Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- A standard-aligned reference model for digital twin systems: foundational work toward the V³ framework for smart manufacturing(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025) Enríquez García, Víctor Hugo; Navarro Durán, David; emimmayorquin, emipsanchez; Tejeda Alejandre, Raquel; Lenz, Jürgen; School of Engineering and Sciences; Campus Ciudad de MéxicoThe increasing adoption of Digital Twin (DTw) technologies in the manufacturing industry has revealed significant gaps in methodological consistency, standard alignment, and interoperability across hierarchical levels and lifecycle phases. Current implementations often lack a unifying structure that connects design, operation, and data continuity, hindering scalability and integration within smart manufacturing environments. This master’s research develops and validates a Reference Model for Digital Twin implementation, establishing the foundational structure for what will evolve into the V³ Framework in subsequent doctoral work. The proposed model aligns with international standards, including RAMI 4.0, ISA-95, IEC 62890, ISO 23247, and ISO/IEC/IEEE 15288, to ensure semantic interoperability and traceability across system layers and lifecycle stages. The research adopts a V-Model approach for systematic validation and verification, combining computational simulation in Siemens Tecnomatix Process Simulate with semantic modeling through AASX Package Explorer. Experimental validation was conducted using the FESTO Meclab System, demonstrating how the proposed structure enables the creation of interoperable digital assets compliant with the Asset Administration Shell (AAS) specification. Results confirm that the Reference Model provides a robust foundation for structuring DTw systems, facilitating standardized information exchange and modular system expansion. This work establishes the conceptual and experimental groundwork for the forthcoming V³ Framework, which will integrate lifecycle synchronization, hierarchical interoperability, and digital-thread continuity to support industrial digital transformation and smart manufacturing ecosystems.
- Anomaly detection in a cost-effective conveyor belt testing system(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-09) Solórzano Souza, Ana Paula; Navarro Durán, David; mtyahinojosa, emipsanchez; Galluzzi Aguilera, Renato; Sotelo Molina, Carlos Gustavo; Navarro Durán, David; School of Engineering and Sciences; Campus Ciudad de MéxicoThe industrial manufacturing sector faces significant challenges regarding energy efficiency and operational sustainability, particularly in the management of motor driven systems such as conveyor belts. While traditional diagnostic methods effectively identify faults, a critical gap remains in closing the control loop to enable autonomous corrective actions, specifically for mechanical tension regulation y conveyor belts. This research addresses this limitation by designing, implementing, and validating a low-cost, small-scale prototype capable of soft real-time energy monitoring, anomaly detection, and automatic physical correction. The system utilizes an ESP32 microcontroller and an INA219 sensor to analyze voltage and current signals, employing unsupervised machine learning algorithms such as Isolation Forest (IF) and One-Class Support Vector Machine (OC-SVM). This serves to diagnose tension states defined by specific deflection thresholds. To execute the physical corrections, the platform integrates a custom built rack and pinion mechanism driven by a servomotor, which automatically regulates the belt tension. Methodologically, the study characterized optimal operational conditions at a 30% PWM duty cycle and applied a median filter that reduced signal variability. Four models were trained using exclusively optimal-state data under univariate and multivariate configurations. Experimental results demonstrate that electrical parameters serve as reliable indicators of mechanical tension in a small scale conveyor belt. The Univariate One-Class SVM model applied to voltage yielded the highest performance, achieving an F1-Score of 0.8624. Meanwhile, the Multivariate OC-SVM model demonstrated high reliability with a Precision of 90.51% and a Recall of 72.95%. Upon detection of anomalies (slippage or excessive tightness), the system successfully triggered autonomous adjustments via a rack and pinion mechanism. These findings validate the feasibility of using accessible monitoring to implement intelligent, self-correcting maintenance systems, offering a scalable solution to minimize downtime and energy waste in industrial environments.