Multi-sensor Information Research Articles

Real-Time Monitoring Method of Icing on Overhead Transmission Lines Based on Multi-Sensor Information Fusion

Real-Time Monitoring Method of Icing on Overhead Transmission Lines Based on Multi-Sensor Information Fusion

Ultraclean Bellows Pump Flowrate Prediction Based on Multisensor Information Fusion With Noncontact Magnetic Tagged Bellow Deformation Derivation

Ultraclean Bellows Pump Flowrate Prediction Based on Multisensor Information Fusion With Noncontact Magnetic Tagged Bellow Deformation Derivation

An Anti-Collision Algorithm for Self-Organizing Vehicular Ad-Hoc Network Using Deep Learning

The rapid increase of the number of motor vehicles over the last several decades has driven a corresponding increase in the severity of traffic congestion, which has exhibited a considerable human impact. Intelligent anti-collision control via inter-vehicle communication technology can facilitate vehicles adhering to spacing speed standards and improve road utilization and traffic efficiency. In this paper, we apply a deep learning image estimation model based on joint attention mechanism. The network framework uses a deep estimation network Yolov5 and a location based VANET information fast transmission strategy to work together. This paper mainly considers vehicle distance measurement technology as a point of entry to study multi-sensor information fusion for vehicle collision prevention technology based on the self-organizing vehicular ad-hoc network (VANET). This paper also proposes a solution based on the strategy of one-way transmission of shared information and dynamic valuation of a cluster distance threshold with vehicle density. The proposed vehicle anti-collision control algorithm is designed to realize dynamic vehicle control via inter-vehicle communication. In this paper, the two-way coupling of traffic flow and network simulator is used to randomly generate vehicle nodes on the road, and the behavior of the anti-collision system is simulated. The experimental results show that the predetermined control goal is achieved, which demonstrates the effectiveness of the proposed algorithm.

Review of the field environmental sensing methods based on multi-sensor information fusion technology

Review of the field environmental sensing methods based on multi-sensor information fusion technology

Multisensor information fusion: Future of environmental perception in intelligent vehicles

Multisensor information fusion: Future of environmental perception in intelligent vehicles

Semi-supervised multi-sensor information fusion tailored graph embedded low-rank tensor learning machine under extremely low labeled rate

This paper investigates a demanding and meaningful task of intelligent fault diagnosis, in which multi-sensors signals are fused for semi-supervised analysis with few labeled fault data. Exploring effective strategies to solve this task from an industrial or academic perspective remains a challenging and resource-consuming task. To this issue, this study develops a new low-rank tensor-based semi-supervised classifier, called graph embedded low-rank tensor learning machine (GE-LRTLM), which can effectively alleviate the foregoing difficulties and increase the diagnosis precision in engineering applications. First, multi-sensor and multi-channel vibration signals are converted into the pixel matrices that are stacked as the multi-sensor information fusion feature tensors, making the coupling relationship between multi-sensor signals remained and achieving a reasonable fusion of multi-source features. Additionally, an advanced tensor decomposition method, tensor nuclear norm (TNN), is introduced in GE-LRTLM model to obtain the low-rank structure information of each feature tensor, implementing the extraction of the most important feature and patterns from the tensor data while ensuring that the tensor data structure remains unchanged. Ultimately, the manifold regularization and tensor-based graph construction method are introduced to obtain potential label information from unlabeled samples, achieving better description of the geometry similarity and distribution of tensor data. Numerous semi-supervised experiments are conducted across multiple datasets, and the results demonstrate that the proposed method can achieve a classification accuracy of 97% even when the number of labeled samples is extremely limited. Simultaneously, it also verifies that the combination of the constructed labeled and unlabeled multi-sensor information fusion tensor samples can promote the improvement of model accuracy.

A belief Sharma-Mittal divergence with its application in multi-sensor information fusion

A belief Sharma-Mittal divergence with its application in multi-sensor information fusion

A fault diagnosis method based on feature-level fusion of multi-sensor information for rotating machinery

Traditionally, most fault diagnosis work on rotating machinery is carried out on single sensor datasets. However, the single feature source may suffer from missing or inaccurate features, which is especially sluggish for fault diagnosis tasks under noise interference. Feature-level fusion of multi-sensor information can obtain more comprehensive and abundant feature information, while improving the feature discrimination. Therefore, through feature-level fusion of multi-sensor information, a parallel multi-scale attentional convolutional neural network (PMSACNN) is proposed in this paper to achieve rotating machinery fault diagnosis. A dilated wide convolutional layer is designed to extract the short-time features of signals with noise by performing sparse sampling on them. The multi-scale structure is constructed to capture the diversity feature information of signals, and the feature-level stitching of multi-sensor information is realized by the parallel input mechanism. Feature fusion is achieved by adaptively correcting the importance of different channel features by using channel attention. The global averaging pooling operation is introduced to reduce the number of parameters and improve the efficiency of the model operation. The effectiveness of PMSACNN is verified by using the bearing dataset acquired from the mechanical comprehensive diagnosis simulation platform. The experimental results indicate that the proposed method outperforms the existing methods of this field in terms of fault diagnosis accuracy and noise immunity, which can improve the reliability and safety of rotating machinery.

MIFDELN: A multi-sensor information fusion deep ensemble learning network for diagnosing bearing faults in noisy scenarios

Owing to the harsh operating environment of rolling bearings, acquired vibration signals contain strong noise interference, which makes it challenging for conventional methods to effectively extract fault information directly from the original signal. Additionally, obtaining satisfactory diagnostic results using traditional methods based on a single sensor is often difficult because the complementary information between different sensors is ignored. To address these challenges, this paper proposes a multi-sensor information fusion deep ensemble learning network (MIFDELN) for diagnosing bearing faults. First, the collected multi-sensor signals are organically fused using the weighted fusion strategy based on the composite index, which not only avoids the selection of poor-quality sensor signals but also suppresses part of the strong noise interference in the original signal. Second, the cross-scale attention feature extraction module (CAFEM) is presented to automatically learn discriminative features from the fused signals and reduce the impact of useless feature information through cross-scale learning and attentional weight enhancement. Subsequently, a weighted topology learning module (WTLM) is introduced to further excavate the spatial structure features and strengthen the distinctness of the learned features. Finally, the softmax classifier is employed to finalize the fault identification. Two experiments were conducted to verify the effectiveness and superiority of the proposed approach. The results indicated that the proposed approach performs best in extracting discriminating features with the highest accuracy and greatest robustness compared with several representative multi-sensor fusion technologies.

Multi-sensor information fusion localization of rare-earth suspended permanent magnet maglev trains based on adaptive Kalman algorithm.

Since the positioning accuracy of sensors degrades due to noise and environmental interference when a single sensor is used to localize a suspended rare-earth permanent magnetically levitated train, a multi-sensor information fusion method using multiple sensors and self-correcting weighting is proposed for permanent magnetic levitated train localization. A decay memory factor is introduced to reduce the weight of the influence of historical measurement data on the fusion estimation, thus enhancing the robustness of the fusion algorithm. The Kalman filtering results suffer from inaccuracy when process noise is present in the system. In this paper, we use a covariance adaptive scheme that replaces the prediction step of the Kalman filter with covariance. It uses the covariance adaptive scheme to search the posterior sequence online and reconstruct the prior error covariance. Since the process noise covariance is not used in the new adaptive scheme, the negative impact of the mismatch noise statistics is greatly reduced. Simulation and experimental results show that the use of multi-sensor information fusion and covariance adaptive Kalman algorithm has significant advantages in terms of adaptability, accuracy and simplicity.

Intelligent Perception in Healing Landscape Design Based on Multi-sensor Information Fusion

Intelligent Perception in Healing Landscape Design Based on Multi-sensor Information Fusion

Diagnosing root causes of faults based on alarm flood classification using transfer entropy and multi-sensor fusion approaches

Process abnormalities can result in serious accidents that may lead to unexpected loss of life and property. Early fault detection and diagnosis are essential to prevent these accidents. In the efficient operation of industrial systems, alarm systems play a crucial role. Developing new sensors and alarm adjustment networks has increased the possibility of generating multiple alarms. The alarm flood is the most important type of this problem. In this study, a new algorithm is proposed to diagnose the root cause of the fault through the classification of alarm floods. The main novelty of this algorithm is to use transfer entropy as a criterion to detect the similarity between the alarm flood sequences. The other innovations include calculating transfer entropy between the process variable and the alarm data, multi-sensor information fusion for large-scale plants and simultaneous alarms, and proposing an online version of the algorithm for the early prediction of the type of fault occurring. Finally, the Tennessee Eastman Process system, as a simulator, and the Saveh rotary cement kiln, as a real industrial system, are applied to evaluate the proposed method.

DVGTformer: A dual-view graph Transformer to fuse multi-sensor signals for remaining useful life prediction

Deep learning-based remaining useful life (RUL) prediction methods have achieved great success due to their powerful capacity of feature representation especially when big data of condition monitoring is available. However, how to fuse multi-sensor information to facilitate RUL prediction accuracy remains a challenging problem due to the complex temporal and spatial dependencies within multi-sensor signals. To address this problem, we propose a dual-view graph Transformer, named as DVGTformer, for RUL prediction, which can fully learn potential degradation patterns from multi-sensor signals by capturing complex correlations within them. The proposed method involves the design of a novel graph Transformer, named as GTformer, by collaboratively integrating learnable graph adjacency matrix and multi-head self-attention to learn structural and dynamic correlations between the nodes of graphs. We then construct the DVGTformer for RUL prediction based on the GTformer. Each layer of a DVGTformer is formed by cascading a temporal-view GTformer layer and a spatial-view GTformer layer to fuse temporal and spatial information across time stamps and sensor nodes. Experimental results on the benchmark CMAPSS dataset and a wind turbine dataset from real applications show that our method consistently provides accurate and robust RUL prediction results compared with the state-of-the-art methods.

Multiscale cyclic frequency demodulation-based feature fusion framework for multi-sensor driven gearbox intelligent fault detection

Accurate fault detection is extremely important to ensure stable gearbox operation. Data-driven schemes using cyclic spectral have received significant attention due to their robust demodulation performance. However, these schemes are mainly applied to process single sensor signals, and they are unable to accurately obtain precise fault features. This paper proposed a novel multiscale cyclic frequency demodulation (MCFD)-based feature fusion framework for multi-sensor driven gearbox intelligent fault diagnosis. Firstly, the MCFD is proposed to analyze the vibration signals from multi-sensor driven gearbox, which acquires the multi-sensor mode information without setting parameters in advance. Thereafter, the grey relational degree between the multi-sensor mode information and original signal is calculated, and its results are normalized to obtain the relationship coefficients. Finally, the acquired coefficients are performed for multi-sensor information fusion to form the covariance matrix for gearbox fault diagnosis. The effectiveness of the proposed feature fusion framework is validated using the gearbox case. The comparative experiments indicate that this framework outperforms comparative algorithms for multi-sensor driven gearbox fault diagnosis.

Research on state-parameter estimation of unmanned Tractor—A hybrid method of DEKF and ARBFNN

Unmanned tractor relies on multi-sensor information collection to obtain the current state or parameters. However, when driving in complex field, it will inevitably suffer from the uneven ground, the impact of crop straw and clods et al., which usually poses considerable challenges for multi-sensor to obtain stable and accurate value of states and parameters to realize tractor automatic lane guidance control. Therefore, a novel state and parameter estimation method by mixing the dual extended Kalman filter (DEKF) technology and adaptive radial basis function neural network (ARBFNN) technology is proposed in this paper. Firstly, DEKF technique is applied to estimate key states and initial model time-varying parameters–front/rear axle cornering stiffnesses at the same time. Then, in order to further improve the accuracy of estimation value of front/rear axle cornering stiffnesses during the automatic lane guidance control process, an ARBFNN technology is investigated by taking the heading error, lateral error and initial estimation value of front/rear axle cornering stiffnesses from DEKF as inputs to approach ideal estimation value. Finally, results of automatic lane guidance control scenarios from both simulation and hardware-in-loop (HIL) implementation show that the proposed hybrid estimated method of DEKF and ARBFNN can robustly obtain satisfactory estimation value and automatic lane guidance control performance for tractor when the control system is characterized by both time-varying model parameters and uncertain external energy-bounded disturbance. A comparative study is also conducted to investigate cases to show its effectiveness when the hybrid DEKF-ARBFNN state and parameter estimation method is used and when it is not.

Semi-supervised diagnosis method of refrigeration compressor hidden defect based on convolutional transformer autoencoder model

Improving product quality and service life by diagnosing defects in compressors through vibration signal analysis of the compressor's shell is challenging due to strong noise interference and limited labeled samples. The paper proposed a semi-supervised diagnosis method based on the convolution Transformer autoencoder (CTAE) for diagnosing hidden defects with small samples and strong noise. Firstly, an optimized variational modal decomposition is utilized to decouple and reduce noise from the raw vibrations, and the decomposition results are concatenated with the vibration signals as input to the model; Secondly, the CTAE is employed to learn the feature distribution of unlabeled samples and to extract and fuse local and global features from the input data; Finally, a labeled samples are used to fine-tune the model and to fuse features from multi-sensor information. The results of using a compressor dataset for validation show that the proposed method has high diagnosis accuracy and robustness with limited labeled samples and different signal-to-noise ratios.

Fault Diagnosis of PMSMs Based on Image Features of Multi-Sensor Fusion.

Permanent magnet synchronous motors (PMSMs) are extensively utilized in production and manufacturing fields due to their wide speed range, high output torque, fast speed response, small size and light weight. PMSMs are susceptible to inter-turn short circuit faults, demagnetization faults, bearing faults, and other faults arising from irregular vibrations and frequent start-brake cycles. While fault diagnosis for PMSMs offers an effective means to enhance operational efficiency, the multi-sensor information fusion is often overlooked. In industrial production processes, the collected data inevitably suffers from noise contamination, which can adversely impact diagnostic outcomes. To enhance the robustness of diagnostic methods in noisy environments and mitigate the risk of overfitting, a PMSM fault diagnosis method based on image features of multi-sensor fusion is proposed. Firstly, the vibration acceleration signals of the PMSM at different positions were acquired. Then, the newly designed multi-signal Gramian Angular Difference Fields (MGADF) method combines sensor signals from three different installation locations into a single image. Next, the multi-texture features are fused to extract the features of the image. Various machine models are compared in the fault feature learning and classification, and the results show that the proposed diagnostic method has good diagnostic accuracy and robustness, with an average diagnostic accuracy of 99.54% and a standard deviation of accuracy of 0.19. It has excellent performance even in noisy environments. The method is non-invasive and can be extended and applied to the condition monitoring and diagnosis of industrial motors.

Image segmentation using convolutional neural networks in multi-sensor information fusion

Image segmentation using convolutional neural networks in multi-sensor information fusion

Multisensor Information Fusion for Fault Diagnosis of Axial Piston Pump Based on Improved WPD and SSA-KSTTM

Multisensor Information Fusion for Fault Diagnosis of Axial Piston Pump Based on Improved WPD and SSA-KSTTM

Convolutional Neural Networks and Feature Fusion for Flow Pattern Identification of the Subsea Jumper

The gas–liquid two-phase flow patterns of subsea jumpers are identified in this work using a multi-sensor information fusion technique, simultaneously collecting vibration signals and electrical capacitance tomography of stratified flow, slug flow, annular flow, and bubbly flow. The samples are then processed to obtain the data set. Additionally, the samples are trained and learned using the convolutional neural network (CNN) and feature fusion model, which are built based on experimental data. Finally, the four kinds of flow pattern samples are identified. The overall identification accuracy of the model is 95.3% for four patterns of gas–liquid two-phase flow in the jumper. Through the research of flow profile identification, the disadvantages of single sensor testing angle and incomplete information are dramatically improved, which has a great significance on the subsea jumper’s operation safety.