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5th International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Kim, Jueun Song, Seunghwan Hwang, Woo Young Baek, Jun-Geol Korea Univ Dept Ind & Management Engn Seoul South Korea

ISBN: (纸本)9781665456456

fault detection and classification (FDC) refers to a data analysis technique that classifies normal and abnormal situations. Data for FDC is being collected in various processes such as manufacturing processes, and various algorithms such as machine learning and deep learning are used in FDC. In particular, convolutional neural network (CNN) models have the advantage of automatically extracting data features and are widely used in analysis, which has a limitation that it is difficult to directly reflect the relationship between variables in the model in multivariate time series data. This work proposes a randomly masked convolutional neural network (RM-CNN) model that enhances FDC performance by adding intervariable relationships to a 1D CNN algorithm. The proposed methodology can directly reflect the relationship between variables in the model to improve FDC performance and identify variables that have a lot of influence. The experiments were conducted by comparison with a typical 1D CNN model, and evaluation indicators used Accuracy, Precision, Recall, and F1 score. As a result of the experiment, the proposed methodology performed better than the comparative models.

关键词： interaction fault detection and classification randomly masked 1-dimensional convolutional neural network

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Redefining Monitoring Rules for Intelligent fault detection and classification via CNN Transfer Learning for Smart Manufacturing

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2022年第2期35卷 158-165页

作者： Chien, Chen-Fu Hung, Wei-Tse Liao, Eddy Ting-Yi Natl Tsing Hua Univ Minist Sci & Technol Artificial Intelligence Intelligent Mfg Syst Res Hsinchu 30013 Taiwan Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 30013 Taiwan Natl Tsing Hua Univ Int Intercollegiate PhD Program Hsinchu 30013 Taiwan

fault detection and classification has been employed to enhance yield and product quality for smart semiconductor manufacturing. For early detection of abnormal events that cause defects, the status variables identification data collected by the sensors embedded in advanced machines can be analyzed to derive the actions for advanced process control and advanced equipment control. However, the validity and effectiveness of fault detection and classification technologies may highly depend on domain knowledge and experience of the process engineers who should redefine the monitoring rules quickly when new process excursion occurred especially when ramping up new technologies and products. Motivated by realistic needs, this study aims to propose a novel strategy to empower intelligent fault detection and classification that employed convolutional neural network to analyze the feature SVID data and determine the conditions of the wafers, while shorten the cycle time for self-learning from domain knowledge and redefining new monitoring rules for fault classification in real time. This approach is validated with an empirical study in a leading semiconductor manufacturing company in Taiwan. The results have demonstrated the practical viability of the developed solution.

关键词： Monitoring Feature extraction fault diagnosis fault detection Convolutional neural networks Process control Data models fault detection and classification convolutional neural networks machine learning yield ramping real time decision

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Methodology for Important Sensor Screening for fault detection and classification in Semiconductor Manufacturing

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IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING 2021年第1期34卷 65-73页

作者： Zhu, Feng Jia, Xiaodong Miller, Marcella Li, Xiang Li, Fei Wang, Yinglu Lee, Jay Univ Cincinnati Dept Mech & Mat Engn Cincinnati OH 45220 USA

Feature design and selection is challenging because of huge data volume and high-mix production systems. Most engineers still rely on human experts to suggest the specific sensor channel and specific time frames of data from which to design the features. This study proposes a novel approach for important sensor screening to prioritize the useful sensor channels for FDC model development in semiconductor manufacturing. The proposed method can be used as a pre-processing step prior to feature extraction, and the selected sensor channels can be leveraged by process engineers for finer feature design. In this research, firstly, time series alignment kernels (TSAKs) are proposed to handle multivariate trace data. Then, the proposed method combines 5 different time series alignment kernels (TSAKs) with a feature selection algorithm, minimum Redundancy Maximum Relevance (mRMR), to identify the important sensor channels. Furthermore, a TSAK+Kernel Principal Component Analysis (KPCA) algorithm is proposed for a visualization tool. Lastly, the TSAK+Support Vector Machine (SVM) is employed for results validation. In this study, validation of the proposed method is based on both open-source datasets and the proprietary datasets from a real production line.

关键词： Feature extraction Kernel Support vector machines Time series analysis Redundancy Principal component analysis Manufacturing Semiconductor time series alignment kernel sensor selection fault detection and classification

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fault detection and classification in oil wells and production/service lines using random forest

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JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2021年 197卷 107879-107879页

作者： Marins, Matheus A. Barros, Bettina D. Santos, Ismael H. Barrionuevo, Daniel C. Vargas, Ricardo E. V. Prego, Thiago de M. de Lima, Amaro A. de Campos, Marcello L. R. da Silva, Eduardo A. B. Netto, Sergio L. Univ Fed Rio de Janeiro Elect Engn Program POB 68504 BR-21947970 Rio De Janeiro RJ Brazil Norwegian Univ Sci & Technol Dept Elect Syst Trondheim Norway Petr Brasileiro SA BR-20211160 Rio De Janeiro RJ Brazil Petr Brasileiro SA BR-29057570 Vitoria ES Brazil CEFET NI BR-26041271 Nova Iguacu RJ Brazil

This papers deals with the automatic detection and classification of faulty events during the practical operation of oil and gas wells and lines. The events considered here are part of the publicly available 3W database developed by Petrobras, the Brazilian oil holding. Seven fault classes are considered, with distinct dynamics and patterns, as well as several instances of normal operation. A random forest classifier is employed with different statistical measures to identify each fault type. Three experiments are devised in order to evaluate the system performance in distinct classification scenarios. An accuracy rate of 94% indicates a successful performance for the proposed system in detecting real events. Also, the system's time of detection was on average 12% of the transient period that precedes the fault steady-state.

关键词： fault detection and classification Oil well monitoring Abnormal event management Random forest classifier Machine learning

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fault detection and classification for Wind Turbine Gearbox via a Time-Frequency Analysis Method 9

Fault Detection and Classification for Wind Turbine Gearbox ...

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9th IEEE Data Driven Control and Learning Systems Conference (DDCLS)

作者： Gu, Caoyuan Zhu, Junwei Huang, Dajian Wang, Xin Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China Heilongjiang Univ Coll Math Sci Harbin 150080 Peoples R China

ISBN: (纸本)9781728159225

Gearbox with complex structure is one of the most fragile components of wind turbines. The fault detection and classification of the gearbox is crucial to reduce the unexpected downtime and economic loss of wind turbines. The main purpose of this paper is to propose a fault detection and classification method based on standard deviation and wavelet entropy for wind turbines. Firstly, the fault detection logic is designed by using the standard deviation and its normalized result. Secondly, the wavelet entropy is used to determine the wind turbine gearbox fault type. The experimental results from historical data are consistent with the actual maintenance report results, which verifies the effectiveness of the method.

关键词： Wind turbine gearbox fault detection and classification Standard deviation Wavelet entropy

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Adaptive Machine-Learning-Based Transmission Line fault detection and classification Connected to Inverter-Based Generators

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ENERGIES 2023年第15期16卷 5775页

作者： Al Kharusi, Khalfan El Haffar, Abdelsalam Mesbah, Mostefa Sultan Qaboos Univ Elect & Comp Engn POB 33 Muscat 123 Oman

Adaptive protection schemes have been developed to address the problem of behavior-changing power systems integrated with inverter-based generation (IBG). This paper proposes a machine-learning-based fault detection and classification technique using a setting-group-based adaptation approach. Multigroup settings were designed depending on the types of power generation (synchronous generator, PV plant, and type-3 wind farm) connected to a transmission line in the 39-Bus New England System. For each system topology, an optimized pretrained ensemble tree classifier was used. The adaptation process has two phases: an offline learning phase to tune the classifiers and select the optimum subset of features, and an online phase where the circuit breaker (CB) status and the active output power of the generators are continuously monitored to identify the current system topology and to select the appropriate setting group. The proposed system achieved an average accuracy of 99.4%, a 99.5% average precision, a 99.9% average specificity, and a 99.4% average sensitivity of classification. The robustness analysis was conducted by applying several fault scenarios not considered during training, which include different transmission network configurations and different penetration levels of IBGs. The case of incorrect selection of the appropriate setting group resulting from selecting the wrong topology is also considered. It was noticed that the performance of developed classifiers deteriorates when the transmission network is reconfigured and the incorrect setting group is selected.

关键词： adaptive protection machine learning fault detection and classification inverter-based generators groups setting

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Discriminative common vector in sufficient data Case: A fault detection and classification application on photovoltaic arrays

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2021年第5期24卷 1168-1179页

作者： Onal, Yasemin Turhal, Umit Cigdem Bilecik Seyh Edebali Univ Dept Elect & Elect Engn TR-11000 Bilecik Turkey

In this study, the derivation of the Discriminative Common Vector (DCV) approach which is first introduced for a face recognition task in the insufficient data case, for the sufficient data case is obtained and it is applied for a photovoltaic (PV) panel fault detection and classification. Two experimental studies are performed including two different fault configurations. In the first experimental study, as the faulty conditions open-circuit, short-circuit, and partial shading conditions are taken and healthy condition is taken as reference. Thus, a four-class fault detection and classification scheme is constructed. In the second experimental study, the serial resistance degradation fault is considered. This fault detection and classification scheme includes four classes that are healthy and three different serial resistance degradation. The data used in the experimental studies are formed to be 1x3 dimensional vectors which include the current, voltage, and power values obtained from the simulations in the PSIM program. In all two experimental studies for each class, a discriminative common vector (DCV) which represents the common properties of that class, thus, having a high discriminative ability is obtained. As a contribution to the literature, the derivation of DCVA which has high discrimination ability for sufficient data case, and usage of it for PV panels fault detection and classification is proposed for the first time in this study. The proposed method's performance is evaluated with the performance of PCA method that is recently used for the fault detection and classification problem in PV panel systems in the literature. In the first experimental study, the proposed method's performance (99%) is obtained significantly higher than the performance of the PCA method (95%). And in the second experimental study, while PCA can only detect the faulty condition but cannot classify the serial resistance degradation, the proposed method can both detect and class

关键词： Photovoltaic array fault fault detection and classification Sufficient data case Discriminative common vector Signal processing

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A novel MODWT-based fault detection and classification scheme in VSC-HVDC transmission line

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第1期221卷

作者： Imani, Amir Moravej, Zahra Pazoki, Mohammad Semnan Univ Fac Elect & Comp Engn Semnan Iran Damghan Univ Sch Engn Damghan Iran

This paper proposes a protection scheme according to the transient wavelet energy of DC pole's current signals by using an illustrative signal processing technique called maximum overlap discrete wavelet (MODWT). The main advantage of the proposed single-end protection scheme is its faster performance in comparison with discrete wavelet transform (DWT). The performance of the scheme is evaluated against different crucial scenarios, including external solid DC and AC faults and sudden load variation in two sample systems in MATLAB/ Simulink and PSCAD/EMTDC softwares with point to point and multi-terminal topologies. Moreover, the effectiveness of the proposed scheme is tested by carrying out a large number of simulations, including different fault locations, resistances, noise levels, window lengths, sampling frequencies, line lengths, and smoothing reactor values. Altogether, analyzing the results shows that the proposed scheme is efficacious in noisy conditions and in terms of detection time.

关键词： HVDC system HVDC protection Maximum overlap discrete wavelet (MODWT) fault detection and classification

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fault detection and classification for Sensor faults of UAV by Deep Learning and Time-Frequency Analysis

Fault Detection and Classification for Sensor Faults of UAV ...

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第40届中国控制会议

作者： Jing Huang Mengna Li Youmin Zhang Lingxia Mu Zihang Ao Haihua Gong Department of Automation and Information Engineering Xi'an University of Technology Jiujiang Precision Test Technology Research Institute Department of Mechanical Industrial and Aerospace EngineeringConcordia University China Satellite Maritime Tracking and Control Department

Sensor faults could occur in unmanned aerial vehicles（UAVs） during a mission of flight,which might deteriorate UAV’s performance or even cause *** different types of sensor faults of a quadrotor UAV,a new fault detection and classification method based on time-frequency analysis（TFA） and deep learning（DL） technologies is proposed in this ***,the data sets including different types of sensor fault in time-domain are generated *** date sets are then transformed into time-frequency domain by short-time Fourier transform（STFT）,resulting in time-frequency graph（TFG）.Secondly,the time-frequency graph sets are used to train the deep network,by which the fault type can be rapidly classified with high ***,the simulations are carried out to verify the performance of the proposed algorithm and the accuracy reaches to 99.6%.

关键词： fault detection and classification sensor faults classification time-frequency analysis（TFA） deep learning

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Fuzzy Logic based fault detection and classification scheme for Series faults in Six Phase Transmission Line 7

Fuzzy Logic based Fault Detection and Classification scheme ...

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7th International Conference on Electrical Energy Systems (ICEES)

作者： Althi, Tirupathi Rao Koley, Ebha Ghosh, Subhojit NIT Raipur Dept Elect Engn Raipur Madhya Pradesh India

ISBN: (纸本)9781728176123

In recent times the power demand has increased considerably. Power demand can be achieved by multi-phase transmission line. In the present work, a protection scheme for six-phase transmission line for series faults or open conductor fault has been presented. The scheme initiates with the estimation of fundamental current component from the measured time-domain current signals at bus located at sending end. These signals are fed to fuzzy logic-based protection approach. The proposed protection scheme has been analyzed for different types of series faults at different locations with various fault inception angles. From simulation results, it is evident that the proposed fuzzy logic-based relay scheme perfectly detects and classifies all types of series faults accurately.

关键词： Six phase transmission line Fuzzy logic Fundamental current component Series fault fault detection and classification

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