Avilés-Castillo, Fátima
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Preferred name
Avilés-Castillo, Fátima
Main Affiliation
Ambato
Email
faviles@indoamerica.edu.ec
ORCID
Scopus Author ID
57845057800

Research Output
Now showing 1 - 2 of 2
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Publication

Performance and Real-World Variability of Predictive Maintenance Models for Vehicle Fleets

2024 , Avilés-Castillo, Fátima , Dayanara Yánez-Arcos , Ayala-Chauvin, Manuel Ignacio , Elena Blanco-Romero

This study presents a comprehensive evaluation of predictive maintenance models for vehicle fleets, detailing a sequence of systematic steps to ensure model performance and address real-world variability. The process begins with database creation and data preprocessing, where relevant maintenance records are filtered, and datetime columns are converted to facilitate time-based calculations. Grouping and aggregation techniques are then applied to count occurrences of specific maintenance activities and identify common failure types. For model training, we define a neural network architecture comprising dense and dropout layers to mitigate overfitting, compile the model with suitable loss functions and optimizers, and train it using the prepared data. The trained model, along with the scaler and encoder, is saved for future use. To augment the dataset, synthetic data is generated using the Faker library and random distributions, with added noise to mimic real-world variability. Preprocessing steps are reapplied to this synthetic data to ensure consistency. By implementing this neural network, we achieved a sensitivity of 0.93 and an ROC of 0.71. Following these detailed steps, we develop a robust predictive maintenance model that effectively identifies failures and non-failures, ultimately enhancing the reliability and efficiency of vehicle fleet management.

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Publication

Predictive Maintenance in Industrial Robotics Using Big Data: Techniques, Challenges, and Opportunities

2024 , Ayala-Chauvin, Manuel Ignacio , Avilés-Castillo, Fátima , Dayanara Yánez-Arcos , Buele, Jorge

In industrial robotics, predictive maintenance is important to improve efficiency and reduce costs, addressing early detection and diagnosis of failures. The use of Big Data allows us to identify patterns and trends that at first glance are complex. This review examines research on the application of big data in predictive maintenance of industrial robots, which use advanced techniques such as cloud-based architectures, filtering algorithms, and machine learning. The review methodology included an analysis of the big data techniques used, the challenges identified, and the opportunities presented. The results show significant improvements in the accuracy of predictions and fault diagnoses. Key anomaly drivers were identified that improved production performance and enabled accurate fault identification and reduced downtime in industrial robots. Despite the benefits, challenges remain in data security and communications latency, underscoring the need to develop innovative algorithms and techniques to balance computing load and minimize delays. The continuous evolution of these techniques promises to improve the failure management capacity in industrial robotics, thus optimizing the operability and efficiency of robotic systems.

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