Ciencias Exactas y Ciencias de la Salud

Now showing 1 - 2 of 2

Wavelets for spindle health diagnosis
(2018-12-04) Villagómes Garzón, Silvia Cristina; Morales Menéndez, Rubén; Vallejo Guevara, Antonio; Hernández Alcántara, Diana
Industrial 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.
Convergence of Industry 4.0 and Regenerative Engineering to boost development of scaffolds created by hybrid additive manufacturing
(2017-12-05) Camargo Camrgo, Belinda; Rodríguez González, Ciro Ángel; Romero Díaz, David Carlos
Industry 4.0 and its underlying technologies, such as Internet of Things (IoT) and Cyber-Physical Systems (CPS), are usually portrayed as a way to enable communication in a workshop between the machinery and an intelligent control system, handle consumer demand for customized products, achieve a near-zero defect manufacturing process, and handle materials, energy consumption, and waste more efficiently, amongst others. Case studies on how the automotive, electronics, or aerospace industry benefit from Industry 4.0 implementation are readily available and surely, there are more to come. By contrast, scaffolds of Regenerative Engineering, are still in Research and Development and yet to be approved as a commercial regenerative procedure. A thorough analysis of the requirements was developed and the product manufacturing phases were modeled using Unified Modeling Language (UML). Business, structure, activity, class, and sequence diagrams, amongst others, are modeled using this standard and an ontology that converges Industry 4.0 technologies applied on Regenerative Engineering is established under the Ontology Web Language Description Logic (OWL-Dl). An architecture to augment a scaffold manufacturing cell with Industry 4.0 technologies is proposed. By using smart sensors, actuators, and the information they generate, a database with material and process variables is populated. This database can then be analyzed by smart algorithms to find the most effective parameters to manufacture a successful scaffold for tissue regeneration. Initial testing shows the feasibility of the proposed architecture and its ability to store relevant information of the produc

Ciencias Exactas y Ciencias de la Salud

Browse

Filters

Settings

Sort By

Results per page

Search Results