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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- LSTM Neural Networks for Remaining Useful Life Estimation of Turbofan Engines(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2020-12-04) Montoya Herrera, Luisa Fernanda; MORALES MENENDEZ, RUBEN; 30452; Morales Menéndez, Rubén; tolmquevedo, emipsanchez; School of Engineering and Sciences; Campus MonterreyCondition-based Maintenance is a maintenance strategy that monitors the actual condition of a system to make predictive decisions whit respect to it. This type of maintenance includes detection, diagnosis, and prediction of system failures. It has become increasingly important because it generates the least losses, reducing total maintenance costs in a business by 5In general, the Remaining Useful Life estimation allows making failure predictions. The complexity of failure prediction in mechanical systems has led to a significant amount of literature. Different solutions have been proposed; however, this still a real problem.Remaining Useful Life estimation can be done from other approaches, for example, using physical models, knowledge-based models, or data-driven models. Extracting relevant features from raw data using physical or knowledge-based techniques alone, in most cases, is not enough due to the complexity of the characteristics present in the data. Literature shows that data-driven approaches are the most used for prediction. In recent years, Deep Learning models for different applications have been used, including failure detection, diagnosis, and prediction. The Deep Learning model’s advantage is that an indepth knowledge of the system is not required, and due to its robustness, complex learning results are satisfactory. For Remaining Useful Life estimation, Long Short Term Memory neural networks are a viable option since they can adequately handle the time series needed for failure predictions using Remaining Useful Life estimation. The three main stages for developing this method based on Long Short Term Memory neural networks were data pre-processing, model training, and model performance evaluation. The methodology uses two datasets of turbofan engines with different operational conditions and faults for its validation. The process evaluates signals obtained from sensors located along with a turbofan engine simulated through a Simulink-based program. This methodology presents a reasonably acceptable performance in terms of Root Mean Squared Error of 2.85 with a standard deviation of 0.39. It means that on average for the engines, the failure prediction will have an error of 3 cycles; and a Score function of 7.26 with a standard deviation of 1.76, which is an asymmetric algorithm where late predictions are more penalized than early predictions, increasing exponentially with the error. The proposed methodology has the advantage of being more straightforward than other methods found in the literature. Besides, the obtained values of the predictions are conservative.
- Sistema inteligente de diagnóstico de fallas en máquinas rotativas usando el enfoque de aprendizaje automático(Instituto Tecnológico y de Estudios Superiores de Monterrey) Chuya Sumba, Jorge Patricio; Morales Menéndez, Rubén; Vallejo Guevara, Antonio Jr.; Campus Monterrey; Ibarra Zarate, David IsaacSpindle failures diagnosis in high-speed machining centers is critical in manufacturing systems, since early detection can save a representative amount of time and cost. The fault diagnosis systems usually have two blocks: feature extraction and classification, the feature extraction affects the performance of prediction model, and the essential information is realized by identification of abstract and representative high-level features. Deep Learning (DL) provides an effective way to extract the features of raw data, without prior knowledge compared with traditional Machine Learning (ML) methods. A feature learning approach was applied using 1D CNN that works directly with raw vibration signals. The network structure consists of small convolutional kernels to realize a nonlinear mapping and extract features, the classifier is a Softmax layer. The method has achieved a satisfactory performance in terms of prediction accuracy reaching an ∼99% using three bearing databases, the processing time is suitable for real-time applications with ∼8ms per signal, the repeatability has a low standard deviation ∼0.25% and achieves an acceptable network generalization ability.
- Prognosis using Deep Learning in CoViD-19 patients(Instituto Tecnológico y de Estudios Superiores de Monterrey) Guadiana Álvarez, José Luis; MORALES MENENDEZ, RUBEN; 30452; Morales Menéndez, Rubén; emipsanchez; Vargas Martínez, Adriana; Ramírez Mendoza, Ricardo Ambrocio; School of Engineering and Sciences; Campus Monterrey; Rojas Flores, Etna AuroraPrognostics study the prediction of an event before it happens, to enable efficient critical decision making. Over the past few years, it has gained a lot of research attention in many fields, i.e. manufacture, economics, and medicine. Particularly in medicine, prognostics are very useful for front line physicians to predict how a disease may affect a patient and react accordingly to save as many lives as possible. One clear example is the recently discovered Coronavirus Disease 2019 (CoViD-19). Because of its novelty, not nearly enough is known about the virus’ behaviour and Key Performance Indicators (KPIs) to asses a mortality prediction. However, using a lot of complex and expensive medical biomarkers could be impossible for many low budget hospitals. This motivates the development of a prediction model that not only maximizes performance, but does so using the least amount of biomarkers possible. For mortality risk prediction, falsely assuming that a patient has a low mortality risk is far more critical than the opposite. Therefore, false negative predictions should be prioritized over false positive ones. This research project proposes a CoViD-19 mortality risk calculator based on a Deep Learning model trained on a data set provided by the HM Hospitales from Madrid, Spain. A pre-processing strategy for unbalanced classes and feature selection is proposed. Benefit of using over-sampling and imputation techniques is evaluated. Also, an imputation method based on the K-Nearest Neighbor (KNN) algorithm for biomarker data is is proposed and its efficiency is evaluated. Results are compared against a Random Forest (RF) model while showing the trade-off between feature input space and the number of samples available. Results on the MPCD score show the proposed DL outperforms the proposed RF on every data set when evaluating even with an over-sampling technique. Finally, the proposed KNN method proves beneficial for data imputation, improving the model’s Recall score from 0:87 to 0:90.