dataset. However, You Only Look Once (YOLO) algorithms such as YOLOv5 and YOLOv8 models exhibited improved performance. The post-processing operation further refined the results obtained from the YOLO algorithm, specifically YOLOv5 and YOLOv8 models. While YOLO provides bounding box coordinates and class labels, the post-processing step enhanced drill bit failure detection through various techniques such as confidence thresholding, etc. By effectively leveraging the YOLO-based models and incorporating post-processing, this research advances failure detection in tricone drill bits. These intelligent methods enable more precise and efficient detection, preventing operational losses and optimizing maintenance processes. The findings underscore the potential of machine learning techniques in the mining industry, particularly in mechanical drilling, driving progress and enhancing overall operational efficiency

Gears are the heart of many machines, being its function transform and transmit torque. This work is a study of adequate design requirements, in particular, the best methodology to early detect gear fatigue failure using a gear test rig, an FZG test machine. The study used the widely proved QFD analysis technique that introduces the client in the design process by using a matrix system. All available relevant literature on the subject and interviews with relevant people in the field were sources of information for the development of this technique. In that way, a mapping is presented, showing the most common fatigue failure modes and available detection methods.  As a result of the investigation, the most suitable technique for the early gear failure detection in the FZG rig to be a combination of vibration analysis and acoustic emissions analysis, these techniques present the best practice at the moment and also possible to implement. However, other technologies are also presented in the report. / Kuggväxlar uppfyller en viktig funktion i många system. I det här arbetet studerades de viktigaste konstruktionskraven hos en tetstrigg för kugg, en FZG-rigg, för att kunna detektera tidiga tecken på kuggskador. Litteraturstudier tillsammans med intervjuer av personer från industrin lade grunden till en jämförelse av produktegenskaper som uppfyller kundkraven med hjälp av en så kallad Quality Function Deployment matrix (QFD-matris). I en QFD omvandlas kundkrav till funktion- och konstruktionskrav, i och med den kunde också de vanligaste kuggskadorna och detekteringsmetoderna kartläggas.  De mest relevanta teknikerna för att detektera tidiga tecken på skador i en FZG-rigg idag visade sig vara en kombination av vibrationsmätningar och akustiska emissionsmätningar. Lösningen är möjlig att implementera. Även andra teknologier finns presenterade i rapporten. / Los engranajes son el corazón de muchas máquinas, siendo su función transformar y transmitir par. En este trabajo se realizó un estudio de los requisitos de diseño más adecuados, en particular, la mejor metodología para detección anticipada de fallo a fatiga de engranajes testeados en un banco de pruebas de engranajes FZG. Durante el estudio se utilizó la técnica de análisis QFD que introduce al cliente en el proceso de diseño mediante el uso de un sistema matricial. Toda la literatura relevante disponible sobre el tema, así como entrevistas a personas relevantes en el campo fueron utilizadas como fuente de información para el desarrollo de dicha técnica. También se realizó un mapeo de los diferentes modos y mecanismos de fallo por fatiga más comunes, así como los métodos de detección disponibles.  Como resultado de la investigación se concretó como metodología más adecuada para la detección anticipada de fallo de engranajes en los bancos de prueba FZG, una combinación de análisis de vibraciones y análisis de emisiones acústicas, estas técnicas presentan las mejores características en función de la dificultad de implementación. Sin embargo, en el informe también se presentan otras tecnologías.

Recent advances in the miniaturization, functionality, and integration of integrated circuits and packages, such as the system-on-package (SOP) methodology, require increasing use of microvias that generates vertical signal paths in a high-density multilayer substrate. A scanning projection excimer laser system has been utilized to fabricate the microvias. In this thesis, a novel technique implementing statistical experimental design and neural networks (NNs) is used to characterize and model the excimer laser ablation process for microvia formation. Vias with diameters from 10 50 micrometer have been ablated in DuPont Kapton(r) E polyimide using an Anvik HexScan(tm) 2150 SXE pulsed excimer laser operating at 308 nm. Accurate NN models, developed from experimental data, are obtained for microvia responses, including ablated thickness, via diameter, wall angle, and resistance. Subsequent to modeling, NNs and genetic algorithms (GAs) are utilized to generate optimal process recipes for the laser tool. Such recipes can be used to produce desired microvia responses, including open vias, specific diameter, steep wall angle, and low resistance. With continuing advancement in the use of excimer laser systems in microsystems packaging has come an increasing need to offset capital equipment investment and lower equipment downtime. In this thesis, an automated in-line failure diagnosis system using NNs and Dempster-Shafer (D-S) theory is implemented. For the sake of comparison, an adaptive neuro-fuzzy approach is applied to achieve the same objective. Both the D-S theory and neuro-fuzzy logic are used to develop an automated inference system to specifically identify failures. Successful results in failure detection and diagnosis are obtained from the two approaches. The result of this investigation will benefit both engineering and management. Engineers will benefit from high yield, reliable production, and low equipment down-time. Business people, on the other hand, will benefit from cost-savings resulting from more production-worthy (i.e., lower maintenance) laser ablation equipment.