Since variational mode decomposition (VMD) was proposed, it has been widely used in condition monitoring and fault diagnosis of mechanical equipment. However, the parameters K and α in the VMD algorithm need to be set before decomposition, which causes VMD to be unable to decompose adaptively and obtain the best result for signal decomposition. Therefore, this paper optimizes the VMD algorithm. On this basis, this paper also proposes a method of multi-domain feature extraction of signals and combines an extreme learning machine (ELM) to realize comprehensive and accurate fault diagnosis. First, VMD is optimized according to the improved grey wolf optimizer; second, the feature vectors of the time, frequency, and time-frequency domains are calculated, which are synthesized after dimensionality reduction; ultimately, the synthesized vectors are input into the ELM for training and classification. The experimental results show that the proposed method can decompose the signal adaptively, which produces the best decomposition parameters and results. Moreover, this method can extract the fault features of the signal more completely to realize accurate fault identification.

Symplectic geometric mode decomposition (SGMD) is a newly proposed signal processing method. Because of its superiority, it has gained more and more attention in the field of fault diagnosis. However, the similar component reorganization problem involved in this method has not been clearly stated. Aiming at this problem, this paper proposes the SGMD-CS method based on the SGMD method and the cosine similarity (CS) and has been compared and verified on the simulation signal and the actual rolling bearing signal. In addition, in order to realize the intelligent diagnosis of the wind turbine bearing fault, the symplectic geometric entropy (SymEn) is extracted as the fault feature and input it into the AdaBoost classification model. In summary, this paper proposes a new wind turbine fault feature extraction method based on the SGMD-CS and AdaBoost framework, and the validity of the method is verified by the rolling bearing vibration data of the Electrical Engineering Laboratory of Case Western Reserve University.

In order to address the challenges of low recognition accuracy and the difficulty in effective diagnosis in traditional converter transformer voiceprint fault diagnosis, a novel method is proposed in this article. This approach takes account of the impact of load factors, utilizes a multi-strategy improved Mel-Frequency Spectrum Coefficient (MFCC) for voiceprint signal feature extraction, and combines it with a temporal convolutional network for fault diagnosis. Firstly, it improves the hunter–prey optimizer (HPO) as a parameter optimization algorithm and adopts IHPO combined with variational mode decomposition (VMD) to achieve denoising of voiceprint signals. Secondly, the preprocessed voiceprint signal is combined with Mel filters through the Stockwell transform. To adapt to the stationary characteristics of the voiceprint signal, the processed features undergo further mid-temporal processing, ultimately resulting in the implementation of a multi-strategy improved MFCC for voiceprint signal feature extraction. Simultaneously, load signal segmentation is introduced for the diagnostic intervals, forming a joint feature vector. Finally, by using the Mish activation function to improve the temporal convolutional network, the IHPO-ITCN is proposed to adaptively optimize the size of convolutional kernels and the number of hidden layers and construct a transformer fault diagnosis model. By constructing multiple sets of comparison tests through specific examples and comparing them with the traditional voiceprint diagnostic model, our results show that the model proposed in this paper has a fault recognition accuracy as high as 99%. The recognition accuracy was significantly improved and the training speed also shows superior performance, which can be effectively used in the field of multiple fault diagnosis of converter transformers.

Due to the complexity of wind power, traditional prediction models are incapable of fully extracting the hidden features of multidimensional strong fluctuation data, which results in poor multi-step prediction performance. To predict continuous power effectively in the future, an improved wind power multi-step prediction model combining variational mode decomposition (VMD) with sequence-to-sequence (Seq2Seq) is proposed. Firstly, the wind power sequence is smoothed using VMD and the decomposition parameters of VMD are optimized by using the squirrel search algorithm (SSA) to effectively optimize the decomposition effect. Then, the subsequence obtained from decomposition, together with the original wind power data, is reconstructed into multivariate time series features. Finally, a Seq2Seq model is constructed, and convolutional neural networks (CNNs) with bidirectional gate recurrent units (BiGRUs) are used to learn the coupling and timing relationships of the input data and encode them. The gate recurrent unit (GRU) is decoded to achieve continuous power prediction. Based on the actual operating data of a wind farm, a case analysis is conducted. Experimental results show that SSA-VMD can effectively optimize the decomposition effect, and the subsequences obtained with its decomposition are highly accurate when applied to predictions. The Seq2Seq model has better multi-step prediction results than traditional prediction methods, and as the prediction step size increases, the advantages are more obvious.

The diagnosis of vibration faults in hydropower units is essential for ensuring the safe and stable operation of these systems. This paper proposes a fault diagnosis method for hydropower units that combines Dynamic Mode Decomposition (DMD) with an optimized Extreme Learning Machine (ELM) utilizing the Hiking Optimization Algorithm (HOA). To address the issue of noise interference in the vibration signals of hydropower units, this study employs DMD technology alongside a thresholding technique for noise reduction, demonstrating its effectiveness through comparative trials. Furthermore, to facilitate a thorough analysis of the operational status of hydropower units, this paper extracts multidimensional features from denoised signals. To improve the efficiency of model training, Principal Component Analysis (PCA) is applied to streamline the data. Given that the weights and biases of the ELM are generated randomly, which may impact the model’s stability and generalization capabilities, the HOA is introduced for optimization. The HOA-ELM model achieved a classification accuracy of 95.83%. A comparative analysis with alternative models substantiates the superior performance of the HOA-ELM model in the fault diagnosis of hydropower units.

The construction of pumped storage power stations (PSPSs) is undergoing rapid expansion globally. Detecting operational faults and defects in pumped storage units is critical, as effective diagnostic methods can not only identify fault types quickly and accurately but also significantly reduce maintenance costs. This study leverages the symmetry characteristics in the vibration signals of pumped storage units to enhance fault diagnosis accuracy. To address the challenges of selecting the key parameters (e.g., decomposition level and penalty factor) of the variational mode decomposition (VMD) algorithm during vibration signal analysis, this paper proposes an algorithm for an improved subtraction-average-based optimizer (ISABO). By incorporating piecewise linear mapping, the ISABO enhances parameter initialization and, combined with a balanced pool method, mitigates the algorithm’s tendency to converge to local optima. This improvement enables more effective vibration signal denoising and feature extraction. Furthermore, to optimize hyperparameter selection in the bidirectional long short-term memory (BILSTM) network—such as the number of hidden layer units, maximum training epochs, and learning rate—we introduce an ISABO-BILSTM classification model. This approach ensures robust fault diagnosis by fine-tuning the neural network’s critical parameters. The proposed method is validated using vibration data from an operational PSPS. Experimental results demonstrate that the ISABO-BILSTM model achieves an overall fault recognition accuracy of 97.96%, with the following breakdown: normal operation: 96.29%, thrust block loosening: 98.60%, rotor-stator rubbing: 97.34%, and rotor misalignment: 99.59%. These results confirm that the proposed framework significantly improves fault identification accuracy, offering a novel and reliable approach for PSPS unit diagnostics.