Work

Embedding Time-Frequency Transform Neural Networks for Efficient Fault Diagnosis

Year: 2025

Type: article

Abstract: To address the challenges of non-stationary signals, noise corruption, and limited interpretability in intelligent fault diagnosis, we propose a Time-Frequency Dual Transformation (TFDT) framework. By embedding a trainable Time-Frequency Convolution (TFConv) layer into a convolutional neural network, TFDT integrates physics-informed time-frequency transforms—STTF, Morlet wavelet, and Chirplet—into the learning process. This allows the model to adaptively extract fault-sensitive features while maintaining physical interpretability. Experimental results on the CWRU dataset show that TFDT outperforms standard CNNs and fixed-transform pipelines in accuracy, convergence speed, and robustness under noisy conditions. Ablation studies confirm the critical role of the TFConv layer, and t-SNE visualizations reveal discriminative and compact feature clusters, supporting the model’s interpretability claims.

Source: Applied Sciences

DOI: 10.3390/app152413082

Authors: Yanfeng Chai, Lihong Zhang, Rui Zhang, Qiang Zhang, Yang Zhang

Institutions: Taiyuan University of Science and Technology, North University of China

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,896

Funder: National Natural Science Foundation of China