A Three Level Strategy for Fault Detection and Diagnosis in Industrial Processes

System Fault Diagnostics, Reliability and Related Knowledge-Based Approaches ◽

10.1007/978-94-009-3929-5_10 ◽

1987 ◽

pp. 219-233

Author(s):

T. Cecchin ◽

J. Ragot ◽

D. Sauter

Keyword(s):

Fault Detection ◽

Fault Detection And Diagnosis ◽

Industrial Processes ◽

Detection And Diagnosis

A neural-network approach to fault detection and diagnosis in industrial processes

IEEE Transactions on Control Systems Technology ◽

10.1109/87.641399 ◽

1997 ◽

Vol 5 (6) ◽

pp. 529-541 ◽

Cited By ~ 81

Author(s):

Y. Maki ◽

K.A. Loparo

Keyword(s):

Neural Network ◽

Fault Detection ◽

Fault Detection And Diagnosis ◽

Industrial Processes ◽

Network Approach ◽

Neural Network Approach ◽

Detection And Diagnosis

Observer-based fault detection and diagnosis strategy for industrial processes

Journal of the Franklin Institute ◽

10.1016/j.jfranklin.2020.07.046 ◽

2020 ◽

Vol 357 (14) ◽

pp. 10054-10081 ◽

Cited By ~ 2

Author(s):

Emanuel Bernardi ◽

Eduardo J. Adam

Keyword(s):

Fault Detection ◽

Fault Detection And Diagnosis ◽

Industrial Processes ◽

Detection And Diagnosis

A Review on Fault Detection and Process Diagnostics in Industrial Processes

Processes ◽

10.3390/pr8091123 ◽

2020 ◽

Vol 8 (9) ◽

pp. 1123 ◽

Cited By ~ 2

Author(s):

You-Jin Park ◽

Shu-Kai S. Fan ◽

Chia-Yu Hsu

Keyword(s):

Fault Detection ◽

Process Variable ◽

Fault Detection And Diagnosis ◽

Industrial Processes ◽

Proper Action ◽

Process Diagnostics ◽

Underlying Process ◽

Process Dynamics ◽

Industrial Sectors ◽

Detection And Diagnosis

The main roles of fault detection and diagnosis (FDD) for industrial processes are to make an effective indicator which can identify faulty status of a process and then to take a proper action against a future failure or unfavorable accidents. In order to enhance many process performances (e.g., quality and throughput), FDD has attracted great attention from various industrial sectors. Many traditional FDD techniques have been developed for checking the existence of a trend or pattern in the process or whether a certain process variable behaves normally or not. However, they might fail to produce several hidden characteristics of the process or fail to discover the faults in processes due to underlying process dynamics. In this paper, we present current research and developments of FDD approaches for process monitoring as well as a broad literature review of many useful FDD approaches.

A Deep Learning Approach for Fault Detection and Diagnosis of Industrial Processes using Quantum Computing

2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) ◽

10.1109/smc42975.2020.9283034 ◽

2020 ◽

Author(s):

Akshay Ajagekar ◽

Fengqi You

Keyword(s):

Quantum Computing ◽

Deep Learning ◽

Fault Detection ◽

Fault Detection And Diagnosis ◽

Industrial Processes ◽

Learning Approach ◽

Detection And Diagnosis

On-line Fault Detection and Diagnosis in Industrial Processes Using Hidden Markov Model

Developments in Chemical Engineering and Mineral Processing ◽

10.1002/apj.5500130322 ◽

2008 ◽

Vol 13 (3-4) ◽

pp. 397-406 ◽

Cited By ~ 1

Author(s):

Sha-Yuan Zhou ◽

Shu-Qing Wang

Keyword(s):

Fault Detection ◽

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Fault Detection And Diagnosis ◽

Industrial Processes ◽

On Line ◽

Detection And Diagnosis

Application of Bayesian belief networks to fault detection and diagnosis of industrial processes

2010 International Conference on Chemistry and Chemical Engineering ◽

10.1109/iccceng.2010.5560369 ◽

2010 ◽

Cited By ~ 4

Author(s):

Maryam Azhdari ◽

Nasir Mehranbod

Keyword(s):

Fault Detection ◽

Bayesian Belief Networks ◽

Fault Detection And Diagnosis ◽

Industrial Processes ◽

Belief Networks ◽

Detection And Diagnosis

Data-driven Fault Detection and Diagnosis for Complex Industrial Processes

IFAC Proceedings Volumes ◽

10.3182/20090630-4-es-2003.00184 ◽

2009 ◽

Vol 42 (8) ◽

pp. 1115-1125 ◽

Cited By ~ 25

Author(s):

S. Joe Qin

Keyword(s):

Fault Detection ◽

Fault Detection And Diagnosis ◽

Data Driven ◽

Industrial Processes ◽

Detection And Diagnosis

Principal Polynomial Analysis for Fault Detection and Diagnosis of Industrial Processes

IEEE Access ◽

10.1109/access.2018.2870140 ◽

2018 ◽

Vol 6 ◽

pp. 52298-52307 ◽

Cited By ~ 5

Author(s):

Xinmin Zhang ◽

Manabu Kano ◽

Yuan Li

Keyword(s):

Fault Detection ◽

Fault Detection And Diagnosis ◽

Industrial Processes ◽

Detection And Diagnosis

A knowledge based approach to fault detection and diagnosis in industrial processes: a case study

Proceedings of 1994 IEEE International Symposium on Industrial Electronics (ISIE'94) ◽

10.1109/isie.1994.333083 ◽

2002 ◽

Cited By ~ 1

Author(s):

C.J.G. Alonso ◽

G.G.L. Acosta ◽

M.C. De Prada ◽

J.M. Mira

Keyword(s):

Fault Detection ◽

Fault Detection And Diagnosis ◽

Industrial Processes ◽

Knowledge Based ◽

Detection And Diagnosis

One-dimensional convolutional neural network-based active feature extraction for fault detection and diagnosis of industrial processes and its understanding via visualization

ISA Transactions ◽

10.1016/j.isatra.2021.04.042 ◽

2021 ◽

Author(s):

Shumei Chen ◽

Jianbo Yu ◽

Shijin Wang

Keyword(s):

Neural Network ◽

Feature Extraction ◽

Fault Detection ◽

Convolutional Neural Network ◽

Fault Detection And Diagnosis ◽

Industrial Processes ◽

One Dimensional ◽

Detection And Diagnosis