ArticleSensors (Basel, Switzerland)2025
A Hybrid Deep Learning Framework for Fault Diagnosis in Milling Machines.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- A degradation index model for maintenance prediction run-to-failure in production systems.Scientific reports · 2026Article
- Physics guided semantic consistency learning for bearing fault diagnosis in agricultural machinery under operating condition shifts.Scientific reports · 2026Article
- A distribution-level statistical framework for reliable pipeline leak detection using multi-domain signal analysis.Scientific reports · 2026Article
- Research on few-shot fault diagnosis and feature extraction mechanism based on ADPCW-ELCNN.Scientific reports · 2026Article
- A multistage transfer learning framework for intelligent fault diagnosis of rotating machinery under variable operating conditions.Scientific reports · 2026Article
- Comparative analysis of deep learning algorithms for rolling element bearing fault classification under variable loads and speeds.Scientific reports · 2026Article
- Adaptive training load optimization for track and field athletes: A reinforcement learning approach.Scientific reports · 2026Article
- A sparrow search algorithm-optimized LSTM framework with EMD denoising for rolling element bearing remaining useful life prediction.Scientific reports · 2026Article
- Explainable LSTM-AdamW based fault diagnosis of aircraft rotating components using airborne acoustic signals under dynamic operating conditions.Scientific reports · 2026Article
- Rolling bearing fault diagnosis method based on fusion of STFT-statistical features and AL-SOA optimized bagging tree.Scientific reports · 2026Article
- An optimized ensemble framework for machinery fault detection in IoT environments.Scientific reports · 2026Article
- Evaluation of deep learning models for segmentation of hippocampus volumes from MRI images in Alzheimer's disease.Scientific reports · 2026Article
- Research on a General-Type Hydraulic Valve Leakage Diagnosis Method Based on CLAF-MTL Feature Deep Integration.Sensors (Basel, Switzerland) · 2026Article
- Advanced Acoustic Monitoring Using Psychoacoustic Heatmap Machine Learning Models for Noise Impact Prediction in Air-Conditioned Building Environments.Sensors (Basel, Switzerland) · 2026Article
- Acoustic-based fault diagnosis of electric motors using Mel spectrograms and convolutional neural networks.Scientific reports · 2025Article
- Advanced fault diagnosis in milling cutting tools using vision transformers with semi-supervised learning and uncertainty quantification.Scientific reports · 2025Article
- Fault diagnosis using ISMA to optimize SVM parameters for aircraft engine damage repair.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
This paper presents a hybrid fault-diagnosis framework for milling cutting tools designed to address three persistent challenges in industrial monitoring: noisy vibration signals, limited fault labels, and variability across operating conditions. The framework begins by removing baseline drift from raw signals to improve the signal-to-noise ratio. Logarithmic continuous wavelet scalograms are then constructed to provide precise time-frequency localization and reveal fault-related harmonics. To enhance feature clarity, a Canny edge operator is applied, suppressing minor artifacts and reducing intra-class variation so that key diagnostic structures are emphasized. Feature representation is obtained through a dual-branch encoder, where one pathway captures localized patterns while the other preserves long-range dependencies, resulting in compact and discriminative fault descriptors. These descriptors are integrated by an ensemble decision mechanism that assigns validation-guided weights to individual learners, ensuring reliable fault identification, improved robustness under noise, and stable performance across diverse operating conditions. Experimental validation on real-world cutting tool data demonstrates an accuracy of 99.78%, strong resilience to environmental noise, and consistent diagnostic performance under variable conditions. The framework remains lightweight, scalable, and readily deployable, providing a practical solution for high-precision tool fault diagnosis in data-constrained industrial environments.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.