<scp>Data‐driven</scp> nonlinear chemical process fault diagnosis based on hierarchical representation learning

Yang, Wang; Jiang, Qingchao

doi:10.1002/cjce.23753

Cited by 6 publications

(3 citation statements)

References 42 publications

(52 reference statements)

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“…[15] A hierarchical representation learning (HRL) method that integrates the DBN and support vector data description (SVDD) was proposed for chemical process fault diagnosis. [16] SAE is currently the most widely used feature learning method and has been successfully applied in the multivariate industrial process monitoring. [17][18][19] The recursive stacked denoising autoencoder (RSDAE) was proposed for the dynamic relationship problem.…”

Section: Introductionmentioning

confidence: 99%

Feature learning based on entropy estimation density peak clustering and stacked autoencoder for industrial process monitoring

Liu

et al. 2022

Can J Chem Eng

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Recently, stacked autoencoder (SAE)‐based deep learning has been widely employed for industrial process fault detection, which can extract representative low‐dimensional features. However, SAE fails to take the distribution structure of raw data into consideration in the unsupervised self‐reconstruction learning, and this may lead to limited monitoring performance. Focusing on this issue, this paper proposes a new fault detection method based on entropy estimation density peak clustering and stacked autoencoder (EEDPC‐SAE) for industrial process monitoring. First, an improved automatic clustering algorithm, namely, entropy estimation density peak clustering (EEDPC), is presented to analyze the distribution structure of process data. Then, the data distribution information is integrated into the learning procedure of SAE to capture the data intrinsic structure. EEDPC‐SAE can describe the distribution characteristics of process data, and more informative high‐level features can be learned for fault detection. Finally, two statistics, that is, Hotelling's T‐squared (T2) and squared prediction error (SPE), are established based on the encoder features and residual features generated by EEDPC‐SAE. The effectiveness of the proposed method is evaluated by the Tennessee Eastman (TE) process and fed‐batch fermentation penicillin (FBFP) process.

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Section: Introductionmentioning

confidence: 99%

Feature learning based on entropy estimation density peak clustering and stacked autoencoder for industrial process monitoring

Liu

et al. 2022

Can J Chem Eng

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“…[3,4] The data-based fault diagnosis method does not need to establish a complex mechanism model; it only utilizes the relevant or causal latent features among data to match the difference between normal and fault. [5,6] Due to the convenience of its implementation, it has occupied a dominant position in recent studies.…”

Section: Introductionmentioning

confidence: 99%

A novel imbalanced fault diagnosis method integrated KLFDA with improved cost‐sensitive learning ANBSVM

Jiang

et al. 2022

Can J Chem Eng

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Fault diagnosis, as an important approach to ensure the safety and stability of industrial processes, has been widely studied in recent years. During the running process, it is noted that the normal data are always much more than the fault data, which demonstrates imbalanced characteristics and leads to a negative effect on the overall accuracy of fault diagnosis. Targeting the problem, a novel imbalanced fault diagnosis method integrated kernel local Fisher discriminant analysis (KLFDA) with improved adaptive near-Bayesian support vector machine (ANBSVM) is proposed in this paper. First, KLFDA is used to extract the non-linear features while maintaining the local spatial structure of the data by introducing flow pattern learning. Second, considering the imbalance characteristics of the data, the data set is divided into a majority class (normal data) and a minority class (fault data). The density distributions of the two classes in their overlapping region are characterized by the proportional function of variance. Third, by minimizing the Bayesian error under the proportion function, the weight factors are adaptively obtained and then introduced into the objective function of the support vector machine (SVM). Namely, a cost sensitivity-based ANBSVM classifier for fault diagnosis is constructed. Finally, by the simulation experiment on the Tennessee Eastman (TE) process, the comparison results show that the proposed ANBSVM-based fault diagnosis method makes progress in the performance of fault diagnosis with higher diagnostic accuracy and F1 score.

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“…On the premise of the sufficient data, it has a good fault identification effect and feature extraction ability for highly nonlinear processes [17]. Therefore, multiple and multivariate statistical methods are combined to identify fault types; for example, the convolutional neural network (CNN) [18,19], dynamic Bayesian network (DBN) [20,21], long short term memory (LSTM) [22,23], etc.…”

Section: Introductionmentioning

confidence: 99%

Early Fault Diagnosis Method for Batch Process Based on Local Time Window Standardization and Trend Analysis

Yao

Dai

Luo

2021

Sensors

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The products of a batch process have high economic value. Meanwhile, a batch process involves complex chemicals and equipment. The variability of its operation leads to a high failure rate. Therefore, early fault diagnosis of batch processes is of great significance. Usually, the available information of the sensor data in batch processing is obscured by its noise. The multistage variation of data results in poor diagnostic performance. This paper constructed a standardized method to enlarge fault information as well as a batch fault diagnosis method based on trend analysis. First, an adaptive standardization based on the time window was created; second, utilizing quadratic fitting, we extracted a data trend under the window; third, a new trend recognition method based on the Euclidean distance calculation principle was composed. The method was verified in penicillin fermentation. We constructed two test datasets: one based on an existing batch, and one based on an unknown batch. The average diagnostic rate of each group was 100% and 87.5%; the mean diagnosis time was the same; 0.2083 h. Compared with traditional fault diagnosis methods, this algorithm has better fault diagnosis ability and feature extraction ability.

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Data‐driven nonlinear chemical process fault diagnosis based on hierarchical representation learning

Cited by 6 publications

References 42 publications

Feature learning based on entropy estimation density peak clustering and stacked autoencoder for industrial process monitoring

Feature learning based on entropy estimation density peak clustering and stacked autoencoder for industrial process monitoring

A novel imbalanced fault diagnosis method integrated KLFDA with improved cost‐sensitive learning ANBSVM

Early Fault Diagnosis Method for Batch Process Based on Local Time Window Standardization and Trend Analysis

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