Toni Prahasto

Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics

This paper deals with the maintenance technique for industrial machinery using the artificial neural network so-called self-organizing map (SOM). The aim of this work is to develop intelligent maintenance system for machinery based on an alternative way, namely, thermal images instead of vibration signals. SOM is selected due to its simplicity and is categorized as an unsupervised algorithm. Following the SOM training, machine fault diagnostics is performed by using the pattern recognition technique of machine conditions. The data used in this work are thermal images and vibration signals, which were acquired from machine fault simulator (MFS). It is a reliable tool and is able to simulate several conditions of faulty machine such as unbalance, misalignment, looseness, and rolling element bearing faults (outer race, inner race, ball, and cage defects). Data acquisition were conducted simultaneously by infrared thermography camera and vibration sensors installed in the MFS. The experimental data are presented as thermal image and vibration signal in the time domain. Feature extraction was carried out to obtain salient features sensitive to machine conditions from thermal images and vibration signals. These features are then used to train the SOM for intelligent machine diagnostics process. The results show that SOM can perform intelligent fault diagnostics with plausible accuracies.

Optimization of knots for the multi curve B-spline approximation

This article presents a method for multi curve approximation with B-splines. The approximation is formulated as a constrained optimization problem with the least squares error as the objective function and the knot vector as the variables. The method presented in this article is designed to eliminate the well-known lethargic behaviour and to maintain the accuracy of control points. The method for fitting skeletal curves was tested on a single-section case and a multi-section case. The tests showed that the method reduces the objective function significantly. The proposed method has potential applications in the shape design of wings, turbine blades and automotive body panels.

Fault diagnosis of roller bearing using parameter evaluation technique and multi-class support vector machine

Roller bearing is one of the vital parts of a rotating machine. Bearing failure can result in serious damage of the machine. This paper aims to develop a bearing fault diagnosis method using parameter evaluation technique to improve the diagnosis accuracy. The parameter evaluation technique is used to select five features that are used as predictors in multi-class support vector machine (SVM) classification. The purpose of this feature reduction was to avoid the curse of dimensionality and to increase the accuracy of the diagnosis. The diagnosis process was performed by classification of bearing states using one-against-one method multi-class SVM. Three types of kernel functions i.e., linear, polynomial, and Gaussian RBF were used in the SVM classification. The bearing conditions which is diagnosed in this paper were normal bearing, inner race fault, and outer race fault conditions. As a result, the classification performance of multiclass SVM using five selected features as the parameter have excellent p...

Intelligent Bearing Diagnostics Using Wavelet Support Vector Machine

This paper deals with implementation of intelligent system for fault diagnostics of rolling element bearing. In this work, the proposed intelligent system was basically created using support vector machine (SVM) due to its excellent performance in classification task. Moreover, SVM was modified by introducing wavelet function as kernel for mapping input data into feature space. Input data were vibration signals acquired from bearings through standard data acquisition process. Statistical features were then calculated from bearing signals, and extraction of salient features was conducted using component analysis. Results of fault diagnostics are shown by observing classification of bearing conditions which gives plausible accuracy in testing of the proposed system.