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.
Browse
Search Results
- Wavelets for spindle health diagnosis(2018-12-04) Villagómes Garzón, Silvia Cristina; Morales Menéndez, Rubén; Vallejo Guevara, Antonio; Hernández Alcántara, DianaIndustrial development and customer demands have increased the need to look for high-quality products at low cost and, at the same time, ensure safety during manufacturing. As a result, rotary machinery and its components have become increasingly complex, making their repairs more expensive. Therefore, many efforts must be focused in preventing breakdowns in machines, for which real-time fault diagnosis and prognosis are mandatory. Considering that the element most prone to failure in a machining center is the spindle, and with it its bearing system, the diagnosis of failures of these elements is of paramount importance. To ensure the safe operation of the bearing, some methods of fault detection have been developed based on different techniques. One of the most commonly used is vibration analysis. There are several difficulties when dealing with analyzing vibration signals, they are complex and non-stationary signals with a large amount of noise. Conventional analysis have not been able to solve this problem, thus, alternative methods such as Wavelet Transform have been gaining ground. The following research is focused in detecting bearing faults, as well as the main shaft faults, which eventually also lead to bearing damage, by using wavelets. Different signals, presenting distinct bearing fault conditions, of different data sets are evaluated for validating the proposed methodology. An exhaustive analysis has been developed for selecting the best parameters of this methodology. As results, an improvement around 20% in magnitude of bearing fault frequency peaks was found, compared to the traditional methodology. The proposal of giving more weight to high energy components allows increasing these fault frequencies, as well as reducing low frequency noise. This provides a great advantage in pursuit of an automatic fault detection. An industrial approach was also validated, by proving that the proposed methodology is more immune to noise. Even though, the magnitudes of the bearing fault peaks are diminished by noise, a comparison between the proposal and the traditional methodology reveal an increase of approximately 70% of those magnitudes. Demonstrating that the fault information is barely attenuated by noise. Also, an early diagnosis was proved, which could benefit future studies of fault prognosis. Finally, the filtering property of wavelet decomposition is exploited to limit the frequencies of the signal to few harmonics of the shaft speed. This with the aim of restricting the spectrum for detecting other faults, that mainly affect the spindle shaft, which are diagnosed by analyzing speed harmonics and subharmonics. Thus, a complete methodology is proposed to deal with the main spindle faults.
- Hilbert-Huang transform based methodology for bearing fault detection(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2018-05-16) Campos García, Rubén; Vallejo Guevara, Antonio Jr.; Morales Menéndez, Rubén; Ibarra Zárate, David IsaacRotating machinery is of great importance for manufacturing industry, and therefore huge investments for their acquisition are made every year. Machine preservation plays an important role in the exploitation of this resource. Rotating machines are more susceptible to certain types of faults, investigations report that at least 42 % of the root causes of failure in rotating machinery are related with bearings. To detect the bearing condition many techniques have been developed. One of the most reliable is vibration analysis. The Hilbert-Huang transform (HHT) has been used for vibration analysis and has gained attention in recent years, a topic of controversy in this method is the selection of the Intrinsic Mode Functions (IMFs) with fault information. Statistical parameters can be used to describe the characteristics of vibration signals, this attribute can be exploited to select the IMFs. There are many time domain features used for signal analysis. In this research, a study of 17 statistical parameters was made to determine which one is the best to represent IMFs with fault information. As a result of this analysis a new methodology based on HHT is proposed. This methodology deals with the IMF selection with the use of KR (Kurtosis x RMS) to detect the IMFs with fault information, and can be used to detect incipient bearing faults. The proposed methodology was validated with 18 signals from the Case Western Reserve University (CWRU), Tian-Yau Wu, and the society for Machinery Failure Prevention Technology (MFPT Society) databases. For the 18 analyzed signals, only one IMF was wrongly selected. The cause of this error was the end defect produced in the EMD, this caused the KR amplitude to increase even tough the IMF did not have fault information. The results on the Envelope spectrum from 14 signals were clear with fault components with large amplitude. For the remaining four signals the results on the Envelope spectrum was noisy, but the fault fault components were distinguishable.Rotating machinery is of great importance for manufacturing industry, and therefore huge investments for their acquisition are made every year. Machine preservation plays an important role in the exploitation of this resource. Rotating machines are more susceptible to certain types of faults, investigations report that at least 42 % of the root causes of failure in rotating machinery are related with bearings. To detect the bearing condition many techniques have been developed. One of the most reliable is vibration analysis. The Hilbert-Huang transform (HHT) has been used for vibration analysis and has gained attention in recent years, a topic of controversy in this method is the selection of the Intrinsic Mode Functions (IMFs) with fault information. Statistical parameters can be used to describe the characteristics of vibration signals, this attribute can be exploited to select the IMFs. There are many time domain features used for signal analysis. In this research, a study of 17 statistical parameters was made to determine which one is the best to represent IMFs with fault information. As a result of this analysis a new methodology based on HHT is proposed. This methodology deals with the IMF selection with the use of KR (Kurtosis x RMS) to detect the IMFs with fault information, and can be used to detect incipient bearing faults. The proposed methodology was validated with 18 signals from the Case Western Reserve University (CWRU), Tian-Yau Wu, and the society for Machinery Failure Prevention Technology (MFPT Society) databases. For the 18 analyzed signals, only one IMF was wrongly selected. The cause of this error was the end defect produced in the EMD, this caused the KR amplitude to increase even tough the IMF did not have fault information. The results on the Envelope spectrum from 14 signals were clear with fault components with large amplitude. For the remaining four signals the results on the Envelope spectrum was noisy, but the fault fault components were distinguishable.