Multi-sensor Information Research Articles

Estimation of the state and parameter of full electric driven ground unmanned platform with variable configuration

A ground unmanned platform with variable configuration can better adapt to the field combat environment, which can adjust the wheelbase and wheel track to adapt to the ground. The power system of the platform is distributed drive form, with better controllability, higher drive efficiency, and faster system response. Changes of wheelbase and wheel track can influence the position of the platform’s centroid, which is critical for dynamic control and platform state acquisition. Based on the 9DOF nonlinear vehicle model, this paper introduces the relative position parameters of the vehicle centroid and the inertial measurement unit. And this paper performs coordinate transformation to accurately estimate the state parameters such as the position of the center of mass, the speed of the vehicle, and the centroid side-slip angle according to the measurement data, avoiding estimation error due to the change of centroid position. On the basis of fusing on-board multi-sensor information such as wheel hub torque, a square-root Unscented Kalman Filtering algorithm (SR-UKF) with high stability performance that can adapt to strong nonlinear system is proposed. In the Simulink/Trucksim joint platform, the double lane change experiments with different intermediate axis positions show that the estimation method can observe the driving status and parameters of the vehicle in real time, and the tracking effect is better.

Fault diagnosis of the hydraulic valve using a novel semi-supervised learning method based on multi-sensor information fusion

Hydraulic systems are usually applied in large and complex engineering fields. For hydraulic systems or components in operation, it is difficult to obtain fault data with fault labels due to the high engineering cost. Therefore, a semi-supervised learning (SSL) method based on multi-sensor information fusion is proposed to obtain valuable pseudo label data to diagnose faults of the hydraulic directional valve in operation. In this method, the classification model is trained from a small amount of data with fault labels, thus generating pseudo labels for a large amounts of unmarked data. The contribution of this article is that a multi-sensor fusion algorithm is designed to obtain pseudo labels with high confidence, and an adaptive threshold model similar to generative countermeasure network is designed to intelligently generate thresholds for selecting pseudo labels instead of human intervention. Theoretical and experimental results show that the multi-sensor information fusion algorithm can obtain high confidence pseudo tags, the adaptive threshold model can screen effective pseudo tag samples by generating appropriate thresholds for accelerating the convergence of the classification model. In the hydraulic valve fault diagnostic test, after five iterations, the average diagnosis accuracy of this method can reach 99.72% and 99.00% respectively for different types of hydraulic valves in different engineering fields. This provides a new idea for developing intelligent hydraulic directional valve with self fault diagnosis function.

A Day/Night Leader-Following Method Based on Adaptive Federated Filter for Quadruped Robots

The quadruped robots have superior adaptability to complex terrains, compared with tracked and wheeled robots. Therefore, leader-following can help quadruped robots accomplish long-distance transportation tasks. However, long-term following has to face the change of day and night as well as the presence of interference. To solve this problem, we present a day/night leader-following method for quadruped robots toward robustness and fault-tolerant person following in complex environments. In this approach, we construct an Adaptive Federated Filter algorithm framework, which fuses the visual leader-following method and the LiDAR detection algorithm based on reflective intensity. Moreover, the framework uses the Kalman filter and adaptively adjusts the information sharing factor according to the light condition. In particular, the framework uses fault detection and multisensors information to stably achieve day/night leader-following. The approach is experimentally verified on the quadruped robot SDU-150 (Shandong University, Shandong, China). Extensive experiments reveal that robots can identify leaders stably and effectively indoors and outdoors with illumination variations and unknown interference day and night.

A Heterogeneous Time-Tracking Fusion and Application to Health Evaluation of Aerospace Engines

Heterogeneous information fusion has long been a difficult problem due to the differences in the representation and feature of various physical information. Besides, the multi-sensor signals of large mechanical equipment such as aerospace engines often change in a complicated way during the start-up stage and long-term operation, which makes the multi-sensor fusion based health assessment research impending. To explore a suitable fusion method for the multi-physical signals with different change rates, as well as to monitor the health state of large mechanical equipment based on multi-sensor information, this paper proposes a heterogeneous time-tracking fusion algorithm. Firstly, the time-domain indexes and instantaneous frequencies of the fast-varying harmonic-like signals are obtained by employing index extraction and second-order synchrosqueezing transform respectively, by which the overall and detailed characteristics of the signals are thus obtained. Secondly, after structuring a dynamic time-tracking function consisting of the hyperbolic tangent function and modified arctangent function, the time-dynamic confidence upper limit for fast-varying signals and confidence interval for slow-varying signals are obtained creatively. Finally, the different varying-rate signals are fused into a dynamic normalized time-varying index representing the health state through the aforementioned functions. By applying the proposed method to the health evaluation for ignition start-up stage of gas generators and the long-term performance of the turbopump, its effectiveness and practicability in the aerospace engine health analysis have been validated.

Three-Phase Asynchronous Motor Fault Diagnosis Using Attention Mechanism and Hybrid CNN-MLP By Multi-Sensor Information

Three-Phase Asynchronous Motor Fault Diagnosis Using Attention Mechanism and Hybrid CNN-MLP By Multi-Sensor Information

Research on the Cloud Platform for Urban Rail Transit based on Track Inspection Vehicle

The aim of this paper was to improve the automation of track inspection, data utilization rate, informationization of maintenance dispatch. We designed a track inspection vehicle based on multi-sensor information fusion, then gave the method of coordinate transformation and data fusion. And then researched the service of data displaying, data management and data analysis, proposed the system architecture and network topology, realized the system deployment on the cloud. And then demonstrated stability by comparing the results of two machine tests, and proved accuracy by comparing the results of machine tests with manual tests. The result shows that it had a high accuracy and stability. At last, designed the reserve of cloud platform function extension, discussed the method of data analysis. Research shows that, the track inspection vehicle can measure the track geometric parameter. The application of cloud platform technology can reduce the cost of operation, simplify the function extension. The research result has a positive impact on solving the current problem of track inspection, improving passenger comfort and ensuring safe operation.

Transformer Based Conditional GAN for Multimodal Image Fusion

Multimodal Image fusion is becoming urgent in multi-sensor information utilization. However, existing end-to-end image fusion frameworks ignore a priori knowledge integration and long-distance dependencies across domains, which brings challenges to the network convergence and global image perception in complex scenes. In this paper, a conditional generative adversarial network with transformer (TCGAN) is proposed for multimodal image fusion. The generator is to generate a fused image with the source images content. The discriminators are adopted to distinguish the differences between the fused image and the source images. Adversarial training makes the final fused image to maintain the structural and textural details in the cross-modal images simultaneously. In particular, a wavelet fusion module makes the inputs contain image content from different domains as much as possible. The extracted convolutional features interact in the multiscale cross-modal transformer fusion module to fully complement the associated information. It makes the generator to focus on both local and global context. TCGAN fully considers the training efficiency of the adversarial process and the integrated retention of redundant information. Various experimental results of TCGAN have highlighted targets, rich details, and fast convergence properties on public datasets.

Retracted: DC Motor Control Technology Based on Multisensor Information Fusion.

[This retracts the article DOI: 10.1155/2022/1447333.].

An Active Landing Recovery Method for Quadrotor UAV: Localization, Tracking and Buffering Landing

This paper proposes a principle of fully autonomous ground mobile landing recovery of Unmanned Aerial Vehicles (UAV) for the problems of relatively fixed landing point, passive recovery, poor flexibility, and environmental adaptability, which mainly includes localization, landing point tracking, and buffering landing for quadrotor UAV. Firstly, aiming at the problem that it is difficult to accurately obtain the position of a UAV in dynamic mobile landing recovery, a target location method based on Asynchronous Multisensor Information Fusion(AMIF) and servo turntable focus tracking is proposed. Secondly, to achieve fast and high-precision tracking of UAVs, a tracking control strategy of an independently driven landing recovery system and a Stewart six-degree of freedom platform is proposed. Then, to solve the problems of large impact force and center of gravity instability in the landing process of UAV, a stationarity control algorithm based on model prediction and a compliance control algorithm based on adaptive variable impedance are designed to achieve active compliance control while adjusting the position and attitude of the receiving surface in real-time. Finally, a quadrotor unmanned landing and recovery experimental platform is built to verify the feasibility of the ground mobile landing and recovery strategy proposed in this paper and the effectiveness of the control algorithm.

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.

The Steering Actuator System to Improve Driving of Autonomous Vehicles based on Multi-Sensor Data Fusion

In autonomous vehicles, the control unit must be based on two main goals, first maintains the stability of the car second follows the desired path. All things considered, the controller's effectiveness is heavily dependent on the details of the steering system actuators. The necessary steering is set by a higher-order controller. The time delay of the steering actuator is one of the main features affecting the performance of the controller. While the artificial intelligence and artificial ethic are new apparatuses in autonomous vehicles but their ICs and electrical component are exposed to fusion. This paper primarily presents a more reliable system work during the fusion of multi-sensor information. We design the requirements of the steering system and the sureness of stability control in autonomous vehicles, also finding the suitable parameters for high-level control algorithms to compensate for time delay and ensure vehicle stability. The vehicle's steering angle response was obtained by testing the actuator of electric power steering (EPS) undergoing different speeds. In fact, using the identification of the system has been beneficial because obtaining the transfer function is easier than the actual methods which need the implementation of a mathematical model of the system. The system response of the Input-output has been defined via MATLAB. Full vehicle model simulation results indicate that the found adjustment parameter improves lane-tracking performance in a basic architecture by reducing oscillation and lateral error relative to other instances. The simplified steering system is the primary improvement brought by this effort.

Retracted: Application of 5G Internet of Things Multisensor Information Fusion Model in Piano Performance

Retracted: Application of 5G Internet of Things Multisensor Information Fusion Model in Piano Performance

Multisensor Information Fusion for Condition Based Environment Monitoring

Destructive wildfires are becoming an annual event, similar to climate change, resulting in catastrophes that wreak havoc on both humans and the environment. The result, however, is disastrous, causing irreversible damage to the ecosystem. The location of the incident and the hotspot can sometimes have an impact on early fire detection systems. With the advancement of intelligent sensor-based control technologies, the multi-sensor data fusion technique integrates data from multiple sensor nodes. The primary objective to avoid wildfire is to identify the exact location of wildfire occurrence, allowing fire units to respond as soon as possible. Thus to predict the occurrence of fire in forests, a fast and effective intelligent control system is proposed. The proposed algorithm with decision tree classification determines whether fire detection parameters are in the acceptable range and further utilizes a fuzzy-based optimization to optimize the complex environment. The experimental results of the proposed model have a detection rate of 98.3. Thus, providing real-time monitoring of certain environmental variables for continuous situational awareness and instant responsiveness.

Artificial intelligence-driven wearable technologies for neonatal cardiorespiratory monitoring: Part 1 wearable technology.

With the development of Artificial Intelligence techniques, smart health monitoring is becoming more popular. In this study, we investigate the trend of wearable sensors being adopted and developed in neonatal cardiorespiratory monitoring. We performed a search of papers published from the year 2000 onwards. We then reviewed the advances in sensor technologies and wearable modalities for this application.Common wearable modalities included clothing (39%); chest/abdominal belts (25%); and adhesive patches (15%). Popular singular physiological information from sensors included electrocardiogram (15%), breathing (24%), oxygen saturation and photoplethysmography (13%). Many studies (46%) incorporated a combination of these signals.There has been extensive research in neonatal cardiorespiratory monitoring using both single and multi-parameter systems. Poor data quality is a common issue and further research into combining multi-sensor information to alleviate this should be investigated. IMPACT STATEMENT: State-of-the-art review of sensor technology for wearable neonatal cardiorespiratory monitoring. Review of the designs for wearable neonatal cardiorespiratory monitoring. The use of multi-sensor information to improve physiological data quality has been limited in past research. Several sensor technologies have been implemented and tested on adults that have yet to be explored in the newborn population.

Bearing Fault Diagnosis Based on Multisensor Information Coupling and Attentional Feature Fusion

The effective fault diagnosis of bearing can guarantee the safety of rotating machinery and is very important for its stable operation. The information fusion of multi-sensor data has been a feasible method to enhance the performance of fault diagnosis. However, how to fuse the joint information from different channels or even different kinds of sensors is still an important challenge. The present study proposes a novel multi-sensor information coupling network (MICN) for bearing fault diagnosis, which handles the signals from the same or different types of sensors, and the deeper features can be extracted from multi-sensors independently and simultaneously fused layer by layer. Especially, during the multi-layer feature fusion process, a novel feature-level information coupling model is developed based on the mutual attention mechanism. Finally, to validate the efficiency of the proposed method, several different experiments are designed, and the results show validity and superiority.

Fault diagnosis method of wheel diameter difference based on stacked autoencoder and multiple kernel learning

Tread wear rates of the right and left wheels of a wheelset are not the same because of the complexity of the track condition, which causes the wheel diameter difference (WDD). The WDD can influence vehicle dynamic performances and shorten the service life of the wheelset. To diagnose and recognize the condition of the WDD in time, a data-driven method based on multi-sensor information fusion is proposed. Different statistical features are extracted from the time and frequency domains of the axle-box acceleration signals. The features can be fused by integrating stacked autoencoder and multiple kernel learning. The comparative experimental analysis shows that compared with other commonly used intelligent methods, the proposed method can achieve higher diagnostic accuracy and give better performance with small training sample sizes. The statistical features sensitive to the WDD are also analyzed for industrial application.

Expressway danger warning ICV based on multi-sensor information fusion

Expressway danger warning ICV based on multi-sensor information fusion

Research on Comprehensive Safety Monitoring System for Elderly Care Based on Artificial Intelligence and Information Fusion

Nowadays, China has entered into an aging society; how to ensure safety in elderly care has drawn social attention. Through artificial intelligence and multi-information fusion research, combined with the applications of machine learning algorithms, internet of things devices and cloud computing, this paper presents a comprehensive, intelligent safety monitoring system for the elderly in the community and at home. The system collects the daily life data of the elderly through a series of sensors in an all-round, all-time, and non-intrusive manner, and realizes intelligent alarms for high-risk states such as falls, acute illness, abnormal personnel, and gas smoke for the elderly. Through the innovative research of human pose estimation and behavior recognition, and application of multi-sensor information fusion, the system can greatly reduce the occurrence or injury caused by safety incidents in senior care, bringing safe and healthy living environment for the elderly at homes and communities.

Multisensor information fusion method for intelligent fault diagnosis of reciprocating compressor in shale gas development

To address the problems of the poor feature extraction ability and weak data generalization ability of traditional fault diagnosis methods in reciprocating shale gas compressor fault diagnosis applications, in this study, a fault diagnosis method for reciprocating shale gas was developed. This method uses a novel optimized learning method, free energy in persistent contrastive divergence, in deep belief network learning and training. It solves the problem of the deep belief network classification ability degradation in long-term training. The root mean square error is used as the fitness function to search for the optimal parameter combination of the DBN network by using the sparrow search algorithm. At the same time, the learning rate and batch size of the deep belief network, which have a large impact on the training error, are selected for optimization. Then, the original vibration signal is preprocessed by calculating 13 different time domain indicators, and feature-level data and decision-level data are fused in a parallel superposition method to obtain a fused time domain index dataset. Finally, combined with the powerful adaptive feature extraction and nonlinear mapping ability of deep learning, the constructed sample dataset is input to the deep belief network for training, and the deep belief network based on reciprocating shale gas compressor fault diagnosis model is established.