Nondestructive Acoustic Testing of Ceramic Capacitors Using One-Class Support Vector Machine with Automated Hyperparameter Selection

Abstract

The energy transition and electrification across many industries place increasingly more weight on the reliability of power electronics. A significant fraction of breakdowns in electronic devices result from capacitor failures. Multilayer ceramic capacitors, the most common capacitor type, are especially prone to mechanical damage, for instance, during the assembly of a printed circuit board. Such damage may dramatically shorten the life span of the component, eventually resulting in failure of the entire electronic device. Unfortunately, current electrical production line testing methods are often unable to reveal these types of damage. While recent studies have shown that acoustic measurements can provide information about the structural condition of a capacitor, reliable detection of damage from acoustic signals remains difficult. Although supervised machine learning classifiers have been proposed as a solution, they require a large training data set containing manually inspected damaged and intact capacitor samples. In this work, acoustic identification of damaged capacitors is demonstrated without a manually labeled data set. Accurate and robust classification is achieved by using a one-class support vector machine, a machine learning model trained solely on intact capacitors. Furthermore, a new algorithm for optimizing the classification performance of the model is presented. By the proposed approach, acoustic testing can be generalized to various capacitor sizes, making it a potential tool for production line testing.