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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- Wavelets for spindle fault diagnosis in high speed machining(2017-12-04) Batallas Moncayo, George Francisco; Morales Menéndez, Rubén; Vallejo Guevara, Antonio; Alcántra Hernández, DianaThe spindle of machining centers must provide high rotational speed, transfer torque and power to the cutting tool during continuous periods of time. The constant forces generate faults in its components where the most important are the shaft and bearings. As the fault increases, it affects other components and may lead to a catastrophic damage and a production stoppage. The maintenance strategies have been evolving in order to prevent irreversible damages. Over the last years, great progress has been made in the condition-based maintenance, particularly in the vibration analysis, where the vibration signature can be associated with the fault. In recent years, several signal-processing techniques have been introduced to extract the features from vibration signals. The WT has caught the attention of the scientific community by its characteristics and its limitless number of wavelets. In this thesis a methodology based on the WT is proposed to detect faults in spindle. The approach is capable of extracting the bearing characteristic frequencies related to the fault from the resonance frequency and the low frequencies information associated with shaft faults. The implemented method contemplates the latest advances in the literature to detect robustly the type of the fault, it is focused on industrial environment were the faults are usually tainted by noise from other machines or by errors in the acquisition. The method is applied to different types of bearing faults to demonstrate its effectiveness and robustness when detecting faults at early stages. In the three studied cases the proposed methodology got several properties; for the CWRU signals the characteristic fault frequency peak got an increase from 6 to 32% compared with the traditional methods; when the signal is tainted by Gaussian noise, the method works more effectively, since in these cases the increase percentage reaches up to 57%. Similarly, in the IMS database the characteristic frequency peak increases from 6 to 70%. Finally, in the machining center database there was not an increment but the method acts as filter which eliminates the undesired frequencies. Experimental results indicate the proposed approach is reliable to detect bearing and shaft faults. It also has a superior diagnosis performance compared to traditional methods in extracting fault features. The method removes most of the noise and can be used in future works as preprocessor.
- Time-frequency method for bearing fault diagnosis(Instituto Tecnológico y de Estudios Superiores de Monterrey) Ruiz Quinde, Israel Benjamin; 864379; Morales Menéndez, Rubén; Lozoya Santos, Jorge de Jesús; Vargas Martínez, Adriana; School of Engineering and Sciences; School of Engineering and Sciences; Campus Monterrey; Vallejo Guevara, Antonio JrSpindle bearings are some of the most critical and vulnerable components in rotating machines. Friction, load forces and vibrations actuating over bearings can produce wear, fatigue and impending cracks on these which may end in a full damage of the spindle over time. The Condition-Based Maintenance (CBM) have arose as a strategy to address this problem, in which, analysis of vibration signals can be performed in real time to anticipate the damage of the machine. A wide range of strategies based on digital processing techniques have been developed for vibration analysis. Wigner-Ville Distribution (WVD) is probably the most used non-linear time-frequency distribution for signal processing in fault diagnosis, however, the presence of cross terms can lead to misleading interpretations of their Time-Frequency Representations (TFR). Signal decomposition methods such as Variational Mode Decomposition (VMD) and Local Mean Decomposition (LMD) have been developed to reduce the complexity of vibration signals allowing to reconstruct them only with their main components. Moreover, this can reduce the cross terms in WVD. However, after the signal decomposition procedure, the identification of the relevant components, which contain the fault information, is commonly based in visual inspection and identification of the bearing housing resonance band. A methodology which combines the great characteristics of the VMD and the WVD is proposed to get more reliable and illustrative results of bearing fault diagnosis from TFR of the vibration signals. Kullback-Leibler Divergence (KLD) was included in the analysis to guide the selection of the effective components with the most relevant information about the fault in an automatic way. After applying the proposed method, in some cases, the amplitude of the fault frequencies in the spectrum were increased around 53% for Outer Race (OR) signals, 45% for Inner Race (IR) signals and 73% for Rolling Element (RE) signals, regarding the amplitude of the found peaks by using the traditional envelope-FFT method. An automatic fault diagnosis method based on an Artificial Neural Network (ANN) and WVD was also presented to avoid the visual inspection. The LMD was used as the signal decomposition method. The TFR, obtained by computing the WVD over the effective Product Functions (PF), were used to build the feature vectors. A classification accuracy in average = 98.2% was obtained by testing the proposed methodology with experimental data.