# Online Monitoring of Power Converter Degradation Using Deep Neural Network
## 1 Introduction  
Brushless DC (BLDC) motors have been widely used in a large spectrum of consumer electric applications for their merits such as high efficiency and low maintenance cost [1, 2, 3, 4]. The electronic commutation helps to eliminate the physical brush contact, and the only contact is the ball bearing, which makes the BLDC motor more reliable and quieter than a conventional DC motor. The back EMF can be excited by either the sinusoidal driving method or the trapezoidal driving method. In the BLDC drive system, the power converter plays a vital role in transporting energy between the power source and the motor. However, the reliability of the power converter is of particular concern, since the power devices in the converter are considered vulnerable parts and prone to failures [5, 6, 7, 8, 9]. Monitoring the health states online helps raise awareness of the degradation of the power devices, which may eventually lead to the failure of the power converter, and even damage the whole drive system. This is of importance for devising a maintenance plan andpreventing the failure of the system. Generally, the fault detection and diagnosis can be classified into model-based, signal-based, and knowledge-based categories [10]. The model-based method checks the presence and the type of the fault by comparing the measured data and the model's prediction. The signal-based method examines the signal patterns caused by the faults without the need for an input-output model. The knowledge-based method is aided by artificial intelligence, and it is used in situation where model is too complex to obtain. Thanks to the direct influences of the faults on output signal patterns, signal-based converter monitoring methods have been proposed [11; 12]. A signal-based condition monitoring for power electronics is proposed for hybrid electric vehicles [11]; the system faults are detected by the time domain analysis of current and voltage signals obtained at the input and output terminals of the converter; statistical methods such as skewness and kurtosis are applied to monitor the buck and boost conditions. A comprehensive signal-based real-time condition monitoring algorithm for high-power converters is proposed [12], which can provide open circuit fault detection, short circuit fault detection, overheating detection, and remaining lifetime estimation simply by measuring the voltage between the collector and emitter of the power semiconductor devices. A variety of model-based converter monitoring has also been proposed [13; 14; 15; 16]. Sub-module capacitance condition in the modular multilevel converter is monitored online using four instantaneous capacitor voltage values with specified phase information [13]. Degradation of the transistor in boost converters is monitored using the saturation region resistance instead of the on-resistance, in order to solve the problem that it is difficult to identify the on-resistance due to its small value, even though it is the most significant aging factors in power MOSFETS [14]. With the vast development of artificial intelligence, deep learning algorithms have been used in numerous applications [17; 18; 19], and have also been introduced in monitoring power converters [20; 21; 22; 23]. Even though these knowledge-based methods usually require a huge amount of data, they are receiving more and more attention when the explicit models or the signal patterns of a system are not available straightforward. A real-time reliability modeling and prediction algorithm for high-frequency MOSFET power electronic converters is proposed using long short-term memory and the change of device resistance, which achieves much higher accuracy than traditional approaches such as Kalman filter and particle filter [20]. The proposed approach in [21] monitors the semiconductor on-resistor change as a precursor for diagnostic/prognostic in GaN power converters.