Towards Data-Driven Fault Diagnostics Framework for SMPS-AEC Using Supervised Learning Algorithms

• Published in: Electronics (Basel) Vol. 11; no. 16; p. 2492
• Main Authors: Kareem, Akeem Bayo, Hur, Jang-Wook
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2022
• Subjects: Algorithms; Aluminum; Anomalies; Artificial intelligence; Breakdowns; Capacitors; Circuits; Condition monitoring; Data acquisition; Data collection; Design; Design and construction; Design factors; Dissipation factor; Electric fault location; Electrolytes; Electrolytic capacitors; Electronics; Failure; Fault diagnosis; Feature extraction; Machine learning; Methods; Optimization techniques; Parameter estimation; Parameters; Power converters; Power supply; Power supply (Electronics); Principal components analysis; Resistance factors; Service life; Supervised learning; Switched mode power supplies; South Korea
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics11162492

The service life of aluminium electrolytic capacitors is becoming a critical design factor in power supplies. Despite rising power density demands, electrolytic capacitors and switching devices are the two most common parts of the power supply that age (deteriorate) under normal and diverse working conditions. This study presents a fault diagnostics framework integrated with long-term frequency for a switched-mode power supply aluminium electrolytic capacitor (SMPS-AEC). Long-term frequency condition monitoring (CM) was achieved using the advanced HIOKI LCR meter at 8 MHz. The data acquired during the experimental study can help to achieve the needed paradigm from various measured characteristics of the SMPS/power converter component to detect anomalies between the capacitors selected for analysis. The CM procedure in this study was bound by the electrical parameters—capacitance (Cs), equivalent series resistance (ESR), dissipation factor (DF), and impedance (Z)—-acting as degradation techniques during physical and chemical changes of the capacitors. Furthermore, the proposed methodology was carried out using statistical feature extraction and filter-based correlation for feature selection, followed by training, testing and validation using the selected supervised learning algorithms. The resulting assessment revealed that with increased data capacity, an improved performance was achieved across the chosen algorithms out of which the k-nearest neighbors (KNN) had the best average accuracy (98.40%) and lowest computational cost (0.31 s) across all the electrical parameters. Further assessment was carried out using the fault visualization aided by principal component analysis (PCA) to validate and decide on the best electrical parameters for the CM technique.