Grouping multi‐rate sampling fault detection method for penicillin fermentation process

A new fault-relevant KPCA algorithm is proposed. Then the fault detection approach is proposed based on the fault-relevant KPCA algorithm. The proposed method further decomposes both the KPCA principal space and residual space into two subspaces. Compared with traditional statistical techniques, the fault subspace is separated based on the fault-relevant influence. This method can find fault-relevant principal directions and principal components of systematic subspace and residual subspace for process monitoring. The proposed monitoring approach is applied to Tennessee Eastman process and penicillin fermentation process. The simulation results show the effectiveness of the proposed method.

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Comparison of MPCAV, ARPCA, and BDPCA fault detection performance in a fed-batch penicillin fermentation process

IFAC Proceedings Volumes ◽

10.3182/20090630-4-es-2003.00138 ◽

2009 ◽

Vol 42 (8) ◽

pp. 840-845

Author(s):

J. Vanlaer ◽

G. Gins ◽

J.F.M. Van Impe

Keyword(s):

Fault Detection ◽

Fermentation Process ◽

Detection Performance ◽

Fed Batch ◽

Penicillin Fermentation

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Fault detection and isolation in a FED-batch penicillin fermentation process

2003 European Control Conference (ECC) ◽

10.23919/ecc.2003.7085204 ◽

2003 ◽

Author(s):

Hongwei Zhang ◽

Barry Lennox

Keyword(s):

Fault Detection ◽

Fermentation Process ◽

Fault Detection And Isolation ◽

Fed Batch ◽

Penicillin Fermentation

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Fault Detection for Batch Processes Based on Segmentation MPCA

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1030-1032.1701 ◽

2014 ◽

Vol 1030-1032 ◽

pp. 1701-1708

Author(s):

Ning Lv ◽

Guang Yuan Bai ◽

Lei Lei Jiang

Keyword(s):

Fault Detection ◽

Fermentation Process ◽

Detection Method ◽

Piecewise Linear ◽

Nonlinear Modeling ◽

Principal Component ◽

Batch Processes ◽

Piecewise Linear Approximation ◽

Process Data ◽

Speed And Accuracy

Aiming at the dynamic characteristic of batch production process changes fast and the accurate modeling of it is difficult, so this paper proposes an intermittent fault detection method of the principal component analysis based on process segment. According to the different dynamic characteristics of process data, the process is divided into multiple stages, with the method of piecewise linear approximation of nonlinear modeling to model different stages of the process, in order to make up the deficiency of traditional MPCA fault diagnosis methods. Through the fault detection of the beer fermentation process experiments to verify that the method can detect process faults promptly and improve the speed and accuracy of process monitoring.

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A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset207430 ◽

2020 ◽

pp. 129-139

Author(s):

Weihai Sun ◽

Lemei Han

Keyword(s):

Fault Detection ◽

Signal Analysis ◽

Energy Analysis ◽

Detection Method ◽

Sound Signal ◽

Practical Significance ◽

Time Frequency ◽

Detection Approach ◽

Machine Fault ◽

Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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