Unknown input observer design for fault detection and diagnosis in a continuous stirred-tank reactor

Ali H Al-Shatri,Oladokun Olagoke,Bemgba B Nyakuma,Ahmad Arshad,A Abd Jalil

doi:10.1051/e3sconf/20199002004

Ali H Al-Shatri, Oladokun Olagoke + Show 3 more

Open Access

https://doi.org/10.1051/e3sconf/20199002004

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Abstract

Early and accurate fault detection and diagnosis (FDD) minimises downtime, increases the safety and reliability of plant operation, and reduces manufacturing costs. This paper presents a robust FDD strategy for a nonlinear system using a bank of unknown input observers (UIO). The approach is based on structure residual generation that provides not only decoupling of faults from model uncertainties and unknown input disturbance but also decoupling the effect of a fault from the effects of other faults. The generated residual was evaluated through the statistical threshold to avoid fault missing or false alarm. The performance of the robust FDD scheme was assessed by some sensor fault scenarios created in a continuous stirred-tank reactor (CSTR). The simulation result showed the effectiveness of the proposed approach.

Highlights

Fault detection and diagnosis (FDD) is an important task for improving the reliability and safety of process industries
FDD has become a significant aspect of the early warning system and control system design [1]
The recent methods used are analytical redundancy methods, which only rely on the knowledge of the process model to generate fault features

Summary

Introduction

Fault detection and diagnosis (FDD) is an important task for improving the reliability and safety of process industries. Chemical process plants are susceptible to many common failures such as sensor faults, actuator faults, process faults, noise, and unexpected disturbances. These failures can cause product quality degradation, high operational costs, damage to equipment and instrumentation, and fatal accidents such as explosion, release of harmful materials, and fire that may cause harm to humans and the environment unless the failures are detected and diagnosed early. The recent methods used are analytical redundancy methods, which only rely on the knowledge of the process model to generate fault features. Analytical redundancy methods reduce the cost, weight, and size of a system, and fault detection of the sensor (or the actuator) and inside the system compared to hardware redundancy.

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