The topic of Doppler and Bearing Tracking (DBT) problem is to achieve a target trajectory using the Doppler and Bearing measurements. The difficulty of DBT problem comes from the nonlinearity terms exposed in the measurement equations. Several techniques were studied to deal with this topic, such as the unscented Kalman filter. Nevertheless, the performance of the filter depends directly on the...

Supervised learning method, like support vector machine (SVM), has been widely applied in diagnosing known faults, however this kind of method fails to work correctly when new or unknown fault occurs. Traditional unsupervised kernel clustering can be used for unknown fault diagnosis, but it could not make use of the historical classification information to improve diagnosis accuracy. In this pa...

A rolling bearing fault diagnosis method based on the Volterra series and kernel principal component analysis (KPCA) is proposed. In proposed method, first, improved genetic algorithm (IGA) used to identify model of in four states: normal, element fault, inner ring outer fault. The time-domain as feature vector for classify faults. feasibility level verified by experimental results.

Received Oct 30, 2014 Revised Dec 29, 2014 Accepted Jan 15, 2015 A reliable monitoring of industrial drives plays a vital role to prevent from the performance degradation of machinery. Today’s fault detection system mechanism uses wavelet transform for proper detection of faults, however it required more attention on detecting higher fault rates with lower execution time. Existence of faults on...

This study presents fault diagnosis of low speed bearing using multi-class relevance vector machine (RVM) and support vector machine (SVM). A low speed test rig was developed to simulate various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired from the low speed bearing test rig using two acoustic emission (AE) sensors under constant loading (5 ...

Various diagnostics methods have been applied to machinery condition monitoring and fault diagnosis, with far from satisfactory levels of accuracy. With the development of modern multi-sensor based data acquisition technology often used in advanced signal processing, more and more information is becoming available for the purposes of fault diagnostics and prognostics of machinery integrity. It ...

Fault diagnosis is important for the maintenance of rotating machinery. The detection of faults and fault patterns is a challenging part of machinery fault diagnosis. To tackle this problem, a model for deep statistical feature learning from vibration measurements of rotating machinery is presented in this paper. Vibration sensor signals collected from rotating mechanical systems are represente...

In allusion to more indeterminate information and higher speed request characteristic in fault diagnosis system, according to the intelligence complementary strategy, a new fault diagnosis(SWPSO-BPN) model based on combining improved particle swarm optimization (PSO) algorithm and Back-propagation(BP) neural network is proposed in this paper. In the SWPSO-BPN method, an improved PSO (SWPSO) alg...