A Novel Fault Detection and Diagnosis Scheme Based on Independent Component Analysis-Statistical Characteristics: Application on the Tennessee Eastman Benchmark Process

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

Towards Distributed Diagnosis of The Tennessee Eastman Process Benchmark

A distributed hybrid strategy is outlined for the isolation of faults and disturbances in the Tennessee Eastman Process, which would build on existing structures for distributed control systems, so should be easy to implement, be cheap and be widely applicable. The main emphasis in the paper is on one component of the strategy, a steady state based approach. Results obtained by applying this ap...

Online Statistical Monitoring and Fault Classification of the Tennessee Eastman Challenge Process Based on Dynamic Independent Component Analysis and Support Vector Machine

This paper presents a new online statistical monitoring based on dynamic independent component analysis (DICA) to detect the Tennessee Eastman challenge process faults. The proposed method employs dynamic feature extraction approach to capture most of the inherent dynamic fault information. This leads to an efficient fault detection with superior performance compared to independent component an...

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...

On the development of a sliding mode observer-based fault diagnosis scheme for a wind turbine benchmark model

This paper addresses the design of an observer-based fault diagnosis scheme, which is applied to some of the sensors and actuators of a wind turbine benchmark model. The methodology is based on a modified sliding mode observer (SMO) that allows accurate reconstruction of multiple sensor or actuator faults occurring simultaneously. The faults are reconstructed using the equivalent output err...