English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Convolutional Neural Networks (CNNs) are widely used in fault diagnosis ofmechanical systems due to their powerful feature extraction and classificationcapabilities. However, the CNN is a typical black-box model, and the mechanismof CNN's decision-making are not clear, which limits its application inhigh-reliability-required fault diagnosis scenarios. To tackle this issue, wepropose a novel interpretable neural network termed as Time-Frequency Network(TFN), where the physically meaningful time-frequency transform (TFT) method isembedded into the traditional convolutional layer as an adaptive preprocessinglayer. This preprocessing layer named as time-frequency convolutional (TFconv)layer, is constrained by a well-designed kernel function to extractfault-related time-frequency information. It not only improves the diagnosticperformance but also reveals the logical foundation of the CNN prediction inthe frequency domain. Different TFT methods correspond to different kernelfunctions of the TFconv layer. In this study, four typical TFT methods areconsidered to formulate the TFNs and their effectiveness and interpretabilityare proved through three mechanical fault diagnosis experiments. Experimentalresults also show that the proposed TFconv layer can be easily generalized toother CNNs with different depths. The code of TFN is available onhttps://github.com/ChenQian0618/TFN.

TFN: An Interpretable Neural Network with Time-Frequency Transform Embedded for Intelligent Fault Diagnosis