A novel method of designing LVOT using artificial neural network

Designing an industrial real-time measurement and monitoring system based on embedded system and ZigBee

“…Wirandi, Kulesza, and Lauber (2004) presented a reliable online industrial automated measurement system, improving the sideremoval process as well as product quality analysis and showing that human factors cannot be ignored but that the measurement process can be completely automated. Misgra, Panda, and Das (2005) presented a simpler method to design and develop an LVDT-based sensor system primarily intended to expand the linear adjustment range and to be used in conjunction with a neural network to overcome the difficulties of manual adjustment; Mishra also used computers to produce actual simulation data. Ford, Weissbach, and Loker (2000) considered the difficulties of commercial use of LNDT due to complexities in signal processing, and so developed a communications system using a double sideband suppressed carrier (DSBSC-AM) to improve the problem.…”

Linear Variable Differential Transformer Design and Verification Using MATLAB and Finite Element Analysis

“…These equations suffer from inaccuracy especially from end effects. More novel methods for design employ finite element methods (Syulski et al, 1992), artificial neural networks (Mishra et al, 2006) and (Mishra et al, 2005). The dynamic response of the LVDT is discussed in (Doebelin, 2003).…”

An Evolutionary Optimized Algorithm Approach to Compensate the Non-linearity in Linear Variable Displacement Transducer Characteristics

“…The data obtained by conducting experiments on the two LVDTs are given in Table 2 and Table 3 The percentage of nonlinearity is calculated using the Eq. (9). The lowest range choosen LVDT (LVDT-1) having highest percentage of nonlinearity when compared with the highest range LVDT (LVDT-2).…”

“…Some digital signal processing techniques have been suggested to achieve better sensitivity and to implement the signal conditioning circuits [5,6,13]. It is reported in [7][8][9] that the artificial neural network (ANN)-based inverse model can effectively compensate for the nonlinearity effect of the sensors. LVDTs show a nonlinearity behavior when the core is moved toward any one of the secondary coils.…”

“…Because of that, the range of operation is limited within the primary coil. The nonlinearity estimation and its compensation in the case of a capacitive pressure sensor and an LVDT using different ANNs are proposed in [7][8][9]. In [14], compensation of Capacitive Pressure Sensor (CPS) nonlinearities is done using neurofuzzy algorithms.…”