標(biāo)題: Titlebook: Data-Driven Fault Detection and Reasoning for Industrial Monitoring; Jing Wang,Jinglin Zhou,Xiaolu Chen Book‘‘‘‘‘‘‘‘ 2022 The Editor(s) (i [打印本頁] 作者: concord 時(shí)間: 2025-3-21 19:40
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書目名稱Data-Driven Fault Detection and Reasoning for Industrial Monitoring網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Data-Driven Fault Detection and Reasoning for Industrial Monitoring被引頻次
書目名稱Data-Driven Fault Detection and Reasoning for Industrial Monitoring被引頻次學(xué)科排名
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書目名稱Data-Driven Fault Detection and Reasoning for Industrial Monitoring讀者反饋
書目名稱Data-Driven Fault Detection and Reasoning for Industrial Monitoring讀者反饋學(xué)科排名
作者: CHIP 時(shí)間: 2025-3-22 00:11
Multivariate Statistics Between Two-Observation Spaces,easurement spaces. The vast majority of smart manufacturing problems, such as soft measurement, control, monitoring, optimization, etc., inevitably require modeling the data relationships between the two kinds of measurement variables. This chapter’s subject is to discover the correlation between th作者: 變形 時(shí)間: 2025-3-22 02:56 作者: 冥界三河 時(shí)間: 2025-3-22 04:44
,Soft-Transition Sub-PCA Monitoring of?Batch Processes,s, such as fermentation, polymerization, and pharmacy, is highly sensitive to the abnormal changes in operating condition. Monitoring of such processes is extremely important in order to get higher productivity. However, it is more difficult to develop an exact monitoring model of batch processes th作者: 盡忠 時(shí)間: 2025-3-22 09:49 作者: arbovirus 時(shí)間: 2025-3-22 13:19 作者: arbovirus 時(shí)間: 2025-3-22 18:20
Fault Identification Based on Local Feature Correlation,as PCA, PLS, CCA, and FDA, are only suitable for solving the high-dimensional data processing with linear correlation. The kernel mapping method is the most common technique to deal with the nonlinearity, which projects the original data in the low-dimensional space to the high-dimensional space thr作者: 尖叫 時(shí)間: 2025-3-23 01:02 作者: 沉默 時(shí)間: 2025-3-23 03:08
Locality-Preserving Partial Least Squares Regression,e and structure-preserving properties of LPP into the PLS model. The core of LPPLS is to replace the role of PCA in PLS with LPP. When extracting the principal components of . and ., two conditions must satisfy: (1) . and . retain the most information about the local nonlinear structure of their res作者: 鋼筆尖 時(shí)間: 2025-3-23 08:34
Locally Linear Embedding Orthogonal Projection to Latent Structure, Monitoring process variables and their associated quality variables is essential undertaking as it can lead to potential hazards that may cause system shutdowns and thus possibly huge economic losses. Maximum correlation was extracted between quality variables and process variables by partial least作者: 正式通知 時(shí)間: 2025-3-23 11:45 作者: libertine 時(shí)間: 2025-3-23 15:46
Bayesian Causal Network for Discrete Variables,chapters have discussed the fault detection and identification methods. Fault traceability is also an important issue in industrial system. This chapter and Chap.?. aim at the fault inference and root tracking based on the probabilistic graphical model. This model explores the internal linkages of s作者: 他去就結(jié)束 時(shí)間: 2025-3-23 21:43
Probabilistic Graphical Model for Continuous Variables, the value of probability and cannot be applied to the time series. The model established in Chap.?. is a graphical model similar to a Bayesian network, but its parameter learning method can only handle the discrete variables. This chapter aims at the probabilistic graphical model directly for the c作者: 造反,叛亂 時(shí)間: 2025-3-24 01:32 作者: Fulminate 時(shí)間: 2025-3-24 03:53 作者: 暫時(shí)中止 時(shí)間: 2025-3-24 07:41 作者: Communicate 時(shí)間: 2025-3-24 14:24 作者: 沉積物 時(shí)間: 2025-3-24 15:33 作者: 墻壁 時(shí)間: 2025-3-24 22:32 作者: 狂怒 時(shí)間: 2025-3-25 00:00 作者: 充氣球 時(shí)間: 2025-3-25 04:44 作者: Semblance 時(shí)間: 2025-3-25 10:41
Global Plus Local Projection to Latent Structures,time delay (Ding .; Aumi et?al. .; Peng et?al. .; Zhang et?al. .; Yin et?al. .). Monitoring the process variables related to the quality variables is significant for finding potential harm that may lead to system shutdown with possible enormous economic loss.作者: sleep-spindles 時(shí)間: 2025-3-25 14:13 作者: 使聲音降低 時(shí)間: 2025-3-25 19:28
Locality-Preserving Partial Least Squares Regression,principal components of . and ., two conditions must satisfy: (1) . and . retain the most information about the local nonlinear structure of their respective data sets. (2) The correlation between . and . is the largest. Finally, a quality-related monitoring strategy is established based on LPPLS.作者: AGATE 時(shí)間: 2025-3-25 22:21
Locally Linear Embedding Orthogonal Projection to Latent Structure,m shutdowns and thus possibly huge economic losses. Maximum correlation was extracted between quality variables and process variables by partial least squares analysis (PLS) (Kruger et?al. .; Song et?al. .; Li et?al. .; Hu et?al. .; Zhang et?al. .).作者: 的’ 時(shí)間: 2025-3-26 01:09
New Robust Projection to Latent Structure,ainty part, just as the nonlinearity can be used to represent the uncertainty of a system (Wang et?al. .). So this chapter also focuses on how to deal with the nonlinearity in PLS series method, but starts from an different view, i.e., robust PLS. Here the system nonlinearity is considered as uncertainty and a new robust .-PLS is proposed.作者: arcane 時(shí)間: 2025-3-26 06:47 作者: 魔鬼在游行 時(shí)間: 2025-3-26 09:20
A Further Improvement on?Approximating TTP-2d of a batch. Therefore, the complete batch trajectory must be estimated real time, or alternatively only the measured values at the current moment are used for online diagnosis. Moreover, the above approaches do not consider the problem of inconsistent production cycles.作者: Countermand 時(shí)間: 2025-3-26 16:25 作者: 迅速成長 時(shí)間: 2025-3-26 18:48 作者: 領(lǐng)先 時(shí)間: 2025-3-26 23:00
Kernel Fisher Envelope Surface for Pattern Recognition,d of a batch. Therefore, the complete batch trajectory must be estimated real time, or alternatively only the measured values at the current moment are used for online diagnosis. Moreover, the above approaches do not consider the problem of inconsistent production cycles.作者: 漫不經(jīng)心 時(shí)間: 2025-3-27 01:21
Fault Identification Based on Local Feature Correlation,ough appropriate kernel functions so as to achieve the goal of linear separability in the new space. However, the space projection from the low dimension to the high dimension is contradictory to the actual requirement of dimensionality reduction of the data. So kernel-based method inevitably increases the complexity of data processing.作者: reject 時(shí)間: 2025-3-27 06:03 作者: 孤僻 時(shí)間: 2025-3-27 12:10 作者: Mutter 時(shí)間: 2025-3-27 14:13
Reversible Pebble Game on Treesm shutdowns and thus possibly huge economic losses. Maximum correlation was extracted between quality variables and process variables by partial least squares analysis (PLS) (Kruger et?al. .; Song et?al. .; Li et?al. .; Hu et?al. .; Zhang et?al. .).作者: 演講 時(shí)間: 2025-3-27 21:35
Bin Packing Game with an Interest Matrixainty part, just as the nonlinearity can be used to represent the uncertainty of a system (Wang et?al. .). So this chapter also focuses on how to deal with the nonlinearity in PLS series method, but starts from an different view, i.e., robust PLS. Here the system nonlinearity is considered as uncertainty and a new robust .-PLS is proposed.作者: Patrimony 時(shí)間: 2025-3-27 22:52
Marc Hellmuth,Guillaume E. Scholzk, but its parameter learning method can only handle the discrete variables. This chapter aims at the probabilistic graphical model directly for the continuous process variables, which avoids the assumption of discrete or Gaussian distributions.作者: 使顯得不重要 時(shí)間: 2025-3-28 03:39
Book‘‘‘‘‘‘‘‘ 2022 who are interested in fault diagnosis technology, researchers investigating automation and industrial security, professional practitioners and engineers working on engineering modeling and data processing applications.?.This is an open access book..作者: 深淵 時(shí)間: 2025-3-28 07:05
Data-Driven Fault Detection and Reasoning for Industrial Monitoring作者: 走調(diào) 時(shí)間: 2025-3-28 13:04 作者: Entropion 時(shí)間: 2025-3-28 16:58
Computing Techniques for RobotsThe observation data collected from continuous industrial processes usually have two main categories: process data and quality data, and the corresponding industrial data analysis is mainly for the two types of data based on the multivariate statistical techniques.作者: jumble 時(shí)間: 2025-3-28 19:01
Multivariate Statistics in Single Observation Space,The observation data collected from continuous industrial processes usually have two main categories: process data and quality data, and the corresponding industrial data analysis is mainly for the two types of data based on the multivariate statistical techniques.作者: Acetaminophen 時(shí)間: 2025-3-29 02:31 作者: COKE 時(shí)間: 2025-3-29 03:44 作者: 搖擺 時(shí)間: 2025-3-29 08:49 作者: 大酒杯 時(shí)間: 2025-3-29 13:11 作者: mercenary 時(shí)間: 2025-3-29 16:08
Tom Davot,Lucas Isenmann,Jocelyn Thiebautce (RS), then calculates . and . statistics to detect the abnormality. However, the abnormality by these two statistics are detected from the principle components of the process. Principle components actually have no specific physical meaning, and do not contribute directly to identify the fault var作者: 自負(fù)的人 時(shí)間: 2025-3-29 21:04 作者: 儲(chǔ)備 時(shí)間: 2025-3-30 01:15 作者: Temporal-Lobe 時(shí)間: 2025-3-30 05:02
Kameng Nip,Zhenbo Wang,Wenxun Xingarge number of process and quality variables produced. Therefore, quality-related fault detection and diagnosis are extremely necessary for complex industrial processes. Data-driven statistical process monitoring plays an important role in this topic for digging out the useful information from these作者: 拉開這車床 時(shí)間: 2025-3-30 10:29
https://doi.org/10.1007/978-3-319-21398-9e and structure-preserving properties of LPP into the PLS model. The core of LPPLS is to replace the role of PCA in PLS with LPP. When extracting the principal components of . and ., two conditions must satisfy: (1) . and . retain the most information about the local nonlinear structure of their res作者: prostate-gland 時(shí)間: 2025-3-30 14:22
Reversible Pebble Game on Trees Monitoring process variables and their associated quality variables is essential undertaking as it can lead to potential hazards that may cause system shutdowns and thus possibly huge economic losses. Maximum correlation was extracted between quality variables and process variables by partial least
