Many technologies are available to detect online the failure of electrolytic capacitors. In the case of MOSFETs, most approaches have been proposed to detect them post-fault, including short-circuit and open-circuit faults. Previous work on MOSFETs has focused primarily on three aspects. The first is the reliability design of these components [3]. The second is on predicting the remaining useful life of MOSFETs using off-line accelerated aging tests [4]. An accelerated aging system for the prognostics of discrete power semiconductor devices was built in [5]. Based on accelerated aging with an electrical overstress on the MOSFETs, predictions by gate-source voltage are made in [6]. In [7], collector-emitter voltage is identified as a health indicator.

In [8], the maximum peak current of the collector-emitter ringing at the turn off transient is identified as the degradation variable. The third aspect of focus is on the development of degradation models. Degradation models are set up according to the function of the usage time based on accelerated life tests [9]. For example, gate structure degradation modeling of discrete power MOSFETs exposed to ion impurities was presented in [10]. Above all, traditional studies on the degradation of MOSFETs have focused on analyzing non-real time data. Predictions of the remaining useful life of MOSFETs have been based on off-line, statistical analyses.In recent years, Prognostics and Health Management (PHM) has resulted in a broad range of applications. Many works pay attention to on-line monitoring technology.

Papers have proposed algorithms to extract features to monitor MOSFETs and IGBTs in real-time, but the features are difficult to measure accurately. Paper [11] presents Drug_discovery a real-time method by capturing the changes of ringing signals to diagnose the health state of IGBTs, but no takes account of the nonlinear features of electronic components.In this paper, an online non-intrusive method of obtaining the degradation state of MOSFETs based on Volterra series is proposed. We first use the self-driving signals of MOSFETs as a non-intrusive incentive, and extract the degradation characteristics of MOSFETs using the frequency-domain kernel of the Volterra series.

According to the relationship between health state and kernel, the state of MOSFETs can be given in real-time and the remaining useful life can be predicted, which can help avoid the inconveniences of fatal accidents, so we have time to deal with the occurrence of faults to realize prognosis and manage the health of electronics. This introductory section is followed by Section 2, which begins by addressing the basic features of MOSFETs and failure mechanisms. Then, based on the Volterra series a transform method is proposed. In Section 3, the experimental procedure is described and how to deal with the data process is discussed. In Section 4, the results of the study are discussed.