Broken rotor bars detection via Park's vector approach based on ANFIS
Many attempts have been made on fault diagnosis of induction motors based on frequency and time domain analysis of stator current. In this paper, first the Park's vector transformation and frequency analysis for fault detection of induction motors are introduced. Then a smart approach using Adaptive Neuro Fuzzy Inference System (ANFIS) is proposed. This approach uses the time domain features derived from the Park's vector transformation of stator current. By the proposed method, a partial break including 5 mm crack on a bar, one broken bar and two broken bars using experimental data are investigated. It will be shown that features derived from Park's vector compared to features obtained fro…
Observer-based adaptive stabilization of a class of uncertain nonlinear systems
In this paper, an adaptive output feedback stabilization method for a class of uncertain nonlinear systems is presented. Since this approach does not require any information about the bound of uncertainties, this information is not needed a priori and a mechanism for its estimation is exploited. The adaptation law is obtained using the Lyapunov direct method. Since all the states are not measurable, an observer is designed to estimate unmeasurable states for stabilization. Therefore, in the design procedure, first an observer is designed and then the control signal is constructed based on the estimated states and adaptation law with the σ-modification algorithm. The uniformly ultimately bou…
A robust fault detection design for uncertain Takagi-Sugeno models with unknown inputs and time-varying delays
Abstract This paper investigates the problem of robust fault detection system design for a class of uncertain Takagi–Sugeno (T–S) models. The system under consideration is subject to unknown input and time-varying delay. The fault detection system is designed such that the unknown input is thoroughly decoupled from residual signals generated by the fault detection system. Furthermore, the residual signals show the maximum possible sensitivity to the faults and the minimum possible sensitivity to the external disturbances. The model matching approach is utilized to tackle the effects of parametric uncertainties in the model of the system. The design procedure is presented in terms of Linear …
Convergence analysis of cubature Kalman filter
This paper investigates the stability analysis of cubature Kalman filter (CKF) for nonlinear systems with linear measurement. The certain conditions to ensure that the estimation error of CKF remains bounded are proved. Then, the effect of process noise covariance is investigated and an adaptive process noise covariance is proposed to deal with large estimation error. Accordingly, a modified CKF (MCKF) is developed to enhance the stability and accuracy of state estimation. The performance of the MCKF is compared to the CKF by two case studies. Simulation results demonstrate that the large estimation error may lead to instability of CKF while the MCKF is successfully able to estimate the sta…
Robust Fault Detection of Linear Uncertain Time-Delay Systems Using Unknown Input Observers
This paper deals with the problem of fault detection for linear uncertain time-delay systems. The proposed method for Luenberger observers is developed for unknown input observers (UIOs), and a novel procedure for the design of residual based on UIOs is presented. The design procedure is carried out based on the ${H}_{\infty }$ model matching approach which minimizes the difference between generated residuals by the optimal observer and those by the designed observer in the presence of uncertainties. The optimal observer is designed for the ideal system and works so that the fault effect is maximized while the exogenous disturbances and noise effects are minimized. This observer can give di…
Adaptive output feedback neural network control of uncertain non-affine systems with unknown control direction
Abstract This paper deals with the problem of adaptive output feedback neural network controller design for a SISO non-affine nonlinear system. Since in practice all system states are not available in output measurement, an observer is designed to estimate these states. In comparison with the existing approaches, the current method does not require any information about the sign of control gain. In order to handle the unknown sign of the control direction, the Nussbaum-type function is utilized. In order to approximate the unknown nonlinear function, neural network is firstly exploited, and then to compensate the approximation error and external disturbance a robustifying term is employed. …
Fault diagnosis of induction motors broken rotor bars by pattern recognition based on noise cancelation
Current signal monitoring (CSM) can be used as an effective tool for diagnosing broken rotor bars fault in induction motors. In this paper, fault diagnosis and classification based on artificial neural networks (ANNs) is done in two stages. In the first stage, a filter is designed to remove irrelevant fault components (such as noise) of current signals. The coefficients of the filter are obtained by least square (LS) algorithm. Then by extracting suitable time domain features from filter's output, a neural network is trained for fault classification. The output vector of this network is represented in one of four categories that includes healthy mode, a 5 mm crack on a bar, one broken bar, …
Vibration analysis for bearing fault detection and classification using an intelligent filter
Abstract This paper proposes an intelligent method based on artificial neural networks (ANNs) to detect bearing defects of induction motors. In this method, the vibration signal passes through removing non-bearing fault component (RNFC) filter, designed by neural networks, in order to remove its non-bearing fault components, and then enters the second neural network that uses pattern recognition techniques for fault classification. Four different categories include; healthy, inner race defect, outer race defect, and double holes in outer race are investigated. Compared to the regular fault detection methods that use frequency-domain features, the proposed method is based on analyzing time-d…
Robust fault detection design for unknown inputs Takagi-Sugeno models with parametric uncertainties and time-varying delays
This paper deals with the design of robust fault detection system for Takagi-Sugeno (T-S) modes with parametric uncertainties and time-varying delay. An Unknown Input Observer (UIO) is designed such that the unknown inputs are thoroughly decoupled from residual signals while they show the maximum possible sensitivity to the faults and the minimum possible sensitivity to the external disturbances. Since the system under consideration is subjected to parametric uncertainties, the H ∞ model matching approach is used to design an optimal observer. Design procedure is given in terms of Linear Matrix Inequalities (LMIs). Finally, a numerical example is presented to show the effectiveness of the p…