Fault Detection in State Variable Filter Circuit Using Kernel Extreme Learning Machine (KELM) Algorithm

Shanthi Manivasagam


This paper proposes a new method for fault diagnosis in analog circuits. The proposed method uses Extreme Learning Machine (ELM) and Kernal based ELM for fault detection and classification.  The approach of ELM is characterized by a unified formulation used for regression, binary, and multiclass classification problems, and the related solution is given in an analytical compact form.  ELM is a single hidden layer feed forward neural network (SLFN) which chooses input weight randomly and computes the output weight analytically. The fault dictionary constructed from the features of the CUT is used for fault detection and classification.  Fault detection for the state variable filter is considered as the bench mark circuit and the transfer function is derived and simulated for finding the features gain, pole selectivity and quality factor with and without fault conditions. ELM algorithm is applied for fault classification in the state variable filter. Efficient time saving is possible through random fixing of weights and bias value and calculating the output weight in single iteration.  A new test generation algorithm based on machine learning called Extreme Learning Machine (ELM) and Kernel Extreme Learning Machine (KELM) with much lower time cost and simple process has been proposed in this paper.  Simulation results   shows that KELM have better scalability and achieves much better generalization performance at much faster learning speed than EMLM algorithm. Experimental results confirm the attractive properties of classification accuracy and computation time for ELM and KELM. 


Analog circuits, Neural network, fault detection. Extreme learning machine

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