A CUSUM-based condition monitoring algorithm for power electronics based on SOM-MQE feature extraction method

Ahstract-MOSFET is an important power electronic component and is widely used in high-power, high-availability applications, such as electric vehicles. The anomalous state of a MOSFET has a major impact on the operation of the entire electrical system in a vehicle. Anomaly detection can provide earl...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE Transportation Electrification Conference and Expo (Online) s. 1 - 5
Hlavní autoři: Yang, Qian, Joshi, Shailesh, Ukegawa, Hiroshi, Viviano, Raymond, Pattipati, Krishna R.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 21.06.2023
Témata:
Anomaly detection
Cumulative sum (CUSUM)
Digital signal processors
Feature extraction
MOSFET
Quantization (signal)
Self-organizing feature maps
Self-organizing Map (SOM)
Semiconductor device modeling
Signal processing algorithms
ISSN:2473-7631
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Ahstract-MOSFET is an important power electronic component and is widely used in high-power, high-availability applications, such as electric vehicles. The anomalous state of a MOSFET has a major impact on the operation of the entire electrical system in a vehicle. Anomaly detection can provide early warning to vehicle operators, thereby avoiding unscheduled maintenance and improving the vehicle availability. This paper presents an unsupervised data-driven method that involves a distance-based feature extraction and the Page's Cumulative sum (CUSUM) detection scheme for real-time detection of incipient faults in MOSFETs. The method utilizes the ON-state signals under a healthy condition to train a self-organizing Map (SOM) model of the device and uses the Minimum quantization error (MQE) of a test device from the SOM centroids as a Health Indicator (HI). Once the statistics of HI of a nominal device are determined, a CUSUM model monitor for changes in the device's operation. The proposed algorithm is able to process multiple measurements at once, leading to a shorter detection time and a better detection accuracy. The effectiveness of the proposed method is validated on 60 accelerated aging experimental data sets with both low-frequency and high-frequency switching. The results demonstrate that the proposed method can detect anomalies of MOSFETs under diverse operating conditions.
ISSN:2473-7631
DOI:10.1109/ITEC55900.2023.10186893