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One of the significant research problems in support vector machines (SVM) is the selection of optimal parameters that can establish an efficient SVM so as to attain desired output with an acceptable level of accuracy. The present study adopts ant colony optimization (ACO) algorithm to develop a novel ACO-SVMmodel to solve this problem. The proposed(More)
Mechanical anomaly is a major failure type of induction motor. It is of great value to detect the resulting fault feature automatically. In this paper, an ensemble super-wavelet transform (ESW) is proposed for investigating vibration features of motor bearing faults. The ESW is put forward based on the combination of tunable Q-factor wavelet transform(More)
In information age, reliability of digital manufacturing equipment has a large impact on throughput, productivity and executing predictive maintenance. Accurate reliability forecasts can provide a good assessment of machine performance in order to execute predictive maintenance effectively. This paper investigates a methodology of applying support vector(More)
A new method for intelligent fault diagnosis of rotating machinery based on wavelet packet transform (WPT), empirical mode decomposition (EMD), dimensionless parameters, a distance evaluation technique and radial basis function (RBF) network is proposed in this paper. In this method, WPT and EMD are, respectively, used to preprocess vibration signals to(More)
This paper presents a new approach to intelligent fault diagnosis based on statistics analysis, an improved distance evaluation technique and adaptive neuro-fuzzy inference system (ANFIS). The approach consists of three stages. First, different features, including time-domain statistical characteristics, frequency-domain statistical characteristics and(More)
Due to the widespread application of rolling element bearings, it is necessary to effectively monitor their health status. The shock pulse method (SPM) has been widely used as a quantitative method for bearing condition monitoring. However, the shock value indicating the bearing condition may be mistakenly estimated by direct demodulation in the SPM. To(More)
A new hybrid clustering algorithm based on a three-layer feed forward neural network (FFNN), a distribution density function, and a cluster validity index, is presented in this paper. In this algorithm, both feature weighting and sample weighting are considered, and an optimal cluster number is automatically determined by the cluster validity index. Feature(More)