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18th IEEE International Conference on Software Architecture (ICSA)

作者： Aniculaesei, Adina Vorwald, Andreas Zhang, Meng Rausch, Andreas Tech Univ Clausthal Inst Software & Syst Engn Clausthal Zellerfeld Germany

ISBN: (纸本)9781665439107

Safety-critical automotive functions are required to satisfy stringent safety requirements. To guarantee the safety of such functions, their conformance with industry approved standards as well as statutory regulations must be ensured. Testing is the main method for checking automotive system functions, yet testing is incomplete and cannot show correctness. Inherent uncertainties in the physical environment introduce non-determinism in testing, increasing the difficulty of replicating environmental stimuli relevant for edge cases, and thus, the effort invested in road tests to produce statistical significance. Formal verification techniques are able to show correctness and are recommended for functions with higher automotive safety integrity levels (ASIL), e.g. for ASIL D. However, formal verification has scalability issues in case of highly complex automotive systems and heterogeneous sensor data received as inputs. To address these challenges, this paper proposes a novel architecture-based approach, which combines data-driven methods with formal methods for the verification of safety-critical automotive functions, with consideration of the system decomposition within the functional system architecture. We illustrate the application of our concept on two industrial automotive functions, speed estimation and exhaust aftertreatment, and report on results and lessons learned.

关键词： safety-critical automotive functions data-driven methods formal verification methods automotive software architectures system verification test

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data-driven snapshot methods leveraging data fusion to estimate state of health for maritime battery systems

ENERGY STORAGE

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ENERGY STORAGE 2023年第8期5卷

作者： Vanem, Erik Bruch, Maximilian Liang, Qin Thorbjornsen, Kristian Valoen, Lars Ole Alnes, Oystein Asheim DNV Grp Res & Dev Hovik Norway Univ Oslo Dept Math Oslo Norway Fraunhofer Inst Solar Energy Syst Freiburg Germany Corvus Energy Porsgrunn Norway DNV Maritime Hovik Norway

The number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of these ships, it is important to monitor the capacity that can be stored in the batteries, and classification societies typically require that this can be verified by independent tests-annual capacity tests. However, this paper discusses data-driven alternatives based on operational sensor data collected from the batteries. There are different strategies for such data-driven state of health (SOH) estimation. Some approaches require full operational history of the batteries in order to predict SOH, and this may be impractical due to several reasons. Thus, methods that are able to give reliable estimation of SOH based on only snapshots of the data streams are more attractive from a practical point of view. In this paper, data-driven snapshot methods are explored and applied to degradation data from battery cells cycled in different laboratory tests. Hence, data from different sources are fused together with the aim of achieving better predictions. The paper presents the battery data show how relevant features can be extracted from snapshots of the data and presents data-driven models for SOH estimation. It is discussed how such methods could be utilized in a data-driven classification regime for maritime battery systems. Results are encouraging, and yields reasonable degradation estimates for nearly 40% of the tested cells, although the fusion of data from different laboratory tests did not improve results significantly. Results are greatly improved if data from the actual cell is included in the training data, and indicates that better results can be achieved if more representative training data is available.

关键词： battery degradation battery diagnostics data-driven methods maritime batteries state of health modeling

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data-driven fault detection and diagnosis methods in wastewater treatment systems: A comprehensive review

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ENVIRONMENTAL RESEARCH 2025年 268卷 120822页

作者： Wang, Xinyuan Wei, Wenguang Yoo, Changkyoo Liu, Hongbin Nanjing Forestry Univ Jiangsu Coinnovat Ctr Efficient Proc & Utilizat Fo Nanjing 210037 Peoples R China Shandong Huatai Paper Co Ltd Dongying 257335 Peoples R China Kyung Hee Univ Coll Engn Dept Environm Sci & Engn Yongin 446701 South Korea

Wastewater treatment systems are essential for sustainable water resource management but face challenges such as equipment and sensor malfunctions, fluctuating influent conditions, and operational disturbances that compromise process stability and detection accuracy. To address these challenges, this paper systematically reviews data-driven fault detection and diagnosis (FDD) methods applied in wastewater treatment systems from 2014 to 2024, focusing on their applications, advancements, and limitations. Main contributions include an overview of key treatment processes, a detailed evaluation of fault types (process and sensor faults), advancements in multivariate statistical methods, machine learning (ML), and hybrid FDD techniques, as well as their effectiveness in anomaly detection, managing complex data distributions, and enabling real-time monitoring. Furthermore, the paper highlights critical challenges such as data quality and model interpretability, proposing actionable future directions, including the development of explainable artificial intelligence, adaptive real-time processing, and cross-system generalizability. These insights are intended to guide the development of robust, scalable, and interpretable FDD solutions, ultimately improving the efficiency, reliability, and sustainability of wastewater treatment systems.

关键词： Wastewater treatment systems data-driven methods Fault detection and diagnosis Multivariate statistical analysis Machine learning

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A comparison study of basic data-driven fault diagnosis methods for variable refrigerant flow system

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ENERGY AND BUILDINGS 2020年 224卷 110232-110232页

作者： Zhou, Zhenxin Li, Guannan Wang, Jiangyu Chen, Huanxin Zhong, Hanlu Cao, Zihan Huazhong Univ Sci & Technol Sch Energy & Power Engn Dept Refrigerant & Cryogen Engn Wuhan 430074 Peoples R China Wuhan Univ Sci & Technol Sch Urban Construct Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol China EU Inst Clean & Renewable Energy Wuhan 430074 Peoples R China

HVAC systems occupy a large part of the building's energy consumption. The development of fault detection and diagnosis (FDD) techniques for HVAC system is becoming increasingly essential for building energy saving. The present paper proposes a comparison study on the basic data-driven methods for variable refrigerant flow (VRF) system fault diagnosis. And five widely used data-driven methods were analyzed, which were decision tree (DT), support vector machines (SVM), clustering (CL), shallow neural networks (SNN), and deep neural networks (DNN). The six common types of fault data in VRF system and three evaluation indexes were used to compare the performance of the proposed five FDD methods in single fault and multiple faults. Results indicate that the single fault diagnosis performance of all methods is better than multiple fault diagnosis. The performances of DNN, SNN, and SVM are better than CART and CL no matter single fault or multiple fault diagnosis. Among them, we believe that the SVM method is preferred for single fault diagnosis and the DNN method is preferred for multiple fault diagnosis. And the performance of method CL is the worst, especially in the case of multiple fault classification, the accuracy is only about 48%. The study results are dedicated to providing a reference for subsequent research in FDD of VRF system. Some important remarks are finally concluded in this paper. (C) 2020 Elsevier B.V. All rights reserved.

关键词： data-driven methods Fault diagnosis Variable refrigerant flow system Air conditioning Deep neural networks

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A Comparative Study on methods for Fusing data-driven and Physics-Based Models for Hybrid Remaining Useful Life Prediction of Air Filters

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IEEE ACCESS 2023年 11卷 35737-35753页

作者： Hagmeyer, Simon Zeiler, Peter Esslingen Univ Appl Sci Fac Mech & Syst Engn Res Grp Reliabil Engn & Prognost & Hlth Management D-73728 Esslingen Germany

Approaches for diagnosis and prognosis of the health of engineering systems are divided into data-driven, model-based, and hybrid methods. data-driven methods depend on the availability of data. Model-based methods require knowledge of the degradation process. A great effort of data generation along with the high complexity of degradation processes often limits both approaches. To mitigate these limitations, the combination of data and knowledge through hybrid methods is examined in this paper. This approach is compared to the alternative approach of reducing the effort of generating training data, as both are gaining importance in diagnostics and prognostics. A new categorization of hybrid prognostic methods for combining data-driven and physics-based models is presented, along with references to existing realizations of these methods. Based on the categorization, a case study on the hybrid remaining useful life prediction of a filtration process is conducted. Several hybrid methods are implemented and tested in this study. Through the combination of models, an improvement in predictive accuracy is achieved. In addition, the paper examines systematic attributes of the individual hybrid methods. Statements on the influence of data scarcity on the predictive accuracy, data-driven models with high variance, and the computational efficiency of the hybrid methods are made. It is shown that these statements are supported by the case study's results.

关键词： Machine learning Prognostics and health management Predictive models Degradation data models Medical services data-driven methods filtration hybrid methods model-based methods physics-informed machine learning prognostics and health management PHM remaining useful life prediction

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Summary study of data-driven photometric stereo methods

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Virtual Reality & Intelligent Hardware 2020年第3期2卷 213-221页

作者： Qian ZHENG Boxin SHI Gang PAN School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore National Engineering Laboratory for Video Technology Department of CSPeking UniversityBeijing 100871China State Key Lab of CAD&CG College of Computer Science and TechnologyZhejiang UniversityHangzhou 310058China

Background A photometric stereo method aims to recover the surface normal of a 3D object observed under varying light *** is an ill-defined problem because the general reflectance properties of the surface are *** This paper reviews existing data-driven methods,with a focus on their technical insights into the photometric stereo *** divide these methods into two categories,per-pixel and all-pixel,according to how they process an *** discuss the differences and relationships between these methods from the perspective of inputs,networks,and data,which are key factors in designing a deep learning *** We demonstrate the performance of the models using a popular benchmark *** data-driven photometric stereo methods have shown that they possess a superior performance advantage over traditional ***,these methods suffer from various limitations,such as limited generalization ***,this study suggests directions for future research.

关键词： Photometric stereo data-driven methods Non-Lambertian reflectance

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A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process

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JOURNAL OF PROCESS CONTROL 2012年第9期22卷 1567-1581页

作者： Yin, Shen Ding, Steven X. Haghani, Adel Hao, Haiyang Zhang, Ping Univ Duisburg Essen Inst Automat Control & Complex Syst D-47057 Duisburg Germany Harbin Inst Technol Inst Intelligent Control & Syst Harbin 150001 Peoples R China

This paper provides a comparison study on the basic data-driven methods for process monitoring and fault diagnosis (PM-FD). Based on the review of these methods and their recent developments, the original ideas, implementation conditions, off-line design and on-line computation algorithms as well as computation complexity are discussed in detail. In order to further compare their performance from the application viewpoint, an industrial benchmark of Tennessee Eastman (TE) process is utilized to illustrate the efficiencies of all the discussed methods. The study results are dedicated to provide a reference for achieving successful PM-FD on large scale industrial processes. Some important remarks are finally concluded in this paper. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Process monitoring Fault diagnosis data-driven methods Tennessee Eastman process

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Predicting the flood peak arrival time via a comprehensive machine learning framework: case studies in Changhua and Tunxi basins, China

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JOURNAL OF WATER AND CLIMATE CHANGE 2025年第1期16卷 142-159页

作者： Zhou, Shiyu Liu, Xiaona Hohai Univ Coll Comp Sci & Software Engn Nanjing Peoples R China Shenzhen Polytech Univ Inst IoT Shenzhen Peoples R China

Floods are becoming increasingly frequent and severe due to climate change and urbanization, thereby increasing risks to lives, property, and the environment. This necessitates the development of precise flood forecasting systems. This study addresses the critical task of predicting flood peak arrival times, which is essential for timely warnings and preparations, by introducing a comprehensive machine-learning framework. Our approach integrates interpretable feature engineering, individual model design, and novel model ensembles to enhance prediction accuracy. We extract informative features from historical flood flow and rainfall data, design a suite of machine-learning models, and develop a novel ensemble technique to combine model predictions. We conducted case studies on the Tunxi and Changhua basins in China. Numerical experiments reveal that our method significantly benefits from feature engineering and model ensembles, achieving mean absolute error (MAE) prediction errors of 1.524 h for Tunxi and 2.192 h for Changhua. These results notably outperform the best baseline method, which achieves MAE errors of 1.727 h for Tunxi and 2.737 h for Changhua.

关键词： arrival time prediction data-driven methods feature engineering machine learning model ensemble

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data-driven Quadratic Modeling in the Loewner Framework from Input-Output Time-Domain Measurements

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SIAM JOURNAL ON APPLIED DYNAMICAL SYSTEMS 2025年第1期24卷 457-500页

作者： Karachalios, D. S. Gosea, I., V Gkimisis, L. Antoulas, A. C. Max Planck Inst Dynam Complex Tech Syst D-39106 Magdeburg Germany Univ Lubeck Inst Elect Engn Med D-23562 Lubeck Germany Max Planck Gesell Inst Dynam Complex Tech Syst D-39106 Magdeburg Germany Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA

In this study, we present a purely data-driven method that uses the Loewner framework along with nonlinear optimization techniques to infer quadratic dynamical systems with affine control that admit Volterra series (VS) representations from input-output time-domain measurements. The proposed method extensively employs optimization tools for interpolating the symmetric generalized frequency response functions (GFRFs) derived in the VS framework. The GFRF estimations are obtained from the Fourier spectrum (phase and amplitude) of the quasi-steady-state system response under harmonic excitation. Appropriate treatment of these measurements under the developed framework allows the identification of low-order in-state dimension quadratic state-space models with nontrivial stable equilibria, such as in the Lorenz '63 forced system. We thus can achieve low-order global model identification for certain classes of systems that can bifurcate to multiple equilibria after collecting measurements solely from a local stable operational regime. The advanced framework rigorously tests the identification and reduction features across various examples with increasing dimensions and varying complexities. It effectively addresses the inference of a reduced model from measurements of a viscous flow, assuming adherence to the Burgers' model with Robin boundary conditions. These results provide new prospects for developing low-order predictive models based on input-output data suitable for control.

关键词： data-driven methods model reduction system identification Loewner framework quadratic state-space modeling Lorenz attractor viscous Burgers' equation Volterra series quadratic matrix vector equations

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VANE-IN: Velocity Auto-Encoder for Inertial Navigation

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IEEE INTERNET OF THINGS JOURNAL 2025年第5期12卷 5015-5025页

作者： Teng, Xiaoqiang Li, Chenyang Xu, Shibiao Guo, Deke Li, Haisheng Beijing Technol & Business Univ Sch Comp & Artificial Intelligence Beijing 102401 Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China Natl Univ Def Technol Coll Syst Engn Changsha 410073 Hunan Peoples R China

data-driven inertial navigation is crucial for mobile computing applications, such as navigation, augmented reality, and robotics. It typically depends on a trained velocity regression network (VRN) to estimate velocities from inertial measurement unit (IMU) data, enabling position determination through integration. However, using prior velocity information for feature representation in inertial navigation remains underexplored. This work introduces a framework called velocity auto-encoder for inertial navigation (VANE-IN), which employs a Teacher-Student scheme to enhance VRN performance by encoding the velocity. Specifically, a velocity auto-encoder (VANE) is proposed as a student model to distill prior velocity insights from the training dataset, which is then guided by the VRN acting as the teacher model. Additionally, an attention mechanism is introduced to fuse these insights into the features of the VRN. To this end, the VANE-IN achieves a state-of-the-art position accuracy on the RoNIN benchmarks. Our experimental results demonstrate that the VANE-IN achieves approximately 5% performance improvements over existing methods regarding position accuracy.

关键词： Blades Inertial navigation Training Accuracy Location awareness Dead reckoning Vectors Pedestrians Legged locomotion Indexes data-driven methods indoor localization indoor navigation mobile computing real-time applications

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