Search Results (201)

To address the challenges posed by weak early fault signal features, strong noise interference, low diagnostic accuracy, poor reliability when using single information sources, and the limited availability of high-quality samples in practical applications for permanent magnet synchronous motor (PMSM) bearings, this paper proposes an early bearing fault diagnosis method based on Hippopotamus Optimization Variational Mode Decomposition (HO-VMD) and weighted evidence fusion of current–vibration signals. The HO algorithm is employed to optimize the parameters of VMD for adaptive modal decomposition of current and vibration signals, resulting in the generation of intrinsic mode functions (IMFs). These IMFs are then selected and reconstructed based on their kurtosis to suppress noise and harmonic interference. Subsequently, the reconstructed signals are demodulated using the Teager–Kaiser Energy Operator (TKEO), and both time-domain and energy spectrum features are extracted. The reliability of these features is utilized to adaptively weight the basic probability assignment (BPA) functions. Finally, a weighted modified Dempster–Shafer evidence theory (WMDST) is applied to fuse multi-source feature information, enabling an accurate assessment of the PMSM bearing health status. The experimental results demonstrate that the proposed method significantly enhances the signal-to-noise ratio (SNR) and enables precise diagnosis of early bearing faults even in scenarios with limited sample sizes. Full article

Considering the complexity of the metro pit construction environment, the existing risk early-warning methods cannot ensure high-precision early warning. A high-accuracy metro pit collapse risk fusion early-warning method is proposed in present study. The main contributions include (1) presenting a new input to the fusion model by optimizing the machine learning model through a multi-step rolling method, and then using the basic probability assignment values obtained from the cloud model as input to the fusion model and (2) developing an improved methodology to address the paradoxical results of the fusion of traditional Dempster–Shafer evidence theory when there is a high level of conflict in multi-source risk prediction data. The proposed method is successfully applied to the Guangzhou Metro station project. By analyzing the early-warning results of 240 moments in 6 monitoring points, compared with the single information source method and the traditional D-S method, the early-warning accuracy of this method is increased by 15.8% and 10.8% respectively, the false alarm rate is reduced by 6.3% and 5.5%, respectively, and the missed alarm rate is reduced by 9.5% and 5.3%, respectively. The high-accuracy fusion early-warning method proposed in this paper has good universality and effectiveness in the early warning of subway foundation pit collapse risk. Full article

Lithium-ion batteries (LIBs) are widely used in consumer electronics, electric vehicles, and renewable energy systems, but their performance decays with their lifespan, which poses safety risks. Therefore, it is crucial to develop remaining useful life (RUL) prediction technology. This paper proposes a RUL prediction method for lithium-ion batteries based on an improved Dempster–Shafer (D-S) evidence theory framework, which aims to improve the accuracy and robustness of prediction by integrating the advantages of a wavelet packet decomposition convolutional neural network (WPD-CNN) and an extended Kalman filter (EKF). The results show that the improved D-S theory overcomes the limitations of the classical D-S theory, improves the accuracy and robustness of diagnosis and prediction, and can effectively integrate multi-source information. Experimental verification shows that the fused model is significantly better than a single model in terms of prediction accuracy and robustness, providing an efficient and reliable solution for fault diagnosis and health management of lithium-ion batteries. Full article

Flood disasters are growing more common and severe as a result of global warming and climate change. These factors intensify weather extremes, resulting in more unpredictable and disastrous floods around the world. Effective flood risk assessment is critical for reducing the socioeconomic and environmental consequences of catastrophic events. This work proposes a novel technique for flood risk assessment that combines Event Tree Analysis with Dempster–Shafer evidence theory and an optimization approach. The methodology assesses flood scenarios, as well as probabilities and outcomes, to predict risk pathways and uncertainties. Prevention measures, such as flood defenses, early warning systems, and sustainable land use practices, are evaluated for cost-effectiveness and their contribution to flood resilience. The findings emphasize the relevance of multi-layered mitigation techniques for lowering flood risks and increasing community resilience. The model presented in this paper is modular, and since it depends on expert judgement, it can be used in other geographical or regional settings with adjustments from local data and local expert assessments. Full article

Gait is considered a valuable biometric feature, and it is essential for uncovering the latent information embedded within gait patterns. Gait recognition methods are expected to serve as significant components in numerous applications. However, existing gait recognition methods exhibit limitations in complex scenarios. To address these, we construct a dual-Kinect V2 system that focuses more on gait skeleton joint data and related acoustic signals. This setup lays a solid foundation for subsequent methods and updating strategies. The core framework consists of enhanced ensemble learning methods and Dempster–Shafer Evidence Theory (D-SET). Our recognition methods serve as the foundation, and the decision support mechanism is used to evaluate the compatibility of various modules within our system. On this basis, our main contributions are as follows: (1) an improved gait skeleton joint AdaBoost recognition method based on Circle Chaotic Mapping and Gramian Angular Field (GAF) representations; (2) a data-adaptive gait-related acoustic signal AdaBoost recognition method based on GAF and a Parallel Convolutional Neural Network (PCNN); and (3) an amalgamation of the Triangulation Topology Aggregation Optimizer (TTAO) and D-SET, providing a robust and innovative decision support mechanism. These collaborations improve the overall recognition accuracy and demonstrate their considerable application values. Full article

This paper proposes a pipeline leakage detection technology that integrates machine learning algorithms with Dempster–Shafer (DS) evidence theory. By implementing five machine learning algorithms, this study constructs pipeline pressure and flow signal characteristics through wavelet decomposition. The data were normalized and processed using principal component analysis to prepare the algorithm for training. A new method for constructing basic probability functions using a confusion matrix and a simple support function is proposed and compared with the traditional triangular fuzzy number method. The basic probability function of the identification sample is refined by calculating a comprehensive discount factor. Finally, the results from multiple algorithms are fused using DS evidence theory. Experimental results demonstrate that after combining multiple algorithms, the average accuracy improves by 0.1565%, and the precision of the triangular fuzzy number method is enhanced by 0.091%. Full article

Aero engines are complex coupled systems in which faults in one subsystem can propagate and affect the performance of others. Relying on single-source performance parameters is often insufficient for accurately assessing component degradation. Although multi-source fusion diagnosis methods, such as those based on Bayesian networks, have been widely applied, their diagnostic performance remains limited when prior knowledge is scarce. To address this challenge, this paper proposes a multi-source information fusion diagnosis method for aero engine fault detection based on Dempster–Shafer (D-S) evidence theory. Data from gas path and vibration subsystems are separately processed to extract fault features, and a decision-level fusion strategy is employed to achieve comprehensive diagnoses. A case study based on real operational data from a two-shaft aero engine demonstrates that the proposed method significantly improves diagnostic performance. Specifically, the Bayesian-network-based fusion method achieves a diagnostic confidence of 87.2% without prior knowledge and 91.2% with prior knowledge incorporated, whereas D-S evidence theory attains a higher fault confidence of 99.6% without requiring any prior information. Full article

As global warming and environmental issues become increasingly prominent, steel enterprises, as a carbon-intensive industry, face urgent challenges in energy saving and emission reduction (ESER). This study develops a novel evaluation model integrating the WSR methodology, the cloud matter-element model, and an improved D-S evidence theory to address the fuzziness, randomness, and uncertainty in ESER assessments. A case study demonstrates that this approach can address the correlation between ESER indicators; quantify the evaluation process; and optimize issues related to fuzziness, randomness, and uncertainty. This finding provides a systematic evaluation framework for ESER in steel enterprises operating under the long-process production model (the blast furnace-converter model), offering valuable insights for formulating comprehensive ESER strategies throughout the entire production process. Full article

Microorganisms play a key role in fish spoilage and quality deterioration, making the development of a rapid, accurate, and efficient technique for detecting surface microbes essential for enhancing freshness and ensuring the safety of mandarin fish consumption. This study focused on the total viable count (TVC) and Escherichia coli levels in the dorsal and ventral parts of fish, and we constructed a detection model using hyperspectral imaging and data fusion. The results showed that comprehensive and simplified models were successfully developed for quantitative detection across all wavelengths. The models performed best at predicting microbial growth on the dorsal side, with the RAW-CARS-PLSR model proving the most effective at predicting TVC and E. coli counts in that region. The RAW-PLSR model was identified as the optimal predictor of the E. coli concentration on the ventral side. A fusion model in the decision layer constructed using the Dempster–Shafer theory of evidence outperformed models relying solely on spectral or textural information, making it an optimal approach for detecting surface microbes in mandarin fish. The best prediction accuracy for dorsal TVC concentration achieved an Rp value of 0.9337, whereas that for ventral TVC concentration reached 0.8443. For the E. coli concentration, the optimal R_p values were 0.8180 for the dorsal section and 0.8512 for separate analysis. Full article

Influential node identification is an important and hot topic in the field of complex network science. Classical algorithms for identifying influential nodes are typically based on a single attribute of nodes or the simple fusion of a few attributes. However, these methods perform poorly in real networks with high complexity and diversity. To address this issue, a new method based on the Dempster–Shafer (DS) evidence theory is proposed in this paper, which improves the efficiency of identifying influential nodes through the following three aspects. Firstly, Dempster–Shafer evidence theory quantifies uncertainty through its basic belief assignment function and combines evidence from different information sources, enabling it to effectively handle uncertainty. Secondly, Dempster–Shafer evidence theory processes conflicting evidence using Dempster’s rule of combination, enhancing the reliability of decision-making. Lastly, in complex networks, information may come from multiple dimensions, and the Dempster–Shafer theory can effectively integrate this multidimensional information. To verify the effectiveness of the proposed method, extensive experiments are conducted on real-world complex networks. The results show that, compared to the other algorithms, attacking the influential nodes identified by the DS method is more likely to lead to the disintegration of the network, which indicates that the DS method is more effective for identifying the key nodes in the network. To further validate the reliability of the proposed algorithm, we use the visibility graph algorithm to convert the GBP futures time series into a complex network and then rank the nodes in the network using the DS method. The results show that the top-ranked nodes correspond to the peaks and troughs of the time series, which represents the key turning points in price changes. By conducting an in-depth analysis, investors can uncover major events that influence price trends, once again confirming the effectiveness of the algorithm. Full article

With the rapid advancement of ocean monitoring technology, the types and quantities of underwater sensors have increased significantly. Traditional single-sensor approaches exhibit limitations in underwater target classification, resulting in low classification accuracy and poor robustness. This paper integrates deep learning and information fusion theory to propose a multi-level fusion perception method for underwater targets based on multi-physical-field sensing. We extract both conventional typical features and deep features derived from an autoencoder and perform feature-level fusion. Neural network-based classification models are constructed for each physical field subsystem. To address the class imbalance and difficulty imbalance issues in the collected physical field target samples, we design a C-Focal Loss function specifically for the three underwater target categories. Furthermore, based on the confusion matrix results from the subsystem’s validation set, we propose a neural network-based Dempster–Shafer evidence fusion method (NNDS). Experimental validation using real-world data demonstrates a 97.15% fusion classification accuracy, significantly outperforming both direct multi-physical-field network fusion and direct subsystem decision fusion. The proposed method also exhibits superior reliability and robustness. Full article

Geothermal resources are crucial for sustainable energy development, yet accurately detecting geothermal anomalies in complex terrains remains a significant challenge. This study develops a multi-source thermal infrared approach to enhance geothermal anomaly detection using Landsat 8 and ASTER land surface temperature (LST) data. The Yangbajing–Yangyi Basin in Tibet, characterized by high altitude and rugged topography, serves as the study area. Landsat 8 winter time-series data from 2013 to 2023 were processed on the Google Earth Engine (GEE) platform to generate multi-year average LST images. After water body removal and altitude correction, a local block thresholding method was applied to extract daytime geothermal anomalies. For nighttime data, ASTER LST products were analyzed using global, local block, elevation zoning, and fault buffer strategies to extract anomalies, which were then fused using Dempster–Shafer (D–S) evidence theory. A joint daytime–nighttime analysis identified stable geothermal anomaly regions, with results closely aligning with known geothermal fields and borehole distributions while predicting new potential anomaly zones. Additionally, a 21-year time-series analysis of MODIS nighttime LST data identified four significant thermal anomaly areas, interpreted as potential magma chambers, whose spatial distributions align with the identified anomalies. This multi-source approach highlights the potential of integrating thermal infrared data for geothermal anomaly detection, providing valuable insights for exploration in geologically complex regions. Full article

The variation in dissolved gas concentration in the transformer serves as a crucial indicator for assessing the health status and potential faults of the transformer. However, traditional models and existing machine learning and deep learning models exhibit limitations when applied to real-world scenarios in power systems, lacking adaptability and failing to meet the requirements for accuracy and efficiency of prediction in practical applications. This paper proposes a Dempster–Shafer evidence theory-optimized Bagging ensemble learning model, aiming to improve the accuracy and stability of dissolved gas concentration prediction in transformers. By incorporating Dempster–Shafer evidence theory for the fusion of base learners and optimizing the basic probability distribution parameters by using the sequential least squares programming algorithm, this model significantly improves the adaptability and robustness of prediction. The experimental results show that compared to the ordinary Bagging method and the SARIMA model, the overall mean squared error of the Bagging prediction results optimized by the Dempster–Shafer evidence theory is only 22% of the mean square error of the Bagging prediction results and 38% of the mean square error of the SARIMA prediction results. Full article

This paper addresses the critical challenges of inadequate localization and low quantification precision in structural damage identification by introducing a novel approach that integrates Dempster–Shafer (D-S) evidence theory with the Self-Adaptive Differential Evolution (SDE) algorithm. First, modal parameters are extracted from a simply supported beam using the finite element (FE) method, and the corresponding index values are computed based on the formulated damage identification index equations. Next, these indices are applied to analyze damage localization in both single-position and multi-position scenarios within the simply supported beam. The SDE algorithm is then employed to dynamically optimize the initial weights and thresholds of various algorithms, ensuring the assignment of optimal values. Finally, the resulting data are input into the model for training, yielding a prediction model with enhanced accuracy that can precisely estimate the damage severity of the simply supported beam. The findings demonstrate that the three proposed damage identification indices—DI_1,i,j, DI_2,i,j, and DSDI_i,j—not only achieve high accuracy in damage localization but also significantly improve the precision of algorithms optimized by the SDE. These methods exhibit strong accuracy and robustness, providing a valuable reference for damage identification in small-to-medium-span simply supported beam bridges. Full article

Fatigue crack defects in metallic materials significantly reduce the remaining useful life (RUL) of parts. However, much of the existing research has focused on identifying single-millimeter-scale cracks using individual nonlinear ultrasonic responses. The identification of subtle parameters from complex ultrasonic responses of micro-crack groups remains a significant challenge in the field of nondestructive testing. We propose a novel multi-harmonic nonlinear response fusion identification method integrated with a deep learning (DL) model to identify the subtle parameters of micro-crack groups. First, we trained a one-dimensional convolutional neural network (1D CNN) with various time-domain signals obtained from finite element method (FEM) models and analyzed the sensitivity of different harmonic nonlinear responses to various subtle parameters of micro-crack groups. Then, high harmonics were fused to perform a decoupled identification of multiple subtle parameters. We enhanced the Dempster–Shafer (DS) evidence theory used in decision fusion by accounting for different sensitivities, achieving an identification accuracy of 93.73%. Building on this, we assigned sensor weights based on our proposed new conflict measurement method and further conducted decision fusion on the decision results from multiple ultrasonic sensors. Our proposed method achieves an identification accuracy of 95.68%. Full article