Intelligent work-situation fault diagnosis and fault-tolerant system for the shaft-furnace roasting process

Abstract

During roasting in a shaft furnace (used for the deoxidizing roasting of ore), work-situation faults (WSFs) arise as a result of variations in process conditions and off-spec operation. These work-situation faults can be potentially disastrous and can lead to a total collapse of the control system if they are not detected and diagnosed in time. Furthermore, by their very nature they have to be distinguished from the results addressed by existing methods of diagnosis and tolerance control. This paper presents an innovative work-situation fault diagnosis (WSFD) and fault-tolerance control (FTC) strategy for a control system where a combination of neural networks, expert system, and case-based reasoning is used. As such, a system is established that consists of a magnetic tube recovery rate (MTRR) prediction model, a work-situation fault diagnosis unit, and a fault-tolerance controller. The proposed system diagnoses imminent work-situation faults, and then the fault-tolerance controller adjusts the set-points of the control loops. The outputs of the lower-level control system track the modified set-points, which makes the process deviate gradually from work-situation faults with an acceptable product quality. The proposed system has been applied to the shaft-furnace roasting process in the largest minerals processing factory in China and has reduced the frequency of all work-situation faults by more than 50 per cent, with the ratio of furnace operation increased by 2.98 per cent. It has been proven to provide many benefits to the factory.

Keywords

shaft-furnace roasting process work-situation fault diagnosis fault-tolerance control neural networks expert system case-based reasoning

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References

Gui

W. H.

Liu

X. Y.

Fault diagnosis technologies based on artificial intelligence for complex process

Control Engng of China, 2002, 9 (4), 1–6.

Qian

X. X.

Jiang

Y. R.

Wen

Y. Q.

An expert system for real-time fault diagnosis of complex chemical processes

Expert Syst. with Applic., 2003, 24, 425–432.

Wang

Z. W.

Zhang

R. P.

Research on fault fuzzy diagnosis based on neural network

J. Syst. Engng, 2005, 20 (3), 330–334.

Jiang

Fault-tolerant control systems - an introductory overview

Acta Autom. Sinica, 2005, 31 (1), 161–174.

Tzafestas

C. S.

Rigatos

G. G.

Tzafestas

S. G.

Design of fault-tolerant control systems: Passive and active approaches

Syst. Sci., 1998, 24 (4), 5–28.

Zhang

Y. M.

Jiang

Fault tolerant control system design with explicit consideration of performance degradation

IEEE Trans. Aerospace and Electronic Syst., 2003, 39 (3), 838–848.

Hammett

Design by extrapolation: An evaluation of fault tolerant avionics

IEEE Aerospace and Electronic Syst. Mag., 2002, 17 (4), 17–25.

Marcello

R. N.

Younghwan

Brad

A. S.

A fault tolerant flight control system for sensor and actuator failures using neural networks

Aircr. Des., 2000, 3, 103–128.

McAllister

D. F.

Nagle

H. T.

Toward a fault-tolerant processor for medical applications. IEEE, Computer Soc., Los Alamitos, California; IEEE, Engineering in Medicine and Biology Soc., New York; University of Minnesota, Minnesota, 1988, pp. 101–104.

10.

Adam

G. R.

Ervin

Jiang

Fault diagnosis in machine tools using selective regional correlation

Mech. Syst. and Signal Processing, 2006, 20, 1221–1238.

11.

Miyagi

P. E.

Riascos

L. A. M.

Modeling and analysis of fault-tolerant systems for machining operations based on Petri nets

Control Engng Practice, 2006, 14 (4), 397–408.

12.

Izumikawa

Fault-tolerant control system of flexible arm for sensor fault by using reaction force observer. 8th IEEE International Workshop on Advanced motion control, Kawasaki, Japan, 2004, pp. 583–588.

13.

Shiraishi

Fault-tolerant mobile agents in distributed objects systems

In Proceedings of the 9th IEEE Workshop on Future trends of distributed computing systems, San Juan, Puerto Rico, 2003, pp. 145–151.

14.

Chai

T. Y.

Ding

J. L.

Integrated automation system for hematite ores processing and its applications

Measmt and Control, 2006, 29 (5), 140–146.

15.

Ramasamy

Narayanan

S. S.

Rao

Ch. D. P.

Control of ball mill grinding circuit using model predictive control scheme

J. Process Control, 2005, 15, 273–283.

16.

Yan

A. J.

Chai

T. Y.

Intelligent prediction model of magnetic tube recovery rate for shaft furnace roasting

J. Northeast. Univ. (Nat. Sci.), 2005, 26 (9), 824–827.

17.

Harald

Martin

Comparing performance of feedforward neural nets and k-means for cluster-based market segmentation

Eur. J. Opl Res., 1999, 114, 346–353.

18.

Darken

Chang

Moody

Learning rate schedules for faster stochastic gradient search

In Proceedings of IEEE-SP Workshop Neural networks for signal processing II, Helsingoer, Denmark, 1992, pp. 3–12.

19.

Wagner

W. P.

Otto

Chung

Q. B.

Knowledge acquisition for expert systems in accounting and financial problem domains

Knowledge-Based Syst., 2002, 15, 439–447.

20.

Zlatareva

N. P.

A refinement framework to support validation and maintenance of knowledge-based systems

Expert Syst. with Applic., 1998, 15, 245–252.

21.

She

J. H.

Nakano

Gui

Expert control and fault diagnosis of the leaching process in zinc hydrometallurgy plant

Control Engng Practice, 2002, 10 (4), 433–442.

22.

Monica

H. O.

Dynamic knowledge validation and verification for CBR teledermatology system

Artificial intelligence in medicine, 2006, 39 (1), 79–96. DOI: 10.1016/j.artmed.2006.08.004.

23.

Tan

M. H.

Chai

Modelling of the laminar cooling process with case-based reasoning

Control Theory and Applic., 2005, 22 (2), 248–253, 260.

24.

Zhou

Yue

Zhao

D. Y.

Chai

T. Y.

Soft-sensor approach with case-based reasoning and its application in grinding process

Control and Decision, 2006, 21 (6), 647–650, 655.