Research on Fault Diagnosis of Traction Inverter Based on Compressed Sensing and Deep Wavelet Neural Network

Cheng, Yongqi; Weiguang, Dong

doi:10.1109/cisce.2019.00077

Cited by 1 publication

(1 citation statement)

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“…Another example of DL application is ref. [98], where a fault identification in traction inverter based on Deep Wavelet Neural Network (DWNN) and Deep Support Vector Machine was suggested. The method uses the DWNN to automatically mine and compress hidden fault information from the simulated current signals and after that Deep Support Vector Machines (DSVM) are trained to integrate the recognition results.…”

Section: Energy Conversion Stepmentioning

confidence: 99%

Data-Driven Fault Diagnosis for Electric Drives: A Review

Gonzalez-Jimenez

Olmo

Poza

et al. 2021

Sensors

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The need to manufacture more competitive equipment, together with the emergence of the digital technologies from the so-called Industry 4.0, have changed many paradigms of the industrial sector. Presently, the trend has shifted to massively acquire operational data, which can be processed to extract really valuable information with the help of Machine Learning or Deep Learning techniques. As a result, classical Condition Monitoring methodologies, such as model- and signal-based ones are being overcome by data-driven approaches. Therefore, the current paper provides a review of these data-driven active supervision strategies implemented in electric drives for fault detection and diagnosis (FDD). Hence, first, an overview of the main FDD methods is presented. Then, some basic guidelines to implement the Machine Learning workflow on which most data-driven strategies are based, are explained. In addition, finally, the review of scientific articles related to the topic is provided, together with a discussion which tries to identify the main research gaps and opportunities.

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Section: Energy Conversion Stepmentioning

confidence: 99%