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Bayesian Fault Detection and Localization Through Wireless sensor Networks in Industrial Plants

IEEE INTERNET OF THINGS JOURNAL 2024年第8期11卷 13231-13246页

作者： Tabella, Gianluca Ciuonzo, Domenico Paltrinieri, Nicola Rossi, Pierluigi Salvo SINTEF Energy Res Dept Gas Technol N-7034 Trondheim Norway Norwegian Univ Sci & Technol Dept Elect Syst N-7034 Trondheim Norway Univ Naples Federico II Dept Elect Engn andInformat Technol I-80138 Naples Italy Norwegian Univ Sci & Technol Dept Mech & Ind Engn N-7034 Trondheim Norway

This work proposes a data fusion approach for quickest fault detection and localization within industrial plants via wireless sensor networks. Two approaches are proposed, each exploiting different network architectures. In the first approach, multiple sensors monitor a plant section and individually report their local decisions to a fusion center (FC). The FC provides a global decision after spatial aggregation of the local decisions. A post-processing center subsequently processes these global decisions in time, which performs quick detection and localization. Alternatively, the FC directly performs a spatiotemporal aggregation directed at quickest detection, together with a possible estimation of the faulty item. Both architectures are provided with a feedback system where the network's highest hierarchical level transmits parameters to the lower levels. The two proposed approaches model the faults according to a Bayesian criterion and exploit the knowledge of the reliability model of the plant under monitoring. Moreover, adaptations of the well-known Shewhart and CUSUM charts are provided to fit the different architectures and are used for comparison purposes. Finally, the algorithms are tested via simulation on an active Oil and Gas subsea production system, and performances are provided.

关键词： Data fusion fault detection Industry 4.0 localization monitoring quickest detection reliability wireless sensor network (WSNs)

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Knowledge Transfer for Body sensor Networks: Characterization and Resistance of Negative Transfer in Overconstrained Environments

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IEEE INTERNET OF THINGS JOURNAL 2024年第19期11卷 31959-31973页

作者： Shi, Han Liu, Yang Luo, Hao Zhao, Hai Liaoning Univ Coll Informat Shenyang 110036 Peoples R China Liaoning Univ Sch Phys Shenyang 110036 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110169 Peoples R China

Transfer learning (TL) has shown potential for body sensor network (BSN) applications. However, TL performance is influenced by various factors, such as the BSN environment, user-specific physiological signs, or sensor modalities, leading to negative transfer (NT) effects. Limited research has defined NT from perspectives, such as data quality, domain transferability, and transfer components, but the cognition of NT in multimodal BSNs remains insufficient. In this article, we explore the correlation between transfer modes and NT, proposing a knowledge transfer framework composed of common domain adaptation constraints to reveal the NT effects in overconstrained environments. This effect can explain why strongly constrained transfer algorithms are effective but not necessarily optimal in terms of performance. In the three typical BSN scenarios of emotion recognition, fall detection, and daily activity recognition, we demonstrate the NT in overconstrained environments and its potential influencing factors using different constraint combinations and based on different multimodal body sensor fusion architectures. Extensive experiments focus on discussing the impact of category-condition constraints, domain similarity, and fusion architectures on the NT effect in BSNs. This work can provide a new perspective for the design of multimodal BSN knowledge transfer scheme.

关键词： Transfer learning Task analysis Data models Internet of Things Knowledge transfer Accuracy Training Body sensor network (BSN) information fusion negative transfer (NT) transfer learning (TL)

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RGB-LiDAR fusion for accurate 2D and 3D object detection

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MACHINE VISION AND applications 2023年第5期34卷 1-11页

作者： Mousa-Pasandi, Morteza Liu, Tianran Massoud, Yahya Laganiere, Robert Univeristy Ottawa Ottawa ON Canada

Effective detection of road objects in diverse environmental conditions is a critical requirement for autonomous driving systems. Multi-modal sensor fusion is a promising approach for improving perception, as it enables the combination of information from multiple sensor streams in order to optimize the integration of their respective data. fusion operators are employed within fully convolutional architectures to combine features derived from different modalities. In this research, we present a framework that utilizes early fusion mechanisms to train and evaluate 2D object detection algorithms. Our evaluation shows that sensor fusion outperforms RGB-only detection methods, yielding a boost of +15.07% for car detection, +10.81% for pedestrian detection, and +19.86% for cyclist detection. In our comparative study, we evaluated three arithmetic-based fusion operators and two learnable fusion operators. Furthermore, we conducted a performance comparison between early- and mid-level fusion techniques and investigated the effects of early fusion on state-of-the-art 3D object detectors. Lastly, we provide a comprehensive analysis of the computational complexity of our proposed framework, along with an ablation study.

关键词： Multimodal deep learning sensor fusion fusion operators Object detection Autonomous driving Convolutional neural networks

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Optimizing sensor Placement for Event Detection: A Case Study in Gaseous Chemical Detection

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sensorS 2025年第8期25卷 2397-2397页

作者： Suawa, Priscile Fogou Herglotz, Christian Brandenburg Univ Technol Cottbus Senftenberg Dept Comp Engn D-03046 Cottbus Germany

In dynamic industrial environments, strategic sensor placement is key to accurately monitoring equipment and detecting critical events. Despite progress in Industry 4.0 and the Internet of Things, research on optimal sensor placement remains limited. This study addresses this gap by analyzing how sensor placement impacts event detection, using chemical detection as a case study with an open dataset. Detecting gases is challenging due to their dispersion. Effective algorithms and well-planned sensor locations are required for reliable results. Using deep convolutional neural networks (DCNNs) and decision tree (DT) methods, we implemented and tested detection models on a public dataset of chemical substances collected at five locations. In addition, we also implemented a multi-objective optimization approach based on the non-dominated sorting genetic algorithm ii (NSGA-ii) to identify optimal sensor configurations that balance high detection accuracy with cost efficiency in sensor deployment. Using the refined sensor placement, the DCNN model achieved 100% accuracy using only 30% of the available sensors.

关键词： event detection gaseous substances industrial monitoring applications influence of sensor location multi-objective optimization sensors sensors fusion supervised learning

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Anomaly characterization for the condition monitoring of rotating shafts exploiting data fusion and explainable convolutional neural networks

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2025年

作者： Parziale, Marc Yeung, Yip Fun Youcef-Toumi, Kamal Giglio, Marco Cadini, Francesco MIT Dept Mech Engn Cambridge MA USA Politecn Milan Dept Mech Engn Via Masa 1 I-20156 Milan Italy

Condition monitoring of rotating shafts is a critical task in the maintenance of mechanical systems. Rotating shafts are essential components in many machines, and their failure can result in serious consequences, including system downtime, production loss, equipment damage, and safety issues. Advanced sensor technologies and deep learning algorithms have facilitated data collection and processing, providing vital insights into system health. However, despite the vast availability of sensors, the data used to train these algorithms often consists of single-nature signals, and systems are typically damaged to simulate different faulty scenarios. Additionally, the perceived opacity of deep learning algorithms, often referred to as black-box models, has raised concerns about their credibility in critical domains. Hence, this paper addresses these challenges by (i) proposing various explainable artificial intelligence (XAI) methods in a rotating shaft case study, (ii) using a novel data-fusion approach to combine multiple signals, and (iii) leveraging data from an innovative experimental set-up mimicking real-world industrial machines. The employed experimental set-up is equipped with a diverse array of sensors capable of capturing signals of varying nature, and it streamlines the automated introduction of four distinct fault types in an innovative manner. Applied to convolutional neural networks, the employed XAI methods enhance transparency in deep learning models, providing practical insights for complex systems. This approach not only addresses the limitations associated with single-nature signals and simulated faults but also contributes to the credibility and interpretability of deep learning models in critical applications.

关键词： Condition monitoring anomaly characterization convolutional neural networks explainable artificial intelligence data fusion

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DSP architectures of Covariance Intersection fusion Algorithm for Automotive sensor fusion 14

DSP Architectures of Covariance Intersection Fusion Algorith...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Praharshita, D.S.L. Achala, G. Srihari, Pathipati Acharya, U. Shripathi Pardhasaradhi, Bethi India National Institute of Technology Karnataka ECE Dept. India Continental Autonomous Mobility India Pvt Ltd Adas Dept. India

ISBN: (纸本)9798350335095

The data fusion from sensors within the automotive vehicle is vital for improved accuracy and safety. The centralized and information matrix fusion (IMF) algorithms are famous for providing an optimal fusion estimate. However, the IMF is not viable in automotive sensor fusion applications due to the limited bandwidth and low hardware resources. Hence, distributed fusion technology is widely adopted in the automotive sensor applications to achieve high-speed and low-area realizations. This paper proposes three digital signal processing (DSP) architectures for covariance intersection (CI) fusion algorithm: Pipelined-traditional CI, adder-ladder CI, and pipelined adder-ladder CI. The proposed DSP architectures are evaluated with hardware resource consumption (multipliers, adders, and delays), maximum achievable frequency, and latency of the architecture. In addition, proposed CI algorithms for Digital Signal Processing (DSP) architectures are compared with IMF DSP architectures. The hardware resources and optimal pipeline stages required for CI with respect to N number of sensors are provided. The traditional pipeline algorithm requires N number of stages where as the proposed pipelined version of adder-ladder CI requires a N-1 pipeline stage with additional 7N-1 and 7N-3 delay elements for even and odd number of sensors to achieve the overall system operating frequency to an operation of multiplier. The proposed DSP architectures are suitable for automotive sensor fusion due to their high operating frequency and low hardware resources. © 2023 IEEE.

关键词： Digital signal processing

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Multi-sensor integrated navigation/positioning systems using data fusion: From analytics-based to learning-based approaches

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INFORMATION fusion 2023年第1期95卷 62-90页

作者： Zhuang, Yuan Sun, Xiao Li, You Huai, Jianzhu Hua, Luchi Yang, Xiansheng Cao, Xiaoxiang Zhang, Peng Cao, Yue Qi, Longning Yang, Jun El-Bendary, Nashwa El-Sheimy, Naser Thompson, John Chen, Ruizhi Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan Peoples R China Hubei Luojia Lab Wuhan Peoples R China Wuhan Univ Shenzhen Res Inst Shenzhen Peoples R China Southeast Univ Natl ASIC Ctr Nanjing Peoples R China Wuhan Univ Sch Cyber Sci & Engn Wuhan Peoples R China Arab Acad Sci Technol & Maritime Transport AASTMT Aswan Egypt Univ Calgary Dept Geomat Engn Calgary AB Canada Univ Edinburgh Sch Engn Edinburgh Scotland

Navigation/positioning systems have become critical to many applications, such as autonomous driving, Internet of Things (IoT), Unmanned Aerial Vehicle (UAV), and smart cities. However, it is difficult to provide a robust, accurate, and seamless solution with single navigation/positioning technology. For example, the Global Navigation Satellite System (GNSS) cannot perform satisfactorily indoors;consequently, multi-sensor integrated systems provide the solution, as they compensate for the limitations of single technology by using the complementary characteristics of different sensors. This article describes a thorough investigation into multi-sensor data fusion, which over the last ten years has been used for integrated positioning/navigation systems. In this article, different navigation/positioning systems are classified and elaborated upon from three aspects: (1) sources, (2) algorithms and architectures, and (3) scenarios, which we further divide into two categories: (i) analytics-based fusion and (ii) learning-based fusion. For analytics-based fusion, we discuss the Kalman filter and its variants, graph optimization methods, and integrated schemes. For learning-based fusion, several supervised, unsupervised, reinforcement learning, and deep learning techniques are illustrated in multi -sensor integrated positioning/navigation systems. Design consideration of these integrated systems is discussed in detail from several aspects and their application scenarios are categorized. Finally, future directions for their research and implementation are discussed.

关键词： Machine learning Data fusion Estimation Integrated navigation system Multi-sensor Positioning

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Railway safety through predictive vertical displacement analysis using the PINN-EKF synergy

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MATHEMATICS AND COMPUTERS IN SIMULATION 2024年 223卷 368-379页

作者： Cuomo, Salvatore De Rosa, Mariapia Piccialli, Francesco Pompameo, Laura Univ Naples Federico II Dept Math & Applicat Renato Caccioppoli Via Cintia 26 I-80126 Naples Italy

Scientific Machine Learning (SciML) finds extensive application in daily life, industry, and scientific research. Specifically, in railway data analysis, it utilizes tools such as time series analysis, classification, and data visualization. Among these, monitoring vertical displacement, or the movement of railway components relative to a fixed point, is vital. This indicates changes in the elevation of railway infrastructure, highlighting track settlement and structural shifts. However, relying solely on single sensor measurements for these displacement calculations can introduce inaccuracies. To overcome this, sensor fusion techniques are employed. These techniques employ advanced algorithms to combine data from multiple sensors, thereby enhancing accuracy. They consider the individual characteristics of each sensor, effectively mitigating the limitations of any single sensor. This study introduces a hybrid approach that combines the Extended Kalman Filter (EKF) with the Physics-Informed Neural Network (PINN) to refine predictive data analytics in dynamic railway environments. The experimental results underscore the efficacy of this innovative methodology.

关键词： Numerical methods Deep learning Extended Kalman filter Physics-informed neural network sensor fusion Railway tracking Vertical displacement

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MULTI-sensor INFORMATION fusion TECHNOLOGY: PRINCIPLE, applications AND KEY TECHNOLOGIES EXPLORATIONS 2

MULTI-SENSOR INFORMATION FUSION TECHNOLOGY: PRINCIPLE, APPLI...

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2nd International Conference on Mechatronic Automation and Electrical Engineering, ICMAEE 2024

作者： Wu, Wenchen Xu, Ruizhe Guangdong Country Garden School Foshan528312 China The Affiliated Middle School of Fujian Normal University Fuzhou350007 China

ISBN: (纸本)9781837242672

Multi-sensor information fusion (MSIF) has emerged as a critical technology in various fields due to the growing complexity of systems and the demand for precise information. This paper explores the fundamental principles, key technologies, and potential applications of MSIF. The paper delves into the design and implementation of MSIF systems, discussing various system architectures such as centralized, distributed, and hybrid models. Key technologies crucial to MSIF, including sensor calibration, data association, filtering methods, and fusion algorithms like Bayesian Criterion, Dempster-Shafer Evidence Theory, Fuzzy Theory, and Neural Networks, are thoroughly examined. The study highlights the importance of data preprocessing and feature extraction techniques in achieving efficient data fusion. Furthermore, the potential of MSIF in diagnosing complex systems is demonstrated through applications in industrial automation, environmental monitoring, and healthcare. MSIF also faces challenges related to data synchronization, algorithm complexity, and the integration of diverse data sources. With ongoing advancements in sensor technology and data processing, MSIF is poised to play an increasingly significant role in intelligent systems, including autonomous vehicles and smart cities. © The Institution of Engineering & Technology 2024.

关键词： Information fusion

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Optimizing Long-Term Robot Tracking with Multi-Platform sensor fusion

Optimizing Long-Term Robot Tracking with Multi-Platform Sens...

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IEEE/CVF Winter Conference on applications of Computer Vision (WACV)

作者： Albanese, Giuliano Mitra, Arka Zaech, Jan-Nico Zhao, Yupeng Chhatkuli, Ajad Van Gool, Luc Swiss Fed Inst Technol Zurich Switzerland Katholieke Univ Leuven Leuven Belgium INSAIT Sofia Bulgaria

ISBN: (纸本)9798350318920;9798350318937

Monitoring a fleet of robots requires stable long-term tracking with re-identification, which is yet an unsolved challenge in many scenarios. One application of this is the analysis of autonomous robotic soccer games at RoboCup. Tracking in these games requires handling of identically looking players, strong occlusions, and non-professional video recordings, but also offers state information estimated by the robots. In order to make effective use of the information coming from the robot sensors, we propose a robust tracking and identification pipeline. It fuses external non-calibrated camera data with the robots' internal states using quadratic optimization for tracklet matching. The approach is validated using game recordings from previous RoboCup World Cup tournaments.

关键词： algorithms algorithms and algorithms applications formulations Machine learning architectures Robotics Video recognition and understanding

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