Three Tank System Sensors and Actuators Faults Detection Employing Unscented Kalman Filter

Abstract

Fault detection is critical for industrial applications to maintain a stable operation and to reduce maintenance costs. Many fault detection techniques have been introduced recently to cope with the increasing demand for more safe operations. One of the most promising fault detection algorithms is the Unscented Kalman Filter (UKF). UKF is a model-based algorithm that could be used to detect different fault types for a given system. On the other hand, the three-tank system is a well-known benchmark that simulates many industrial applications. The fault detection of the three-tank system is quite challenging as it is a Multi-Input Multi-Output (MIMO) nonlinear system. Therefore, UKF will be employed as a fault detection strategy for this system to detect sensor and actuator faults. The performance of the UKF will be investigated under different operating and fault conditions to show its merits for the given case study.

Attack Resilient Cloud-Based Control Systems for Industry 4.0

“…Then, by comparing the measured signals that we get from sensors with the estimated ones, a residual signal is generated, and by performing statistical tests such as generalized likelihood ratio (GLR) or cumulative sum (CUSUM) on this signal, an anomaly is detected. This anomaly detection method has been applied to different applications like power systems [24]- [27], automated vehicles [28]- [30], industrial control systems [31]- [33], etc.…”