Automated Estimation of Equivalent Circuit Model from Impedances with Long Short-Term Memory

Electrochemical Impedance Spectroscopy (EIS) is a widely used, non-destructive technique for characterizing electrochemical systems, and its analysis typically relies on fitting the measured spectra to an Equivalent Circuit Model (ECM). Selecting an appropriate ECM, however, remains a major bottleneck: knowledge-based selection requires expert judgment and is difficult to reproduce, while existing automated approaches either choose from a fixed set of candidate circuits or, in the case of Gene Expression Programming, require repeated equivalent-circuit fitting and a predetermined circuit scale. Here, we propose a machine learning method that estimates an ECM directly from an impedance spectrum by representing the circuit as a serialized string of symbols and generating this string with a Long Short-Term Memory (LSTM) network coupled to a convolutional feature extractor. Because the LSTM inherently handles variable-length sequences, the method produces the circuit topology directly, without any fitting during estimation nor prior assumption for the number of elements. A fourth-root transformation of the impedance is introduced to emphasize the mid-frequency features essential for distinguishing circuits, and an adaptive beam search yields multiple ranked candidates. Evaluated on 100,000 synthetic datasets generated from 119 circuit topologies with 1% added noise on impedances, the method identified the correct topology as the most probable ECM in 77.8% of cases and among the top five candidates in 98.8% of cases, with an average estimation time of 17.8 milliseconds per dataset - several orders of magnitude faster than reported fitting-based approaches. These results indicate that direct topology generation with a neural network is a promising route toward fully automated, expert-independent ECM estimation.

Fault Detection Method for Power Conversion Circuits Using Thermal Image and Convolutional Autoencoder

A fault detection method for power conversion circuits using thermal images and a convolutional autoencoder is presented. The autoencoder is trained on thermal images captured from a commercial power module at randomly varied load currents and augmented image2 generated through image processing techniques such as resizing, rotation, perspective transformation, and bright and contrast adjustment. Since the autoencoder is trained to output images identical to input only for normal samples, it reconstructs images similar to normal ones even when the input images containing faults. A small heater is attached to the circuit board to simulate a fault on a power module, and then thermal images were captured from different angles and positions, as well as various load currents to test the trained autoencoder model. The areas under the curve (AUC) were obtained to evaluate the proposed method. The results show the autoencoder model can detect anomalies with 100% accuracy under given conditions. The influence of hyperparameters such as the number of convolutional layers and image augmentation conditions on anomaly detection accuracy was also investigated.