Fault Detection and Monitoring of Length Loop Control System in Pickling Process

Abstract

Modern manufacturing facilities are large scale, highly complex, and operate with large number of variables under closed loop control. Early and accurate fault detection and diagnosis for these plants can minimise down time, increase the safety of plant operations, and reduce manufacturing costs. Fault detection and isolation is more complex particularly in the case of the faulty analog control systems. Analog control system are not equipped with monitoring function where the process parameters are continually visualised. We consider in this paper an approach to fault detection and isolation in the pickling process in cold rolling. The fault appeared on one of the power unit driving a rotary machine, this machine can not track a reference speed given by another machine. Using a computerised data acquisition system, the main machine parameters have been monitored. The fault has been detected and isolated on basis of analysis of monitored data. Normal and faulty situation have been obtained by an artificial neural network (ANN) model which is implemented to simulate the normal and faulty status of rotary machine.

Quality Monitoring Using Principal Component Analysis and Fuzzy Logic Application in Continuous Casting Process

“…In most cases, this phase is followed by an additional feature selection procedure, necessary to keep only relevant descriptors which retain a maximum of information. This method leads to new descriptors determined by linear or non linear combination of initial ones according to an appropriate criterion [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] . The second phase consists in building the system diagnosis.…”

Fault Tolerance for Two Wheel Mobile Robot Using FSM (Finite State Machine)

“…Most researches focused on how to identify the system fault, including failure of the actuator and sensor as well as the error of the system. Researchers have proposed various solutions to the problem including (Magni, Scattolini, & Rossi, 2000) the Finite State Machine (FSM) method to identify the type of fault, (Apley, Shi, & Arbor, n.d., 1998) the GLRT mathematical method to identify the fault, (Bouhouche, Lahreche, Ziani, & Bast, 2005) artificial neural network, (Lee, Alena, & Robinson, 2005) fault decision method, (Bai, Dick, Dinda, & Chou, 2011) Fault-Aware Code Transformation For Sensor Network (FACT), (Mathew et al, 2014) genetic algorithm, and (Kamel, Yu, & Zhang, 2016) two-stage Kalman filter. On the other hand, computation methods proposed to solve this problem are (Benowitz, Calhoun, & Alderson, 1975) the Advanced Avionics Fault Isolation System (AAFIS) concept utilizes bit (build in test) and etc.…”