# WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis

arXiv: https://arxiv.org/abs/1911.07925 · HTML: https://mlsanity.com/paper/1911.07925/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2019.

## Built on by (most cited first, 23 of 23)

- [Adaptive wavelet distillation from neural networks through interpretations](https://mlsanity.com/paper/2107.09145/) (2021)
- [Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series](https://mlsanity.com/paper/2105.00899/) (2021)
- [An Explainable Artificial Intelligence Approach for Unsupervised Fault Detection and Diagnosis in Rotating Machinery](https://mlsanity.com/paper/2102.11848/) (2021)
- [Explainable Predictive Maintenance: A Survey of Current Methods, Challenges and Opportunities](https://mlsanity.com/paper/2401.07871/) (2024)
- [ChatGPT-Like Large-Scale Foundation Models for Prognostics and Health Management: A Survey and Roadmaps](https://mlsanity.com/paper/2305.06472/) (2023)
- [Convolutional Neural Networks Demystified: A Matched Filtering Perspective Based Tutorial](https://mlsanity.com/paper/2108.11663/) (2021)
- [Roadmap on Signal Processing for Next Generation Measurement Systems](https://mlsanity.com/paper/2111.02493/) (2021)
- [A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecture](https://mlsanity.com/paper/2207.04585/) (2022)
- [Brain-Inspired Spiking Neural Networks for Industrial Fault Diagnosis: A Survey, Challenges, and Opportunities](https://mlsanity.com/paper/2401.02429/) (2023)
- [Taking ROCKET on an Efficiency Mission: Multivariate Time Series Classification with LightWaveS](https://mlsanity.com/paper/2204.01379/) (2022)
- [TFN: An Interpretable Neural Network with Time-Frequency Transform Embedded for Intelligent Fault Diagnosis](https://mlsanity.com/paper/2209.01992/) (2022)
- [A Lightweight Dual-Branch System for Weakly-Supervised Video Anomaly Detection on Consumer Edge Devices](https://mlsanity.com/paper/2410.21991/) (2024)
- [Iterative Low-rank Network for Hyperspectral Image Denoising](https://mlsanity.com/paper/2509.00356/) (2025)
- [Explainable AI (XAI) for PHM of Industrial Asset: A State-of-The-Art, PRISMA-Compliant Systematic Review](https://mlsanity.com/paper/2107.03869/) (2021)
- [Time Scale Network: A Shallow Neural Network For Time Series Data](https://mlsanity.com/paper/2311.06170/) (2023)
- [Uncertainty-Aware Artificial Intelligence for Gear Fault Diagnosis in Motor Drives](https://mlsanity.com/paper/2412.01272/) (2024)
- [Rational Gaussian wavelets and corresponding model driven neural networks](https://mlsanity.com/paper/2502.01282/) (2025)
- [SHapley Estimated Explanation (SHEP): A Fast Post-Hoc Attribution Method for Interpreting Intelligent Fault Diagnosis](https://mlsanity.com/paper/2504.03773/) (2025)
- [SigWavNet: Learning Multiresolution Signal Wavelet Network for Speech Emotion Recognition](https://mlsanity.com/paper/2502.00310/) (2025)
- [EGR-Net: A Novel Embedding Gramian Representation CNN for Intelligent Fault Diagnosis](https://mlsanity.com/paper/2502.19199/) (2025)
- [WaveletInception Networks for on-board Vibration-Based Infrastructure Health Monitoring](https://mlsanity.com/paper/2507.12969/) (2025)
- [Classification and detection of multiple UAVs using rational Gaussian wavelet neural networks](https://mlsanity.com/paper/2605.26310/) (2026)
- [Autonomous Model Lifecycle Management for Digital Twin-Based Manufacturing Control](https://mlsanity.com/paper/2609.22701/) (2026)

## Abstract (arXiv)

Convolutional neural network (CNN), with ability of feature learning and nonlinear mapping, has demonstrated its effectiveness in prognostics and health management (PHM). However, explanation on the physical meaning of a CNN architecture has rarely been studied. In this paper, a novel wavelet driven deep neural network termed as WaveletKernelNet (WKN) is presented, where a continuous wavelet convolutional (CWConv) layer is designed to replace the first convolutional layer of the standard CNN. This enables the first CWConv layer to discover more meaningful filters. Furthermore, only the scale parameter and translation parameter are directly learned from raw data at this CWConv layer. This provides a very effective way to obtain a customized filter bank, specifically tuned for extracting defect-related impact component embedded in the vibration signal. In addition, three experimental verification using data from laboratory environment are carried out to verify effectiveness of the proposed method for mechanical fault diagnosis. The results show the importance of the designed CWConv layer and the output of CWConv layer is interpretable. Besides, it is found that WKN has fewer parameters, higher fault classification accuracy and faster convergence speed than standard CNN.