Modified partial least square for diagnosing key‐performance‐indicator‐related faults

He, Siyuan; Wang, Youqing; Liu, Changqing

doi:10.1002/cjce.23002

Cited by 36 publications

(25 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…where α and γ are constants. Initial loudness A 0 and initial pulse frequency r 0 are generated randomly in the range of [1,2] and [0, 1].…”

Section: Generate New Location By Perturbing the Best Positionmentioning

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

“…where α and γ are constants. Initial loudness A 0 and initial pulse frequency r 0 are generated randomly in the range of [1,2] and [0, 1].…”

Section: Generate New Location By Perturbing the Best Positionmentioning

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

“…Since product quality is an important economic indicator in industrial processes, quality‐related fault detection, one of the monitoring tasks, has recently become a common research topic . If quality‐related faults occur in industrial processes that mainly focus on product quality, an effective traceability diagnosis and fault location would be performed.…”

Section: Introductionmentioning

confidence: 99%

Distributed process monitoring framework based on decomposed modified partial least squares

et al. 2019

View full text Add to dashboard Cite

With the growing complexity of industrial processes, the scale of production processes tends to be large. The significant amount of measurement data in large‐scale processes poses challenges in data collection, management, and storage. In order to perform effective process monitoring in large‐scale processes, the distributed process monitoring strategy is widely applied. Meanwhile, product quality is an important indicator for industrial production. Therefore, a novel quality‐based distributed process monitoring scheme is proposed. Firstly, the Girvan‐Newman (GN) algorithm in complex network divides process variables into multiple sub‐blocks. Secondly, greedy algorithm‐based high‐dimensional mutual information (HDMI) is used to extract quality‐related variables in each sub‐block, through which the irrelevant and redundant variables are eliminated. Thirdly, the decomposed modified partial least squares (DMPLS) approach is used to detect whether a fault is quality‐related or not in each sub‐block. Finally, the Bayesian inference strategy is adopted to combine the detection results of all sub‐blocks. The effectiveness of the distributed DMPLS approach is illustrated through a numerical simulation and the Tennessee Eastman (TE) process. The results show the superiority of our proposed monitoring scheme.

show abstract

“…Multi‐way partial least squares (MPLS) and dynamic time warping (DTW) were used for predicting the final quality of industrial processes . Modified partial least squares was proposed to find a suitable decomposition . They can be used for multimode process modelling .…”

Section: Introductionmentioning

confidence: 99%

“…Modified partial least squares was proposed to find a suitable decomposition . They can be used for multimode process modelling . The hidden semi‐Markov model (HSMM) was combined with PCA by using the mode affiliation information of the historical data, the mode shifting probability, and the mode duration probability for mode identification to address the multimode problem in process monitoring, which can detect the mode disorder fault .…”

Section: Introductionmentioning

confidence: 99%

Multimode processes monitoring based on hierarchical mode division and subspace decomposition

Zhou

Shi

et al. 2018

Can J Chem Eng

View full text Add to dashboard Cite

To handle multimodal uncertainty and dynamics which are common in actual industrial processes, this paper proposes a multimode process monitoring method based on hierarchical mode division and subspace decomposition. First, hierarchical mode division is presented to achieve hierarchical mode information for multimode process modelling, which can improve the adaptability of industrial processes with multimodal uncertainty. Then, hierarchical subspace decomposition is presented to decompose multimode processes into multiple global subspaces and multiple local subspaces for monitoring. Lastly, the most similar mode with various hierarchical modes at the current sampling point is dynamically determined for monitoring, which considers multimodal dynamics. The simulation tests of the penicillin fermentation processes are developed to show feasibility and superiority of the proposed method.

show abstract

Modified partial least square for diagnosing key‐performance‐indicator‐related faults

Cited by 36 publications

References 24 publications

Chemical process fault diagnosis based on enchanted machine‐learning approach

Chemical process fault diagnosis based on enchanted machine‐learning approach

Distributed process monitoring framework based on decomposed modified partial least squares

Multimode processes monitoring based on hierarchical mode division and subspace decomposition

Contact Info

Product

Resources

About