1D-CNN-Based Fault Diagnosis for Traction Motors Using Vibration and Current Signals

Adaptive CNN-BO-XGBoost framework for traction motor fault diagnosis

A data-driven fault diagnosis method for rolling bearings: a model based on MorletConv CNN interpretability

The validation results on multiple typical bearing fault datasets show that the proposed MorletConv CNN model is characterised by enhanced physical interpretability and generalisation ability while maintaining high diagnostic accuracy, providing new ideas and method support for achieving highly reliable rolling bearing fault diagnosis.

Fault Detection in Unbalanced Data of Brushed DC Motor Worm and Helical Gears for Automotive Applications Based on CNN-LSTM-Attention

Direct Current motors (DC motors) and their worm-helical gear pairs are critical to the NVH performance and driving safety of new energy vehicles. To address the lack of dedicated fault datasets and the severe sample imbalance in existing research, this paper establishes a test bench to collect a real-world vibration dataset under typical operating conditions. A 1,000-point sliding window preprocessing strategy is proposed to mitigate sample imbalance while preserving complete gear meshing characteristics. Furthermore, a hybrid fault detection model integrating CNN, LSTM, and attention mechanisms is designed to effectively extract local impact features, capture temporal dependencies, and adaptively weight fault-sensitive data. Experimental results show that the proposed model achieves 99.23% accuracy and a 99.18% F1 score, with a fault recall rate exceeding the baseline by over 6%, validating the superiority of this approach.

Experimental Verification of Vibration-Based Unbalance Detection in a PMSM Drive Using a CNN-Based Classification Framework

This paper presents an experimental verification of a convolutional neural network (CNN) method for vibrationbased unbalance diagnosis (also known as imbalance diagnosis) in a PMSM drive test stand. Vibration data were acquired using an industrial accelerometer and converted into grayscale image representations for CNN classification using a band-pass-filter-based signal-to-image pipeline. The study considers three variants of the moment of inertia, each measured at three sampling frequencies (1, 8, and 48 kHz) and under two operating conditions: normal and with added unbalance on a metal disk. The results confirm high classification accuracy for the training data at 900 rpm, but reduced performance at unseen speeds (800 and 1000 rpm), indicating limited generalization across operating points and the need for improved robustness of the method.

Rotatory Machine Fault Detection Using CNNs on Spectrogram Signal Data

Rotary machines are vital in industrial and electrical systems, and prompt defect detection is crucial to prevent operational failures and financial losses. This article presents a framework using a Convolutional Neural Network (CNN) for defect detection via spectrogram images derived from simulated voltage, current, and load signals of rotary machines. The dataset, generated using MATLAB simulations and accessible on Kaggle, comprises spectrograms depicting normal operation and three fault conditions: $10 \Omega, 30 \Omega$, and $60 \Omega$. The CNN model proficiently extracts time-frequency characteristics from the spectrograms, attaining an overall classification accuracy of 96.3%, with precision, recall, and F1-scores continuously above 95% across all fault categories. The findings illustrate the model’s capacity to identify nuanced differences in machine behavior resulting from varying fault resistances. In contrast to traditional vibration- and signal-based techniques, the proposed method offers a resilient, non-invasive, and automated alternative for monitoring the state of rotary machines, facilitating predictive maintenance and mitigating the risk of unforeseen breakdowns. This research highlights the efficacy of integrating deep learning with spectrogram analysis for precise industrial problem identification.

Comparative Evaluation of Machine Learning Algorithms for Fault Diagnosis in Automotive Press Lines

The results show that the proposed Condition Monitoring (CM) approach significantly reduces resource waste and prevents costly downtime, offering a practical and scalable asset management model for industrial applications.