Detection and management of actuator faults in controlled particulate processes using population balance models

El‐Farra, Nael H.; Giridhar, Arthi

doi:10.1016/j.ces.2007.07.019

Cited by 16 publications

(7 citation statements)

References 36 publications

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“…By properly tuning the observer, however, T k b can be made as small as desired. This idea can be justified using singular perturbation analysis (see [20]). …”

Section: Remarkmentioning

confidence: 99%

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Fault Detection and Fault-Tolerant Control of Particulate Processes using Population Balance Models

El‐Farra

Giridhar

2007

2007 American Control Conference

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This paper presents a Fault-Tolerant Control (FTC) architecture for particulate processes described by population balance models with control constraints, actuator faults and limited process measurements. The architecture integrates model-based nonlinear feedback, state estimation, fault detection and supervisory control on the basis of an appropriate reduced-order model that captures the dominant process dynamics. Appropriate fault detection thresholds and controller reconfiguration laws are derived to ensure robustness of the FTC architecture to model reduction and state estimation errors when implemented on the particulate process. The proposed methodology is applied to the problem of constrained, actuator fault-tolerant stabilization of an unstable steady-state of a continuous crystallizer.

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“…By properly tuning the observer, however, T k b can be made as small as desired. This idea can be justified using singular perturbation analysis (see [20]). …”

Section: Remarkmentioning

confidence: 99%

“…Due to space limitations, the mathematical proofs of the results presented here will be omitted. The reader is referred to the full version of this work [20] for the proofs and for a more in-depth discussion of the results.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection and Fault-Tolerant Control of Particulate Processes using Population Balance Models

El‐Farra

Giridhar

2007

2007 American Control Conference

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“…Major bottlenecks in the design of model-based faulttolerant control systems for particulate processes include the infinite-dimensional nature of the process model as well as the complex and uncertain dynamics of particulate processes. An effort to address these problems was initiated in El-Farra and Giridhar (2008) where a methodology for the detection and handling of control actuator faults in particulate processes was developed based on low-order models that capture the dominant process dynamics. These results were generalized in Giridhar and El-Farra (2009) to address the problems of fault isolation and robustness against model uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Data-based Fault Identification and Accommodation in the Control of Sampled-Data Particulate Processes

Napasindayao

El‐Farra

2014

IFAC Proceedings Volumes

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“…An effort to address these problems was initiated where a methodology for the detection and handling of control actuator faults in particulate processes was developed based on low-order models that capture the dominant process dynamics. 8 These results were generalized to address the problems of fault isolation and robustness against model uncertainty. 9 Various implementation issues arise in the design of any fault-tolerant control system.…”

Section: ■ Introductionmentioning

confidence: 99%

Fault Detection and Accommodation in Particulate Processes with Sampled and Delayed Measurements

Napasindayao

El‐Farra

2013

Ind. Eng. Chem. Res.

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A model-based framework is developed for component fault detection and accommodation in particulate processes with discretely sampled and delayed measurements. An observer-based output feedback controller is initially designed based on a suitable reduced-order model that captures the dominant process dynamics. The controller includes an intersample model predictor that compensates for measurement intermittency and a propagation unit that compensates for the delays. The intersample model predictor provides the observer with process output estimates between sensor measurements, and the model states are updated using current output estimates obtained from the propagation unit. The fault-free stability properties are characterized in terms of model accuracy, sampling rate, and delay size. These properties are used to derive appropriate rules for fault detection and accommodation. The difference between the output estimates from the state observer and the propagation unit is compared against a time-varying alarm threshold for fault detection. Once the threshold is breached, controller design parameters are adjusted to preserve closed-loop stability. The proposed methodology is illustrated using a continuous crystallizer example.

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Detection and management of actuator faults in controlled particulate processes using population balance models

Cited by 16 publications

References 36 publications

Fault Detection and Fault-Tolerant Control of Particulate Processes using Population Balance Models

Fault Detection and Fault-Tolerant Control of Particulate Processes using Population Balance Models

Data-based Fault Identification and Accommodation in the Control of Sampled-Data Particulate Processes

Fault Detection and Accommodation in Particulate Processes with Sampled and Delayed Measurements

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