Multi-source Information Fusion Research Articles

Fault diagnosis method for machinery based on multi-source conflict information fusion

Multi-source information fusion diagnosis is usually more reliable than fault diagnosis with a single source employed. However, fusion results may be absurd when fusing highly conflicting information. To address this problem, the Dempster–Shafer (DS) evidence theory is updated by weighting each piece of evidence according to the corresponding contribution to the decision, and a novel fault diagnosis method based on multi-source conflict information fusion is proposed. First, the basic probability assignment of evidence corresponding to the sensor information is given by introducing the feature parameters of electromyographic signals and using the back-propagation neural network. Then, the importance of each piece of evidence is determined by solving the difference degree and exclusion degree among the evidence, and the evidence is assigned weights according to the degree of importance of each piece of evidence in the fusion decision-making process. Next, the weighted evidence is combined for making decisions and further diagnosis after weighted averaging of the evidence with different weights. Finally, the performance of the proposed method is assessed using receiver operating characteristic (ROC) curves. The experimental results show that the areas under the ROC curves for the proposed method are 0.3229, 0.0729 and 0.9271 higher than those of the traditional DS method, Murphy’s method and Yager’s method, respectively, which proves that the proposed method has better diagnostic performance and reliability.

A post-processing algorithm based on factor graph optimization for autonomous underwater vehicle integrated navigation

Aiming at the requirements of offline high-precision navigation and positioning in the mapping of ocean floor, survey, and other fields for autonomous underwater vehicle (AUV)integrated navigation system, a post-processing algorithm based on factor graph optimization is proposed in this paper. First, the factor graph model is established by the factor graph formulation to address the asynchronous and heterogeneous problem of multi-source information fusion. Then the equivalent inertial measurement unit (IMU) factor is introduced to replace several consecutive IMU factors between two adjacent measurements to further aid high-rate navigation solutions. Second the forward and backward message passing processes are performed independently using the sum-product algorithm in the factor graph model. Finally, the optimal navigation solution is obtained by weighted smoothing the results of the forward and backward message passing processes. Simulation results show this algorithm proposed in this paper provides more accurate navigation accuracy and smoothness, especially in the case of signals loss or sensors fault. Compared with the federal Kalman filtering method, the horizontal positioning errors of the un-simplified factor graph, the simplified factor graph, and the factor graph optimization are improved by about 30%, 34%, and 80%, respectively. The semi-physical environment results also clearly verify the reliability and effectiveness of the proposed method, and its horizontal positioning accuracies are kept within −2∼2 m.

Early warning of reciprocating compressor valve fault based on deep learning network and multi-source information fusion

An early warning method of compressor valve fault based on multi-parameter signals (vibration, pressure, and temperature) is presented in this work. Due to the complexity working condition, the run data of the compressor are of problems like noise and feature aliasing, which makes it difficult to extract useful features and find out the running law from the original signals. In this work, an improved deep learning network Multi-Level Fusion long short-term memory based on Component Evaluating Empirical Mode Decomposition and Fuzzy C-Means (CEEMD-FCM & MLF-LSTM) for parameter prediction of reciprocating compressor and an information fusion strategy is proposed for compressor valve fault warning. The CEEMD-FCM & MLF-LSTM network consists of data processing block, information learning block, and prediction output block, which is mainly responsible for parameter prediction. In the data processing block, the CEEMD-FCM algorithm is used for parameter decomposition, noise removal, and fuzzy mode (FM) reconstruction, which generates the input for the information learning block to ensure the predicting accuracy and reduce model complexity. MLF-LSTM is constructed to predict the parameter in the future by learning the temporal and spatial characteristics of FMs of the run data. Then, an early warning strategy for compressor valve fault based on multi-source information fusion is developed. Experimental results have verified that the proposed CEEMD-FCM & MLF-LSTM model and early warning strategy could realize early warning of compressor valve fault effectively.

Multi-source information fusion technology for risk assessment of water inrush from coal floor karst aquifer

In order to effectively solve the technical problem of the prediction of coal floor water inrush, according to the spatial distribution differences of geological conditions, combined with the typical coal mine, a partition calculation map of the development depth of the mineral pressure failure zone was made, and the mining process under different geological conditions has been numerically simulated and analyzed. And the empirical formula is modified by the numerical simulation calculation results, the calculation formula of the development depth of the mineral pressure failure zone of a typical coal mine was established, which effectively improves the accuracy of the calculation of the depth of the mineral pressure failure zone and lays the foundation for the quantification of evaluation indicators related to the aquifuge. According to the hydrogeological characteristics of southwest mines in China, 12 evaluation indicators determined from the three aspects of aquifer, aquifuge and geological structure, and an evaluation index system of water inrush risk of the karst aquifer in the coal floor has been constructed. On this basis, further using GIS technology and entropy weight theory, a multi-source information evaluation method for the risk of water inrush from coal floor was proposed, and verified the evaluation results.

A belief Rényi divergence for multi-source information fusion and its application in pattern recognition

A belief Rényi divergence for multi-source information fusion and its application in pattern recognition

Abnormal Concentration Detection Method of Chemical Pollutants Based on Multisensor Fusion

China is a big industrial producer, but also a big producer and user of chemical materials. Although the use of chemical materials has improved the level of industrialization, it has also caused harm to the environment and ecosystem. With the acceleration of China’s industrialization, more and more attention has been paid to the problem of chemical pollution. The pollution of water resources in China has seriously damaged the balance of ecological environment and is also an important factor threatening people’s own health. The detection of chemical pollutants in water resources, especially organic pollutants, has a long way to go. To solve this problem, this paper designs a method of chemical pollutant concentration detection based on multisource information fusion and analyzes the performance of the detection system. Firstly, this paper introduces the main types of situations of chemical pollution at present. Secondly, a multisensor fusion model based on BP neural network is established, and the collected chemical pollutant samples were input into the model. Finally, the quantitative and qualitative analysis of the detected pollutant concentration results shows that the proposed method not only has good detection effect of chemical pollutant concentration but also has good practicability. In a word, the proposed method not only has good theoretical significance but also has certain potential application value.

Quantitative Nondestructive Testing for Wire Rope Based on Multi-Source Information Fusion

Quantitative Nondestructive Testing for Wire Rope Based on Multi-Source Information Fusion

Design and development of orchard autonomous navigation spray system.

Driven by the demand for efficient plant protection in orchards, the autonomous navigation system for orchards is hereby designed and developed in this study. According to the three modules of unmanned system “perception-decision-control,” the environment perception and map construction strategy based on 3D lidar is constructed for the complex environment in orchards. At the same time, millimeter-wave radar is further selected for multi-source information fusion for the perception of obstacles. The extraction of orchard navigation lines is achieved by formulating a four-step extraction strategy according to the obtained lidar data. Finally, aiming at the control problem of plant protection machine, the ADRC control strategy is adopted to enhance the noise immunity of the system. Different working conditions are designed in the experimental section for testing the obstacle avoidance performance and navigation accuracy of the autonomous navigation sprayer. The experimental results show that the unmanned vehicle can identify the obstacle quickly and make an emergency stop and find a rather narrow feasible area when a moving person or a different thin column is used as an obstacle. Many experiments have shown a safe distance for obstacle avoidance about 0.5 m, which meets the obstacle avoidance requirements. In the navigation accuracy experiment, the average navigation error in both experiments is within 15 cm, satisfying the requirements for orchard spray operation. A set of spray test experiments are designed in the final experimental part to further verify the feasibility of the system developed by the institute, and the coverage rate of the leaves of the canopy is about 50%.

Multi-Source Data High-Performance Indoor Positioning considering Genetic Optimization Neural Network Algorithm

In order to effectively solve the problem of relatively large errors in individual positioning strategies in indoor environments, this paper applies the genetic optimization neural network algorithm to indoor location based on multi-source information fusion. The range of the geomagnetic fitness is constrained based on the results obtained by using the wireless WiFi positioning for combination and matching, which can reduce the value of the matching error effectively. Subsequently, the global optimal value of the indoor network is calculated based on the genetic algorithm, which can optimize the initial value and threshold of the neural network after genetic optimization so as to improve the accuracy of the network to the greatest extent possible while accelerating the convergence speed at the same time. After the optimization processing is completed, fusion training can be performed on the coordinates of the actual positions based on the obtained combination positioning situation and the predicted positioning result in the indoor network. Finally, the optimal positioning result can be obtained accordingly. Through the analysis of practical cases, it can be known that the mean square error predicted based on the genetic optimization neural network calculated by using the genetic algorithm can be effectively reduced by 76%, and the accuracy of the fusion positioning can be increased by 48% on average compared with the accuracy of a single positioning strategy. Hence, the method put forward in this paper has effectively improved the positioning accuracy, which suggests that its positioning performance is superior.

A novel carbon price combination forecasting approach based on multi-source information fusion and hybrid multi-scale decomposition

Accurate carbon price forecasting is essential to reduce carbon dioxide emissions and slow down global warming. However, a key issue in the carbon trading market is the diversity and uncertainty of external factors. Some studies began to focus on the impact of a single external factor, but few of them considered the application of multi-source information on carbon prices. In addition, the selection of the decomposition method is still controversial, making carbon price forecasting inefficient and unstable. Therefore, this paper proposes a carbon price forecasting method based on multi-source information fusion (MSIF) and hybrid multi-scale decomposition (HMSD). First, MSIF can provide complete, interactive, and timely information for raw carbon prices, including historical data, influencing factors (coal prices, oil prices), and unstructured data (Baidu index, social media sentiment). Second, HMSD is used to completely extract the internal features of multi-source information and avoid the problem of decomposition method selection. Third, due to the linear and nonlinear characteristics of carbon prices, a combination strategy based on Holt, ARIMA, SVR, BPNN, and LSTM can achieve satisfactory results. Finally, to evaluate the effectiveness of the proposed framework, seven types of comparative experiments (based on historical data, influencing factors, Baidu index, and sentiment analysis) are carried out. The results show that MSIF is superior to single-source information in improving carbon price forecasting performance. Furthermore, the HMSD is stronger than the single multi-scale decomposition method in information extraction. Therefore, the proposed hybrid framework is a state-of-the-art carbon price forecasting approach.

The Application of the Data Mining Method Based on Ensemble Learning to the Research on Early Warning of Financial Crisis in Small- and Medium-Sized Enterprises

In the big data environment, the factors affecting the operation of small- and medium-sized enterprises are becoming more and more complex. In order to more accurately measure the financial crisis of small- and medium-sized enterprises, from the perspective of multisource information fusion, based on the traditional financial data reflecting the private information of enterprises, the public information data for measuring the macroeconomic and market environment are integrated and analyzed. Considering the multidimensional heterogeneity of multisource information data, BPNN, SVM, KNN, LOG, and MDA are introduced to build models, and ensemble learning is used to integrate the results of different early warning models to reduce the risk of inconsistent results from different models. The empirical results show that the early warning model integrating multisource information can improve the early warning accuracy. The research results show that the financial crisis early warning of small- and medium-sized enterprises should not only pay attention to financial indicators but also pay attention to the impact of corporate governance, industry, and the overall economic environment of the country; the accuracy of the model in this paper is significantly higher than that of the other models in terms of the accuracy of financial crisis early warning. It shows that the model in this paper can better predict the real financial status of small- and medium-sized enterprises.

Construction of Holographic Immersion Chamber Based on Multisource Information Fusion and Interactive Virtual Reality Technology

Nowadays, many visual artists actively respond to the drastic changes taking place in today’s society, build a communication bridge between real places and virtual places, and use holographic image technology to organically integrate them with each other. We are increasingly shuttling through the network of unbounded space. These spaces have no fixed geographical location. With the help of modern science and technology, they often fall into the field of vision of the viewer. With the development of the times, virtual reality technology is a computer simulation system that can create and experience the virtual world. It mainly uses computers to generate a simulated environment. It is a multisource information fusion interactive virtual reality technology to help users immerse themselves in the virtual environment. This paper studies the holographic immersion chamber based on multisource information fusion interactive virtual reality technology. The approximation index of the transformation matrix calculated by the registration algorithm based on SIFT+MSA is 1.7073, which is less than 4.0017 obtained by SIFT matching algorithm. It can be seen that the error of CCD image registration parameters calculated by this algorithm is smaller. In the process of the development of multisource information fusion interactive virtual reality technology, the technical feature of virtual reality is gradually integrated into the user and application object, becoming more closely connected and finally causing the user of the technology the feeling of “immersion” in the use experience.

Enhanced mass Jensen–Shannon divergence for information fusion

Conflict issue has been a topic of immense interest in evidence theory because the current methods still do not accurately reflect the conflict degree between evidence bodies. Thus, this paper defines mass Jensen–Shannon divergence (MJSD) to reflect the conflict degree. MJSD considers both the external difference and the external difference of the mass function. Furthermore, this paper proposes an enhanced mass Jensen–Shannon divergence (EMJSD). EMJSD has all the advantages of MJSD. It is a distance-like measure, which satisfies some axiom of distance measure, such as non-negativity and symmetry. Compared with the current methods, EMJSD fully measures the internal and external difference between the mass functions. Therefore, EJMSD overcomes the drawbacks of the classical methods when measuring differences between mass functions. Some numerical examples are used to explain its properties and advantages. Additionally, this paper presents a new method to fuse multi-source information. A numerical example shows that this fusion method is more useful for decision-making under the framework of multi-source information fusion. We then construct a new generation method of mass function in the data-driven environment. The experimental results show that when compared with other method, the new generation method more accurately reflects the support degree of proposition. Finally, this paper applies the generated mass functions to the new fusion method and other methods. The results indicate that the new fusion method has better recognition performance in practical applications.

DC Motor Control Technology Based on Multisensor Information Fusion.

To solve these uncertain problems by studying the motor fault diagnosis technology, so as to ensure the normal operation of the motor equipment is the primary problem to be solved in the field of motor fault diagnosis. The traditional DC motor is one of the most widely used motors at present. It has excellent speed regulation performance and is easy to control. It is widely used in applications that require high motor startup and speed regulation characteristics. This research mainly discusses DC motor control technology. Evidence theory can combine various fault information at different levels to enhance mutual support between pieces of evidence, thereby improving the accuracy of motor fault detection. Based on the steps of signal processing, feature extraction, feature dimensionality reduction, and state recognition, the research on the state recognition method of belt conveyor drive motor based on multisource information fusion is carried out. By studying the multisource information fusion, this paper proposes a two-stage belt conveyor drive motor information fusion model based on the optimal D-S evidence theory. The correct identification rate of broken rotor bars during fault monitoring is 99.8%. This method divides the specific motor fault feature set into multiple fault subspaces and uses different diagnostic neural networks and different fault feature parameters for local diagnosis, respectively. The scheme designed in this study significantly improves the recognition accuracy of the motor in the same working condition and under variable working conditions. The drive motor state recognition and intelligent decision-making system designed by combining the results of multisource information fusion can effectively describe the fault type and has strong operability.

Design of English Teaching Sharing System Combining Internet of Things and Memory Mechanism

In the era of Internet of Everything, English teaching has evolved continuously undergoing radical changes. Informatization and modern technologies are continuously promoting the reform of education, and the application of these technological tools makes information sharing a reality. Timely information sharing contributes to the development of English teaching as a part of curriculum requirements. As a consequence of this rapid progress, the theoretical and practical research on IoT-based English teaching sharing systems is also increasing. In this paper, we initiate an exhaustive research of the popular methodologies and then adopt LSTM network management platform data to address the shortcomings of the current teaching system has memory mechanism issues. At the outset, the standard English curriculum is discussed to establish the background, and then, data fusion among various types of information is considered. This is done by completely utilizing independent and scattered heterogeneous data in the information platform without changing or minimizing the information available in the shared system. Then, the information fusion is thoroughly analyzed resulting in the proposal of a multisource and multigranularity information fusion method based on a neural network. The method implements a study considering multisource heterogeneous data, which is followed by carrying out of functional design and process design. The paper also conducts a study on CNN-based multisource information association and designs information association algorithms to customize the dataset. Finally, the study of multigranularity feature fusion based on CNN and LSTM is implemented. The design relevant to granularity calculation algorithm helps in the customization of the dataset. The multigranularity feature fusion is conducted integrating CNN and LSTM. The experimental results show that the proposed method effectively integrates the scattered teaching information in the system, which is conducive to improving the utilization rate of teaching resources and simultaneously promoting the development of teaching information.

Multi-Source Information Fusion Fault Diagnosis for Gearboxes Based on SDP and VGG

A decision-level approach using multi-sensor-based symmetry dot pattern (SDP) analysis with a Visual Geometry Group 16 network (VGG16) fault diagnosis model for multi-source information fusion was proposed to realize accurate and comprehensive fault diagnosis of gearbox gear teeth. Firstly, the SDP technique was used to perform a feature-level fusion of the fault states of gearbox gear collected by multiple sensors, which could initially visualize the vibration states of the gear teeth in different states. Secondly, the SDP images obtained were combined with the deep learning VGG16. In this way, the local diagnostic results of each sensor can be easily obtained. Finally, the local diagnostic results of each sensor were combined with the DS evidence theory to achieve decision-level fusion, which can better realize comprehensive fault detection for gearbox gear teeth. Before fusion, the accuracies of the three sensors were 96.43%, 93.97%, and 93.28%, respectively. When sensor 1 and sensor 2 were fused, the accuracy reached 99.93%, which is 3.52% and 6.34% better than when using sensors 1 and 2, respectively, alone. When sensor 1 and sensor 3 were fused, the accuracy reached 99.96%, marking an improvement of 3.36% and 6.85% over individual use of sensors 1 and 3, respectively. When sensor 2 and sensor 3 were fused, the accuracy reached 99.40%, which is 5.78% and 6.56% better than individual use of sensors 2 and 3, respectively. When the three sensors were fused simultaneously, the accuracy reached 99.98%, which is 3.68%, 6.40%, and 7.18% better than individual use of sensors 1, 2, and 3, respectively.

The Application of Internet of Things in Robot Route Planning Based on Multisource Information Fusion.

With the advent of the Internet of Everything era, multi-information integration, and development, technology has penetrated into all aspects of life, promoting the continuous progress of social development, and people's requirements for a happy life are getting higher and higher. In this, robots play an extremely important role. It is especially important for the route planning of robots. This paper analyzes the DWA dynamic window algorithm through the verification of the global route planning algorithm, namely, the Dijkstra algorithm and the A∗ algorithm; the verification of the local route planning algorithm; the application of the DWA mobile robot algorithm in the robot route planning. The three aspects of the evaluation function are described by a formula, the experimental simulation is carried out, the relationship between the evaluation function terms is obtained, and the feasibility of the algorithm is verified.

A Multisource Dense Adaptation Adversarial Network for Fault Diagnosis of Machinery

Deep learning theory has made great progress in the field of intelligent fault diagnosis, and the development of domain adaptation has greatly promoted fault diagnosis under polytropic working conditions (PWC). Extensive studies have been conducted to solve the problem of fault diagnosis under PWC. However, the existing fault diagnosis methods based on domain adaptation have the following shortcomings. First, multisource information fusion is rarely considered. Second, the utilization of inherent labels is also insufficient in classification problems. To deal with the above problem, a novel multisource dense adaptation adversarial network is proposed, which leverages multisensor vibration information and classification label information. Specifically, the frequency spectrum of multisensor data is first employed to make full use of fault information. Afterwards, the dense convolution and fusion convolution blocks are used for deep feature extraction and fusion. Finally, a joint loss function is reconstructed under the framework of unsupervised learning, which considers the distribution differences of the features and the label information simultaneously. The experimental results from various working conditions, including still distant working conditions, all demonstrate that the proposed method can achieve state-of-the-art performances, which has shown great promise as an intelligent fault diagnosis method.

Regenerative Braking Control Strategy Based on Multi-source Information Fusion under Environment Perception

Regenerative Braking Control Strategy Based on Multi-source Information Fusion under Environment Perception

A novel belief χ2 ${\chi }^{2}$ divergence for multisource information fusion and its application in pattern classification

Dempster–Shafer (D-S) evidence theory is invaluable in the domain of multisource information fusion for handing uncertainty problems. However, there may be counter-intuitive phenomenon when facing highly conflicting information. In this paper, a novel symmetric enhanced belief χ 2 ${\chi }^{2}$ divergence measure, called S E B χ 2 $SEB{\chi }^{2}$ , is proposed to measure the discrepancy between basic probability assignments (BPAs). The S E B χ 2 $SEB{\chi }^{2}$ divergence consider the features of BPAs as the influence of both single-element subsets and multielement subsets is taken into account. Furthermore, the S E B χ 2 $SEB{\chi }^{2}$ divergence is proven to be symmetric, nonnegative and nondegenerate, which are desirable properties for conflict management. Then, a new algorithm for multisource information fusion based on the S E B χ 2 $SEB{\chi }^{2}$ divergence measure is derived. Finally, an application for pattern classification is used to illustrate the superiority of the proposed S E B χ 2 $SEB{\chi }^{2}$ divergence measure-based fusion method over other existing well-known and recent related works with a better classification accuracy of 94.39%.