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1.
    
Industrial processes often encounter disturbances that propagate through the process units and their control elements, leading to poor process performance and massive economic losses. Thus, one major concern in the chemical industry is the detection of disturbances and identification of their propagation path. Causal analysis based on process data is frequently applied to identify causal dependencies among process measurements and thereby obtain the propagation path of disturbances. One significant challenge in data-based causal analysis is investigating industrial systems with a high degree of connectivity due to multiple causal pathways. This paper proposes a new hybrid approach for detecting causality based on the transfer entropy (TE) method by incorporating process connectivity information using an explicit search algorithm. Based on the hybrid approach, initially, the TE is only calculated for pathways that are considered as direct pathways based on the process topology. Then, the direct transfer entropy (DTE) is employed to discriminate spurious and/or indirect pathways obtained by the initial TE results. To facilitate the DTE calculation, the search algorithm is invoked once again to extract the intermediate pathways. This concept is demonstrated on an industrial board machine. In particular, the propagation path of an oscillation due to valve stiction within multiple control loops in the drying section of the machine is studied. Finally, the results are discussed and evaluated.  相似文献   

2.
因果图模型可以表示整个复杂系统中各因素见的复杂因果关系,故障树是故障分析和安全可靠性分析的常用模型,如果因果图模型能够转换为故障树模型,将扩大因果图的应用范围,方便故障树建模。因此研究因果图向故障树的转换显得十分重要。文章在分析故障树与因果图概念和表示符号的基础上,提出了因果图转换为故障树的算法。通过因果图向故障树转换可以得到任何事件为顶事件的故障树模型,然后可以采用成熟的FTA方法进行分析,这样因果图模型中的一些问题也可以采用故障树的方法来解决。在假设因果图中的连接事件表示必然因果关系条件下,因果图可以转换为含有可能的因果关系的故障树。研究表明通过微因果树化,因果图可以转换为故障树,所以因果图模型具有广泛的适用性和应用前景。  相似文献   

3.
故障树向因果图转换的研究   总被引:1,自引:0,他引:1  
梁新元  张勤 《计算机仿真》2005,22(10):144-146
故障树和因果图都采用图形表示因果关系,都主要用于故障诊断和故障分析。由于二者的相似点较多,因此研究故障树向因果图的转换显得十分重要。这样可以进一步扩大因果图的应用范围。文章在分析故障树与因果图概念和表示符号的基础上,探讨了故障树与因果图之间的相互转换,提出了故障树转换为因果图的方法。研究结果表明:含有“与门”、“或门”、“禁门”和“表决门”的故障树可以转换为因果图,这样对于这类故障树处理的故障诊断问题都可以采用因果图来解决。  相似文献   

4.
梁新元 《计算机工程》2005,31(5):204-206,209
因果图理论是一种基于概率论的图形化的知识表达推理方法,文中对因果图理论在故障分析和安全评估中的应用进行了研究。将这种新方法运用于煤矿机械设备的故障分析,并与故障树分析法进行了比较和分析。研究结果表明因果图方法比故障树分析法效果好。  相似文献   

5.
讨论了控制系统的符号有向图(SDG)模型描述和故障在控制系统中的传播方式及分析方法.该方法按控制回路信息流向的正反推理,分析初始和稳态响应的故障传递规律,通过基本单元的组合可以扩展到各类控制系统的SDG描述和故障传播分析.以锅炉水位控制系统为例,验证了该方法的有效性.  相似文献   

6.
7.
多元统计过程控制要求观测数据服从正态分布,而实际的5-业过程数据大都不满足正态分布条件.独立源分析(ICA)近几年才发展起来的一种新的统计方法,可以克服对数据分布的依赖性.对此,以ICA算法为核心,引入一种新型的过程监测方法,应用ICA提取独立源,利用I^2图,Ic^2图和SPE图进行故障检测.最后以3水箱系统为例进行了实验研究,取得了很好的效果.  相似文献   

8.
Early detection and diagnosis of faults in industrial machines would reduce the maintenance cost and also increase the overall equipment effectiveness by increasing the availability of the machinery systems. In this paper, a semi-nonparametric approach based on hidden Markov model is introduced for fault detection and diagnosis in synchronous motors. In this approach, after training the hidden Markov model classifiers (parametric stage), two matrices named probabilistic transition frequency profile and average probabilistic emission are computed based on the hidden Markov models for each signature (nonparametric stage) using probabilistic inference. These matrices are later used in forming a similarity scoring function, which is the basis of the classification in this approach. Moreover, a preprocessing method, named squeezing and stretching is proposed which rectifies the difficulty of dealing with various operating speeds in the classification process. Finally, the experimental results are provided and compared. Further investigations are carried out, providing sensitivity analysis on the length of signatures, the number of hidden state values, as well as statistical performance evaluation and comparison with conventional hidden Markov model-based fault diagnosis approach. Results indicate that implementation of the proposed preprocessing, which unifies the signatures from various operating speeds, increases the classification accuracy by nearly 21% and moreover utilization of the proposed semi-nonparametric approach improves the accuracy further by nearly 6%.  相似文献   

9.
    
Root cause diagnosis is an important step in process monitoring, which aims to identify the sources of process disturbances. The primary challenge is that process disturbances propagate between different operating units because of the flow of material and information. Data-driven causality analysis techniques, such as Granger causality (GC) test, have been widely adopted to construct process causal maps for root cause diagnosis. However, the generated causal map is over-complicated and difficult to interpret because of the existence of process loops and the violation of statistical assumptions. In this work, a two-step procedure is proposed to solve this problem. First, a causal map is built by adopting the conditional GC analysis, which is viewed as a graph in the next step. In this graph, each vertex corresponds to a process variable under investigation, while the weight of the edge connecting two vertices is the F-value calculated by conditional GC. This graph is then simplified by computing its maximum spanning tree. Thus, the results of the causality analysis are transformed into a directed acyclic graph, which eliminates all loops, highlights the root cause variable, and facilitates the diagnosis. The feasibility of this method is illustrated with the application to the Tennessee Eastman benchmark process. In the investigated case studies, the proposed method outperforms the conditional GC test and provides an easy way to identify the root cause of process disturbances.  相似文献   

10.
    
Fault detection and diagnosis (FDD) in chemical process systems is an important tool for effective process monitoring to ensure the safety of a process. Multi-scale classification offers various advantages for monitoring chemical processes generally driven by events in different time and frequency domains. However, there are issues when dealing with highly interrelated, complex, and noisy databases with large dimensionality. Therefore, a new method for the FDD framework is proposed based on wavelet analysis, kernel Fisher discriminant analysis (KFDA), and support vector machine (SVM) classifiers. The main objective of this work was to combine the advantages of these tools to enhance the performance of the diagnosis on a chemical process system. Initially, a discrete wavelet transform (DWT) was applied to extract the dynamics of the process at different scales. The wavelet coefficients obtained during the analysis were reconstructed using the inverse discrete wavelet transform (IDWT) method, which were then fed into the KFDA to produce discriminant vectors. Finally, the discriminant vectors were used as inputs for the SVM classification task. The SVM classifiers were utilized to classify the feature sets extracted by the proposed method. The performance of the proposed multi-scale KFDA-SVM method for fault classification and diagnosis was analysed and compared using a simulated Tennessee Eastman process as a benchmark. The results showed the improvements of the proposed multiscale KFDA-SVM framework with an average 96.79% of classification accuracy over the multi-scale KFDA-GMM (84.94%), and the established independent component analysis-SVM method (95.78%) of the faults in the Tennessee Eastman process.  相似文献   

11.
    
Small-sample properties of a nonparametric estimator of conditional quantiles based on optimal quantization, that was recently introduced (Charlier et al., 2015), are investigated. More precisely, (i) the practical implementation of this estimator is discussed (by proposing in particular a method to properly select the corresponding smoothing parameter, namely the number of quantizers) and (ii) its finite-sample performances are compared to those of classical competitors. Monte Carlo studies reveal that the quantization-based estimator competes well in all cases and sometimes dominates its competitors, particularly when the regression function is quite complex. A real data set is also treated. While the main focus is on the case of a univariate covariate, simulations are also conducted in the bivariate case.  相似文献   

12.
Reconstruction-based contribution for process monitoring   总被引:2,自引:0,他引:2  
This paper presents a new method to perform fault diagnosis for data-correlation based process monitoring. As an alternative to the traditional contribution plot method, a reconstruction-based contribution for fault diagnosis is proposed based on monitored indices, SPE, T2 and a combined index φ. Analysis of the diagnosability of the traditional contributions and the reconstruction-based contributions is performed. The lack of diagnosability of traditional contributions is analyzed for the case of single sensor faults with large fault magnitudes, whereas for the same case the proposed reconstruction-based contributions guarantee correct diagnosis. Monte Carlo simulation results are provided for the case of modest fault magnitudes by randomly assigning fault sensors and fault magnitudes.  相似文献   

13.
A regression model whose regression function is the sum of a linear and a nonparametric component is presented. The design is random and the response and explanatory variables satisfy mixing conditions. A new local polynomial type estimator for the nonparametric component of the model is proposed and its asymptotic normality is obtained. Specifically, this estimator works on a prewhitening transformation of the dependent variable, and the results show that it is asymptotically more efficient than the conventional estimator (which works on the original dependent variable) when the errors of the model are autocorrelated. A simulation study and an application to a real data set give promising results.  相似文献   

14.
8031单片机在自动剪板机上的应用   总被引:2,自引:0,他引:2  
介绍了自动剪板机系统的结构组成和工作原理,叙述了8031单片微机实现其生产过程控制的硬件与软件的设计方法。  相似文献   

15.
    
As the key indicators of chemical processes, the quality variables, unlike process variables, are often difficult to obtain at the high frequency. Obtaining the data of quality variables is expensive, so the data are only collected as a small portion of the whole dataset. It is common to see in both continuous and batch processes that the sample sizes of process variables and quality variables are unequal. To effectively integrate two different observation sources, including quality variables collected at a low frequency and process variables sampled at a high rate, a semi-supervised probabilistic latent variable regression model (SSPLVR) is proposed in this article. It enhances the performance monitoring of the variations of process variables and quality variables. The proposed semi-supervised model is applied to continuous and batch processes respectively. The SSPLVR model calibrated by the expectation-maximization algorithm is derived and the corresponding statistics is also systematically developed for the fault detection. Finally, two simulated case studies, TE benchmark for a continuous process problem and the penicillin fermentation for a batch process problem, are presented to illustrate the effectiveness of the proposed method.  相似文献   

16.
    
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17.
Let G1 and G2 be two connected graphs. The Kronecker product G1×G2 has vertex set V(G1×G2)=V(G1V(G2) and the edge set . In this paper, we show that if G is a bipartite graph with κ(G)=δ(G), then G×Kn(n?3) is super-κ.  相似文献   

18.
19.
The problem of specifying a smooth and simple function that approximates noisy data is considered. A new automatic method is described that is based on solving a constrained optimisation problem. The target functional to be minimised is the sum of the squared residuals penalised by the curve length of the approximation. Multiresolution and monotonicity constraints provide a good approximation to the data with a small number of local extreme values. The new method can also be applied to density estimation.  相似文献   

20.
Most dimension reduction methods based on nonparametric smoothing are highly sensitive to outliers and to data coming from heavy-tailed distributions. Two recently proposed methods, minimum average variance estimation and outer product of gradients, can be and are made robust in such a way that preserves all advantages of the original approach. Their extension based on the local one-step M-estimators is sufficiently robust to outliers and data from heavy-tailed distributions, it is relatively easy to implement, and surprisingly, it performs as well as the original methods when applied to normally distributed data.  相似文献   

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