Morales Menéndez, Rubén2019-08-292019-08-292018Chuya-Sumba, J. P. (2019). Sistema Inteligente de Diagnóstico de Fallas en Máquinas Rotativas usando el Enfoque de Aprendizaje Automático (Tesis de Maestría). Instituto Tecnológico y de Estudios Superiores de Monterrey, Monterrey, Nuevo León, México.http://hdl.handle.net/11285/633052Spindle 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.TextoOpen Accesshttp://creativecommons.org/licenses/by-sa/4.0/INGENIERÍA Y TECNOLOGÍA::CIENCIAS TECNOLÓGICAS::TECNOLOGÍA E INGENIERÍA MECÁNICASIngeniería y Ciencias Aplicadas / Engineering & Applied SciencesTesis de maestríaCNN de 1DDeep LearningDiagnóstico de FallasExtracción de característicasAnálisis de vibración