2019
Autores
Rocha, A; Adeli, H; Reis, LP; Costanzo, S;
Publicação
WorldCIST (3)
Abstract
2019
Autores
Martins, J; Pinto, A; Stollenwerk, N;
Publicação
ECOLOGICAL COMPLEXITY
Abstract
In this work, we introduce the concept of maximum curvature to separate the low from high reinfection levels. For each temporary immunity transition rate, the threshold value is the infection rate where the positive curvature of the endemic stationary state attains its maximum value. Hence, the maximum curvature reinfection threshold can be interpreted as the moment when the graph of the stationary state of infected attains the maximum change in its direction. When the temporary immunity transition rate tends to zero, the limiting point of the maximum curvature reinfection threshold coincides with the Gomes' reinfection threshold and the curvature blows up to infinity.
2019
Autores
Gonçalves, J; Batista, J; Paula, M; Braz César, M;
Publicação
COMPDYN Proceedings
Abstract
This work describes an experimental setup that was developed in order to automate the one-dimensional consolidation and the direct shear Tests. This experimental setup assures repeatability in the data acquisition, avoiding human errors, mainly when the tests data vary with a high dynamic. The described setup is based on LabVIEW, LVDT sensors and a 16 Bit Data Acquisition Board. For the one-dimensional consolidation test it was used a Load device and a consolidometer, being the experimental setup developed according to the standard ASTM D2435 / D2435M - 11. For the direct shear Test it was used an apparatus, covered in ASTM standard D-3080 / D3080M - 11,”Standard Method for direct shear test on soils under consolidated drained conditions”. © 2019 The authors.
2019
Autores
Nogueira, R; Moreira, AC;
Publicação
Higher Education and the Evolution of Management, Applied Sciences, and Engineering Curricula - Advances in Higher Education and Professional Development
Abstract
2019
Autores
Sousa, R; Antunes, J; Coutinho, F; Silva, E; Santos, J; Ferreira, H;
Publicação
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
Abstract
This paper proposes the linear frequency cepstral coefficients as highly discriminative features for anomaly detection in ball bearings using vibration sensor data. These features are based on cepstral analysis and are capable of encoding the patterns of a spectral magnitude profile. Incipient damages on bearings can grow rapidly under normal use resulting in vibration and harsh noise. If left undetected, this damage will worsen, leading to high maintenance costs or even injury. Multiple interferences in an industrial environment contaminate the signal, making it a challenge to correctly identify the bearings' condition. Many studies have attempted to overcome this issue at the signal level. However, the discriminative capacity of the current vibration signal features is still vulnerable to interference, which motivates this work. In order to demonstrate the benefits of these features, we (1) show that they are computationally efficient and suitable for real-time incremental training; (2) conduct discriminative analysis by evaluating the separability performance and comparing it with the state of the art; and (3) test the robustness of the proposed features under noise interference, which is ideal for use in the harsh operating conditions of industrial machinery. The data was obtained from a laboratory workbench setting that reproduces bearing fault scenarios. Results show that the proposed features are fast, competitive when compared to state-of-the-art features, and resilient to high levels of interference. Despite the higher performance when using the quadratic model, the proposed features remain highly discriminative when used with several other discriminant function.
2019
Autores
Antunes,; Pontes,; Monte,; Barbosa,; Ferreira,;
Publicação
Proceedings
Abstract
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