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Joint time-serial variation analysis for fault monitoring of chemical processes

process SAFETY AND ENVIRONMENTAL PROTECTION 2025年 196卷

作者： Hu, Guowei Tong, Chudong Zeng, Jia Luo, Lijia Ningbo Polytech Inst Intelligent Elect & Control Ningbo 315800 Zhejiang Peoples R China Zhejiang Univ Technol Coll Mech Engn Hangzhou 310014 Zhejiang Peoples R China

As the classical analytics in the field of data-driven fault monitoring, time neighborhood preserving embedding (TNPE), dynamic inner canonical correlation analysis (DiCCA), and slow feature analysis (SFA) provided three different choices for characterizing time-serial variation inherent in sequential samples. Considering the unsupervised nature of these three algorithms as well as their variants, it could be more appropriate to jointly exploit time-serial variation from multiple perspectives in a comprehensive manner. This recognition then motivates us to propose a novel dynamic modeling algorithm titled as joint time-serial variation analysis (JTSVA) for fault monitoring. The proposed JTSVA aims to extract dynamic latent variables (DLVs) with respect to a joint integration of time-manifold embedding, latent auto-regressive, and slow-varying capabilities own by TNPE, DiCCA and SFA, respectively. Furthermore, an additional orthogonality constraint is further assigned to the problem definition of JTSVA, so that the extracted DLVs could have enhanced discriminant in uncovering valuable information for satisfactory fault monitoring performance. Finally, the superiority of JTSVA in fault monitoring, in terms of false alarm rate and fault detection rate, is validated through comparative experiments on two industrial-scale examples, i.e., the Tennessee Eastman benchmark process and a real-world multiphase flow facility.

关键词： Fault monitoring Dynamic latent variable Feature extraction Time-serial variation

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A Heterogeneous Effects analysis Method of Highway Crash Factors Based on Causal Framework

A Heterogeneous Effects Analysis Method of Highway Crash Fac...

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2024 International conference on Smart Transportation Interdisciplinary Studies, ICSTIS 2024

作者： Liang, Xiaoxi Li, Shuang Xu, Nuo Guo, Xiucheng Pu, Ziyuan Southeast University School of Transportation China Southeast University School of Transportation Hangzhou Ins China

The analysis of heterogeneous effects on traffic crashes is crucial for understanding their causal mechanisms and enhancing targeted safety management strategies. However, current methodologies for modeling crash heterogeneous effects lack smooth methods for selecting optimal controls. This study proposes an intuitive variable selection method to improve heterogeneity analysis of crash data, as well as performance evaluation and validation tests. The method utilizes causal discovery algorithms to obtain causal diagrams for selecting confounders, moderators, and neutral control factors in observational collision data. The effectiveness and performance of these methods are assessed through the quality of Heterogeneous Treatment Effects (HTE) estimation. Using a real-world highway crash data, the proposed variable selection process based on causal framework is illustrated. Results indicate that most HTE estimation models perform well in terms of goodness-of-fit and robustness when employing the graphical variable selection method. Notably, models based on adjusted causal diagram and forest-based double-robust learning estimators perform the best across all model. This approach overcomes the challenges in selecting control and moderator factors in crash heterogeneity analysis, leading to more accurate effect estimation results. This study contributes to encouraging discussions on the causal pathways of crash occurrence and provides recommendations for optimizing road safety modeling, analysis and management in the field of crash analysis. © 2025 SAE International.

关键词： Highway accidents

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Application of physics-informed neural networks in fault diagnosis and fault-tolerant control design for electric vehicles: A review

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MEASUREMENT 2025年 246卷

作者： Amin, Arslan Ahmed Mubarak, Ansa Waseem, Saba FAST Natl Univ Comp & Emerging Sci Dept Elect Engn Chiniot Faisalabad Campus Chiniot 35400 Punjab Pakistan

The application of physics-informed neural networks (PINNs) in fault-tolerant control (FTC) systems of electric vehicles has gathered considerable interest in using underlying physics to improve the fault diagnosis and mitigation process. PINNs, which include the governing physical equations in the neural network training process, allow for accurate modeling of the EV components, such as motors and inverters. This review aims to evaluate neural networks, especially PINNs, for fault diagnosis and FTC development in the context of EVs. It includes neural network structures, algorithms for training, methods based on physical analogies, and the application of physical principles to enhance the algorithms. The comparative analysis presents the merits of PINNs against conventional techniques, including PID, LQR, and Kalman Filters, regarding model fitness, data utilization, adaptability, computational footprint, resilience, and extensibility. Future research directions include extension works of PINNs integrating them into conventional approaches, dynamic adaptation, multidisciplinary, and EV self-powered systems.

关键词： Neural network Fault tolerance Electric vehicles Kalman filtering Fault diagnosis

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Per-example gradient regularization improves learning signals from noisy data

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MACHINE LEARNING 2025年第3期114卷 1-20页

作者： Meng, Xuran Cao, Yuan Zou, Difan Umich Dept Biostat Ann Arbor MI 48109 USA HKU Dept Stat & Actuarial Sci Hong Kong 999077 Peoples R China HKU Inst Data Sci Dept Comp Sci Hong Kong 999077 Peoples R China

Gradient regularization, as described in Barrett and Dherin (in: International conference on learning representations, 2021), is a highly effective technique for promoting flat minima during gradient descent. Empirical evidence suggests that this regularization technique can significantly enhance the robustness of deep learning models against noisy perturbations, while also reducing test error. In this paper, we explore the per-example gradient regularization (PEGR) and present a theoretical analysis that demonstrates its effectiveness in improving both test error and robustness against noise perturbations. Specifically, we adopt a signal-noise data model from Cao et al. (Adv Neural Inf process Syst 35:25237-25250, 2022) and show that PEGR can learn signals effectively while suppressing noise memorization. In contrast, standard gradient descent struggles to distinguish the signal from the noise, leading to suboptimal generalization performance. Our analysis reveals that PEGR penalizes the variance of pattern learning, thus effectively suppressing the memorization of noises from the training data. These findings underscore the importance of variance control in deep learning training and offer useful insights for developing more effective training approaches.

关键词： Gradient regularization Noise perturbations Variance control

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Using system dynamics modeling approach to strengthen health systems to combat cancer: a systematic literature review

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JOURNAL OF MEDICAL ECONOMICS 2025年第1期28卷 168-185页

作者： Chen, Xianwen Cai, Kuangyuan Xue, Yan Ung, Carolina Oi Lam Hu, Hao Jakovljevic, Mihajlo Univ Macau Inst Chinese Med Sci State Key Lab Qual Res Chinese Med Zhuhai Macao Peoples R China Univ Macau Ctr Pharmaceut Regulatory Sci Zhuhai Macao Peoples R China Univ Macau Fac Hlth Sci Dept Publ Hlth & Med Adm Zhuhai Macao Peoples R China World Acad Sci UNESCO TWAS Trieste Italy Shaanxi Univ Technol Hanzhong Peoples R China Univ Kragujevac Dept Global Hlth Econ & Policy Kragujevac Serbia

AimDynamic cancer control is a current health system priority, yet methods for achieving it are lacking. This study aims to review the application of system dynamics modeling (SDM) on cancer control and evaluate the research *** were searched in PubMed, Web of Science, and Scopus from the inception of the study to 15 November 2023. Inclusion criteria were English original studies focusing on cancer control with SDM methodology, including prevention, early detection, diagnosis and treatment, and palliative care. Exclusion criteria were non-original research, and studies lacking SDM focus. analysis involved categorization of studies and extraction of relevant data to answer the research question, ensuring a comprehensive synthesis of the field. Quality assessment was used to evaluate the SDM for cancer *** studies were included in this systematic review predominantly from the United States (7, 43.75%), with a focus on breast cancer research (5, 31.25%). Studies were categorized by WHO cancer control modules, and some studies may contribute to multiple modules. The results showed that included studies comprised two focused on prevention (1.25%), ten on early detection (62.50%), six on diagnosis and treatment (37.50%), with none addressing palliative care. Seven studies presented a complete SDM process, among which nine developed causal loop diagrams for conceptual models, ten utilized stock-flow charts to develop computational models, and thirteen conducted *** review's macrofocus on SDM in cancer control missed detailed methodological analysis. The limited number of studies and lack of stage-specific intervention comparisons limit comprehensiveness. Detailed analysis of SDM construction was also not conducted, potentially overlooking nuances in cancer control *** in cancer control is underutilized, focusing mainly on early detection and treatment. Inconsistencies suggest a need for sta

关键词： System dynamics modeling cancer control cancer screening cancer diagnosis cancer treatment I I0 I1 I10

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Practical Considerations and Solutions in NLP-Based analysis of Code Review Comments - An Experience Report 25th

Practical Considerations and Solutions in NLP-Based Analysis...

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25th International conference on Product Focused Software process Improvement

作者： Iftikhar, Umar Blekinge Inst Technol Karlskrona Sweden

ISBN: (纸本)9783031783852;9783031783869

Context: Automated analysis of code review comments (CRCs) can aid in highlighting frequently discussed issues by reviewers from large repositories. Topic modeling is a promising approach to analyzing large natural language repositories. However, CRCs contain natural language text and code references;thus, data pre-processing and topic modeling approaches must be carefully selected. Objective: This work aims to discuss the various decisions taken and considerations involved in the analysis of CRCs. Method: We utilized 5,560 CRCs from an open-source system to study the decisions and considerations faced during the analysis of CRCs using topic modeling, followed by an evaluation of the interpretability of identified themes by a domain expert. Results: We report several observations and challenges in improving the quality of the identified themes, including choices regarding the pre-processing, topic modeling parameters, embedding model, and objective measures of coherence used, which impact the subjective interpretability of the identified themes. Conclusions: This work offers unique considerations, and the impact of these decisions can facilitate future studies in conducting topic modeling-based analyses of CRCs. Future studies can utilize the technical demonstrator to explore the interpretability of the topics generated from CRCs.

关键词： modeling languages

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MISMS: a multistep-ahead and interpretable sequential modelling scheme for the long-term dynamic forecasting in process industries

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INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE 2025年

作者： Song, Wenshuo Cao, Weihua Yuan, Yan Liu, Kang-Zhi Wu, Min China Univ Geosci Sch Automat Wuhan 430074 Peoples R China Hubei Key Lab Adv Control & Intelligent Automat Co Wuhan 430074 Peoples R China Minist Educ Engn Res Ctr Intelligent Technol Geoexplorat Wuhan 430074 Peoples R China Chiba Univ Dept Elect & Elect Engn Chiba Japan

This paper introduces Multistep-ahead and Interpretable Sequential modeling Scheme (MISMS), a pioneering approach for long-term dynamic forecasting that enhances model interpretability in process industries. MISMS is distinguished by its innovative integration of multivariate analysis, multistep-ahead forecasting, and sequential model interpretability. The scheme incorporates knowledge embedding and Exploratory data analysis (EDA) to structure multiple variables and extract their temporal attributes. Based on this groundwork, sequence-to-sequence (Seq2Seq) submodels are developed to capture domain-wide dynamics rather than one single value. Crucially, MISMS has pioneered the application of sequence interpretation for model decomposition and transparency, improving interpretability. The proposed scheme employs different temporal models in practical scenarios and can ensure a better prediction with long-term accuracy and stability. Additionally, discussions on the model explainer's expected and unexpected outcomes are conducted, providing potential avenues for future research in process industries.

关键词： Multistep-ahead forecasting process industries multivariate process explainable artificial intelligence time Shapley values

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A novel co-modulation and hybrid resolution strategy (CHRS) for fault diagnosis of planetary gearboxes

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MECHANICAL SYSTEMS AND SIGNAL processING 2025年 230卷

作者： Zhang, Mian Xie, Ruitong Kang, Tianbo Chen, Jiwei Wang, Yongshan Feng, Xu Zhao, Mengxiong Tianjin Univ Technol Tianjin Key Lab Adv Mechatron Syst Design & Intell Tianjin 300384 Peoples R China Tianjin Univ Technol Natl Demonstrat Ctr Expt Mech & Elect Engn Educ Tianjin Peoples R China Xian Modern Control Technol Res Inst Xian 710065 Peoples R China MEM Tianjin Fire Sci & Technol Res Inst Tianjin 300381 Peoples R China

Planetary gearboxes (PGs) serve as vital transmission links in rotating machinery, and diagnosing faults within them is crucial for effective maintenance. Traditional deep learning methods often operate as "black boxes," offering limited transparency in interpreting results, especially when analyzing the complex vibration signals of PGs. To address this issue, this paper proposes a co-modulation model combined with a hybrid resolution strategy (CHRS), leveraging amplitude modulation (AM) and frequency modulation (FM) intensities, to enhance the interpretability of fault diagnosis. First, a more comprehensive and adaptable expression of the co-modulation model is developed to describe gear faults. Second, CHRS links the model's generated signal with the actual monitoring data, establishing an intrinsic connection between the mathematical model and the data. An updating mechanism based on partial differential analysis is established for model parameter estimation. A partial differential-based updating mechanism is employed for model parameter estimation, enabling the quantitative analysis of model coefficients (including AM and FM), even with a limited number of training samples. Finally, the support vector machine (SVM) is employed to train and test these model parameters, facilitating the identification of different fault types through experimental data, thus validating the effectiveness of CHRS. In summary, CHRS significantly improves the interpretability of PG fault diagnosis by enhancing both the modeling process and quantitative analysis of vibration signals.

关键词： Planetary gearboxes Modulation effect Co-modulation signal model Hybrid resolution strategy Partial differential

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Graph Neural Networks: Architectures, Applications, and Future Directions

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IEEE ACCESS 2025年 13卷 62870-62891页

作者： Ponzi, Valerio Napoli, Christian Sapienza Univ Rome Dept Comp Control & Management Engn I-00185 Rome Italy Italian Natl Res Council Inst Syst Anal & Comp Sci I-00185 Rome Italy Silesian Tech Univ Dept Math Applicat & Methods Artificial Intelligen PL-44100 Gliwice Poland

In recent years, deep learning has revolutionized fields such as computer vision, speech recognition, and natural language processing, primarily through techniques applied to data in Euclidean spaces. However, many real world applications involve data from non-Euclidean domains, where graphs naturally represent entities and their complex interdependencies. Traditional machine learning methods have often struggled to process such data in an effective manner. Graph Neural Networks represent a crucial advance in the use of deep learning to interpret and extract knowledge from graph-based data. They have opened up new possibilities for tasks such as node categorization, link inference, and comprehensive graph analysis. This paper provides a detailed analysis of Graph Neural Network (GNN) methodologies, emphasizing their architectural diversity and wide ranging applications. GNN models are systematically categorized into fundamental frameworks such as message passing paradigms, spectral and spatial methods, and advanced extensions such as hypergraph neural networks and multigraph approaches. This paper also explores domains such as social network analysis, molecular biology, traffic forecasting, and recommendation systems. In addition, it emphasizes some critical open challenges, including scalability, dynamic graph modeling, and robustness against noisy or incomplete data. The paper concludes with a proposal for future research directions to improve the scalability, interpretability, and adaptability of GNNs in this fast-evolving field.

关键词： Deep learning Graph neural networks Social networking (online) Scalability data models Feature extraction Analytical models Vectors Roads Message passing Graph neural network deep learning message passing spectral methods spatial methods hypergraph neural networks multigraph neural networks scalability

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A systematic review of discretisation methods for time-delay systems

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JOURNAL OF control AND DECISION 2025年第3期12卷 335-350页

作者： Sharma, Pooja Neeli, Satyanarayana MNIT Jaipur Rajasthan India 63 Krishi Anusandhan nagar Jaipur 302029 Rajasthan India

Time delay is an inherent characteristic of real-world phenomena which may affect the system's characteristic. The systems including delay are known as time-delay systems, they are represented using delay differential equations. modeling, discretisation, stability and control design for time-delay systems are still challenging in modern control theory. This paper systematically overviews available discretisation methods of linear and nonlinear time-delay systems. Emphasis is placed on illustrating fundamental results and recent progress on discretisation methods for delay systems. Numerous methods for the discretisation of linear and nonlinear systems considering input delays, state or output delays in the system's dynamics have been presented. A particular attention will be paid to illustrate effects of the discretisation process on the stability of discretised systems. Examples of mathematical descriptions, problems, and performance analysis for delay systems are presented. The presentation of discretisation methods is as easy as possible, focussing more on the main ideas and mathematical concepts by analogy. Finally, some possible future research directions to be tackled by researchers in this field are discussed.

关键词： Delay linear nonlinear discretisation method sample data control system

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