Fault Detection in Tennessee Eastman Process Using Fisher’s Discriminant Analysis and Principal Component Analysis Modified by Genetic Algorithm

Fault detection in the Tennessee Eastman benchmark process using dynamic principal components analysis based on decorrelated residuals (DPCA-DR)

Sensor fault detection and isolation by robust principal component analysis

Sensors are essential components of modern control systems. Any faults in sensors will affect the overall performance of a system because their effects can easily propagate to manipulative variables through feedback control loops and also disturb other process variables. The task for sensor validation is to detect and isolate faulty sensors and estimate fault magnitudes afterwards to provide fa...

Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis

Ž . Principal component analysis PCA is the most commonly used dimensionality reduction technique for detecting and diagnosing faults in chemical processes. Although PCA contains certain optimality properties in terms of fault detection, and Ž . has been widely applied for fault diagnosis, it is not best suited for fault diagnosis. Discriminant partial least squares DPLS has been shown to impro...

Fault detection using CUSUM based techniques with application to the Tennessee Eastman Process

In this paper, a cumulative sum based statistical method is used to detect faults in the Tennessee Eastman Process (TEP). The methodology is focused on three particular faults that could not be observed with other fault detection methodologies previously reported. Hotelling’s-T charting based on the cumulative sums of the faults’ relevant variables was successful in detecting these faults, howe...

Feature Extraction Using Genetic Algorithm Based Principal Component Analysis