Multi-Milling-Insert Wear Assessment Using Nonlinear Virtual Sensor, Time-Frequency Distribution and Neural Networks

Abstract

The objective of this study is to establish a signal processing methodology that can infer the state of milling insert wear from translational vibration measured on the spindle housing of a milling machine. First, the tool wear signature in a translational vibration is accentuated by mapping the translational vibration into a torsional vibration using a previously identified nonlinear relationship between the two, i.e., a virtual sensor. Second, a time-frequency distribution, i.e., a Choi-Williams distribution, is calculated from the torsional vibration. Third, scattering matrices and orthogonalization are employed to identify the time-frequency components that are best correlated to the state of wear. Fourth, a neural network is trained to estimate the extent of wear from these critical time-frequency components. The combination of the virtual sensor, time frequency analysis and neural network is then validated with data obtained from real cutting tests.

Diagnostico de falhas em maquinas rotativas utilizando transformada de wavelet e redes neurais artificiais

“…Quando o momento é igual a 0 o método do gradiente descendente com momento torna-se semelhante ao método do gradiente descendente (Haykin, 1999). Várias aplicações utilizando redes neurais no diagnóstico de falhas de máquinas rotativas podem ser encontradas nos seguintes estudos (Wu et al, 1992;Alguindigue et al, 1993;Chow et al, 1993;Liu et al, 1996;McCormick & Nandi, 1997;Marwala & Hunt, 1999;Oliveira, 1999;Dellomo, 1999;Li & Tzeng, 2000;Zang & Imregun, 2001;Vyas & Satishkumar, 2001, Brito, 2002Santiago & Pederiva, 2002a;Eduardo, 2003;Samanta & Al-Balushi, 2003).…”