Diagnosis of tapping process using spindle motor current

A two-stage pair-wise feature selection and classification method is proposed to diagnose the tapping process using the electrical current signal of the spindle motor. The motor current is measured with a non-intrusive Hall effect sensor. The diagnostic process is divided into two stages. In the first stage, wavelet coefficients of the current signals are computed and a subset of these coefficients is chosen as the initial diagnostic features based on their discriminatory powers for each pair of the fault classes. At the second stage, principal component analysis (PCA) is applied to reduce the dimensionality of the initial diagnostic features. Non-linear radial basis probabilistic neural networks (RPNNs) are employed for pair-wise feature classification. The final diagnostic decision is made based on a statistical voting procedure. The proposed method has been demonstrated with experimental data. On average a 93% success rate has been achieved. (C) 2002 Elsevier Science Ltd. All rights reserved.