An Intelligent Evaluation Method of Electromagnet Quality Based on K-Means Clustering and Principal Component Analysis

The quality evaluation of electromagnets plays a key role in improving product reliability level and performance. However, the current quality evaluation methods have some subjectivity and do not fully exploit the potential of data in testing phase. This paper presents a quality evaluation method based on K-Means clustering and Principal Component Analysis (PCA). Firstly, a product quality evaluation index set is constructed by using test data. In order to enhance the quality characteristics and similarities among different electromagnets, K-Means cluster is employed to categorize and group the evaluation information. Subsequently, PCA is used for a comprehensive evaluation of the electromagnets quality, and the quality of different samples can be compared more widely. Additionally, the data dimensionality of quality evaluation is reduced to facilitate understanding and interpretation. Finally, the effectiveness and practicability of the proposed method is demonstrated through a real case study.