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A robust diagnosis method specifically for similar faults in nuclear power plant multi-systems based on data segmentation and stacked convolutional autoencoders

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ANNALS OF NUCLEAR ENERGY 2025年 213卷

作者： Ai, Xin Liu, Yongkuo Shan, Longfei Gao, Jiarong Zhang, Wanzhou Harbin Engn Univ Key Subject Lab Nucl Safety & Simulat Technol Harbin 150001 Peoples R China

Nuclear power plants consist of multiple subsystems characterized by nonlinear correlations, numerous monitoring parameters, and faults with similar characteristics. These similar faults often exhibit closely distributed data points, resulting in unclear classification boundaries and reduced diagnostic accuracy for fault detection algorithms. A single deep learning model cannot establish a diagnostic model that is broad in scope, highly accurate, and robust. This paper proposes a robust, multi-system similar fault diagnosis model for nuclear power plants, combining K-Means and Stacked Convolutional Autoencoders (KSCAE). First, K-Means data segmentation model partitions complex multi-system fault data into several similar data clusters. Then, specialized diagnostic models are developed for each cluster using powerful stacked convolutional autoencoders to focus on learning and classifying similar faults. Testing across three nuclear power plant fault diagnosis scenarios based on data from the Fuqing nuclear power plant simulator demonstrates that KSCAE outperforms single deep learning models in diagnostic accuracy, particularly when fault severity differs between training and testing sets. The results show that the KSCAE algorithm achieves a maximum accuracy of 99.31% and a minimum accuracy of 79.11% under severe noise and data distribution differences. Compared to the baseline algorithms, KSCAE achieves an average accuracy improvement of up to approximately 20% across multiple tests, particularly in Scenario 2. This study demonstrates the effectiveness and robustness of the proposed model for diagnosing similar faults, providing a reliable approach for multi-system fault diagnosis in nuclear power plants.

关键词： Nuclear power plant Similar faults diagnosis Stacked convolutional autoencoders data segmentation Robust diagnostics

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A note on data segmentation, sample size, and model specification for crash injury severity modeling

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ANALYTIC METHODS IN ACCIDENT RESEARCH 2025年 45卷

作者： Hou, Qinzhong Zhuang, Jinglun Zhai, Chenrui Huo, Xiaoyan Mannering, Fred Harbin Inst Technol Sch Automot Engn 2 West Wenhua Rd Weihai 264209 Peoples R China Univ S Florida Dept Civil & Environm Engn 4202 E Fowler Ave Tampa FL 33620 USA

In recent years, the statistical assessment of crash injury severity data has increasingly begun to segment the available crash data into observational groups to explore the possibility that such groups may share the same estimated parameters. This method is commonly used to account for parameters that may shift over time, where the data is often segmented into groups based on observational year. Unfortunately, such data segmentation can lead to small samples within each group, which has caused some concern about decreasing sample size. However, concerns about diminishing sample size are often misplaced and not well understood. In this paper, the impact of data segmentation is assessed by estimating models that address the possibility of temporally shifting parameters. Starting with a large 80,000 observation sample, the process involves randomly segmenting the data into groups with sample sizes varying from 1000 to 40,000, and then assessing the difference between the estimated data-segmented models and the overall model (using all available data) using likelihood ratio tests. The results indicate that: 1) model specification is extremely important, regardless of sample size, 2) statistical tests should be used to determine the suitability of simple versus complex models, not sample size, and 3) the variance/covariance structure of the data being considered determines model specification and sample size effects, which means sample-size requirements are data-specific, and that general statements regarding minimum sample size requirements for specific model types cannot be made.

关键词： Crash-injury severity data segmentation Sample size Model specification Temporal instability Random parameters model

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data segmentation and Augmentation Methods Based on Raw data Using Deep Neural Networks Approach for Rotating Machinery Fault Diagnosis

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IEEE ACCESS 2019年 7卷 79510-79522页

作者： Meng, Zong Guo, Xiaolin Pan, Zuozhou Sun, Dengyun Liu, Shuang Yanshan Univ Key Lab Measurement Technol & Instrumentat Hebei Qinhuangdao 066004 Hebei Peoples R China

Intelligent fault diagnosis has been widely used for mechanical fault diagnosis. Most intelligent diagnostic methods extract fault features from the frequency domain or other domains, instead of from raw data. Given that converting raw data to other domains will cause a partial loss of information, this study is based on feature extraction from raw vibration data. However, there are some difficulties in extracting features from time domain data: 1) raw vibration data lacks regularity compared to the frequency domain data signal, making it difficult to extract features from it;2) the number of labeled samples is so small that the deep neural networks can easily be over-fitted, making their generalization ability excessively poor;and 3) too many parameters in the neural networks need to be adjusted, such as learning rate, and the convergence speed is slow. To overcome the aforementioned difficulties, this study proposes the following three methods: 1) a sample segmentation method to effectively improve the feature extraction from raw data;2) a data augmentation method for raw vibration data, which can increase the number of samples;and 3) the use of the scaled conjugate gradient (SCG) algorithm in the networks to quickly learn sample features without other parameters such as learning rate. Bearing and rotor datasets are used to validate the performance of the proposed methods. The results indicate that the methods obtain superior performance for feature learning and classification compared with the existing ones in the field of induction rotating machinery fault diagnosis.

关键词： data segmentation data augmentation raw data deep neural networks fault diagnosis

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data segmentation of stereo images with complicated background

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4th International Conference on Image Analysis and Recognition

作者： Wei, Yi Hu, Shushu Li, Yu Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China

ISBN: (纸本)9783540742586

With the development of computer science, there is an increasing demand on the object recognition in stereo images. As a binocular image pair contains larger and more complicated information than a monocular image, the stereo vision analysis has been a difficult task. Therefore how to extract the region of user's interest is a vital step to reduce the data redundancy and improve the robustness and reliability of the analysis. The original stereo sequences used in the paper are obtained from two parallel video cameras mounted on a vehicle driving in a residential area. This paper targets the problem of data segmentation of those stereo images. It proposes a set of algorithms to separate the foreground from the complicated changing background. Experiments show that the whole process is fast and efficient in reducing the data redundancy, and improves the overall performance for the further obstacle extraction.

关键词： stereo image data segmentation data redundancy least square fitting

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data segmentation algorithms: Univariate mean change and beyond

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ECONOMETRICS AND STATISTICS 2024年 30卷 76-95页

作者： Cho, Haeran Kirch, Claudia Univ Bristol Sch Math Bristol England Otto von Guericke Univ Dept Math Magdeburg Germany Ctr Behav Brain Sci CBBS Magdeburg Germany

data segmentation a.k.a. multiple change point analysis has received considerable attention due to its importance in time series analysis and signal processing, with applications in a variety of fields including natural and social sciences, medicine, engineering and finance. The first part reviews the existing literature on the canonical data segmentation problem which aims at detecting and localising multiple change points in the mean of univariate time series. An overview of popular methodologies is provided on their computational complexity and theoretical properties. In particular, the theoretical discussion focuses on the separation rate relating to which change points are detectable by a given procedure, and the localisation rate quantifying the precision of corresponding change point estimators, and a distinction is made whether a homogeneous or multiscale viewpoint has been adopted in their derivation. It is further highlighted that the latter viewpoint provides the most general setting for investigating the optimality of data segmentation algorithms. Arguably, the canonical segmentation problem has been the most popular framework to propose new data segmentation algorithms and study their efficiency in the last decades. The second part of this survey motivates the importance of attaining an in-depth understanding of strengths and weaknesses of methodologies for the change point problem in a simpler, univariate setting, as a stepping stone for the development of methodologies for more complex problems. This point is illustrated with a range of examples showcasing the connections between complex distributional changes and those in the mean. Extensions towards high-dimensional change point problems are also discussed where it is demonstrated that the challenges arising from high dimensionality are orthogonal to those in dealing with multiple change points. (c) 2021 EcoSta Econometrics and Statistics. Published by Elsevier B.V. All rights reserved.

关键词： data segmentation Change point analysis Time series analysis High -dimensional statistics

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data segmentation Methods for Activity Recognition in Ambient Assisted Living 8

Data Segmentation Methods for Activity Recognition in Ambien...

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8th IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW) - Driving World to 5G and Beyond with Consumer Technology

作者： Zhao, Yuyan Chen, Haibao Zhao, Ting Chen, Jie Li, Siying Zhao, Shenghui Chuzhou Univ Chuzhou Anhui Peoples R China

ISBN: (纸本)9781665433280

The segmentation of sensor data plays an important role in the Recognition of the activities of daily living. In this paper, we summarize the state of the art of data stream segmentation in the past five years and divide them into four categories, i.e., change point detection based method, semantics based method, window based method, and deep learning-based methods. The summary shows that there are relatively few studies on segmentation using deep learning methods. Besides, there is a lack of a general platform, which can be used in comparing different methods.

关键词： data segmentation Activity Recognition Elderly

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A profile based data segmentation for in-home activity recognition

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INTERNATIONAL JOURNAL OF SENSOR NETWORKS 2019年第1期29卷 28-37页

作者： Al Nadi, Rania Al Zamil, Mohammed G. H. Yarmouk Univ Dept Comp Informat Syst Defaa St Irbid 21163 Jordan

A major problem in smart-home activity recognition is the ambiguity of interpreting the actions that formulate activities. It resulted from the redundancy of irrelevant actions and the concurrent interleaving among activities themselves. In this paper, we present a framework to minimise the effect of such ambiguity using profile based data segmentation and actions refinement. The proposed methodology relies on defining a profile for each sensor in the environment for enriching existing features with semantic ones. Furthermore, according to these profiles, irrelevant actions within data segments are removed. Moreover, the proposed methodology addresses the interleaving among different activities. Experiments have been conducted to measure the performance of the proposed framework on different datasets. We evaluated our methodology using three different classifiers. The results indicated that the proposed framework achieved statistically significant improvements. Such enhancements resulted from minimising the effect of irrelevant actions and resolving the concurrent interleaving among activities within datasets.

关键词： IoT internet of things smart home activity recognition data segmentation data mining sensor profile

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Two-stage data segmentation permitting multiscale change points, heavy tails and dependence

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ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS 2022年第4期74卷 653-684页

作者： Cho, Haeran Kirch, Claudia Univ Bristol Sch Math Inst Stat Sci Fry Bldg Bristol BS8 1UG Avon England Otto Von Guericke Univ Inst Math Stochast Ctr Behav Brain Sci CBBS Dept Math Univ Pl 2 D-39106 Magdeburg Germany

The segmentation of a time series into piecewise stationary segments is an important problem both in time series analysis and signal processing. In the presence of multiscale change points with both large jumps over short intervals and small jumps over long intervals, multiscale methods achieve good adaptivity but require a model selection step for removing false positives and duplicate estimators. We propose a localised application of the Schwarz criterion, which is applicable with any multiscale candidate generating procedure fulfilling mild assumptions, and establish its theoretical consistency in estimating the number and locations of multiple change points under general assumptions permitting heavy tails and dependence. In particular, combined with a MOSUM-based candidate generating procedure, it attains minimax rate optimality in both detection lower bound and localisation for i.i.d. sub-Gaussian errors. Overall competitiveness of the proposed methodology compared to existing methods is shown through its theoretical and numerical performance.

关键词： Change point detection data segmentation Schwarz criterion Localised pruning Multiscale procedure

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A Novel Power-Band Based data segmentation Method for Enhancing Meter Phase and Transformer-Meter Pairing Identification

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IEEE TRANSACTIONS ON POWER DELIVERY 2024年第4期39卷 2327-2339页

作者： Lee, Han Pyo Rehm, P. J. Makdad, Matthew Miller, Edmond Lu, Ning North Carolina State Univ Future Renewable Energy Delivery & Management Syst Dept Elect & Comp Engn Raleigh NC 27606 USA ElectriCities Raleigh NC 27604 USA New River Light & Power NRLP Boone NC 28607 USA

This paper presents a novel power-band-based data segmentation (PBDS) method to enhance the identification of meter phase and meter-transformer pairing. Meters that share the same transformer or are on the same phase typically exhibit strongly correlated voltage profiles. However, under high power consumption, there can be significant voltage drops along the line connecting a customer to the distribution transformer. These voltage drops significantly decrease the correlations among meters on the same phase or supplied by the same transformer, resulting in high misidentification rates. To address this issue, we propose using power bands to select highly correlated voltage segments for computing correlations, rather than relying solely on correlations computed from the entire voltage waveforms. The algorithm's performance is assessed by conducting tests using data gathered from 13 utility feeders. To ensure the credibility of the identification results, utility engineers conduct field verification for all 13 feeders. The verification results unequivocally demonstrate that the proposed algorithm surpasses existing methods in both accuracy and robustness.

关键词： Transformers Voltage measurement Meters Correlation Smart meters Topology Power measurement data segmentation ensemble clustering machine learning phase identification smart meter data analysis topology identification transformer identification

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Determining yield monitoring system delay time with geostatistical and data segmentation approaches

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TRANSACTIONS OF THE ASAE 2002年第4期45卷 915-926页

作者： Chung, SO Sudduth, KA Drummond, ST Univ Missouri Biol Engn Dept Columbia MO 65211 USA USDA ARS Cropping Syst & Water Qual Res Unit Columbia MO USA

In combine harvesting, knowledge of the delay time from cutting the crop to sensing the grain flow is required for accurate spatial location of grain yield data. Currently, either an assumed, fixed delay time is used or the delay time is determined by visual inspection of yield maps. Geostatistical and data segmentation methods were developed to estimate yield monitoring system delay time using objective criteria. The methods were validated with an ideal dataset and with elevation and soil electrical conductivity datasets having known delay times. When applied to yield and moisture content measurements collected with a commercial yield monitoring system, the methods successfully estimated delay time. In most cases, the results agreed (+/-1 s) with results achieved using a visual method. Grain yield and grain moisture content exhibited different delay times at different locations within test fields. Thus, it may be appropriate to apply delay time corrections to homogeneous sub-field areas, instead of on a whole-field basis. Use of these new estimation methods could allow for more accurate and efficient processing of yield monitor data.

关键词： precision agriculture yield monitor yield map delay time geostatistics data segmentation

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