Induction Machines Fault Detection Based on Subspace Spectral Estimation
نویسندگان
چکیده
منابع مشابه
Stator Fault Detection in Induction Machines by Parameter Estimation Using Adaptive Kalman Filter
This paper presents a parametric low differential order model, suitable for mathematically analysis for Induction Machines with faulty stator. An adaptive Kalman filter is proposed for recursively estimating the states and parameters of continuous–time model with discrete measurements for fault detection ends. Typical motor faults as interturn short circuit and increased winding resistance ...
متن کاملRobust Subspace Based Fault Detection
Subspace methods enjoy some popularity, especially in mechanical engineering, where large model orders have to be considered. In the context of detecting changes in the structural properties and the modal parameters linked to them, some subspace based fault detection residual has been recently proposed and applied successfully. However, most works assume that the unmeasured ambient excitation l...
متن کاملBlind Multiuser Detection Based on Subspace Estimation
In this paper, we study an enhanced subspace based approach for the mitigation of multiple access interference (MAI) in direct-sequence code-division multiple-access (DS-CDMA) systems over frequencyselective channels. Blind multiuser detection based on signal subspace estimation is of special interest in mitigating MAI in CDMA systems since it is impractical to assume perfect knowledge of param...
متن کاملFault Detection and Estimation based on Closed-loop Subspace Identification for Linear Parameter Varying Systems
This paper presents a data driven solution of the Fault Identification approach Connected to Subspace Identification (FICSI) for Linear Parameter Varying (LPV) systems. The proposed solution links system identification to fault detection and estimation in affine LPV systems. As an extension of the model-based FICSI-LPV, the data driven solution is also formulated based on the affine LPV model s...
متن کاملFault Detection and Diagnosis of Induction Machines based on Wavelet and Probabilistic Neural Network
In this paper, a prototype wavelet and probabilistic based neural network classifier for recognizing rotor bar defects is implemented and tested under various transient signals. The wavelet transform (WT) technique is integrated with the neural network model to extract rotor fault features. Firstly, the multiresolution analysis technique of WT and the particle swarm optimization (PSO) theorem a...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Industrial Electronics
سال: 2016
ISSN: 0278-0046,1557-9948
DOI: 10.1109/tie.2016.2570741