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Fault Detection and Fault-Tolerant Control for Discrete-Time Multiagent Systems With Sensor Faults: A data-driven method

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IEEE SENSORS JOURNAL 2024年第14期24卷 22601-22609页

作者： Zhang, Ji Ma, Linlin Zhao, Jingbo Zhu, Yanzheng Qingdao Univ Technol Sch Informat & Control Engn Qingdao 266525 Peoples R China Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China

This article investigates the fault detection and fault-tolerant control (FTC) problems for discrete-time multiagent systems (MASs) with sensor faults. First, the dynamic linearization method is introduced to describe the unknown MASs with sensor faults. Afterward, a decentralized fault detection method based on data-driven observers is proposed. And a fault estimator based on RBF neural networks for estimating multiple sensor faults is designed for estimating multiple sensor faults. Then, on the basis of the designed estimator, a distributed model-free sliding mode FTC strategy is provided to ensure the stability of the considered MASs when suffering from certain sensor faults. Finally, a simulated example is used to illustrate the efficiency of the proposed method.

关键词： data-driven method fault detection (FD) fault-tolerant control (FTC) multiagent systems (MASs) sensor faults data-driven method fault detection (FD) fault-tolerant control (FTC) multiagent systems (MASs) sensor faults

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A data-driven method based on deep belief networks for backlash error prediction in machining centers

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JOURNAL OF INTELLIGENT MANUFACTURING 2020年第7期31卷 1693-1705页

作者： Li, Zhe Wang, Yi Wang, Kesheng Norwegian Univ Sci & Technol Dept Prod & Qual Engn N-7491 Trondheim Norway Plymouth Univ Sch Business Plymouth PL4 8AA Devon England

Backlash error occurs in a machining center may lead to a series of changes in the geometry of the components and subsequently deteriorate the overall performance of the equipment. Due to the uncertainty of mechanical wear between kinematic pairs, it is challenging to predict backlash error through physical models directly. An alternative method is to leverage data-driven models to map the degradation. This paper proposes a data-driven method for backlash error predication through Deep Belief Network (DBN). The proposed method focuses on the assessment of both current and future geometric errors for backlash error prediction and subsequent maintenance in machining centers. During the process of prognosis, a DBN via stacking Restricted Boltzmann Machines is constructed for backlash error prediction. Energy-based models enable DBN to mine information hidden behind highly coupled inputs, which makes DBN a feasible method for fault diagnosis and prognosis when the target condition is beyond the historical data. In the experiment, to confirm the effectiveness of deep learning for backlash error prediction, similar popular regression methods, including Support Vector Machine Regression and Back Propagation Neural Network, are employed to present a comprehensive comparison in both diagnosis and prognosis. The experimental results show that the performances of all these regression methods are acceptable in the diagnostic stage. In the prognostic stage, DBN demonstrates its superiority and significantly outperforms the other models for backlash error prediction in machining centers.

关键词： data mining Machining centers data-driven method Deep belief network Backlash error

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Stationary response probability density of nonlinear random vibrating systems: a data-driven method

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NONLINEAR DYNAMICS 2020年第3期100卷 2337-2352页

作者： Tian, Yanping Wang, Yong Jiang, Hanqing Huang, Zhilong Elishakoff, Isaac Cai, Guoqiang Hangzhou Dianzi Univ Coll Mech Engn Hangzhou 310018 Peoples R China Zhejiang Univ Dept Engn Mech Hangzhou 310027 Peoples R China Arizona State Univ Sch Engn Matter Transport & Energy Tempe AZ 85287 USA Florida Atlantic Univ Dept Ocean & Mech Engn Boca Raton FL 33431 USA

A data-driven method is established to derive the (approximately) analytical expression of the stationary response probability density of nonlinear random vibrating system, which explicitly includes system features and intensity of excitation. The stationary response probability density is first assumed as an exponential form by using the principle of maximum entropy. Through the rule of dimensional consistency, the power of exponential function is expressed as a linear combination of a set of nondimensional parameter clusters which are constituted by system features, intensity of excitation, and state variables. By comparing the power of exponential function with the approximate logarithm probability density evaluated from simulated data statistically, the determination of unknown coefficients comes down to the solution of (overdetermined) simultaneous linear algebraic equations. The data-driven method rediscovers the exact stationary response probability density of random-excited Duffing oscillator and derives an approximately analytical expression of stationary response probability density of van der Pol system from the simulated data of six cases with different values of system features and intensity of excitation. This data-driven method is a unique method which can explicitly include the information of system and excitation in the analytical expression of stationary response probability density. It avoids the solution of simultaneous nonlinear algebraic equations encountered in the maximum entropy method and closure methods and, in the meanwhile, avoids the sophisticated selection of weighting functions in closure methods.

关键词： Stationary response probability density Nonlinear random vibrating system data-driven method Dimensional analysis Simulated data

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An interpretable data-driven method for degradation prediction of proton exchange membrane fuel cells based on temporal fusion transformer and covariates

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第66期48卷 25958-25971页

作者： Li, Hongwei Qiao, Binxin Hou, Zhicheng Liu, Junnan Yang, Yue Lu, Guolong Northeast Elect Power Univ Sch Energy & Power Engn Jilin 132012 Peoples R China Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Peoples R China Jilin Univ Coll Biol & Agr Engn Key Lab Bion Engn Minist Educ Changchun 130022 Jilin Peoples R China

The durability of proton exchange membrane (PEM) fuel cells is an obstacle to industrialization. PEM fuel cells need a robust incentive to improve their durability. Traditional data driven methods combined with deep learning for degradation prediction methods lack interpretability, which prevents users from fully trusting the results and limits the application. Moreover, traditional data-driven methods not making full use of the valuable information in the experimental data. This study presents an easy to interpret data-driven method that improves the reliability of the model, the heterogeneity of input features, and the effects of significant covariates. The method's primary objective is to provide a long and short-term degradation prediction of PEM fuel cells for a temporal fusion transformer. This method achieves high accuracy in predicting the degradation performance of PEM fuel cells with a root mean square error of 0.0067. The model's root means square error is reduced by 58% compared to the attention-based gated recurrent unit model. Based on the interpretability analysis, the degradation performance directly correlates with power, the cathode inlet temperature, the anode inlet temperature, time, the cathode inlet pressure, and the cathode outlet pressure. The method can also be applied to fuel cell systems for optimizing control, management, and maintenance.& COPY;2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： PEM fuel cells data-driven method Degradation prediction Temporal fusion transformer Explainable artificial intelligence

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A RankBoost-Based data-driven method to Determine Maintenance Priority of Circuit Breakers

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IEEE TRANSACTIONS ON POWER DELIVERY 2018年第3期33卷 1044-1053页

作者： Zhong, Jun Li, Wenyuan Wang, Caisheng Yu, Juan Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing 400065 Peoples R China State Grid Chongqing Shiqu Power Supply Co Chongqing 100031 Peoples R China Wayne State Univ Elect & Comp Engn Dept Detroit MI 48202 USA

Prioritizing equipment maintenance activities has been an important and challenging task in developing an equipment maintenance strategy for electric power utility companies. This paper presents a RankBoost-based data-driven approach for determining the maintenance priority of circuit breakers. Compared with conventional methods, the proposed method can consider both equipment-level condition-monitoring parameters and system-level reliability impacting indices, automatically create the rational weights among the multiple parameters/indices, and handle missing and invalid data. The simulation results based on the data of 33 circuit breakers indicate that the maintenance priority list obtained using the proposed method is closer to reality than the ranking lists obtained using a subjective weighting method or only using system-level reliability indices.

关键词： Condition monitoring data-driven method equipment maintenance RankBoost algorithm system reliability

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Incorporating medical domain knowledge into data-driven method: A vessel attention guided multi-granularity network for automatic cataract classification

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 241卷

作者： Xu, Xi Zhao, Linna Li, Jianqiang Li, Li Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Capital Med Univ Beijing Childrens Hosp Beijing 100045 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing Engn Res Ctr IoT Software & Syst Beijing 100124 Peoples R China

Early cataract screening is essential to help patients prevent irreversible vision loss. Existing data-driven methods can automatically extract deep fundus features for automatic cataract classification. However, due to the lack of medical domain knowledge, these deep features may contain large pathological irrelevant information, thus reducing the classification accuracy of data-driven methods. To fully take the advantages of domain knowledge to enhance the discriminative representation and avoid the irrelevant impact, we comprehensively investigate how to incorporate it into data-driven models. Considering the vessel information and multi-granularity representation are two key points of domain knowledge for cataract diagnosis, we propose a vessel attention guided multi-granularity network (VAM-Net), which contains three main components. Specifically, the vessel-level segmentation subnet is firstly designed by leveraging U-shape segmentation network. The refined vessel information obtained in this step can be regarded as visual attention to enable deep network extract discriminative features. With the guidance of vessel attention, the global-local classification subnet is further proposed to eliminate the interference of irrelevant information and enhance the multi-granularity feature learning (including global structural features and local subtle features). Finally, the multiple levels predictions are integrated into the final cataract diagnosis decision via constructing multi-granularity ensemble network. In this manner, both domain knowledge and deep networks are organically combined for pursuing better diagnosis performance. By setting extensive comparative and ablation experiments, we evaluate the effectiveness of the proposed VAM-Net on the real-world cataract dataset (92.78% detection accuracy and 87.68% grading accuracy).

关键词： Automatic cataract classification data-driven method Vessel attention Multi-granularity Fundus image

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A data-driven method for Trip Ends Identification Using Large-Scale Smartphone-Based GPS Tracking data

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2017年第8期18卷 2096-2110页

作者： Zhou, Chaoran Jia, Hongfei Juan, Zhicai Fu, Xuemei Xiao, Guangnian Jilin Univ Sch Transportat Changchun 130022 Jilin Peoples R China Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai 200240 Peoples R China

Using tracking data obtained from the smartphone and Internet survey, a data-driven machine learning method is proposed to identify trip ends. In previous literature, this is usually done based on some predefined rules, which have been confirmed to be valid. Nonetheless, these rule-based methods largely depend on researchers' own knowledge, which is inevitably subjective and arbitrary. Moreover, they are not effective enough to process the huge amount of data in the era of big data. In this paper, millions of smartphone-based GPS tracking data are targeted. A group of attributes, such as travel speed, distance, and heading, are derived to characterize the smartphone holders' travel status. In other words, the tracking points could be identified as being at the state of traveling or non-traveling, based on which the trip ends are easily detected. In contrast to those rule-based methods, a random forest is utilized in this paper as the classification model, with no subjective rules predefined for classification. This data-driven model is automatically built. The results show that after training the GPS tracking data of 1393 days and the prompted recall (PR) survey data using the random forest, the accuracy of trip ends identification on tracking data of 697 days is 96.17%. The current analysis is free from personal experiences, which is expected to be useful for the smartphone-based survey data in the era of big data.

关键词： GPS tracking data processing trip ends identification random forest data-driven method

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Gas Production Prediction Model of Volcanic Reservoir Based on data-driven method

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ENERGIES 2024年第21期17卷 5461页

作者： Zhang, Haijie Pu, Junwei Zhang, Li Deng, Hengjian Yu, Jihao Xie, Yingming Tong, Xiaochang Man, Xiangjie Liu, Zhonghua Chong Qing Shale Gas Explorat & Dev Co Ltd Chongqing 401121 Peoples R China Chongqing Univ Sci & Technol Sch Petr & Nat Gas Engn Chongqing 401331 Peoples R China CNOOC EnerTech Drilling & Prod Co Beijing 100028 Peoples R China Pancasia Holding Co Ltd Chongqing 400000 Peoples R China

Based on on-site construction experience, considering the time-varying characteristics of gas well quantity, production time, effective reservoir thickness, controlled reserves, reserve abundance, formation pressure, and the energy storage coefficient, a data-driven method was used to establish a natural gas production prediction model based on differential simulation theory. The calculation results showed that the average error between the actual production and predicted production was 12.49%, and the model determination coefficient was 0.99, indicating that the model can effectively predict natural gas production. Additionally, we observed that the influence of factors such as reserve abundance, the number of wells in operation, controlled reserves, the previous year's gas production, formation pressure, the energy storage coefficient, effective matrix thickness, and annual production time on the annual gas production increases progressively as the F-values decrease. These insights are pivotal to a more profound understanding of gas production dynamics in volcanic reservoirs and are instrumental in optimizing stimulation treatments and enhancing resource recovery in such reservoirs and other unconventional hydrocarbon formations.

关键词： production prediction model volcanic reservoir data-driven method data nondimensionalization dimension recovery

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A data-driven method to predict service level for call centers

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IET COMMUNICATIONS 2022年第10期16卷 1241-1252页

作者： Hou, Chenyu Cao, Bin Fan, Jing Zhejiang Univ Technol Dept Comp Sci & Technol Hangzhou Peoples R China

In call centers, the service level is an important metric to measure the reasonability of the staffing schedule. Traditional service level calculation methods are based on the queue theory, which has very strict restrictions and is not suitable for real scenarios. Therefore, in this paper, a data-driven method to solve the service level prediction problem is proposed to be used. To this end, the relationship between service level and other factors, such as number of calls, number of agents, time, is explored. Then some features are extracted based on empirical analyses and propose to use decision tree based ensemble methods, like random forest and GBDT, to model the relationship between service level and input features. Finally, extensive experimental results show that the proposed method outperforms other baselines significantly. Especially compared with the traditional queue theory methods, our method improves the performance by 6% and 9% in terms of MAE and MAPE.

关键词： decision trees decision tree based ensemble methods random forests learning (artificial intelligence) call centers queueing theory service level prediction problem feature extraction staffing scheduling traditional queue theory methods empirical analysis data-driven method GBDT random forest call centres traditional service level calculation methods MAE telecommunication computing MAPE

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A Multi-Agent Reinforcement Learning-Based data-driven method for Home Energy Management

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IEEE TRANSACTIONS ON SMART GRID 2020年第4期11卷 3201-3211页

作者： Xu, Xu Jia, Youwei Xu, Yan Xu, Zhao Chai, Songjian Lai, Chun Sing Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen 518055 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Hong Kong Polytech Univ Shenzhen Res Inst Hong Kong Peoples R China Hong Kong Polytech Univ Dept Elect Engn Hong Kong Peoples R China Brunel Univ London Dept Elect & Comp Engn London UB8 3PH England Guangdong Univ Technol Dept Elect Engn Guangzhou 510006 Peoples R China

This paper proposes a novel framework for home energy management (HEM) based on reinforcement learning in achieving efficient home-based demand response (DR). The concerned hour-ahead energy consumption scheduling problem is duly formulated as a finite Markov decision process (FMDP) with discrete time steps. To tackle this problem, a data-driven method based on neural network (NN) and Q-learning algorithm is developed, which achieves superior performance on cost-effective schedules for HEM system. Specifically, real data of electricity price and solar photovoltaic (PV) generation are timely processed for uncertainty prediction by extreme learning machine (ELM) in the rolling time windows. The scheduling decisions of the household appliances and electric vehicles (EVs) can be subsequently obtained through the newly developed framework, of which the objective is dual, i.e., to minimize the electricity bill as well as the DR induced dissatisfaction. Simulations are performed on a residential house level with multiple home appliances, an EV and several PV panels. The test results demonstrate the effectiveness of the proposed data-driven based HEM framework.

关键词： Reinforcement learning data-driven method home energy management finite Markov decision process neural network Q-learning algorithm demand response

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