Aug 2026· Energies· Vol 19, pp. 3694· 0 citations· 32 references
TL;DR
Experimental results demonstrate that the proposed T-HMM accurately tracks state evolution trends and effectively identifies fault categories, achieving significantly superior state recognition accuracy and multi-step prediction hit rates compared to conventional HMM, with substantially reduced mean absolute error.
Abstract
Addressing multi-factor coupling, progressive degradation, and time-varying operating conditions in substation equipment under high-penetration renewable energy integration, this paper proposes a Time-varying Hidden Markov Model (T-HMM) for transformer fault diagnosis using multi-source heterogeneous data. Unlike conventional HMM with fixed transition matrices and initial parameter sensitivity, the proposed framework introduces a forgetting-factor-driven online transition matrix updating mechanism, enabling adaptive state tracking under varying operating conditions. A multi-dimensional feature system is constructed incorporating fundamental state, coupling correlation, and temporal evolution characteristics, with state-dependent sliding window standardization and adaptive wavelet denoising to enhance early-stage fault discriminability. Parameter optimization employs pre-clustering and multi-start strategies to circumvent local optima in the Baum–Welch algorithm, while an improved decoding strategy achieves real-time health state identification and multi-step probability prediction. Experimental results demonstrate that the proposed method accurately tracks state evolution trends and effectively identifies fault categories, achieving significantly superior state recognition accuracy and multi-step prediction hit rates compared to conventional HMM, with substantially reduced mean absolute error. The method effectively captures subtle precursors during normal-to-fault transitions, providing reliable theoretical foundations for early fault warning and condition-based maintenance.
A deep learning framework based on a Mamba-driven state-space model architecture for comprehensive PQ disturbance classification is proposed and results indicate that the proposed method is well-suited for real-time smart grid monitoring and intelligent protection systems.
Pintu Das, Chandan Jana, Sannistha Banarjee et al.· Engineering Research Express· 0 citations
A hybrid deep learning architecture is proposed for robust vibration-based fault diagnosis in industrial machinery by jointly modeling time-domain, frequency-domain, and temporal dynamics, enabling coherent cross-domain interaction and robust fault characterization.
Canan Taştimur· Information Technology and C...· 0 citations
Large-scale integration of high-proportion new energy sources and continuous expansion of network scale complicate the transient characteristics of distribution networks. Conventional fault diagnosis methods suffer from insufficient feature extraction and weak capture of topological correlation, which degrade diagnosis accuracy. To tackle this issue, this paper proposes a complex fault diagnosis strategy for distribution networks based on analysis of the dynamic variation law of zero-sequence current. First, multivariate variational mode decomposition (MVMD) is adopted to process zero-sequence current signals, which effectively fuses multi-dimensional zero-sequence current data and fully excavates fault features. Moreover, the zebra optimization algorithm is utilized to optimize the parameters of MVMD for further improving feature extraction performance. Subsequently, a graph convolutional neural network is employed to extract temporal features from the processed waveforms, enhancing the model’s recognition capability under high-resistance faults and typical disturbance conditions. Finally, multiple IEEE test systems are used for verification, which demonstrates the effectiveness and feasibility of the proposed method.
Ruihao Zhou, Penghui Liu, Wenxiang Li et al.· Processes· 0 citations
Experimental results show that the proposed few-shot fault diagnosis method consistently outperforms comparison methods under different rotational speeds, training sample scales, and 8-way 1-shot/5-shot tasks, validating its effectiveness and robustness for few-shot rotating machinery fault diagnosis.
State of Health (SOH) is a critical metric for evaluating the efficient and reliable operation of lithium-ion batteries (LIBs), although it cannot be directly measured. Accurate SOH prediction throughout the entire lifecycle of LIBs remains a significant challenge, primarily due to severe signal fluctuations and complex degradation mechanisms. In this paper, a novel hybrid Transformer-based architecture for SOH prediction is introduced, termed KF–SAMformer–GRU, which integrates a Kalman filter (KF) optimizer, a sharpness-aware minimization Transformer (SAMformer) model, and a multi-layer gated recurrent unit (GRU). To overcome the challenges of multivariate long-term forecasting, we innovatively integrate SAMformer to extract robust feature indicators, actively mitigating data distribution shifts via sharpness-aware minimization. Furthermore, reversible instance normalization (RevIN) is first introduced to tackle non-stationarity in multi-source datasets, effectively eliminating uncertainty and significantly boosting generalization capability. Complementing this, the KF mechanism is uniquely employed to fuse multi-dimensional features, reducing computational overhead while accelerating training. Finally, the multi-layer GRU precisely refines the mapping between SOH metrics and predicted values. Experimental validation on the NASA and CALCE datasets demonstrates the superiority of our approach, achieving a MAPE of 0.01, an RMSE of 0.91%, and an R2 of 0.99. Notably, the method accurately captures phenomena such as battery capacity regeneration, exhibiting superior performance at peaks and valleys while maintaining high computational efficiency.
Lei Xu, Peng Sun, Nan Zhou· Batteries· 0 citations
Accurate prediction of the State of Health (SOH) of lithium batteries is essential for ensuring safe operation, prolonging battery service life and optimizing energy management. Battery degradation is characterized by strong nonlinearity, multi-factor coupled interference and data redundancy. Furthermore, prevailing hybrid prediction models are constrained by several inherent limitations: insufficient feature extraction, limited optimization capacity of conventional algorithms, and oversimplified modular integration. To tackle these challenges, this paper proposes an SOH prediction method based on Variational Mode Decomposition (VMD) and the Transformer-Bidirectional Long Short-Term Memory (BiLSTM) framework optimized by the Multi-Strategy Adaptive Beaver Behavior Optimizer (MSA-BBO). First, multi-dimensional health features are extracted from raw data. Second, the VMD algorithm is employed to decompose and reconstruct multi-dimensional health features (such as charging voltage and current) in the training set, thereby suppressing noise and extracting critical aging characteristics. The obtained processing scheme is then directly applied to the test set. Subsequently, a Transformer-BiLSTM hybrid framework is established, where the Transformer captures long-range temporal dependencies and the BiLSTM extracts bidirectional correlation features of data, thereby synergistically overcoming the limitations of single-structure models. To address the deficiencies of the original Beaver Behavior Optimizer (BBO), including insufficient utilization of elite information, convergence oscillation, and non-targeted perturbation, the algorithm is improved in four aspects: population evolution, exploration-exploitation balance, local optimization, and global escape. The developed MSA-BBO is employed for adaptive hyperparameter optimization. In contrast to existing dual-module hybrid models, the proposed method establishes a three-stage collaborative framework that integrates feature processing, feature mining, and hyperparameter optimization. Finally, comparative experiments with five other models are conducted to objectively evaluate the prediction accuracy, stability, and generalization ability of the proposed model. The experimental results demonstrate that the proposed model achieves the optimal overall performance on five test sets, with all coefficients of determination (R2) above 0.94 and all Mean Absolute Percentage Error (MAPE) values below 0.04. This research can provide reliable technical support and theoretical reference for the full-life cycle health management of lithium batteries.
