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Rolling element bearing is an important part of rotary machines. Bearing fault is a big issue because it can cause huge cost of time and money for fixing broken machines. Thus, early detecting fault of bearing is a critical task in machine health monitoring. This paper presents an automatic fault diagnosis of bearing based on the feature extraction using Wavelet Packet Analysis, feature selection using Autoencoder, and feature classification using Particle Swarm Optimization - Support Vector Machine. First, bearing vibration signals are decomposed at different depth levels by Wavelet Packet Analysis. Then the wavelet packet coefficients are used to compute the energy value of the corresponding wavelet packet node. After that, an Autoencoder is exploited to select the most sensitive features from the feature set. Finally, classification is done by using a Support Vector Machine classifier whose parameters are optimized by Particle Swarm Optimization. The effectiveness of the proposed intelligent fault diagnosis scheme is validated by experiments with bearing data of Case Western Reserve University bearing data center.

In this paper, we propose a new bearing fault diagnosis method without the feature extraction, based on Convolutional Neural Network (CNN). The 1-D vibration signal is converted to 2-D data called vibration image. Then, the vibration images are fed into the CNN for bearing fault classification. Experiments are carried out with bearing data from the Case Western Reserve University Bearing Fault Database and its result are compared with the results of other methods to show the effectiveness of the proposed algorithm.

A fuzzy neural sliding mode controller (FNNSMC) is proposed for robot manipulators. Sliding mode controller is implemented based on two radial basic function neural networks and a fuzzy system. The first neural network is used to estimate the robot dynamic function. The second neural network combines with a fuzzy system to present the switching control term of sliding mode control. This combination resolves the chattering phenomenon. The stability of proposed controller is proven. Finally, simulation is done on a 2-link serial robot manipulator to verify the effectiveness.