TL;DR
The proposed EGFD-FDWSN-VIPINN approach improves gearbox fault detection and recognition performance in noisy and resource-constrained wireless sensor network environments.
Motor drive systems operating in embedded environments are frequently affected by noise, dynamic loading conditions, and electromagnetic interference, making timely fault diagnosis difficult. To improve diagnostic accuracy and real-time performance, this study proposes an intelligent fault diagnosis framework based on embedded multi-source data acquisition and feature fusion. High-precision sensors are employed to synchronously collect vibration and current signals, while improved wavelet packet decomposition and principal component analysis are combined to extract discriminative multi-dimensional fault features and eliminate redundant information. A lightweight convolutional neural network optimized for embedded deployment is then developed to perform low-latency fault classification and edge inference. Experimental results show that the proposed method achieves an average F1-score exceeding 97% under complex mixed-fault conditions, while maintaining an average detection latency of approximately 45 ms. The proposed framework demonstrates strong robustness and computational efficiency, providing practical support for intelligent industrial maintenance and offering reference solutions for embedded sensing, signal processing, and electromagnetic compatibility environments.
This study presents an applied comparative evaluation of automated rolling bearing fault identification using Artificial Neural Networks optimized through Bayesian Optimization, Particle Swarm Optimization, and Genetic Algorithm, highlighting optimized ANNs as competitive and computationally efficient solutions for bearing fault diagnosis.
The findings affirm the efficacy of XGBoost in bearing fault classification and emphasise the diagnostic value of carefully selected time-domain features, as well as suggesting strong potential for deploying such models in real-time condition monitoring and predictive maintenance systems.
Aiming at the difficulty of extracting fault features from nonlinear and non-stationary vibration signals of planetary gearboxes, a planetary gearbox fault diagnosis method based on Refined Composite Multiscale Fuzzy Entropy (RCMFE), Parametric-t-distributed Stochastic Neighbor Embedding (P-t-SNE), and Artificial Jellyfish Search Algorithm Optimized Support Vector Machine (JS-SVM) is proposed. Firstly, RCMFE is used to calculate and combine the feature vectors of the original fault signals of the planetary gearbox to construct the original high-dimensional fault feature set. Secondly, Parametric-t-SNE (P-t-SNE) based on a deep feedforward neural network is employed to reduce the dimensionality of the high-dimensional features, thereby extracting sensitive low-dimensional features and achieving out-of-sample mapping. Finally, the low-dimensional features are inputted into the JS-SVM for the identification of fault types. The experimental results of planetary gearbox fault diagnosis show that the proposed method can accurately identify common faults in planetary gearboxes, demonstrating promising application prospects.
In response to the serious noise interference in the fault signals obtained by the vibration sensors and the difficulty in effectively extracting the fault characteristics, a rolling bearing fault diagnosis method based on adaptive modal decomposition and correlation kurtosis feature enhancement is proposed. This method is based on the time-domain variation model of the vibration sensor output signal to obtain fault characterization features with enhanced impact and noise suppression capabilities. It uses feature reconstruction and classification networks as the core to achieve rolling bearing fault identification. The Zebra optimization algorithm (ZOA) is employed to adaptively optimize the penalty factor and the number of modal decompositions in the Variational mode decomposition (VMD), using the regularized envelope entropy as the fitness function to achieve adaptive decomposition and modal feature extraction of the vibration signal. The weighted correlation kurtosis (WCK) is used to evaluate and select the effective modes for signal reconstruction, thereby effectively suppressing modal aliasing and noise interference. On this basis, to address the issue of weak impact features being not obvious in the reconstructed vibration signal, the ZOA-optimized maximum correlation kurtosis deconvolution (MCKD) is introduced. By adaptively adjusting the filtering length and shift order, the periodic impact feature expression ability is further enhanced, and the fault characterization effect of the vibration sensor signal is improved. The experimental results show that the proposed method can effectively suppress noise interference and enhance the ability to express fault features. This method has achieved excellent diagnostic results on the self-built bearing dataset, the publicly available CWRU dataset, and the publicly available PU dataset. The fault identification accuracy rates reached 96.5%, 97.0%, and 95.9% respectively. Compared with the traditional VMD-MCKD method, the method proposed in this paper has achieved significant performance improvements on different datasets, verifying that the proposed method has better fault feature extraction ability and generalization performance under different operating conditions.
Accurate diagnosis of rolling bearing faults is critical to the reliability of industrial equipment. However, rolling bearings often operate under complex operating conditions, and with data imbalances and noise interference, fault diagnosis of them remains extremely challenging. To address these issues, a novel mode entropy knowledge machine (MEKM) framework for robust bearing fault diagnosis is proposed in this study. For MEKM, the mode entropy space is firstly constructed to decompose the vibration signal into intrinsic mode components, and the noise-resistant feature extraction and dimensionality reduction are realized by principal component analysis. Secondly, a fast classifier based on extreme learning machines is introduced, and its parameters are automatically adjusted through a particle swarm optimization to establish an adaptive extreme learning machine diagnosis model, ensuring optimal generalization under different load and speed levels. Then, a collaborative optimization paradigm is developed to coordinate mode entropy features and classifier parameters through fully automated learning, in which entropy-driven feature characterization guides the iterative refinement of decision boundaries, while classifier feedback dynamically improves the selectivity of entropy features. Finally, validation is performed on bearings with multiple operating conditions, and the results indicated that the MEKM outperformed conventional deep learning methods in terms of diagnostic accuracy and generalization ability. The work provides a theoretical basis and an industrially feasible solution for health monitoring of mechanical equipment.
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