scholarly journals DRAFT: an on-line fault detection method for dynamic and partially reconfigurable FPGAs

Author(s):  
M.G. Gericota ◽  
G.R. Alves ◽  
M.L. Silva ◽  
J.M. Ferreira
2010 ◽  
Vol 21 (1) ◽  
pp. 475-488 ◽  
Author(s):  
Jordi Cusido ◽  
Luis Romeral ◽  
Antonio Garcia Espinosa ◽  
Juan Antonio Ortega ◽  
Jordi-Roger Riba Ruiz

Author(s):  
Henrique Raduenz ◽  
Fábio José Souza ◽  
Pedro P. C. Bastos ◽  
Desyel Ferronatto ◽  
Victor J. De Negri ◽  
...  

This paper presents the analysis of an on-line fault detection method for proportional directional hydraulic valves applied on speed governors of hydroelectric power plants. This application area is very sensitive for unexpected maintenance or long duration stops since most of power plants are interconnected on an electrical power grid. A plant stop must be programmed previously and approved by a regulatory agency. Consequently, the implementation of a fault detection and monitoring system can reduce maintenance and operational costs as well as safety risks of equipment and operators. The developed method is based on monitoring both the valve supply current and spool position related to an input control signal. Therefore, it is applicable for so called servoproportional valves, it means, those including spool position measurement and with embedded electronics. Valve static and dynamic behaviour depends on spool friction, flow forces, solenoid force and valve closed loop controller. Such characteristics are influenced by the spool size, overlapping and manufacturing tolerances. In this paper, the effectiveness of the method to monitor and detected faults in valves with different sizes and constructive parameters is demonstrated experimentally using five proportional valves.


Author(s):  
Weihai Sun ◽  
Lemei Han

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.


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