Nondestructive Magnetisation Estimation for Permanent Magnets Using Autoencoder‐Based Dimensionality Reduction

A magnetisation estimation method that uses an autoencoder (AE) model with a convolutional neural network (CNN) is proposed. The proposed method achieved high accuracy even for complex magnetisation distributions, including defective regions. The proposed model improved estimation accuracy by over 50.62% compared to the conventional multi‐layer perceptron method. Additionally, the proposed model is effective for multi‐output regression problems involving multidimensional vectors. This method enables non‐destructive and rapid estimation of internal magnetisation in permanent magnets from external magnetic flux density.