When classical particle filter (PF) techniques are used for dynamic system state estimation, they have some limitations: for example, when the weights of simulated samples are not sufficiently large, these classical PFs may suffer from sample impoverishment. In addition, the degraded diversity in sampling particles will limit the estimation accuracy, since the particles cannot capture the entire probability density function (pdf) effectively. To tackle these problems, a mutated PF (MPF) technique is proposed in this paper to approximate the posterior pdf of system states. In the MPF, a novel mutation approach is proposed to search extended areas of the prior pdf using mutated particles to make more comprehensive exploration of the posterior pdf. In addition, a particle selection scheme is suggested in the MPF to detect and process low-weight particles so as to explore the high-likelihood area of the posterior pdf more thoroughly. The effectiveness of the developed MPF technique is verified by simulation tests using a benchmark test model. It is implemented for predicting remaining useful life of batteries. Test results show that the developed MPF can capture a system's dynamics effectively and track system characteristics accurately.
Induction motors (IMs) are commonly used in various industrial applications. A spectrum synch (SS) technique is proposed in this paper for early IM defect detection using electric current signals; fault detection in this paper will focus on defects in rolling element bearings and rotor bars, which together account for more than half of IM imperfections. In bearing fault detection, the proposed SS technique will highlight the peakedness of the fault frequency components distributed over several fault-related local bands. These bands are synchronized to form a fault information spectrum to accentuate fault features.
A central kurtosis indicator is proposed to extract representative features from the fault information spectrum and formulates a fault index for incipient IM fault diagnosis. The effectiveness of the developed SS technique is demonstrated on IM with broken rotor bars and IM with damaged bearing. Test results show that the developed SS technique can detect incipient IM faults effectively.
Index Terms-Bearing fault detection, broken rotor bars, current signal, induction motor (IM), spectrum synch (SS) analysis.De Z. Li received the B.Sc. degree in electrical engineering from Shandong University,
Induction motors (IMs) are widely used in various domestic and industrial applications. A reliable IM health condition monitoring system is very useful to detect IM fault at its incipient stage to prevent performance degradation of IMs and their driven equipment. An enhanced bispectrum (EB) technique with auxiliary frequency (AF) injection (EB-AF) is proposed in this paper to conduct incipient IM fault detection. The AFs are injected into the electric current signals to enhance fault characteristic frequency components. The EB technique transforms the traditional 2-D bispectrum with massive features into a one-scale spectral representation to facilitate feature extraction for IM defect detection. The effectiveness of the developed EB-AF technique is examined by IM bearing fault detection with different speed and load conditions. Test results show that the developed EB-AF technique can reveal fault features effectively for initial IM fault detection.
A novel sliding mode (SM) control system with an embedded neuro-fuzzy approximator is developed in this paper to provide more effective vibration suppression, especially in flexible structures. It aims to force system state to move to, and maintain on, the defined sliding surface without chattering. A new hybrid training technique based on an extended gradient method is proposed to optimize the neuro-fuzzy system to approximate unknown nonlinear functions and to enhance control performance. When the principle of the terminal attractor is incorporated into the classical gradient method and/or SM control systems, some implementation problems arise especially when the error is close to its origin. The proposed extended gradient method can enhance the SM control to not only speed up convergence but also overcome the existing implementation problems of the terminal attractor. The Lyapunov stability analysis demonstrates that the approximation with the proposed hybrid training technique is stable and can converge to the optimal approximation. The effectiveness of the developed control system and the hybrid training technique is verified experimentally corresponding to nonlinear and time-varying system control.
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