Evidential KNN-based condition monitoring and early warning method with applications in power plant

Chen, Xiaolong; Wang, Peihong; Hao, Yongsheng; Zhao, Ming

doi:10.1016/j.neucom.2018.05.018

Cited by 64 publications

(23 citation statements)

References 69 publications

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“…In [13], a distinction was made between the so-called modelbased approach, which uses estimated class-conditional distributions and the "Generalized Bayes Theorem", an extension of Bayes theorem [14,15], and the case-based, or distance-based approach, in which mass functions m j are constructed based on distances to learning instances or to prototypes. Evidential classifiers in the latter category have been used in a wide range of applications [16][17][18]. They include the evidential k-nearest neighbor rule [19] and its variants (see, e.g.…”

Section: Introductionmentioning

confidence: 99%

Logistic regression, neural networks and Dempster–Shafer theory: A new perspective

Denœux

2019

Knowledge-Based Systems

108

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We revisit logistic regression and its nonlinear extensions, including multilayer feedforward neural networks, by showing that these classifiers can be viewed as converting input or higher-level features into Dempster-Shafer mass functions and aggregating them by Dempster's rule of combination. The probabilistic outputs of these classifiers are the normalized plausibilities corresponding to the underlying combined mass function. This mass function is more informative than the output probability distribution. In particular, it makes it possible to distinguish between lack of evidence (when none of the features provides discriminant information) from conflicting evidence (when different features support different classes). This expressivity of mass functions allows us to gain insight into the role played by each input feature in logistic regression, and to interpret hidden unit outputs in multilayer neural networks. It also makes it possible to use alternative decision rules, such as interval dominance, which select a set of classes when the available evidence does not unambiguously point to a single class, thus trading reduced error rate for higher imprecision. Dempster-Shafer theory Mass functionLet Θ = {θ 1 , . . . , θ K } be a finite set. A mass function on Θ is a mapping m : 2 Θ → [0, 1] such that m(∅) = 0 and A⊆Θ

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Section: Introductionmentioning

confidence: 99%

Logistic regression, neural networks and Dempster–Shafer theory: A new perspective

Denœux

2019

Knowledge-Based Systems

108

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“…The KNN, basically a pattern classifier technique was applied for detection of speech and non‐speech events 25 and also for classifying chemical skin burn images based on their severity 26 . A model for monitoring abnormal conditions in power plants (PP) was developed in Reference 27 Various power quality issues, terms, and definitions, standards, measurement of disturbance and mitigation techniques were widely discussed by Math H.J. Bollen 28…”

Section: Introductionmentioning

confidence: 99%

Power quality disturbance classification based on efficient adaptive Arrhenius artificial bee colony feature selection

Dawood¹,

Babulal²

2021

Int Trans Electr Energ Syst

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Summary Automatic power quality disturbance (PQD) classification is a challenging task for industry and utility. This work presents an algorithm for classifying the PQDs based on the combination of novel feature selection with machine learning (ML) techniques. In this work, discrete wavelet transform (DWT) is utilized to decompose the PQD signals and hence minimize the size of input vectors with multi‐resolution analysis. Novel optimal feature selection‐based classification is performed using probabilistic neural network and adaptive Arrhenius artificial bee colony (PNN‐ adaptive AABC) algorithm. The selection of optimal features may discard the redundant features and retain the useful features. Also, this adaptive AABC algorithm provides a higher convergence rate, and this is used for accurate feature selection since this work is applied for 16 PQD events. In other case, features extracted with DWT are processed and trained with various ML algorithms such as Decision Tree, support vector machine (SVM), and K‐nearest neighbor (KNN). Finally, the comparison accuracy of the proposed classifier is compared with the existing classifiers. The proposed technique found to give improved results in most of the cases like accuracy, prediction speed, and training time, and the outcomes acquired as 99.98%, ~1260 obs/seconds, and 13.113 seconds, respectively.

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“…Rostek et al [13] adopted artificial neural networks (ANN) to realize early detection of leaks in fluidized-bed boilers. For abnormal condition monitoring and diagnosis, handdesigned feature extraction techniques, which require prior knowledge of k-nearest neighbors (KNN) [14] and support vector regression [15], are used.…”

Section: Introductionmentioning

confidence: 99%

Early Warning of Critical Blockage in Coal Mills Based on Stacked Denoising Autoencoders

Hong

Liang

et al. 2020

IEEE Access

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Coal mills have a significant influence on the reliability, efficiency, and safe operation of a coal-fired power plant. Coal blockage is one of the main reasons for coal mill malfunction. It is highly essential to accurately detect the critical blockage in coal mills to ensure a safe and stable operation of the unit. Taking advantage of unsupervised learning methods and historical process data from distributed control systems (DCS), a stacked denoising autoencoder (SDAE) network-based approach for monitoring critical blockage in a coal mill is proposed in this work. The SDAE model with optimized parameters is applied to reconstruct the operating data during normal operating conditions. The intrinsic relationship between all input variables was captured by training a multilayer network model. The monitoring indicator was obtained from the reconstruction errors, and the threshold for monitoring indicators was obtained using kernel density estimation (KDE) during normal operation. The proposed network is independent of fault data, requires a reduced on-line calculation, and demonstrates a better real-time performance compared to conventional methods. The abnormal variables analysis may provide a theoretical evidence for critical blockage. The effectiveness of the proposed method is validated using operating data collected from an actual coal-fired power plant in China. The results demonstrated that the proposed method can effectively detect critical blockage in a coal mill and issue a timely warning, which allows operators to detect potential faults.

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Evidential KNN-based condition monitoring and early warning method with applications in power plant

Cited by 64 publications

References 69 publications

Logistic regression, neural networks and Dempster–Shafer theory: A new perspective

Logistic regression, neural networks and Dempster–Shafer theory: A new perspective

Power quality disturbance classification based on efficient adaptive Arrhenius artificial bee colony feature selection

Early Warning of Critical Blockage in Coal Mills Based on Stacked Denoising Autoencoders

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