Multi-source Sensor Research Articles

A hybrid clustering multi-source fault diagnosis method for chiller temperature sensors

ABSTRACT Sensor faults have been observed to negatively impact the operation of the HVAC system. Among these faults is the complexity of multi-source sensor faults, which may result in fault confusion due to multiple fault points and different fault patterns. This paper proposes a fault diagnosis model applicable to single- and multi-source faults of HVAC system sensors. Based on the distribution patterns of chillers sensor data, the ensemble empirical mode decomposition soft threshold denoising Gaussian mixture model (EEMDSTD-GMM) is proposed. The study suggests a K-means-based pre-classification method for potentially confusing types of sensor faults. EEMDSTD-GMM-K-means has shown a better fault diagnosis capability under four single-source sensor faults and five multi-source sensor faults. Under the three examined fault levels, the results indicate a satisfactory performance with an average diagnosis rate of 98.7% for single-source faults and 96.5% for multi-source faults.

Learning the micro-environment from rich trajectories in the context of mobile crowd sensing

With the rapid advancements of sensor technologies and mobile computing, Mobile Crowd Sensing (MCS) has emerged as a new paradigm to collect massive-scale rich trajectory data. Nomadic sensors empower people and objects with the capability of reporting and sharing observations on their state, their behavior and/or their surrounding environments. Processing and mining multi-source sensor data in MCS raise several challenges due to their multi-dimensional nature where the measured parameters (i.e., dimensions) may differ in terms of quality, variability, and time scale. We consider the context of air quality MCS and focus on the task of mining the micro-environment from the MCS data. Relating the measures to their micro-environment is crucial to interpret them and analyse the participant’s exposure properly. In this paper, we focus on the problem of investigating the feasibility of recognizing the human’s micro-environment in an environmental MCS scenario. We propose a novel approach for learning and predicting the micro-environment of users from their trajectories enriched with environmental data represented as multidimensional time series plus GPS tracks. We put forward a multi-view learning approach that we adapt to our context, and implement it along with other time series classification approaches. We extend the proposed approach to a hybrid method that employs trajectory segmentation to bring the best of both methods. We optimise the proposed approaches either by analysing the exact geolocation (which is privacy invasive), or simply applying some a priori rules (which is privacy friendly). The experimental results, applied to real MCS data, not only confirm the power of MCS and air quality (AQ) data in characterizing the micro-environment, but also show a moderate impact of the integration of mobility data in this recognition. Furthermore, and during the training phase, multi-view learning shows similar performance as the reference deep learning algorithm, without requiring specific hardware. However, during the application of models on new data, the deep learning algorithm fails to outperform our proposed models.

Design of Multisensor Mobile Robot Vision Based on the RBPF-SLAM Algorithm

The existing algorithms are mainly based on the laser or visual inertial odometer, which has problems such as large particle recommendation distribution error, particle consumption, and long running time of the algorithm. An SLAM scheme based on multisensor fusion is proposed, which integrates multisource sensor data including lidar, camera, and IMU and constructs a set of real-time mapping and positioning system with better robustness and higher accuracy. The experimental results show that the proposed SLAM method uses less running time, and the accuracy of map construction is better.

Design of University Educational Administration Management System Based on Sensor Data and Multidimensional Information Fusion

With the rapid development of education informatization, especially the promotion and reengineering of education business processes by big data and artificial intelligence, how to participate in this major change is the main issue that needs to be considered. Therefore, under the guidance of multisource sensor data fusion technology, this paper adopts the traditional cloud computing operation method, that is, centralized data center processing, which is difficult to adapt to the rapidly expanding data scale. We also propose a new perceptual adaptive data processing method. By designing an edge node adaptive data type sensing mechanism based on counting bloom filter, we can automatically identify the ubiquitous University Internet of things data categories and corresponding fields. The simulation results show that this method improves the recognition accuracy of different data categories in university management by more than 1.3%, which can help teachers use educational technology to do well in the teaching work before, during and after class, and promote the practical application of new teaching modes such as inquiry teaching.

UAV Localization Algorithm Based on Factor Graph Optimization in Complex Scenes.

With the increasingly widespread application of UAV intelligence, the need for autonomous navigation and positioning is becoming more and more important. To solve the problem that UAV cannot perform localization in complex scenes, a new multi-source fusion framework factor graph optimization algorithm is used for UAV localization state estimation in this paper, which is based on IMU/GNSS/VO multi-source sensors. Based on the factor graph model and the iSAM incremental inference algorithm, a multi-source fusion model of IMU/GNSS/VO is established, including the IMU pre-integration factor, IMU bias factor, GNSS factor, and VO factor. Mathematical simulations and validations on the EuRoC dataset show that, when the selected sliding window size is 30, the factor graph optimization (FGO) algorithm can not only meet the requirements of real time and accuracy at the same time, but it also achieves a plug-and-play function in the event of local sensor failures. Finally, compared with the traditional federated Kalman algorithm and the adaptive federated Kalman algorithm, the positioning accuracy of the FGO algorithm in this paper is improved by 1.5–2-fold, and can effectively improve autonomous navigation system robustness and flexibility in complex scenarios. Moreover, the multi-source fusion framework in this paper is a general algorithm framework that can satisfy other scenarios and other types of sensor combinations.

Resilient Multi-Source Integrated Navigation Method for Aerospace Vehicles Based on On-Line Evaluation of Redundant Information

Aerospace vehicle navigation systems are equipped with multi-source redundant navigation sensors. According to the characteristics of the above navigation system configuration, building a resilient navigation framework to improve the accuracy and robustness of the navigation system has become an urgent problem to be solved. In the existing integrated navigation methods, redundant information is only used for backup. So, it cannot use the redundant navigation information to improve the accuracy of the navigation system. In this paper, a resilient multi-source fusion integrated navigation method based on comprehensive information evaluation has been proposed by combining of qualitative analysis and quantitative analysis in information theory. Firstly, this paper proposes a multi-layer evaluation framework of redundant information and carries out quantitative analysis of redundant information with the information disorder analysis theory to improve the reliability of the navigation system. Secondly, a navigation output effectiveness evaluation system has been established to analyze the output of heterogeneous navigation subsystems qualitatively to improve the fusion accuracy. Finally, through the mutual correction of multi-level information evaluation results, the error decoupling between the output parameters of heterogeneous navigation sensors has been realized to improve the robustness of the system. The experimental results show that the method proposed in this paper can adaptively allocate and adjust the weight of navigation information at all levels, realize the “non-stop” work of the navigation system and enhance the resilient of the navigation architecture. The navigation accuracy is improved compared with the existing multi-source fusion algorithm, which reflects the reliability and robustness of this algorithm.

Wireless Sensor System of UAV Infrared Image and Visible Light Image Registration Fusion

The application of multisource sensors to drones requires high-quality images to ensure it. In simultaneous interpreting two or more multisensor images based on the same scene or target, the image obtained by the UAV sensor is limited by the imaging time and the shooting angle. The images obtained may not be aligned in the spatial position, thus affecting the fusion effect. Therefore, different sensor images must be registered before image fusion. During the shooting process of the drone imaging sensor, imaging angle, and environmental conditions, the obtained various sensor images will have rotation, translation, and other deformations in the spatial position so that they do not reach the spatial position. Therefore, it is impossible to directly perform image fusion directly. Therefore, before the multisensor image fusion, the image registration process must be completed to ensure that the two images are aligned in space. This paper analyzes the principles; based on the principles of the Powell search algorithm and improved walking algorithm, an algorithm combining Powell and improved walking algorithm is proposed. This paper also studies several traditional image neutrosophic fusions. The algorithm combines the fusion optimization algorithm proposed in this paper greatly reduces the calculation speed and improves the performance of the optimization algorithm and success rate.

An Indoor and Outdoor Multi-Source Elastic Fusion Navigation and Positioning Algorithm Based on Particle Filters

In terms of indoor and outdoor positioning, in recent years, researchers at home and abroad have proposed some multisource integrated navigation and positioning methods, but these methods are navigation and positioning methods for a single scene. When it comes to the switching of indoor and outdoor complex scenes, these methods will cause the results of position with a marked jump and affect the accuracy of navigation and positioning. Aiming at the navigation and positioning problem in the case of indoor and outdoor complex scene switching, this paper proposes a multisource elastic navigation and positioning method based on particle filters, which makes full use of the redundant information of multisource sensors, constructs an elastic multisource fusion navigation and positioning model after eliminating abnormal data, elastically gives different particle weights to multisource sensors according to environmental changes and realizes the elastic fusion and positioning of multisource sensors through filtering. The test results show that this method has high navigation and positioning accuracy, the dynamic positioning accuracy is better than 0.7 m and there will be no jumping of positioning results in the process of scene switching, which improves the navigation and positioning accuracy and stability in complex and changeable indoor and outdoor environments.

Multi-Source Data Processing and Fusion Method for Power Distribution Internet of Things Based on Edge Intelligence

With the rapid advancement of the Energy Internet strategy, the number of sensors within the Power Distribution Internet of Things (PD-IoT) has increased dramatically. In this study, an edge intelligence-based PD-IoT multi-source data processing and fusion method is proposed to solve the problems of confusing storage and insufficient fusion computing performance of multi-source heterogeneous distribution data. First, a PD-IoT multi-source data processing and fusion architecture based on edge smart terminals is designed. Second, the multi-source sensor data in the distribution network is unified in dimension and magnitude. By introducing the Box–Cox transform to improve the data offset problem in the Z-score normalization process, a multi-source heterogeneous data processing method for distribution networks based on the Box–Cox transform Z-score is proposed. Then, the conflicting phenomena of DS inference methods in data source fusion are optimally handled based on the PCA algorithm. A multi-source data fusion model based on DS inference with conflict optimization is constructed to ensure the effective fusion of distribution data sources from different domains. Finally, the effectiveness of the proposed method is verified by an experimental analysis of an IEEE39 node system in a regional distribution network in China.

Collaborative Extraction of Paddy Planting Areas with Multi-Source Information Based on Google Earth Engine: A Case Study of Cambodia

High-precision spatial mapping of paddy planting areas is very important for food security risk assessment and agricultural monitoring. Previous studies have mainly been based on multi-source satellite imagery, the fusion of Synthetic Aperture Radar (SAR) with optical data, and the combined use of multi-scale and multi-source sensors. However, there have been few studies on paddy spatial mapping using collaborative multi-source remote sensing product information, especially in tropical regions such as Southeast Asia. Therefore, based on the Google Earth Engine (GEE) platform, in this study, Cambodia, which is dominated by agriculture, was taken as the study area, and an extraction scheme for paddy planting areas was developed from collaborative multi-source information, including multi-source remote sensing images (Sentinel-1 and Sentinel-2), multi-source remote sensing land cover products (GFSAD30SEACE, GLC_FCS30-2015, FROM_GLC2015, SERVIR MEKONG, and GUF), paddy phenology information, and topographic features. Evaluation and analysis of the extraction results and the SERVIR MEKONG and ESACCI-LC paddy products revealed that the accuracy of the paddy planting areas extracted using the proposed method is the highest, with an overall accuracy of 89.90%. The results of the proposed method are better than those of the other products in terms of the outline of the paddy planting areas and the description of the road information. The results of this study provide a reference for future high-precision paddy mapping.

Material identification of construction machinery based on multisource sensor information fusion

In order to improve the recognition accuracy of construction machinery and equipment and materials in low contrast scenes, a construction machinery material recognition algorithm based on multisource sensor information fusion is proposed. In the paper, the millimeter wave radar is fused with the camera considering its strong penetration ability in rainy and foggy days and dim environments. Firstly, the spatial coordinates of radar and camera are unified by establishing a spatial fusion model of millimeter wave and camera; then the target acquired by millimeter wave is projected onto the image and the detection frame intersection and ratio model is used to generate the region of interest of the camera; finally, the improved YOLOv2 algorithm is used to identify the region of interest, and in the improved idea, the low-level information is first connected with the high-level information in multilayer depth. At the same time, a multiscale feature pyramid network structure is used to achieve recognition of objects of different scales. This model effectively reduces interference from other feature categories while improving the recognition efficiency of the system. The algorithm can effectively improve the recognition accuracy of mechanical materials in low-contrast scenes, as demonstrated by the validation of different scenes.

A novel objective evaluation method of drivability for passenger cars considering subjective and objective consistency

In this paper, aiming at the problems that the random mixed working conditions are difficult to identify, the insufficiency of drivability evaluation indicators system and the lack of robustness for the evaluation model, a novel objective evaluation method of drivability for passenger cars is proposed. First, combining the sliding time window method and Naive Bayesian model (NBM), a hybrid working condition recognition model of multi-source sensor information fusion is constructed, and the real vehicle test under random mixed working conditions shows that the accuracy of working condition identification can reach 95.6%. Then, the objective evaluation index for crawling conditions, starting conditions and tip-in conditions are studied according to the suggestions of subjective evaluation engineers, an objective indicator evaluation system considering expert knowledge and information redundancy between indicators is constructed, among them, R-type clustering and principal component analysis (PCA) are used to eliminate the information redundancy between objective indicators. Finally, based on the back propagation (BP) neural network to optimize the fuzzy membership function, an improved fuzzy comprehensive evaluation (Improve-FCE) method considering subjective and objective consistency is designed. Real vehicle test and analysis results demonstrate that the development of intelligent drivability objective evaluation tool (I-DOET) has good reliability and stability, the maximum subjective and objective relative error of the improved FCE model is 5.13%, and the pass rate reaches 93.3%.

A Traffic Event Detection Method Based on Random Forest and Permutation Importance

Although the video surveillance system plays an important role in intelligent transportation, the limited camera views make it difficult to observe many traffic events. In this paper, we collect and combine the traffic flow variables from the multi-source sensors, and propose a PITED method based on Random Forest (RF) and Permutation importance (PI) for traffic event detection. This model selects the suitable traffic flow variables by means of permutation arrangement of importance, and establishes the whole process of acquisition, preprocessing, quantization, modeling and evaluation. Moreover, the real traffic data are collected and tested in this paper for evaluating the experiment performance, including the miss/false rate of traffic event, and average detection time. The experimental results show that the detection rate is more than 85% and the false alarm rate is less than 3%. It means the model is effective and efficient in the practical application regardless of both workdays and holidays.

Attention Analysis and Information Dissemination Effect of Young People Based on Multisource Sensors under the Background of Wireless Communication and Artificial Intelligence

In recent years, the development of wireless communication and artificial intelligence technology has brought tremendous convenience to human life. Especially in the fields of education and medical treatment, the functions of wireless communication are the most representative. In order to enhance the attention of young people and the effect of information dissemination, scholars have conducted many analyses on its influencing factors. Studies have found that strong noise environments have obvious negative effects on attention and information dissemination effects. Therefore, this paper proposes a multisource sensor-based method for young people’s attention analysis and information dissemination effect enhancement in a strong noise environment. Multisource sensors are selected to perform young people’s attention analysis on speech in a strong noise environment, combined with the information dissemination effect to strengthen the model obtained by combining attention analysis and information dissemination effect strengthened speech. Through the multisource sensor system, it is demonstrated that the method in this paper is superior to the traditional method in a strong noise environment. And through the comparative analysis of multisource sensors, the corresponding improvement plan for the strong noise environment was formulated, and the numerical simulation experiment of young people's attention analysis and information dissemination effect was carried out to verify the optimization results. This research, based on wireless communication and artificial intelligence decision-making technology, proposes to evaluate and optimize the attention analysis and information dissemination effect of young people in a strong noisy environment based on multisource sensors and gives useful ideas to ensure the system design method in a strong noisy environment and the environmental quality after implementation. It also provides support and help for people to more specifically prevent the adverse effects on people’s attention analysis and information dissemination effects in a strong noise environment. This paper designs a model based on multisource sensors to study the effect of young people's attention analysis and information dissemination in a strong noise environment, which is of great significance to the development of people’s high-quality life.

Comparison of the Vegetation Index of Reclamation Mining Areas Calculated by Multi-Source Remote Sensing Data

Following vegetation reclamation in mining areas, secondary damage may occur at any time, especially in locations that have been mined for decades or even hundreds of years. Effective monitoring strategies are required to accurately assess plant growth and to detect the ecological effects of reclamation. Single satellite monitoring is often difficult to ensure vegetation monitoring needs, therefore multi-source remote sensing is preferred. Different sensor parameters and variation in spectral bands can lead to differences in the type of data obtained, and subsequently, methods for evaluating these differences are required for simultaneous sensor/band use. In this study, NDVI was selected to characterize the vegetation growth of the Antaibao Open-pit Coal Mine Dump by analyzing the correlation between different types of sensors (Landsat 8, HJ, Sentinel-2) and vegetation greenness in order to facilitate satellites’ replacement and supplement. Results show that: (1) Landsat 8 and Sentinel-2 satellite have a high relevance for monitoring the vegetation, but the correlation between these two sensors and HJ is relatively low, (2) the correlation between NDVI values varied by vegetation type, tree (R = 0.8698) > combined grass, shrub and tree (R = 0.7788) > grass (R = 0.7619) > shrub (R = 0.7282), and (3) the phenomenon of “Low value is high, high value is low” in the NDVI value with HJ satellite monitoring may have been caused by a weak signal strength and low sensitivity of the HJ sensor. Comparing the correlation of multi-source sensors to monitor the vegetation in the mining areas can be helpful to determine the alternative supplement of sensors through conversion formulas, which are helpful in realizing the long-term monitoring of dumps and detecting reclamation response in mining areas.

Optimal Placement of Sensors Based on Data Fusion for Condition Monitoring of Pulley Group under Speed Variation Condition

Pulley group plays an important role in the transmission of large mechanical equipment. To obtain informative data for condition monitoring, it is very important to optimize sensor placement on the pulley group. However, due to sharp speed fluctuation, heavy load and complex internal structure, sensor placement for acquiring optimal monitoring points is still a challenging task. Therefore, a novel sensor optimization method based on data fusion is proposed. In this method, the Kalman filter is firstly used to refine the collected signal for dealing with the variable noises. Subsequently, the variable periodicity strength of the signal is calculated to recognize the non-stationary characteristics of the measured signal. A data fusion technique based on maximum likelihood estimation (MLE) is then introduced to estimate sensitive components from the multi-source sensor signals for finding out optimal sensor placement points. The method is validated experimentally on a test rig of the pulley group with variable speed conditions. Analysis results show that the proposed method can recognize the optimal sensor placement points for the pulley group.

Multi-source fault diagnosis of chiller plant sensors based on an improved ensemble empirical mode decomposition Gaussian mixture model

As the core of the heating, ventilation, and air-conditioning (HVAC) control system, the sensor directly determines whether the HVAC control system is running normally. Poor natural and artificial environments can cause sensors to simultaneously fault, called multi-source sensor faults. Compared to the other methods, the Gaussian mixture model (GMM) has a better effect on high-dimensional data classification. In this paper, an improved ensemble empirical mode decomposition hard threshold denoising (EEMD-HTD) is first proposed to denoise the collected data and highlight each sensor’s fault characteristics. Secondly, the single-source and multi-source faults of the sensors are analyzed by the GMM at different points. The above fault diagnosis processes are collectively referred to as EEMD-HTD-GMM. Experimental results show that the EEMD-HTD-GMM has a higher diagnostic ability and distinguishes between the fault types that cause GMM classification confusion.

A new Bayesian finite element model updating method based on information fusion of multi-source Markov chains

• A new Bayesian methodology is presented for structural finite element model updating. • Initial variances of proposal distributions are analogous to the accuracy index of sensors. • Information fusion of screened Markov chains to obtain high-quality posterior samples. • Delayed rejection and adaptive strategies are introduced to improve the acceptance rate of samples. • Two case studies demonstrate the accuracy and reliability of the proposed method. In this paper, we present a new Bayesian model updating method that could overcome the problem of low sampling efficiency and over-reliance on single-chain proposal distribution of traditional Markov chain Monte Carlo algorithm. The delayed rejection and adaptive strategies are introduced in sampling process to obtain a certain number of Markov chains from different proposal distributions, which can independently adjust the variances of proposal distributions and improve the acceptance rate of candidate samples. The abnormal chain detection criterion is adopted to eliminate abnormal Markov chains. Then, the initial variances of different proposal distributions are analogous to the accuracy index of multi-source sensors in the signal domain. And the multi-source sensors grouping weighted fusion algorithm is introduced to fuse the screened Markov chains to approach the posterior probability distribution with high accuracy. The implicit relationship between the parameters to be updated and the responses of the finite element model is fitted by the Kriging surrogate model to improve the computational efficiency. The results of study cases demonstrate that the proposed method has good updating efficiency, excellent updating accuracy, and a higher acceptance rate of samples, which provides a new idea for solving the stochastic model updating.

Bearing Fault Diagnosis Method Based on Multidomain Heterogeneous Information Entropy Fusion and Model Self-Optimisation

Incomplete diagnostic information, inadequate multisource sensor information, weak diagnosis models, and subjective experience result in difficulty in predicting rotating machinery faults. To overcome these limitations, we proposed a multiple domain and heterogeneous information entropy fusion model based on an optimisation of bearing fault diagnosis. The spatiotemporal approach uses a multiscene domain fusion strategy based on heterogeneous sensors (HSMSF) to extract feature fusion strategies and analyses the characteristics of the bearing fault features by multichannel processes with convolutional neural networks to vibration signals. After the mapping of multiple quality characteristics, the high-quality features are combined with each other, and the adaptive entropy weighted fusion method is used to analyse and make decisions on sensor information from different detection points. Nineteen key model parameters that were required for HSMSF construction were selected by adaptive optimisation using the chaos elitist modified sparrow search algorithm (CEI-SSA), and a self-learning diagnostic model that is suitable for multiple detection points was constructed. The validity and feasibility of the proposed fault diagnosis method were verified experimentally on two common reference-bearing datasets, CWRU and IMS, and compared with other fault diagnosis methods.

A novel multi-source sensing data fusion driven method for detecting rolling mill health states under imbalanced and limited datasets

Monitoring and maintaining the health states of the rolling mill is a constant concern of the steel industry. Therefore, in this paper, multi-source sensors are mounted on the rolling mill to collect various data. Meanwhile, for better health states monitoring with multi-source sensing data, a new deep learning (DL) method based on the improved one-dimension Convolutional Neural Network (I1DCNN) and the improved two-dimension Convolutional Neural Network (I2DCNN) is proposed. First, I2DCNN is fed the 2D kurtogram images generated from the vibration signals by fast kurtogram, while I1DCNN is fed the acoustic signals. Meanwhile, Group Normalization (GN) is embedded to improve the robustness. More importantly, Global averaging pooling (GAP) replaces the traditional fully connected layer to improve model spatial feature extraction. Then, the overfitting problem is mitigated by introducing the dropout layer. Finally, the imbalanced and limited datasets are conducted to test and evaluate the proposed method. Experimental results suggest that the proposed method can achieve efficient and accurate health states monitoring with multi-source sensing data, compared to the other states of the art DL methods.