Fault detection of petrochemical process based on space-time compressed matrix and Naive Bayes

Deng, Zhenyu; Han, Te; Cheng, Zhonghai; Jiang, Jiajia; Duan, Fajie

doi:10.1016/j.psep.2022.01.048

Cited by 22 publications

(8 citation statements)

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“…implementation of NB classifers have led to their widespread use in classifcation tasks [32,33]. Te follow equation defnes the Bayes theory that is utilized in NB [34]:…”

Section: Numbermentioning

confidence: 99%

Fault Identification of UHVDC Transmission Based on DF-AD and Ensemble Learning

Zhao,

Yang,

Han

et al. 2024

International Transactions on Electrical Energy Systems

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High-resistance ground faults are difficult to detect with existing ultrahigh voltage direct current (UHVDC) transmission fault detection systems because of their low sensitivity. To address this challenge, a straightforward mathematical method has been proposed for fault detection in UHVDC system based on the downsampling factor (DF) and approximation derivatives (AD). The signals at multiple sampling frequencies were analysed using the DF, and the AD approach was used to generate various levels of detail and approximation coefficients. Initially, the signals were processed with different DF values. The first, second, and third order derivatives of the generated signals were calculated by the AD method. Next, the entropy features of these signals were computed, and the Random Forest-Recursive feature elimination with cross-validation (RF-RFECV) algorithm was used to select a high-quality feature subset. Finally, an ensemble classifier consisting of Light Gradient Boosting Machine (LightGBM), K Nearest Neighbor (KNN), and Naive Bayes (NB) classifiers was utilized to identify UHVDC faults. The MATLAB/Simulink simulation software was used to develop a ±800 kV UHVDC transmission line model and perform simulation experiments with various fault locations and types. Based on the experiments, it has been established that the suggested approach is highly precise in detecting several faults on UHVDC transmission lines. The method is capable of accurately identifying low or high resistance faults, irrespective of their incidence, and is remarkably resistant to transitional resistance. Furthermore, it exhibits excellent performance in identifying faults using a small sample size and is highly reliable.

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“…implementation of NB classifers have led to their widespread use in classifcation tasks [32,33]. Te follow equation defnes the Bayes theory that is utilized in NB [34]:…”

Section: Numbermentioning

confidence: 99%

Fault Identification of UHVDC Transmission Based on DF-AD and Ensemble Learning

Zhao,

Yang,

Han

et al. 2024

International Transactions on Electrical Energy Systems

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“…In their study, early fault detection is regarded as a time-dependent sequence learning problem, and fault symptoms are forecast using LSTM from the previous data pattern to monitor an early fault [12]. Deng et al proposed an online fault detection approach by combining a spacetime compressed matrix and Naive Bayes, which largely reduces the learning complexity and ensures early fault detection [13]. Cheded et al proposed a novel integrated framework for incipient fault detection in terms of process safety.…”

Section: Introductionmentioning

confidence: 99%

Incipient Fault Detection in a Hydraulic System Using Canonical Variable Analysis Combined with Adaptive Kernel Density Estimation

Wang,

Zhao,

Wang

et al. 2023

Sensors

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Incipient fault detection in a hydraulic system is a challenge in the condition monitoring community. Existing research mainly monitors abnormal working conditions in hydraulic systems by separately detecting the key working parameter, which often causes a high miss warning rate for incipient faults due to the oversight of parameter dependence. A principal component analysis provides an effective method for incipient fault detection by taking the correlation of multiple parameters into consideration, but this technique assumes the systems are Gaussian-distributed, making it invalid for a dynamic non-Gaussian system. In this paper, we combine a canonical variable analysis (CVA) and adaptive kernel density estimation (AKDE) for the early fault detection of nonlinear dynamic hydraulic systems. The collected hydraulic system data set was used to construct the typical variable space, and the state space and residual space are divided to represent the characteristics of different correlations between the two variables, which are quantitatively described using Hotelling’s T2 and Q. In order to investigate the proper upper control limits, AKDE was utilised to estimate the underlying probability density functions of T2 and Q by taking the nonlinearity of the hydraulic system variables into consideration. The advantages of the proposed approach for incipient fault detection are illustrated via a marine power plant lubrication system.

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“…Deng et al. 16 proposed a fault detection method based on the integration of the spatial compression matrix and NB, which reduces the complexity of learning and helps to speed up the management of production risks. Machine learning can achieve a better effect of FDD when a small sample is used.…”

Section: Introductionmentioning

confidence: 99%

“…This method achieved good diagnostic performance in a continuous stirred tank heater and binary distillation column because it used the correlation dimension to select the principal component and combined a vine copula and the BN theorem to capture the nonlinear dependence of high dimensional process data. Deng et al 16 proposed a fault detection method based on the integration of the spatial compression matrix and NB, which reduces the complexity of learning and helps to speed up the management of production risks. Machine learning can achieve a better effect of FDD when a small sample is used.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of the Dynamic Chemical Process Based on the Optimized CNN-LSTM Network

et al. 2022

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Deep learning provides new ideas for chemical process fault diagnosis, reducing potential risks and ensuring safe process operation in recent years. To address the problem that existing methods have difficulty extracting the dynamic fault features of a chemical process, a fusion model (CS-IMLSTM) based on a convolutional neural network (CNN), squeeze-and-excitation (SE) attention mechanism, and improved long short-term memory network (IMLSTM) is developed for chemical process fault diagnosis in this paper. First, an extended sliding window is utilized to transform data into augmented dynamic data to enhance the dynamic features. Second, the SE is utilized to optimize the key fault features of augmented dynamic data extracted by CNN. Then, IMLSTM is used to balance fault information and further mine the dynamic features of time series data. Finally, the feasibility of the proposed method is verified in the Tennessee-Eastman process (TEP). The average accuracies of this method in two subdata sets of TEP are 98.29% and 97.74%, respectively. Compared with the traditional CNN-LSTM model, the proposed method improves the average accuracies by 5.18% and 2.10%, respectively. Experimental results confirm that the method developed in this paper is suitable for chemical process fault diagnosis.

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Fault detection of petrochemical process based on space-time compressed matrix and Naive Bayes

Cited by 22 publications

References 50 publications

Fault Identification of UHVDC Transmission Based on DF-AD and Ensemble Learning

Fault Identification of UHVDC Transmission Based on DF-AD and Ensemble Learning

Incipient Fault Detection in a Hydraulic System Using Canonical Variable Analysis Combined with Adaptive Kernel Density Estimation

Fault Diagnosis of the Dynamic Chemical Process Based on the Optimized CNN-LSTM Network

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