Industrial process fault detection based on deep highly-sensitive feature capture

Liu, Bowen; Chai, Yi; Yu-hu, Liu; Cheng-hong, Huang; Wang, Yiming; Tang, Qiu

doi:10.1016/j.jprocont.2021.04.003

Cited by 21 publications

(6 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The large number of features derived from various domains results in an HD dataset, as one could expect. As a result, features are chosen [16] and methodslikePCA (principal component analysis) [17] or LDA (linear discriminant analysis) [18] are typically employed to reduce dimensionality of these features. In addition, [19], for example, used data entropy to preprocess raw time series data.RPCA (Recursive PCA) [20], DPCA (dynamic PCA) [21] and KPCA (kernel PCA) [22] are used to monitor a variety of industrial processes, including adaptive, dynamic, and nonlinear processes [24].…”

Section: Related Workmentioning

confidence: 99%

Application of soft sensors and ant colony optimiation for monitoring and managing defects in the automation industry

Wongchai¹,

Abourehab

Ahmed

et al. 2023

Int J Adv Manuf Technol

View full text Add to dashboard Cite

In modern industrial processes, various types of soft sensors are used, which play essential roles in process monitoring, control and optimization. Emerging new theories, advanced techniques and the information infrastructure have enabled the elevation of the performance of soft sensing. Once soft sensors are designed, a mechanism to maintain or update these models is highly desirable in industry. This paper proposed novel technique in monitoring and control optimization of soft sensors in automation industry for fault detection. Here fault detection has been carried out using Probabilistic Multi-Layer Fourier Transform Perceptron (PMLFTP). The input collected plant data has been pre-processed for removal of samples containing null values.Then fault detection and diagnosis process have been carried out based on probability of the data with Fourier transform-based detection and multi-layer perceptronbased diagnosis of the fault in the manufacturing process. Then controlling of data in soft sensors has been optimized using auto-regression based ant colony optimization (AR_ACO) which has effect in increasing the production of industry automatically. The experimental results have been carried out in terms of computational rate of 40%, QoS of 78%, RMSE of 45%, fault detection rate of 90%, control optimization of 93% has been obtained for various historical data-based evaluations.

show abstract

Section: Related Workmentioning

confidence: 99%

Application of soft sensors and ant colony optimiation for monitoring and managing defects in the automation industry

Wongchai¹,

Abourehab

Ahmed

et al. 2023

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…The number of normal data (23) In the TE process. Fault3, 9 and 15 were ignored while analyzing the result of process monitoring, because their mean, variance and peak value had not changed obviously [30][31][32].…”

Section: Te Processmentioning

confidence: 99%

“…Liu et al proposed a highly sensitive feature selection framework based on DBN and implemented fault detection on this framework. This method eliminated the redundant features in the DBN network and improved the fault detection performance [23]. Wang et al proposed a stacked supervised auto-encoder to obtain deep features for fault classification and applied it to industrial process fault diagnosis [24].…”

Section: Introductionmentioning

confidence: 99%

Industrial Fault Detection Based on Discriminant Enhanced Stacking Auto-Encoder Model

et al. 2022

Self Cite

View full text Add to dashboard Cite

In the recent years, deep learning has been widely used in process monitoring due to its strong ability to extract features. However, with the increasing layers of the deep network, the compression of features by the deep model will lead to the loss of some valuable information and affect the model’s performance. To solve this problem, a fault detection method based on a discriminant enhanced stacked auto-encoder is proposed. An enhanced stacked auto-encoder network structure is designed, and the original data is added to each hidden layer in the model pre-training process to solve the problem of information loss in the feature extraction process. Then the self-encoding network is combined with spectral regression kernel discriminant analysis. The fault category information is introduced into the features to optimize the features and enhance the discrimination of the extracted features. The Euclidean distance is used for fault detection based on the extracted features. From the Tennessee Eastman process experiment, it can be found that the detection accuracy of this method is about 9.4% higher than that of the traditional stacked auto-encoder method.

show abstract

“…To ensure the safe operation of equipment, extracting fault characteristics from signals collected by the sensors is necessary to achieve the purpose of fault diagnosis [1]. Sensors collect a large amount of image [2,3] and data information [4][5][6][7][8], based on which many functions can be implemented.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of sensor pulse signals based on improved energy fluctuation index and VMD

Yu-hu

Chen²,

Mao³

et al. 2023

Front. Phys.

Self Cite

View full text Add to dashboard Cite

Variational mode decomposition (VMD) has been widely applied in sensors. However, the mode number and balance parameter seriously limit VMD application. To solve this problem, this study proposes a novel method, which combines an improved energy fluctuation index (IEFI) and modified VMD (MVMD). In the proposed method, IEFI provided better performance to resist interference from random impulses by considering the periodicity of fault feature components. Consequently, it is applied to determine the initial center frequency for MVMD, which fixed the problem of the mode number. Moreover, a novel balance parameter search strategy, which can adaptively determine the optimal balance parameter, is combined with MVMD whose stop condition is replaced by kurtosis to extract the fault feature. Simulation results indicated that the proposed method does well in detecting the feature of a periodic impulse signal from the signal polluted by some interference impulses. Moreover, the bearing fault diagnosis results demonstrate that the proposed method can accurately detect bearing fault features. Furthermore, the method was validated with bearing fault data. The results showed that the method can accurately extract the fault characteristics of the impulse signal and achieve fault diagnosis.

show abstract

Industrial process fault detection based on deep highly-sensitive feature capture

Cited by 21 publications

References 31 publications

Application of soft sensors and ant colony optimiation for monitoring and managing defects in the automation industry

Application of soft sensors and ant colony optimiation for monitoring and managing defects in the automation industry

Industrial Fault Detection Based on Discriminant Enhanced Stacking Auto-Encoder Model

Fault diagnosis of sensor pulse signals based on improved energy fluctuation index and VMD

Contact Info

Product

Resources

About