Bearing Fault Classification based on the Adaptive Orthogonal Transform Method

Abstract

Abstract-In this work, we propose an approach based on building an adaptive base which permits to make accurate decisions for diagnosis. The orthogonal adaptive transformation consists of calculating the adaptive operator and the standard spectrum for every state, using two sets of vibration signal records for each type of fault. To classify a new signal, we calculate the spectral vector of this signal in each base. Then, the similarity between this vector and other standard spectra is computed. The experimental results show that the proposed method is very useful for improving the fault detection.

Comparative study to realize an automatic speaker recognition system

“…Our approach consists in searching the informative features of the signal by using the operator H, which is a matrix operator of the transform (dimension N×N) whose number of rows corresponds to the number of basic functions. To decompose the vector X, the calculation of the discrete spectrum Y with the numerical methods can be represented by the following matrix [1], [15], [26]:…”

“…In addition, other authors have developed new methods by addressing the limitations of MFCCs in order to obtain an improved algorithm, which is not sensitive to noise, and has a fast execution time. The goal of this study is to solve the problems mentioned above by developing a fast algorithm based on adaptive orthogonal transformations for the extraction of the informative features from the voice signal using the smallest possible training dataset, inspired by references [1], [15]. This paper is organized as follows: Section 2 describes the new approach of orthogonal operators, then the comparison results obtained between MFCCs and the proposed method are discussed in section 3, and finally section 4 concludes the paper.…”

Voice Pathology Detection Using the Adaptive Orthogonal Transform Method, SVM and MLP.

“…The proposed approach can extract the informative features of the voice signal with a minimal dimension by creating an operator H that will be adaptable to any input signal. First, it is necessary to calculate the average of the statistical features obtained at the compression phase to form the ˆsd R vector [15], [26], [27]. H is adapted to a class of signals represented by a standard vector ˆsd R when the following condition is verified:…”