Semiparametric PCA and bayesian network based process fault diagnosis technique

Wang, Yazhen; Liu, Yi; Khan, Faisal; Imtiaz, Syed

doi:10.1002/cjce.22829

Cited by 27 publications

(17 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the index of component G (XMEAS [35]) is regarded as product quality variable, process faults IDV (3,4), IDV (9,11), IDV (14,15), and IDV (19) have little impact on product quality, while other faults have a significant impact on quality variables, ie, faults IDV (1,2), IDV (5,6,7,8), IDV (12,13), and IDV (20,21) are more serious. [37] Detailed process monitoring charts of two typical faults IDV (8) and IDV (12) are given by Figures 4 and 5.…”

Section: Resultsmentioning

confidence: 99%

“…Many potential or minor failures may cause unimaginable consequences. Therefore, the significance of fault diagnosis for maintaining the stability of the production system has gained an increasing amount of attention . As a result, in recent years many new methods have been proposed to effectively diagnose process faults, mainly to improve the use of the production process and decrease maintenance costs .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Chemical process fault diagnosis based on enchanted machine‐learning approach

et al. 2019

View full text Add to dashboard Cite

In the chemical industry, fault diagnosis is a challenging task due to the complexity of chemical equipment. This paper proposes a machine learning‐based approach to achieve the goal of fault diagnosis. First, in order to reduce the impact of redundant features, support vector machine recursive feature elimination (SVMRFE) is used to select important features. The trained probabilistic neural network (PNN) is then used for fault diagnosis. Considering that the diagnostic performance is affected by its hidden layer element smoothing factor (σ), the modified bat algorithm (MBA) is used to optimize the PNN to obtain optimal global parameter values. The MBA adopts a better optimization mechanism than the basic algorithm and achieves excellent global convergence. It can globally optimize the smoothing factor, which effectively improves the fault diagnosis ability of the PNN. During the testing of the Tennessee Eastman (TE) process data set, we evaluate the performance of the proposed model by comparing the F1‐score and accuracy of the different methods. The charts provided describe the fault diagnostic results and classification for the different models. The results indicate that the MBA has a better optimization ability than other traditional optimization algorithms. At the same time, the combination method proposed in this paper is also superior to others and can significantly improve the accuracy of TE process fault diagnosis.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Chemical process fault diagnosis based on enchanted machine‐learning approach

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Approaches having seen fusion with PCA for fault diagnosis in NPPs include FDA (Jamil et al, 2016), conditional Gaussian networks (Atoui et al, 2015), multilevel flow modeling (Peng et al, 2018b), ENNs (Liu et al, 2017), and SVMs (Xin et al, 2019). Additionally, Wang et al (2017) used a semiparametric PCA in combination with a BN.…”

Section: Data-driven Methodsmentioning

confidence: 99%

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

et al. 2021

View full text Add to dashboard Cite

In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

show abstract

“…The historical data‐based FDD methods, also known as data‐driven FDD methods, can be classified into statistical methods, shallow machine learning methods, and deep learning methods. The statistical methods include principal component analysis (PCA), partial least square (PLS), independent component analysis (ICA), Fisher discriminant analysis (FDA), and Bayesian theory . Shallow machine learning methods refer to the FDD methods that based on traditional machine learning models other than deep neural networks, including shallow artificial neural network (ANN), support vector machine (SVM), artificial immune system (AIS), k‐nearest neighbour (KNN), and Gaussian mixture model (GMM) .…”

Section: Related Workmentioning

confidence: 99%

A process fault diagnosis method using multi‐time scale dynamic feature extraction based on convolutional neural network

2020

View full text Add to dashboard Cite

Unlike many other techniques used in process control, which are widely applied in practice and play significant roles, abnormal situation management (ASM) still relies heavily on human experience, not least because the problem of fault detection and diagnosis (FDD) has not been well addressed. In this paper, a process fault diagnosis method using multi-time scale dynamic feature extraction based on convolutional neural network (CNN) consisting of similarity measurement, variable ranking, and multi-time scale dynamic feature extraction is proposed. The CNN-based model containing the fixed multiple sampling (FMS) layer can extract dynamic characteristics of process data at different time scales. The benchmark Tennessee Eastman (TE) process is used to verify the performance of the proposed method. K E Y W O R D S convolutional neural network, dynamic feature extraction, fault diagnosis, TE process

show abstract

Semiparametric PCA and bayesian network based process fault diagnosis technique

Cited by 27 publications

References 19 publications

Chemical process fault diagnosis based on enchanted machine‐learning approach

Chemical process fault diagnosis based on enchanted machine‐learning approach

Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

A process fault diagnosis method using multi‐time scale dynamic feature extraction based on convolutional neural network

Contact Info

Product

Resources

About