Multi-sensor Information Research Articles

Application of Improved MFDFA and D-S Evidence Theory in Fault Diagnosis

To improve the accuracy of centrifugal pump fault diagnosis, a novel fault diagnosis method based on improved multiple fractal detrended fluctuation analysis (MFDFA), the fusion of multi-sensing information derived from the back propagation (BP) neural network and the Dempster–Shafter (D-S) evidence theory, is accordingly proposed. Firstly, the multifractal spectral parameters of four sensor signals under four different operating conditions were extracted as centrifugal pump fault feature vectors using improved MFDFA and input to the BP neural network. Then, the basic trust assignment function was constructed by calculating trustworthiness (both local and global) as priori information, which is based on the output results of the neural networks specific to of each group of sensors. Finally, the basic trust assignment function was fused with decision processing in accordance with the D-S evidence combination rule in order to effectively achieve the multi-sensor information fusion centrifugal pump fault diagnosis. The experimental results show the multiple fractal spectrum feature parameters extracted by the improved MFDFA can accurately reflect the signal essence, and can be used as the fault feature vector. On this basis, this multi-sensor fault diagnosis reduces the uncertainty of fault classification and demonstrates improved accuracy compared to the single-sensor fault diagnosis thanks to being based on a combination of the BP neural networks and D-S evidence theory. Thereby, this method can facilitate accurate diagnosis of the centrifugal pump fault type with high confidence, subsequently providing a novel and alternative method to existing methods of diagnosis.

A Weighted Evidence Combination Method for Multisensor Data Fusion

<p>Multisensor information fusion exerts a key part in lots of practical usages. Dempster-shafer evidence theory has drawn extensive attention in many scopes of information fusion due to its flexibility and effectiveness in dealing with uncertain data without aforehand data. But when combining highly contradictory evidence with Dempster’s combinatorial principles, it can result in counterintuitive results. To solve the issue, the study proposes a multi-sensor data weighted evidence combination fusion method based on inter-evidence difference measure. Firstly, different measures including evidence distance and conflict are with the definition of characterizing distinctions between the two pieces of evidence. Then, according to the difference between each evidence and the average evidence, the weight coefficients of each evidence are calculated. In the end, initial evidence is discounted according to weighting factor, as well as Dempster’s combination principle is adopted to discount the evidence for fusion. Many instances show that this way can efficiently treat highly conflicting evidence and has good convergence performance.</p> <p> </p>

Research on velocity measurement and relative positioning method of maglev train based on multi-sensor information fusion

This paper focuses on the optimal method of the non-contact velocity measurement and relative positioning system for medium and low speed maglev trains. By analyzing the working principle of the velocity measurement method based on counting sleepers and analyzing the speed system error and random error model, it is shown that the velocity measurement accuracy based on the counting sleeper speed measurement method needs to be further improved. Therefore, a method of using multi-sensor information fusion is proposed to improve the accuracy of velocity measurement and relative positioning. Firstly, aiming at the disturbance problem of traction, braking and suspension vibration in the attitude angle calculation and the cumulative error problem of attitude angle, a posture solution method combining the optimized second-order complementary filter and the velocity adaptation Unscented Kalman filter with maximum noise reduction is proposed; Then, in order to further reduce the accumulated error of the attitude angle and the high performance requirements of the gyroscope, the bias instability of the gyroscope is analyzed, and an adaptive wavelet de-noising algorithm based on threshold optimization is proposed; Finally, to weaken the colored noise interference caused by the suspension vibration and to weaken the velocity accumulation error, a fusion velocity measurement and positioning algorithm of multi-loop Kalman filter with acceleration fusion correction and velocity accumulation error correction is researched. The effectiveness of the proposed fusion method is verified through simulation comparison analysis and on-board engineering test. Compared with the velocity measurement method based on counting sleepers, the velocity measurement accuracy is improved by an order of magnitude, and its accuracy is comparable to the high-precision velocity measurement method based on the induction loop and GPS/INS. It has certain engineering applicability and application value.

Thermal Fault Detection and Severity Analysis of Mechanical and Electrical Automation Equipment

Effectively diagnosing thermal faults in key parts of mechanical and electrical automation equipment before they become too serious is of crucial importance for the safe and continuous operation of these equipment. However, existing algorithms are not able to establish stable connections among sensors, so the overall control of thermal faults is not ideal enough. To cope with this issue, this paper aims to study the thermal fault detection of mechanical and electrical automation equipment and analyze the severity of the faults. At first, this paper studied the heterogeneous Multi-Sensor Information Fusion (MSIF) problem of sensors installed in key parts inside the mechanical and electrical automation equipment, and proposed a MSIF algorithm based on the D-S evidential theory. Then, the paper evaluated the influence of damages caused by thermal faults on the different parts of the equipment, providing evidences for the installation of sensors in key parts of the equipment. At last, experimental results proved the effectiveness of the proposed algorithm, and the thermal fault detection results were attained.

Analysis of the Effect of Branched Chain Amino Acids on Muscle Health Information of Swimmers Based on Multisensor Fusion and Deep Learning.

Swimmers must fully mobilize the muscles of the whole body during exercise, and it is necessary to study the protection of swimmers from muscle damage. Now, muscle damage is increasing year by year, and more athletes are affected. Therefore, studying the causes of muscle injuries and exploring more effective treatments have become important research topics in the field of sports medicine. This study is mainly based on deep learning to analyze the protective effect of branched-chain amino acids on swimming athletes' muscle injury. Due to the complex and changeable environment and the interference of unknown factors, a single sensor cannot meet the needs of obtaining information. Therefore, people have developed the technology of multisensor information fusion to obtain enough information. Multisensor data fusion technology can synthesize the information of each sensor and then obtain more comprehensive and accurate decision-making information. This study is mainly based on multisensor fusion and deep learning to analyze the impact of branched chain amino acids on Swimmers' muscle health information. Finally, two experiments were designed in this article. The first experimental result showed that the pain level of the experimental group who took BCAA supplements was 19% lower than that of the control group that did not take the BCAA supplement within three days after exercise. The results of the second experiment show the following: after exercise, the creatine kinase activity value of the experimental group taking BCAA supplement was 4.38 ± 1.45, and the creatine kinase activity value of the control group taking placebo was 5.42 ± 2.12. It proves that BBCA can protect muscle damage by reducing the activity of creatine kinase.

Design of a multi-sensor information acquisition system for mannequin reconstruction and human body size measurement under clothes

Although the measurement of human sizes and the reconstruction of mannequins have been extensively studied, there has been little research on systems applicable for measurement and reconstruction under clothes. This work presents a method for three-dimensional (3D) mannequin reconstruction and human body size measurement based on information fusion of multiple sensors. For this purpose, a multi-sensor information acquisition system (MIAS) is developed allowing one to acquire 11 key size data of the human body under clothes. The MIAS is based on the proposed parametric mannequin method and the Laplace mesh deformation technique, thus allowing one to reconstruct the mannequin conforming to the MIAS measurements. All mannequins reconstructed by this method have the same topology, which will greatly facilitate subsequent processing, such as garment design, customization and virtual fitting. The results of the reconstruction experiments show that our system can reconstruct a mannequin that matches the size of the feature parts very well. In addition, performed comparison experiments with manual and laser measurements shows that the measurement results of our system are close to those of manual and laser measurements. This provides a viable method for measuring key body sizes under clothes and reconstructing the corresponding 3D mannequin.

Active suspension control strategy of heavy rescue vehicle based on multi-sensor information fusion

This study proposes the active suspension control strategy of heavy rescue vehicles based on multi-sensor information fusion to address the problem of poor ride comfort and stability of heavy rescue vehicles on unstructured pavement. Firstly, an attitude fusion algorithm with acceleration compensation is designed. According to the driving speed, the appropriate cut-off frequency of acceleration filter is selected, and the extended Kalman filter is designed to complete the optimal estimation of position and attitude information. Secondly, the vehicle nine degree of freedom model is established, and the inverse matrix of attitude control is solved to obtain the suspension actuator output. Lastly, an active suspension control strategy based on multi-sensor information fusion is designed. The body attitude information after the information fusion is used for the control of active suspension system to realize the real-time adjustment of body attitude. Test results show that the root mean square values of vertical displacement, pitch angle, and roll angle under active suspension are reduced by 37.01%, 26.96%, and 38.90%, respectively, compared with those under in passive suspension. This reduction improves the ride comfort and stability of the heavy rescue vehicles on unstructured pavement.

Mix-VAEs: A novel multisensor information fusion model for intelligent fault diagnosis

Multisensor information are usually required to recognize the health condition of machinery by domain experts, since redundancy and complementarity of multisensor information can enhance robustness of fault diagnosis. However, the mainstream approaches of intelligent fault diagnosis based on deep neural networks only focus on applications of single sensor. In this paper, a novel intelligent fault diagnosis model using mixture of Gaussians (MoGs) and variational auto-encoders (VAEs), named Mix-VAEs, is proposed to leverage redundancy and complementarity of multisensor information. The proposed Mix-VAEs mainly contains two modules. First, feature extracting module is constructed by several parallel and independent VAEs which are used to learn data representation from multisensor data respectively, and their latent variable distributions extracted by VAEs are used for fusion. Second, in multisensor information fusion module, a novel fusion method is proposed where latent variable distributions are combined to form MoGs, and the fused features are obtained in a sampling manner from MoGs. The performance of the proposed model is verified through two datasets consisting of multisensor data. Experimental results demonstrate the robustness of the proposed model in two scenarios, sensor failure and local signal missing, while also showing the better performance than other fusion models.

Image Data Fusion Algorithm Based on Virtual Reality Technology and Nuke Software and Its Application

As an important branch of multisensor information fusion, image fusion is widely used in various fields. As a hot research technology, virtual reality technology can bring different levels of experience to image fusion. At the same time, with the development of existing image processing software, it is conducive to the further analysis and processing of images. In today’s market, image processing technology still faces many problems, and with the advancement of technology, virtual technology is widely used in various fields, so combining virtual technology with images is conducive to improving image processing technology. This article mainly introduces the image fusion algorithm and its application research based on virtual reality technology and Nuke software. This paper first proposes a picture fusion model and a picture fusion system through the analysis of virtual technology and Nuke software and, on this basis, proposes a particle algorithm and a picture edge algorithm. Secondly, the optimal fusion of images is studied on Nuke software, and finally the experimental results are analyzed through image fusion algorithm. Studies have shown that the best image fusion greatly improves the security and privacy of the image, and the difficulty of cracking is as high as 80%. The data in the experimental analysis of the graphic fusion algorithm shows that the execution efficiency and time consumption of the algorithm are greatly shortened, and the time consumption is greatly reduced. The rate is reduced by about 50%, and a good image fusion effect has been achieved.

A new time–space attention mechanism driven multi-feature fusion method for tool wear monitoring

In order to accurately monitor the tool wear process, it is usually necessary to collect a variety of sensor signals during the cutting process. Different sensor signals can provide complementary information in the feature space. In addition, monitoring signals are time series data, which also contains a wealth of time dimension tool degradation information. However, how to fuse multi-sensor information in time and space dimensions is a key issue that needs to be solved. In this paper, a new time–space attention mechanism driven multi-feature fusion method is proposed for tool wear monitoring and residual useful life (RUL) prediction. A time–space attention mechanism is innovatively introduced into the tool wear monitoring model, and features are weighted from two dimensions of space and time. It can more accurately capture the complex spatio-temporal relationship between tool wear values and features, so that the model can accurately predict wear values even if it gives up cutting force signals with good trends. The experimental results show that the correlation of the predicted wear and the actual wear is greater than 0.95, and the relative accuracy of the RUL predicted by the predicted wear combined with the particle filter can also be around 0.78. Compared with other feature fusion models, the proposed method realizes the tool wear monitoring more accurately and has better stability.

Dependency Factors in Evidence Theory: An Analysis in an Information Fusion Scenario Applied in Adverse Drug Reactions.

Multisensor information fusion brings challenges such as data heterogeneity, source precision, and the merger of uncertainties that impact the quality of classifiers. A widely used approach for classification problems in a multisensor context is the Dempster–Shafer Theory. This approach considers the beliefs attached to each source to consolidate the information concerning the hypotheses to come up with a classifier with higher precision. Nevertheless, the fundamental premise for using the approach is that sources are independent and that the classification hypotheses are mutually exclusive. Some approaches ignore this premise, which can lead to unreliable results. There are other approaches, based on statistics and machine learning techniques, that expurgate the dependencies or include a discount factor to mitigate the risk of dependencies. We propose a novel approach based on Bayesian net, Pearson’s test, and linear regression to adjust the beliefs for more accurate data fusion, mitigating possible correlations or dependencies. We tested our approach by applying it in the domain of adverse drug reactions discovery. The experiment used nine databases containing data from 50,000 active patients of a Brazilian cancer hospital, including clinical exams, laboratory tests, physicians’ anamnesis, medical prescriptions, clinical notes, medicine leaflets packages, international classification of disease, and sickness diagnosis models. This study had the hospital’s ethical committee approval. A statistically significant improvement in the precision and recall of the results was obtained compared with existing approaches. The results obtained show that the credibility index proposed by the model significantly increases the quality of the evidence generated with the algorithm Random Forest. A benchmark was performed between three datasets, incremented gradually with attributes of a credibility index, obtaining a precision of 92%. Finally, we performed a benchmark with a public base of heart disease, achieving good results.

Map Construction and Path Planning Method for a Mobile Robot Based on Multi-Sensor Information Fusion

In order to solve the path planning problem of an intelligent vehicle in an unknown environment, this paper proposes a map construction and path planning method for mobile robots based on multi-sensor information fusion. Firstly, the extended Kalman filter (EKF) is used to fuse the ambient information of LiDAR and a depth camera. The pose and acceleration information of the robot is obtained through the pose sensor. The SLAM algorithm based on a fusion of LiDAR, a depth camera, and the inertial measurement unit was built. Secondly, the improved ant colony algorithm was used to carry out global path planning. Meanwhile, the dynamic window method was used to realize local planning and local obstacle avoidance. Finally, experiments were carried out on a robot platform to verify the reliability of the proposed method. The experiment results showed that the map constructed by multi-sensor information fusion was closer to the real environment, and the accuracy and robustness of SLAM were effectively improved. The turning angle of the path was smoothed using the improved ant colony algorithm, and the real-time obstacle avoidance was able to be realized using the dynamic window method. The efficiency of path planning was improved, and the automatic feedback control of the intelligent vehicle was able to be realized.

Design of Intelligent Perception Module Based on Wireless Sensor Network and Basketball Sports Attitude

In recent years, along with microelectromechanical (MEMS) technique and wireless body area network (WBAN), wearable health monitoring systems have emerged. With sustainability posture detection technique, people pay more and more attention to mankind posture detection. Human posture detection technique has been widely used in medical, film and television, industry, sports, and other fields. Motion capture is a technique for measuring the motion of moving objects in three-dimensional space with great accuracy. It is the most efficient method for producing computer 3D animation and collecting human motion data. The use of motion capture systems in animation is becoming increasingly common. The problem of a single sensor having a large error in monitoring human motion and attitude is addressed. The use of multisensor data fusion technology is used to propose a human motion pattern recognition method based on data fusion of accelerometer and gyroscope. The system must effectively integrate the information of various sensors in order to achieve the goal of accuracy, timeliness, and reliability processing, and multisensor information fusion systems for various complex application objects are constantly emerging.

Fault diagnosis of high voltage circuit breaker based on multi-sensor information fusion with training weights

To achieve more accurate identification of mechanical faults for high voltage circuit breaker (HVCB) with higher speed, multi-sensor information fusion has been proposed in this paper. The wavelet packet decomposition is used to decompose the signals collected by various sensors. Then, the energy of the wavelet packets in different frequency band can be obtained to constitute eigenvectors. After that, Dempster/Shafer (D-S) evidence theory can be applied for fault identification, where neural networks are built to train the weight of sensors without prior information. The conflicts existing in information fusion can be solved and the accuracy of fault diagnosis is improved. Finally, experimental validation is carried out to show the effectiveness of the proposed fault diagnosis strategy for the HVCB.

Measurement and prediction of wear volume of the tool in nonlinear degradation process based on multi-sensor information fusion

Excessive tool wear seriously affects the surface quality of the workpiece and reduces the processing efficiency. Therefore, real-time monitoring of the tool wear status is very important. This paper proposes a new tool wear prediction method based on multi-sensor hybrid domain information fusion. First, the collected multi-sensor signals are decomposed by wavelet packet to extract the energy values of 16 frequency bands; in addition, the energy values are combined with the time domain and frequency domain features to construct a hybrid domain feature set; then, the gated recurrent unit model is used to adaptively explore the internal relationship between the hybrid domain features and tool wear, which overcomes the low efficiency of manual feature fusion monitoring; finally, the wear milling cutter data is used to verify the superiority of the proposed method. The results show that the prediction accuracy of tool wear based on multi-sensor feature fusion is significantly better than that based on a single sensor. Also, compared with traditional wear prediction methods, it again verifies the advancement of the proposed method in predicting tool wear.

Human-computer interaction based interface design of intelligent health detection using PCANet and multi-sensor information fusion

Background and Objective: At present, because health monitoring using human-computer interaction (HCI) has become a demand in society, an intelligent health detector with HCI characteristics is urgently needed. Our device and software framework can provide natural human-machine interaction and facilitate the use of effective, efficient and safe electronic equipment for health detection. This paper integrates the research results of human subjects testing with analysis of our computational algorithms to build the proposed interaction platform.Methods: We collected the pulse signals of normal and sub-health people and used them as pre-processed signals. Then, we input them into the Principal Componcent Analysis Network (PCANet) layer by layer, and extracted the corresponding mapping features in each layer. The extracted features are hash coded, and histogram blocks are used as the feature matrix. Next, the accuracy obtained by the classical classifier is compared with the classification results of other feature extraction methods. The HCI integrated intelligent health detector based on PCANet neural network and multi-sensor information fusion has significantly improved the accuracy of human health detection.Results: The experimental results show that the proposed method achieves high accuracy for sub-health state recognition. Compared with the traditional feature extraction method, our PCANet method improves the recognition rate by more than 10%, which proves the effectiveness of PCANet model in the field of sub-health pulse signal detection. Because the PCANet is a multi-layer architecture model, in order to verify the influence of the number of extended network layers on the experimental results, experiments are carried out on the three-layer architectures represented by PCANet-1, PCANet-2 and PCANet-3 respectively. The Experimental results show that PCANet-3 model is 2.4% higher than PCANet-1, but only 0.6% higher than PCANet-2. The running time is about 2 times and 1.6 times higher than that of PCANet-1 and PCANet-2; Compared with traditional feature extraction algorithms such as MP and Gabor transform, the accuracy of pcanet model in pulse signal sub-health detection is significantly improved. Therefore, this product can effectively distinguish between the health and sub-health states.Conclusion: Our research shows that the intelligent health detector is efficient and convenient to use, and has higher accuracy for health detection. The HCI integrated platform provides a new reference basis for the detection of sub-health state.

Monitoring and Analysis of Physical Exercise Effects Based on Multisensor Information Fusion

In this paper, multiple sensors are used to track human physiological parameters during physical exercise, and data information fusion technology is used to extract useful information for monitoring and analyzing the effects of physical exercise. This paper explores the interaction and developmental dynamics of multisensor information fusion technology and physical exercise data monitoring based on the interrelationship and interpenetration between the two. The design ideas and principles that should be followed for the software designed in this study are discussed from the perspective of the portable design of measurement instruments and the perspective of multisensor information fusion, and then, the overall architecture and each functional module are studied to propose a scientific and reasonable design model. The general methodological model to be followed for the development of this resource is designed, and the basic development process of the model is explained and discussed, especially the requirement analysis and structural design, and how to build the development environment are explained in detail; secondly, based on the course unit development process in this model, we clarify the limitations of the system through meticulous analysis of the measurement results, which provides a solid foundation for the next step of system optimization. Finally, with a focus on future development, we elaborate on the potential possible role and development trend of multisensor information fusion in the future period. In this paper, we propose to apply the multisensor data fusion algorithm to the monitoring, analysis, and evaluation of the effect of physical exercise, by collecting multiple human physiological parameters during physical exercise through multiple sensors and performing data fusion processing on the collected physiological parameters to finally evaluate the effect of physical exercise.

Integrating Biologging and Behavioral State Modeling to Identify Cryptic Behaviors and Post-capture Recovery Processes: New Insights From a Threatened Marine Apex Predator

Multisensor biologging provides a powerful tool for ecological research, enabling fine-scale observation of animals to directly link physiology and movement to behavior across ecological contexts. However, applied research into behavioral disturbance and recovery following human interventions (e.g., capture and translocation) has mostly relied on coarse location-based tracking or unidimensional approaches (e.g., dive profiles and activity/energetic metrics) that may not resolve behaviors and recovery processes. Biologging can improve insights into both disturbed and natural behavior, which is critical for management and conservation initiatives, although challenges remain in objectively identifying distinct behavioral modes from complex multisensor datasets. Using white sharks (Carcharodon carcharias) released from a non-lethal catch-and-release shark bite mitigation program, we explored how combining multisensor biologging (video, depth, accelerometers, gyroscopes, and magnetometers), track reconstruction and behavioral state modeling using hidden Markov models (HMMs) can improve our understanding of behavioral processes and recovery. Biologging tags were deployed on eight white sharks, recording their continuous behaviors, movements, and environmental context (habitat, interactions with other organisms/objects) for periods of 10–87 h post-release. Dive profiles and tailbeat analysis (as a standard, activity-based method for assessing recovery) indicated an immediate “disturbed” period of offshore movement, displaying rapid tailbeats and an average tailbeat-derived recovery period of 9.7 h, with evidence of smaller individuals having longer recoveries. However, further integrating magnetometer-derived headings, track reconstruction and HMM modeling revealed a cryptic shift to diurnal clockwise-counterclockwise circling behavior, which we argue represents compelling new evidence for hypothesized unihemispheric sleep amongst elasmobranchs. By simultaneously providing critical information toward conservation-focused shark management and understudied aspects of shark behavior, our study highlights how integrating multisensor information through HMMs can improve our understanding of both post-release and natural behavior, especially in species that are difficult to observe directly.

Automatic Docking Optimization Method of Bed and Chair Based on Multi-sensor Information Fusion

<p>Automatic docking is a basic function of intelligent wheelchair bed system. In order to achieve high-precision docking between intelligent wheelchair and U-shaped bed, this paper proposes an optimization method of automatic docking between bed and chair based on multi-sensor information fusion. By installing a variety of sensors, such as depth vision camera and lidar ranging, there is usually cross sensitivity when multiple sensors work together. Based on real sensing technology，wireless sensor network technology and lidar ranging equipment l, a dynamic Bayesian multi-mode information fusion method with asynchronous constraints is proposed. Wireless sensors are used to collect wheelchair pose information in real time. By analyzing the characteristics of the observation information of various sensors, the complementary and redundant information of various sensors in space and time is combined according to optimization criteria. The data fitting between multi-sensor information is carried out efficiently and quickly by surface fitting method. Based on the extended Kalman filter algorithm, the information fusion of IMU and combined sensing unit is realized, and the analysis model is established. Finally, the feasibility of the method is verified by Matlab simulation and the use of Gazebo in ROS to build a simulation experiment environment for fusion algorithm verification.</p> <p> </p>

Information Fusion for Classification of Hyperspectral and LiDAR Data Using IP-CNN

Joint use of multisensor information has attracted considerable attention in the remote sensing community. While applications in land-cover observation benefit from information diversity, multisensor integration technique is confronted with many challenges, including inconsistent size of data, different data structures, uncorrelated physical properties, and scarcity of training data. In this article, an information fusion network, named interleaving perception convolutional neural network (IP-CNN), is proposed for integrating heterogeneous information and improving joint classification performance of hyperspectral image (HSI) and light detection and ranging (LiDAR) data. Specifically, a bidirectional autoencoder is designed to reconstruct hyperspectral and LiDAR data together, and the reconstruction process is trained with no dependence upon annotated information. Both HSI-perception constraint and LiDAR-perception constraint are imposed on multisource structural information integration. Accordingly, fused data are fed into a two-branch CNN for final classification. To validate the effectiveness of the model, the experiments were conducted using three datasets (i.e., Muufl Gulfport data, Trento data, and Houston data). The final results demonstrate that the proposed framework can significantly outperform state-of-the-art methods even with small-size training samples.