Information Fusion Method Research Articles

Information Fusion-Based Fault Diagnosis Method Using Synthetic Indicator

Multisensor information fusion technology plays an essential role in fault diagnosis. Uncertain reasoning is the core of information fusion, and the DS evidence theory (DSE) has founded a general and popular framework for uncertainty reasoning. Under this framework, a novel information fusion method is developed in this article, to solve the problems of counterintuitive results, poor robustness, and “one-vote veto” when fusing highly conflicting evidence using DSE. We proposed a new synthetic indicator, which can effectively eliminate the conflicts between evidences. This synthetic indicator is composed of two indexes: measurement index of indirect conflicts and that of the evidence’s information itself. First, the divergence is used to measure the degree of conflict between bodies of evidence, and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) model is employed to deal with indirect conflicts. Second, the Deng entropy is utilized to quantitatively measure the uncertainty of evidences, and the relative credibility preference of the evidence is expressed by the fuzzy preference relation. Finally, the initial body of evidence frame is weighted and revised by synthetic indicator before adopting the Dempster Shafer (DS) fusion rule. The superiority of the developed method is illustrated by numerical examples and application cases. The results show that fault diagnosis using the developed information fusion method can have a higher diagnostic accuracy.

Zero-Shot Image Classification Method Based on Attention Mechanism and Semantic Information Fusion.

The zero-shot image classification (ZSIC) is designed to solve the classification problem when the sample is very small, or the category is missing. A common method is to use attribute or word vectors as a priori category features (auxiliary information) and complete the domain transfer from training of seen classes to recognition of unseen classes by building a mapping between image features and a priori category features. However, feature extraction of the whole image lacks discrimination, and the amount of information of single attribute features or word vector features of categories is insufficient, which makes the matching degree between image features and prior class features not high and affects the accuracy of the ZSIC model. To this end, a spatial attention mechanism is designed, and an image feature extraction module based on this attention mechanism is constructed to screen critical features with discrimination. A semantic information fusion method based on matrix decomposition is proposed, which first decomposes the attribute features and then fuses them with the extracted word vector features of a dataset to achieve information expansion. Through the above two improvement measures, the classification accuracy of the ZSIC model for unseen images is improved. The experimental results on public datasets verify the effect and superiority of the proposed methods.

Empathy structure in multi-agent system with the mechanism of self-other separation: Design and analysis from a random walk view

In a socialized multi-agent system, the preferences of individuals will be inevitably influenced by others. This paper introduces an extended empathy structure to characterize the coupling process of preferences under specific relations and make it cover scenarios including human society, human–machine system, and even abiotic engineering applications. In this model, empathy is abstracted as a stochastic experience process in the form of Markov chain, and the coupled empathy utility is defined as the expectation of obtaining preferences under the corresponding probability distribution. The self-other separation is the core concept with which our structure can exhibit social attributes, including attraction of implicit states, inhibition of excessive empathy, attention of empathetic targets, and anisotropy of the utility distribution. Compared with the previous empirical models, our model has a better performance on the data set and can provide a new perspective for designing and analyzing the cognitive layer of the human–machine network, as well as the information fusion and semi-supervised clustering methods in engineering.

MFGAD: Multi-fuzzy granules anomaly detection

Unsupervised anomaly detection is an important research direction in the process of unsupervised knowledge acquisition. It has been successfully applied in many fields, such as online fraud identification, loan approval, and medical diagnosis. Multi-granularity thinking is an effective information fusion method for solving problems in a multi-granular environment, which allows people to understand and analyze problems from multiple perspectives. However, there are few studies on building anomaly detection models using the idea of multi-fuzzy granules. To this end, this paper constructs a multi-fuzzy granules anomaly detection method by using a fuzzy rough computing model. In this method, a hybrid metric is first used to calculate the fuzzy relations. Then, two ranking sequences are constructed based on the significance of attributes. Furthermore, forward and reverse multi-fuzzy granules are constructed to define anomaly scores based on the ranking sequences. Finally, a multi-fuzzy granules-based anomaly detection algorithm is designed to detect anomalies. The experimental results compared with existing algorithms show the effectiveness of the proposed algorithm.

A New Decision-Making Framework for Site Selection of Electric Vehicle Charging Station With Heterogeneous Information and Multigranular Linguistic Terms

As a key technology to perform sustainable development in transportation, electric vehicles have been largely welcomed due to their advantages in energy savings and low carbon emission. The main step in promoting these vehicles is the selection of the appropriate electric vehicle charging station (EVCS) site. EVCS site selection is a laborious task because it involves a series of conflicting quantitative and qualitative criteria from several dimensions. The quantitative criteria are usually expressed by numerical data, while qualitative criteria are commonly represented by linguistic terms. Furthermore, the linguistic terms generated by different decision makers are usually defined on multigranular linguistic term sets. In this article, we present a new decision-making framework to select sustainable EVCS sites within the context of heterogeneous information and multigranular linguistic terms. First, three information transformation mechanisms are defined to unify the heterogeneous information and multigranular linguistic terms into interval-valued belief structures. Afterward, shadowed sets theory is utilized to reflect the personalized individual semantics of linguistic terms. Then, with the aid of the evidential reasoning (ER) algorithm, a new information fusion method is proposed to generate the interval-valued expected utilities of alternatives. Subsequently, an improved minimax regret approach is developed to compare and rank the interval-valued expected utilities. The proposed decision-making framework is then implemented to solve a case study for EVCS site selection. Further analysis and comparisons with other methods are also conducted to show the applicability and feasibility of the current proposal.

A Multi-Task-Based Deep Multi-Scale Information Fusion Method for Intelligent Diagnosis of Bearing Faults

The use of deep learning for fault diagnosis is already a common approach. However, integrating discriminative information of fault types and scales into deep learning models for rich multitask fault feature diagnosis still deserves attention. In this study, a deep multitask-based multiscale feature fusion network model (MEAT) is proposed to address the limitations and poor adaptability of traditional convolutional neural network models for complex jobs. The model performed multidimensional feature extraction through convolution at different scales to obtain different levels of fault information, used a hierarchical attention mechanism to weight the fusion of features to achieve an accuracy of 99.95% for the total task of fault six classification, and considered two subtasks in fault classification to discriminate fault size and fault type through multi-task mapping decomposition. Of these, the highest accuracy of fault size classification reached 100%. In addition, Precision, ReCall, and Sacore F1 all reached the index of 1, which achieved the accurate diagnosis of bearing faults.

Identification of abnormal conditions for gold flotation process based on multivariate information fusion and double‐channel convolutional neural network

AbstractAccurate and in‐time working condition identification plays a great role in industrial processes. However, most of the current flotation process identification models only use the characteristics of flotation froth as an identification basis, which often causes identification errors due to the instability of the froth, and a large amount of process data is not fully utilized. In this paper, an abnormal condition identification method based on multivariate information fusion and double‐channel convolutional neural network (double‐channel CNN) is proposed to achieve higher accuracy. First, a double‐channel CNN is used to extract depth features from different distributions of froth images and process data in parallel. Then, double normalized attention mechanism (double normalized AM) and multivariate information fusion methods are used to attach weights to the features and fuse them so as to ensure a higher response of key features and increase the reliability of the identification model. The method shows better performance than existing methods in offline simulations and has been validated online at a mineral processing plant in Shandong.

Semantic-Structure-Aware Multi-Level Information Fusion for Robust Global Orientation Optimization of Autonomous Mobile Robots.

Multi-camera-based simultaneous localization and mapping (SLAM) has been widely applied in various mobile robots under uncertain or unknown environments to accomplish tasks autonomously. However, the conventional purely data-driven feature extraction methods cannot utilize the rich semantic information in the environment, which leads to the performance of the SLAM system being susceptible to various interferences. In this work, we present a semantic-aware multi-level information fusion scheme for robust global orientation estimation. Specifically, a visual semantic perception system based on the synthesized surround view image is proposed for the multi-eye surround vision system widely used in mobile robots, which is used to obtain the visual semantic information required for SLAM tasks. The original multi-eye image was first transformed to the synthesized surround view image, and the passable space was extracted with the help of the semantic segmentation network model as a mask for feature extraction; moreover, the hybrid edge information was extracted to effectively eliminate the distorted edges by further using the distortion characteristics of the reverse perspective projection process. Then, the hybrid semantic information was used for robust global orientation estimation; thus, better localization performance was obtained. The experiments on an intelligent vehicle, which was used for automated valet parking both in indoor and outdoor scenes, showed that the proposed hybrid multi-level information fusion method achieved at least a 10-percent improvement in comparison with other edge segmentation methods, the average orientation estimation error being between 1 and 2 degrees, much smaller than other methods, and the trajectory drift value of the proposed method was much smaller than that of other methods.

Multi-source electricity information fusion methods: A survey

With the vigorous development of the global economy, the demand for electricity quality from all walks of life is also increasing, so it is essential to ensure the electric power grid’s safe, stable, and efficient operation. Multi-source electric power information fusion, as the core technology of electric power grid data processing, has become the foundation to promote the intelligent and automatic development of the electric power grid. This paper presents the first work on the survey of the methods of electricity information fusion. It first gives an overview of the process of electricity information fusion and shows the types of electricity data. Then, we provide different classifications of existing methods in view of communication annotation and electric power data, and conduct a thorough comparison and analysis of them. Moreover, we introduce the relevant data sets and evaluation criteria of electric power information and summarize the corresponding evaluation scenarios. Finally, we conclude the maturity of existing works and provide an outlook on future multi-source electric power information fusion methods.

Recommendation model based on intention decomposition and heterogeneous information fusion.

In order to solve the problem of timeliness of user and item interaction intention and the noise caused by heterogeneous information fusion, a recommendation model based on intention decomposition and heterogeneous information fusion (IDHIF) is proposed. First, the intention of the recently interacting items and the users of the recently interacting candidate items is decomposed, and the short feature representation of users and items is mined through long-short term memory and attention mechanism. Then, based on the method of heterogeneous information fusion, the interactive features of users and items are mined on the user-item interaction graph, the social features of users are mined on the social graph, and the content features of the item are mined on the knowledge graph. Different feature vectors are projected into the same feature space through heterogeneous information fusion, and the long feature representation of users and items is obtained through splicing and multi-layer perceptron. The final representation of users and items is obtained by combining short feature representation and long feature representation. Compared with the baseline model, the AUC on the Last.FM and Movielens-1M datasets increased by 1.83 and 4.03 percentage points, respectively, the F1 increased by 1.28 and 1.58 percentage points, and the Recall@20 increased by 3.96 and 2.90 percentage points. The model proposed in this paper can better model the features of users and items, thus enriching the vector representation of users and items, and improving the recommendation efficiency.

Super-resolution reconstruction of binocular image based on multi-level fusion attention network

Super-resolution reconstruction of binocular image based on multi-level fusion attention network

A Novel Ensemble Learning-Based Multisensor Information Fusion Method for Rolling Bearing Fault Diagnosis

It is meaningful to learn fault-related information from multisensor signals automatically and provide accurate diagnostic results. To further improve the multisensor information fusion effect, reduce the discrepancy between the real value and predicted value, and improve the rolling bearing fault diagnosis accuracy, in this article, a novel ensemble learning-based multisensor information fusion method is proposed. First, a multiscale convolutional neural network (MSCNN) is constructed as a base learning model to learn multiscale features of raw vibration signals. Second, based on the ensemble learning framework, a multibranch MSCNN is built to realize the simultaneous extraction of multiple sensor signal features and output the decision score of each sensor. Then, considering the confidence of multiple different sensors, fuzzy rank is utilized to fuse the decision scores of each sensor, minimizing the discrepancy between the real value and predicted value. Finally, the usefulness and robustness of the proposed method are validated on two different types of rolling bearing datasets. The dataset and code are available under request and the contact e-mail is pantc2006@163.com.

Robust Unscented Kalman Filter-Based Decentralized Multisensor Information Fusion for INS/GNSS/CNS Integration in Hypersonic Vehicle Navigation

INS/GNSS/CNS (inertial navigation system/global navigation satellite system/celestial navigation system) integration is a favourable navigation mode for hypersonic vehicles. However, since the measurements from GNSS and CNS are easily interfered during highly dynamic maneuvers, this integration is very difficult to achieve optimal navigation solution with existing information fusion techniques. This paper proposes a new method of decentralized multi-sensor information fusion based on robust UKF (unscented Kalman filter) for INS/GNSS/CNS integration to solve the above issue. Firstly, a fault detection-based robust UKF is established for local state estimation, in which a hypothesis test is constructed via the Mahalanobis distance of innovation to detect abnormal measurements in GNSS and CNS; and subsequently, a scalar factor is determined and further introduced into the innovation covariance to decrease the Kalman gain to improve the UKF robustness against abnormal measurements. Secondly, the traditional multi-sensor optimal data fusion technique is extended to nonlinear systems by use of unscented transformation in the framework of minimum variance estimation to fuse the local state estimations from INS/GNSS and INS/CNS subsystems. The proposed information fusion method can achieve the globally optimal fusion estimation results against abnormal measurements for hypersonic vehicle navigation with INS/GNSS/CNS integration. Semi-physical simulations and comparison analysis have validated the superior performance of the proposed method.

Building extraction from remote sensing images based on multi-scale information fusion method under Transformer architecture

Building extraction from remote sensing images based on multi-scale information fusion method under Transformer architecture

A Heterogeneous Information Fusion Method for Maritime Radar and AIS Based on D-S Evidence Theory

A Heterogeneous Information Fusion Method for Maritime Radar and AIS Based on D-S Evidence Theory

A Fusion Recognition Method Based on Temporal Evidence Reasoning

In order to improve the effectiveness of system decision‐making, the use of the evidence theory to identify target intentions has always been a research hotspot. In information fusion using the evidence theory, there are relatively few research studies on temporal domain evidence information fusion. Due to the obvious dynamic, sequential, and real‐time characteristics of temporal domain information fusion, traditional spatial domain information fusion methods are not suitable. Therefore, it is very necessary to study new methods for the temporal evidence fusion problem. In this article, a temporal evidence fusion method under the framework of the evidence reasoning rule (the ER rule) is proposed. The method uses complementary reliability integration rules and the time‐series evidence distance function to obtain the reliability of evidence at adjacent moments. According to the temporal domain evidence credibility decay model, the evidence weight of the temporal domain evidence is determined. Then, through the integration of the ER rule, the temporal domain evidence reliability and evidence weight are used to combine the evidence. The capability of this method is verified by numerical experiments and compared with other methods. The results show that the proposed method can effectively deal with the temporal domain evidence combination problem, has strong anti‐interference ability, and can support target intent recognition.

Calculation Method’s Research of Interruption Probability of Online Economic Transaction Based on Bayes Information Fusion

The problem of economic transaction interruption under the crisis of online economic transaction interruption is analyzed. It is pointed out that there is an unreliable supplier in the online economic transaction manufacturer. When the supplier fails, there is a problem in the selection of two standby reliable suppliers, and the customer service factor is taken into account. By using a Bayes information fusion method and lingo software, we establish a mathematical model to calculate the procurement cost, supplier transaction cost and customer shortage cost and solve the optimal selection scheme. Finally, a sensitivity analysis is carried out on the relevant scenario parameters to obtain the numerical test and sensitivity analysis results. The expected total income of online economic transactions is the largest in the CPD model by comparing the four models of online economic transactions. Compared with three non-centralized online economic transaction models, the expected total income of online economic transactions is the largest in the NGPD model.

A multi-source information fusion method for tool life prediction based on CNN-SVM

For milling tool life prediction and health management, accurate extraction and dimensionality reduction of its tool wear features are the key to reduce prediction errors. In this paper, we adopt multi-source information fusion technology to extract and fuse the features of cutting vibration signal, cutting force signal and acoustic emission signal in time domain, frequency domain and time-frequency domain, and downscale the sample features by Pearson correlation coefficient to construct a sample data set; then we propose a tool life prediction model based on CNN-SVM optimized by genetic algorithm (GA), which uses CNN convolutional neural network as the feature learner and SVM support vector machine as the trainer for regression prediction. The results show that the improved model in this paper can effectively predict the tool life with better generalization ability, faster network fitting, and 99.85% prediction accuracy. And compared with the BP model, CNN model, SVM model and CNN-SVM model, the performance of the coefficient of determination R2 metric improved by 4.88%, 2.96%, 2.53% and 1.34%, respectively.

Scarce Data in Intelligent Technical Systems: Causes, Characteristics, and Implications

Technical systems generate an increasing amount of data as integrated sensors become more available. Even so, data are still often scarce because of technical limitations of sensors, an expensive labelling process, or rare concepts, such as machine faults, which are hard to capture. Data scarcity leads to incomplete information about a concept of interest. This contribution details causes and effects of scarce data in technical systems. To this end, a typology is introduced which defines different types of incompleteness. Based on this, machine learning and information fusion methods are presented and discussed that are specifically designed to deal with scarce data. The paper closes with a motivation and a call for further research efforts into a combination of machine learning and information fusion.

Health State Estimation of On-Board Lithium-Ion Batteries Based on GMM-BID Model

As a single feature parameter cannot comprehensively evaluate the health status of a battery, a multi-source information fusion method based on the Gaussian mixture model and Bayesian inference distance is proposed for the health assessment of vehicle batteries. The missing and abnormal data from real-life vehicle operations are preprocessed to extract the sensitive characteristic parameters which determine the battery performance. The normal state Gaussian mixture model is established using the fault-free state data, whereas the Bayesian inference distance is constructed as an index to quantitatively evaluate the battery performance state. In order to solve the problem that abnormal data may exist in the measured data and introduce errors into evaluation results, the determination rules of abnormal data are formulated. The verification of real-life vehicle operation data reveals that the proposed method can accurately evaluate the onboard battery state and reduce safety hazards of electric vehicles during the normal operation process.