Circuit Health State Estimation via An Integrated Deep Neural Network

accelerating the industry to become more integrated and intricated. It is almost inevitable for a system to encounter failures during its whole life span. Thus, it is imperative to monitor the operating system from a system-level perspective to avoid potential catastrophes. Intuitively, inclusive prior knowledge is required for prognostics and health management (PHM). However, due to time-varying parameters and external conditions, the system is usually too complex to neatly fit into a prior-built model. This paper presents a novel pragmatic method, encompassing the convolutional autoencoder (CAE) and long short-term memory recurrent neural network (LSTM-RNN), to track the health state of a circuit. Briefly, the proposed method can be divided into two steps. First, degradation characteristics are extracted by using the time-domain features and CAE to prepare for the later health state estimation step. Then, the LSTM-RNN is used to finish the predictive process, i.e., to map the extracted abstract features to the health state. In addition, the degradation of a practical circuit considering the angular distance is discussed to quantify the health state of the circuit system. Furthermore, a case study based on that prognostics scheme is conducted to verify the proposed method. The comparison with other existing popular methods indicates the superiority of the proposed methodology.