A Misfire event is an unwanted phenomenon that compromises the regular operation of the engine, increasing the pollution of emission gases and decreasing its efficiency. Therefore, smart systems that detect misfire ensure better engine operation and compliance with government environmental regulations. This paper presents a comparative study of two smart systems that use two different methodologies for misfire detection in a 2006 Ford Zetec-Rocam engine, which is a 4-stroke sparkignition engine. The first methodology tested was vibration analysis, which consists of collecting vibration data from the engine block using a vibration acquisition system. After the acquisition stage, we extracted and selected the signal features using the fast Fourier transform (FFT) in order to recognize the fault patterns through the use of an artificial neural network (ANN), which presented an accuracy of 99.30%. The second methodology was the acoustic analysis of the engine sound. The data were collected by a sound acquisition system, and then the feature extraction and selection were done by using the same technique as we did in the vibration analysis. For this technique, the ANN developed presented an accuracy of 98.70%. Although the vibration analysis is slightly more accurate than the acoustic analysis for misfire diagnosis, the acoustic system has the advantage of not being necessary the physical contact between the data acquisition system and the engine. Therefore, both techniques may be applied with great accuracy, and the decision of using one approach or another will depend on the equipment availability and the user's specialty.
Among many rotating machinery vibration sources, there is one due to resonance, when the machine operation frequency crosses the natural frequency region. This study proposes a smart bearing that employs shape memory alloy NiTi helical springs for vibration-level reduction. This smart bearing is capable of dynamically changing its stiffness during machine acceleration or deceleration, keeping its natural frequency far from resonance. Activated by Joule effect and cooled by forced air convection, the prototype installed in horizontal rotating machinery reaches reduction of vibration amplitude of about 63% (root mean square) and 73% (Peak) at critical speed, with response time between 12–15 s. Compared with the results of the reference articles, satisfactory amplitude reduction and better response time were observed.
In the forward flight, wind loads affect the helicopters and cause vibration. This paper analyzes the behavior of a helicopter prototype composed by two blades when subjected to a front wind load, similar to the forwarding flight condition. An Artificial Neural Network (ANN) processes the experimental data in order to identify the pattern of its dynamic behavior. The tests led to Vibration analysis for different wind speeds. Also, the data indicates that vibration amplitude increases when the blades are subjected to the fundamental frequency and its first harmonic on tests conducted without rotor plane tilt (hover flight). On the other hand, the second test performs a 5-degree tilt on the rotor disc. In this test, the vibration amplitude decreased in the fundamental frequency, and the amplitude related to the first harmonic increased. The ANN achieved 100% efficiency in recognizing the flight conditions of the prototype.
This work presents an experimental study related to the mechanical performance of a special design spring fabricated with a superelastic shape memory alloy (SMA-SE). For the experimental testing, the spring was coupled in a rotor machine, aiming to attenuate the mechanical vibration when the system went through a natural frequency without any external power source. It was verified that the reduction in instabilities stemmed from the better distribution of vibration force in the proposed device, as well as the damping capacity of the spring material. These findings showed that the application of the M-Shape device of SMA-SE for three different cases could reduce vibration up to 23 dB when compared to the situations without, and with, 1.5 mm of preload. The M-Shape device was shown to be efficient in reducing the mechanical vibration in a rotor system. This was due to the damping capacity of the SMA-SE material, and because the application did not require any external source of energy to generate phase transformation.
The present work aims to develop a case study using the casing well interface (CWELL), developed by the Federal University of Alagoas, Brazil. Considering the well critical environment during the gas kick along with wait on cement (WOC) test and cement displacement, we sought to observe the behavior of different casing pipes regarding the stress submitted. The case study analyzes an offshore vertical well, with a water depth of 1574 meters. The well was subjected to a kick of 50 bbl and with an inflow gradient of 2.0 lb/gal. Triaxial loads were obtained through the von-Mises and American Petroleum Institute (API) envelopes, which can be used to analyze the integrity of the tubes. Through the analysis between the resistances returned by Petroleum Engineering Applications System (SAEP), it is possible to verify the possibility of failure of the tubes for each project scenario. This analysis is important for determining the sizing of the columns to obtain the best performance of the structures.
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