scholarly journals Fault Detection for Industrial Processes

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Yingwei Zhang ◽  
Lingjun Zhang ◽  
Hailong Zhang
Keyword(s):  
Fault Detection ◽  
Fermentation Process ◽  
Industrial Processes ◽  
Statistical Techniques ◽  
Tennessee Eastman Process ◽  
Detection Approach ◽  
Principal Directions ◽  
Residual Space ◽  
Simulation Results ◽  
Penicillin Fermentation

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.

scholarly journals Design of Robust Fault Detection Scheme for Penicillin Fermentation Process

2015 ◽  
Vol 48 (21) ◽  
pp. 589-594 ◽  
Author(s):  
Abdul Rehman Khan ◽  
Abdul Qayyum Khan ◽  
Muhammad Taskeen Raza ◽  
Muhammad Abid ◽  
Ghulam Mustafa
Keyword(s):  
Fault Detection ◽  
Fermentation Process ◽  
Detection Scheme ◽  
Robust Fault Detection ◽  
Penicillin Fermentation

scholarly journals A Fault Detection System for The Yaw Control of A HAWT Based on Neural Networking

2021 ◽  
Vol 85 (1) ◽  
pp. 135-142
Author(s):  
Ahmed R. El-Mallawany ◽  
Sameh Shaaban ◽  
Aida Abdel Hafiz
Keyword(s):  
Fault Detection ◽  
Wind Turbine ◽  
Detection System ◽  
Operating Conditions ◽  
Horizontal Axis Wind Turbine ◽  
Yaw Control ◽  
Detection Approach ◽  
Simulation Results ◽  
Early Fault Detection ◽  
The Time Domain

The objective of the yaw control system in a horizontal axis wind turbine (HAWT) is to follow the wind direction with a minimum error. In this paper, a data driven fault detection approach of a HAWT is applied. Three simulation programs were utilized in order to model a 1.5 MW HAWT. These programs are Fatigue, Aerodynamics, Structures, and Turbulence(FAST), TurbSim, and MATLAB. The approach is implemented under normal operating scenarios while considering different wind velocities. Different kinds of faults were applied to the system for a nacelle-yaw angle error ranging from -10° to +20°. The simulation results of the Tower Top Deflection (TTD) in the time domain were transferred into frequency domain by Fast Fourier Transform (FFT). The output variables were used in order to build a Neural Networking, which will monitor the performance of the wind turbine. The built Neural Networking will also provide an early fault detection to avoid the operating conditions that lead to sudden turbine breakdown. The present work provides initial results that are useful for remote condition monitoring and assessment of a 1.5MW HAWT. The simulation results indicate that the implemented Neural Networking can achieve improvement of the wind turbine operation and maintenance level.

scholarly journals Online process monitoring and fault-detection approach based on adaptive neighborhood preserving embedding

2019 ◽  
Vol 52 (5-6) ◽  
pp. 387-398
Author(s):  
Lei Tan ◽  
Peng Li ◽  
Aimin Miao ◽  
Yong Chen
Keyword(s):  
Fault Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Process Monitoring ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Monitoring Method ◽  
Tennessee Eastman Process ◽  
Detection Approach ◽  
Adaptive Neighborhood

This study aims to solve the problem involving the high false alarm rate experienced during the detection process when using the traditional multivariate statistical process monitoring method. In addition, the existing model cannot be updated according to the actual situation. This article proposes a novel adaptive neighborhood preserving embedding algorithm as well as an online fault-detection approach based on adaptive neighborhood preserving embedding. This approach combines the approximate linear dependence condition with neighborhood preserving embedding. According to the newly proposed update strategy, the algorithm can achieve an adaptive update model that realizes the online fault detection of processes. The effectiveness and feasibility of the proposed approach are verified by experiments of the Tennessee Eastman process. Theoretical analysis and application experiment of Tennessee Eastman process demonstrate that in this article proposed fault-detection method based on adaptive neighborhood preserving embedding can effectively reduce the false alarm rate and improve the fault-detection performance.

scholarly journals Data-driven fault diagnostics for industrial processes: An application to Penicillin fermentation process

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Muhammad Asim Abbasi ◽  
Abdul Qayyum Khan ◽  
Ghulam Mustafa ◽  
Muhammad Abid ◽  
Aadil Sarwar Khan ◽  
...  
Keyword(s):  
Fermentation Process ◽  
Data Driven ◽  
Industrial Processes ◽  
Fault Diagnostics ◽  
Penicillin Fermentation

scholarly journals Fault Detection in Tennessee Eastman Process Using Fisher’s Discriminant Analysis and Principal Component Analysis Modified by Genetic Algorithm

2011 ◽  
Vol 110-116 ◽  
pp. 4255-4262
Author(s):  
Mostafa Noruzi Nashalji ◽  
Seyed Mohammad Razeghi ◽  
Mahdi Aliyari Shoorehdeli ◽  
Mohammad Teshnehlab
Keyword(s):  
Genetic Algorithm ◽  
Principal Component Analysis ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Industrial Processes ◽  
Tennessee Eastman Process ◽  
Residual Space ◽  
Fisher’S Discriminant Analysis ◽  
Process Simulator

This paper describes hybrid multivariate methods: Fisher’s Discriminant Analysis and Principal Component Analysis improved by Genetic Algorithm. These methods are good techniques that have been used to detect faults during the operation of industrial processes. In this study, score and residual space of modified PCA and modified FDA are applied to the Tennessee Eastman Process simulator and show that modified PCA and modified FDA are more proficient than PCA and FDA for detecting faults.

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

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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