Motor online novelty detection scheme based on one-class hyperdisk

Abstract

Online novelty detection is of great importance in the series production of motors. This study developed an online novelty detection scheme for motors based on a one-class hyperdisk (OCHD) model. In the OCHD approach, the decision boundary is estimated using a hyperdisk (HD), which is derived from the training sample set. The HD model addresses the underestimation issue commonly associated with convex-hull-based methods by providing a more accurate estimation of the class region. Furthermore, an optimal separating hyperplane is constructed at the nearest point on the HD by solving a quadratically constrained quadratic program problem. Statistical features refined by the Laplacian score are employed in the proposed novelty detection scheme. This study introduces an online novelty detection scheme for assessing motor quality in actual series production. The test results from the offline experiment demonstrate the superiority of the OCHD method. Datasets collected at the end of the production line were evaluated using the proposed novelty detection scheme. The inspection results for motor components confirm that the proposed method effectively identifies faulty motors during the series production process.