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A review on multi sensor data fusion technique in CNC machining of tailor-made nanocomposites

SN APPLIED SCIENCES 2020年第5期2卷 931页

作者： Mouli, K. V. V. N. R. Chandra Prasad, Balla Srinivasa Sridhar, A., V Alanka, Sandeep GITAM Deemed Be Univ GIT Mech Engn Visakhapatnam 530045 Andhra Pradesh India GITAM Deemed Be Univ Dept Mech Engn GIT Visakhapatnam 530045 Andhra Pradesh India

In this study, a multiple sensor data fusion system is anticipated as essential for monitoring of cutting operations, by identifying suitable sensor locations to obtain feedback signals periodically. The sensor signal derives the failure during machining owing to complex cutting tool geometry even in machining of composites. Nano metal matrix composites (NMMC) being extremely upright in mechanical characteristics, consequently machining of these hybrid nano metal matrix composites reinforced with difficult-to-cut nano particles leads to reduced tool life thereby causing rapid flank wear. Therefore, it is a challenge to identify the wear features caused during machining of tailor made NMMC's, reducing wastage and preventing machine malfunction. This paper presents a comprehensive review on the machining strategies in extreme output conditions which rely on input parameters of speed, feed and depth of cut influencing tool life during CNC machining. This can be achieved only with multiple sensor data fusion technique during CNC machining.

关键词： multi-sensor data fusion sensors CNC machining Nanocomposites Tool wear

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Botanical origin identification of Sicilian honeys based on artificial senses and multi-sensor data fusion

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EUROPEAN FOOD RESEARCH AND TECHNOLOGY 2018年第1期244卷 117-125页

作者： Di Rosa, Ambra R. Leone, Francesco Scattareggia, Carmelo Chiofalo, Vincenzo Univ Messina Dept Vet Sci Messina Italy

Quality assessment of honey is often related to its botanical origin, which is traditionally evaluated by a melissopalynology analysis. This technique is quite laborious, time-consuming and requires a high-skilled and trained technician. Another major limitation of this method is fraud addition of pollen for authenticity. Conversely, artificial senses, coupled with chemometrics, have been shown to be rapid and reliable tools for the discrimination of honeys botanical origin. Further, the combined use of these techniques should improve sample classification;however, the main challenge is how to combine data coming from each device. In this study, a comparison between data from single modality and fusion methods, to classify different Sicilian honey varieties, was presented. Combining a potentiometric electronic tongue and a computer vision system, a satisfying recognizing percentage was achieved. This approach proved to be fast, simple and inexpensive;furthermore, any sample preparation or the use of chemicals was required.

关键词： Honey Electronic tongue Electronic nose Computer vision system multi-sensor data fusion Discriminant function analysis

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Pedestrian indoor navigation using foot-mounted IMU with multi-sensor data fusion

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2018年第4期30卷 261-272页

作者： Liu, Shengkai Su, Tingli Wang, Binbin Peng, Shiyu Jin, Xuebo Bai, Yuting Dou, Chao Beijing Technol & Business Univ Sch Comp & Informat Engn Beijing 100048 Peoples R China Beijing Technol & Business Univ Beijing Key Lab Big Data Technol Food Safety Beijing 100048 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China AVIC China Aeropolytechnol Estab Ctr Qual Engn Beijing 100081 Peoples R China

As a widely used indoor navigation technology, the inertial measurement unit (IMU)-based method has caught considerate research interest. However, owing to the significant and inherent drift of the sensors, it is difficult to get the accurate trajectory for pedestrian movement estimation. In this paper, a foot-mounted IMU system was used to improve the accuracy of pedestrian trajectory, by fusing information from the multiple sensors. With the Kalman filter combined with the zero-velocity update (ZUPT) method, a reasonably accurate pedestrian trajectory was then obtained. Furthermore, some adjustable parameters were introduced to better correct the estimation of position and velocity. Effectiveness of the proposed method was well verified through the indoor experiments and the long track performance was also tested in runway verification.

关键词： inertial measurement unit IMU trajectory tracking multi-sensor data fusion Kalman filter zero-velocity update ZUPT

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Enhanced multi-sensor data fusion Methodology based on multiple Model Estimation for Integrated Navigation System

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2018年第1期16卷 295-305页

作者： Wang, Lei Li, Shuangxi Anhui Sci & Technol Univ Sch Elect & Elect Engn Donghua Rd 9 Fengyang Cty 233100 Anhui Peoples R China

A novel multi-sensor data fusion methodology is presented in this paper with respect to noise with unknown or randomly varying statistics properties and outliers in the SINS/GPS/Odometer integrated navigation system. The proposed methodology combines an adaptive interacting multiple model filtering (AIMM) and federated Kalman algorithm. The former implements dynamic interaction and dynamic change of multiple modes based on the Markov chain process of system models. To achieve the adaptive outlier detection and processing in the measurement signal, modified Kalman filter based on orthogonality of innovation serves as the parallel model filters in the AIMM approach. The advantage of decentralized filter architecture of the latter federated algorithm is flexibility and modularity. It has received considerable attention because of its outstanding fault detection and isolation capability. Experiment results show that the proposed multi-sensor data fusion methodology significantly improves the navigation estimation accuracy and reliability as compared to the federated extend Kalman filter and federated IMM filter approaches.

关键词： Interacting multiple model innovation correction multi-sensor data fusion SINS/GPS/odometer

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The design of an embedded multi-sensor data fusion system for unmanned surface vehicle navigation based on real time operating system

The design of an embedded multi-sensor data fusion system fo...

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OCEANS - MTS/IEEE Kobe Techno-Oceans Conference (OTO)

作者： Liu, Wenwen Liu, Yuanchang Song, Rui Bucknall, Richard UCL Mech Engn London England

ISBN: (纸本)9781538616543

This paper describes the design and implementation of a practical multi-sensor data fusion system for unmanned surface vehicle (USV) navigation. The system employs an embedded Linux board as the main on-board control module to extract and preprocess raw measurements from various navigational sensors using the real time operating system (RTOS). An unscented Kalman Filter (UKF) based data fusion algorithm has been developed to fuse the obtained and preprocessed sensor measurements and provide more reliable and accurate estimations of USV's navigational data in real time. The results demonstrate the effectiveness of the data fusion algorithm in reducing unpredicted errors of a standalone sensor.

关键词： multi-sensor data fusion real time operating system unmanned surface vehicle

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multi-sensor data fusion for Real-Time Surface Quality Control in Automated Machining Systems

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sensorS 2018年第12期18卷 4381-4381页

作者： Garcia Plaza, E. Nunez Lopez, P. J. Beamud Gonzalez, E. M. Univ Castilla La Mancha Higher Tech Sch Ind Engn Inst Energy Res & Ind Applicat INEI Dept Appl Mech Avda Camilo Jose Cela S-N E-13071 Ciudad Real Spain Univ Castilla La Mancha Higher Tech Sch Ind Engn Inst Energy Res & Ind Applicat INEI Project Engn Avda Camilo Jose Cela S-N E-13071 Ciudad Real Spain Univ Castilla La Mancha Min & Ind Engn Sch Dept Appl Mech Plaza Manuel Meca 1 Almaden 13400 Ciudad Real Spain Univ Castilla La Mancha Min & Ind Engn Sch Project Engn Plaza Manuel Meca 1 Almaden 13400 Ciudad Real Spain

multi-sensor data fusion systems entail the optimization of a wide range of parameters related to the selection of sensors, signal feature extraction methods, and predictive modeling techniques. The monitoring of automated machining systems enables the intelligent supervision of the production process by detecting malfunctions, and providing real-time information for continuous process optimization, and production line decision-making. Monitoring technologies are essential for the reduction of production times and costs, and an improvement in product quality, discarding the need for post-process quality controls. In this paper, a multi-sensor data fusion system for the real-time surface quality control based on cutting force, vibration, and acoustic emission signals was assessed. A total of four signal processing methods were analyzed: time direct analysis (TDA), power spectral density (PSD), singular spectrum analysis (SSA), and wavelet packet transform (WPT). Owing to the nonlinear and stochastic nature of the process, two predictive modeling techniques, multiple regression and artificial neural networks, were evaluated to correlate signal parametric characterization with surface quality. The results showed a high correlation of surface finish with cutting force and vibration signals. The signal processing methods based on signal decomposition in a combined time and frequency domain (SSA and WPT) exhibited better signal feature extraction, detecting excitation frequency ranges correlated to surface finish. The artificial neural network model obtained the highest predictive power, with better behavior for the whole data range. The proposed on-line multi-sensor data fusion provided significant improvements for in-process quality control, with excellent predictive power, reliability, and response times.

关键词： surface quality control multi-sensor data fusion cutting forces vibration acoustic emission signal feature extraction methods predictive modeling techniques

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Asynchronous multi-sensor data fusion with Decentralized IMM-PDAF

Asynchronous Multi-sensor Data Fusion with Decentralized IMM...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEE IEEM)

作者： Park, W. J. Kang, C. H. Kim, S. Y. Park, C. G. Seoul Natl Univ Dept Mech & Aerosp Engn Seoul 08826 South Korea

ISBN: (纸本)9781538667866

In this paper, asynchronous multi-sensor fusion with decentralized IMM-PDAF is designed. To improve the estimation performance for the maneuvering target in clutter, interacting multiple model (IMM) based probabilistic data association filter (PDAF) is utilized, and multiple sensors are assumed to be asynchronous to reflect the real multi-sensor application. To verify the tracking filter, simulations for tracking maneuvering target with asynchronous multi-sensor fusion are carried out. The simulation results show that the designed filter tracks target robustly, and fusing asynchronous multi-sensor reduces the estimation error.

关键词： multi-sensor data fusion maneuvering target tracking IMM

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Neural computing for walking gait pattern identification based on multi-sensor data fusion of lower limb muscles

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NEURAL COMPUTING & APPLICATIONS 2017年第1-Sup期28卷 S65-S77页

作者： Triloka, Joko Senanayake, S. M. N. Arosha Lai, Daphne Univ Brunei Darussalam Mot Anal Lab Integrated Sci BldgJalan Tungku Link Gadong BE1410 Brunei

The use of neural computing for gait analysis widely known as computational intelligent gait analysis is addressed recently. This research work reports multilayer feed-forward neural networks for walking gait pattern identification using multi-sensor data fusion;electromyography (EMG) signals and soft tissue deformation analysis using successive frames of video sequence extracted from lower limb muscles according to each gait phase within the considered gait cycle. Neural computing framework for walking gait pattern identification consists of system hardware and intelligent system software. System hardware comprises a wireless surface EMG sensor unit and two video cameras for measuring the neuromuscular activity of lower limb muscles, and a custom-developed artificial neural network for classifying the gait patterns of subjects during walking. The system uses root mean square and soft tissue deformation parameter as the input features. multilayer feed-forward back propagation neural networks (FFBPNNs) with different network training functions were designed, and their classification results were compared. The intelligent gait analysis system validation has been carried out for a group of healthy and injured subjects. The results demonstrated that the overall accuracy of 98 % prediction is achieved for gait patterns classification established by multi-sensor data fusion of lower limb muscles using FFBPNN with Levenberg-Marquardt training function resulting better performance over FFBPNN with other training functions.

关键词： Electromyography Soft tissue deformation Gait patterns multi-sensor data fusion

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A deep learning-based recognition method for degradation monitoring of ball screw with multi-sensor data fusion

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MICROELECTRONICS RELIABILITY 2017年 75卷 215-222页

作者： Zhang, Li Gao, Hongli Wen, Juan Li, Shichao Liu, Qi Southwest Jiaotong Univ Sch Mech Engn Chengdu 610031 Sichuan Peoples R China

In this paper, a novel intelligent ball screw degradation recognition method based on deep belief networks (DBN) and multi-sensor data fusion is proposed. First, the derived method calculates frequency spectrums of raw signals, and the fused frequency spectrums are calculated by the multi-sensor data fusion. Then, a deep learning based recognition model that can estimate the degradation condition of ball screw automatically is established with the fused dataset. The effectiveness of the proposed method is validated using dataset collected from the degradation test of ball screw. The dataset contains massive samples involving 7 degradation stages under 9 working conditions by 3 accelerometers. The classification results indicate that the proposed DBN-based method is able to mine intrinsic characteristics from the fused frequency spectrums adaptively and obtain a superior recognition accuracy. Finally, two comparative studies are performed to show the advantage of the proposed DBN-based method in ball screw degradation condition recognition. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Deep learning Deep belief networks multi-sensor data fusion Ball screw Degradation recognition

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Extending lifetime of wireless sensor networks using multi-sensor data fusion

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2017年第7期42卷 1083-1090页

作者： Das, Soumitra Barani, S. Wagh, Sanjeev Sonavane, S. S. Sathyabama Univ Dept Comp Sci & Engn Madras 600119 Tamil Nadu India Sathyabama Univ Dept Elect & Control Engn Madras 600119 Tamil Nadu India KJ Coll Engn Management Res Dept Comp Engn Pune 411048 Maharashtra India Dr DY Patil Sch Engn Dept Elect Pune 412105 Maharashtra India

In this paper a multi-sensor data fusion approach for wireless sensor network based on bayesian methods and ant colony optimization techniques has been proposed. In this method, each node is equipped with multiple sensors (i.e., temperature and humidity). Use of more than one sensor provides additional information about the environmental conditions. The data fusion approach based on the competitive-type hierarchical processing is considered for experimentation. Initially the data are collected by the sensors placed in the sensing fields and then the data fusion probabilities are computed on the sensed data. In this proposed methodology, the collected temperature tand humidity data are processed by multi-sensor data fusion techniques, which help in decreasing the energy consumption as well as communication cost by fusing the redundant data. The multiple data fusion process improves the reliability and accuracy of the sensed information and simultaneously saves energy, which was our primary objective. The proposed algorithms were simulated using Matlab . The executions of proposed arnd low-energy adaptive clustering hierarchy algorithms were carried out and the results show that the proposed algorithms could efficiently reduce the use of energy and were able to save more energy, thus increasing the overall network lifetime.

关键词： multi-sensor data fusion optimization techniques energy aware routing ant colony optimization wireless sensor network

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