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DC-DC converter reliability is of primary interest due to their key role in electronic systems. This paper proposes a diagnostic algorithm, which can be easily implemented in digital programmable device for MOSFET fault detection and identification. An improvement over the existing technique is presented, and its rapid fault detection capability is demonstrated. The two basic faults, namely the short...
An effective approach, based on neural networks, to the fault diagnosis of dc-ac resonant inverters is presented. A MultiLayer MultiValued Neuron neural Network (MLMVNN) with a complex QR-decomposition is used to identify parameter value changing (i.e. fault detection) on a Class-E resonant inverter through steady state measurements of voltages and currents.
This paper presents an effective approach to the fault diagnosis of PWM DC-DC power converters. It is based on a MultiLayer Multi-Valued Neuron Neural Network (MLMVNN) with a complex QR decomposition. The network is used to identify the converter parameter values running out of tolerance. This technique is applied in the fault detection of parameters of a Buck DC-DC PWM converter and is based on steady...
This paper presents a new method for fault detection and fault isolation of analog circuits. The theoretical approach is formulated by considering the response of the circuit under test which is obtained by measuring the output voltage at an accessible node when a stimulus constituted by a signal with a particular test frequency is applied at its input. In a fault condition, such a response represents...
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