Power quality monitoring is a theme in vogue and accurate frequency measurement of the power line is a major issue. This problem is particularly relevant for power generating systems since the generated signal must comply with restrictive standards. The novelty of this work is the development of a smart sensor for real-time high-resolution frequency measurement in accordance with international standards for power quality monitoring. The proposed smart sensor utilizes commercially available current clamp, hall-effect sensor or resistor as primary sensor. The signal processing is carried out through the chirp z-transform. Simulations and experimental results show the efficiency of the proposed smart sensor.
Sistema basado en redes neuronales artificiales para el monitoreo de la herramienta en fresadoras CNC
AbstractMost of the companies have as objective to manufacture high-quality products, then by optimizing costs, reducing and controlling the variations in its production processes it is possible. Within manufacturing industries a very important issue is the tool condition monitoring, since the tool state will determine the quality of products. Besides, a good monitoring system will protect the machinery from severe damages. For determining the state of the cutt ing tools in a milling machine, there is a great variety of models in the industrial market, however these systems are not available to all companies because of their high costs and the requirements of modifying the machining tool in order to att ach the system sensors. This paper presents an intelligent classifi cation system which determines the status of cutt ers in a Computer Numerical Control (CNC) milling machine. This tool state is mainly detected through the analysis of the cutt ing forces drawn from the spindle motors currents. This monitoring system does not need sensors so it is no necessary to modify the machine. The correct classifi cation is made by advanced digital signal processing techniques. Just after acquiring a signal, a FIR digital fi lter is applied to the data to eliminate the undesired noisy components and to extract the embedded force components. A Wavelet Transformation is applied to the fi ltered signal in order to compress the data amount and to optimize the classifi er structure. Then a multilayer perceptron-type neural network is responsible for carrying out the classifi cation of the signal. Achieving a reliability of 95%, the system is capable of detecting breakage and a worn cutt er.
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