Research on TE process fault diagnosis method based on DBN and dropout

2020 ◽  
Vol 98 (6) ◽  
pp. 1293-1306 ◽  
Author(s):  
Yuqin Wei ◽  
Zhengxin Weng
Keyword(s):  
Fault Diagnosis ◽  
Diagnosis Method ◽  
Process Fault Diagnosis

scholarly journals A Hybrid Intelligent Fault Diagnosis Strategy for Chemical Processes Based on Penalty Iterative Optimization

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1266
Author(s):  
Yuman Yao ◽  
Jiaxin Zhang ◽  
Wenjia Luo ◽  
Yiyang Dai
Keyword(s):  
Fault Diagnosis ◽  
Gas Absorption ◽  
Noise Levels ◽  
Adaptive Wavelet ◽  
Penalty Term ◽  
Penalty Factor ◽  
Diagnosis Method ◽  
Adaptive Noise ◽  
Process Fault Diagnosis ◽  
The Impact

Process fault is one of the main reasons that a system may appear unreliable, and it affects the safety of a system. The existence of different degrees of noise in the industry also makes it difficult to extract the effective features of the data for the fault diagnosis method based on deep learning. In order to solve the above problems, this paper improves the deep belief network (DBN) and iterates the optimal penalty term by introducing a penalty factor, avoiding the local optimal situation of a DBN and improving the accuracy of fault diagnosis in order to minimize the impact of noise while improving fault diagnosis and process safety. Using the adaptive noise reduction capability of an adaptive lifting wavelet (ALW), a practical chemical process fault diagnosis model (ALW-DBN) is finally proposed. Then, according to the Tennessee–Eastman (TE) benchmark test process, the ALW-DBN model is compared with other methods, showing that the fault diagnosis performance of the enhanced DBN combined with adaptive wavelet denoising has been significantly improved. In addition, the ALW-DBN shows better performance under the influence of different noise levels in the acid gas absorption process, which proves its high adaptability to different noise levels.

Phase partition and identification based on kernel entropy component analysis and multi-class support vector machines-fireworks algorithm for multi-phase batch process fault diagnosis

2020 ◽  
Vol 42 (12) ◽  
pp. 2324-2337
Author(s):  
Min Zhang ◽  
Ruiqi Wang ◽  
Zhenyu Cai ◽  
Wenming Cheng
Keyword(s):  
Fault Diagnosis ◽  
Batch Process ◽  
Support Vector ◽  
Fireworks Algorithm ◽  
Vector Machines ◽  
Diagnosis Method ◽  
Structure Similarity ◽  
Multi Phase ◽  
Process Fault Diagnosis ◽  
Entropy Component

For the characteristics of nonlinear and multi-phase in the batch process, a self-adaptive multi-phase batch process fault diagnosis method is proposed in this paper. Firstly, kernel entropy component analysis (KECA) method is used to achieve multi-phase partition adaptively, which makes the process data mapped into the high-dimensional feature space and then constructs the core entropy and the angular structure similarity. Then a multi-phase KECA failure monitoring model is developed by using the angular structure similarity as the statistic, which is based on the partitioned phases and the effective failure features by the KECA feature extraction method. A multi-phase batch process fault diagnosis method, which applies the multi-class support vector machines (MSVM) and fireworks algorithm (FWA), is proposed to recognize each sub-phase fault diagnosis automatically. The effectiveness and advantages of the proposed multi-phase fault diagnosis method are illustrated with a case study on a fed-batch penicillin fermentation process.

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

Towards High Accuracy Fault Diagnosis of Digital Circuits

2004 ◽  
Author(s):  
Camelia Hora ◽  
Stefan Eichenberger
Keyword(s):  
Fault Diagnosis ◽  
Fault Location ◽  
Digital Circuits ◽  
High Accuracy ◽  
Manufacturing Processes ◽  
Diagnosis Method ◽  
Technology Trend

Abstract Due to the development of smaller and denser manufacturing processes most of the hardware localization techniques cannot keep up satisfactorily with the technology trend. There is an increased need in precise and accurate software based diagnosis tools to help identify the fault location. This paper describes the software based fault diagnosis method used within Philips, focusing on the features developed to increase its accuracy.

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