A1 Journal article (refereed), original research

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

Open Access publication

Publication Details
Authors: Levikari Saku, Kärkkäinen Tommi J., Andersson Caroline, Tamminen Juha, Nykyri Mikko, Silventoinen Pertti
Publisher: Institute of Electrical and Electronics Engineers (IEEE): OAJ / IEEE
Publication year: 2020
Language: English
Related Journal or Series Information: IEEE Access
eISSN: 2169-3536
JUFO-Level of this publication: 2
Open Access: Open Access publication


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.

Last updated on 2021-16-03 at 12:47