Detection and isolation of sensor faults on nonlinear processes based on local linear models

Belle, P.; Füssel, Dominik; Hecker, Oliver

doi:10.1109/acc.1997.611843

Cited by 10 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of main applications of distributed "intelligent data processing" is "autonomous fault detection and isolation" in measurement systems [1]- [3]. It is necessary to evaluate the reliability of a system during measurement [4], [5], and "artificial intelligence" features could increase the reliability of measurement systems, such as those intended for use in food transportation. In order to process data, the data are first approximated.…”

Section: Introductionmentioning

confidence: 99%

Application of data approximation and classification in measurement systems - comparison of “neural network” and “Least Squares” approximation

Jabbari

Jedermann

Lang

2008

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications

View full text Add to dashboard Cite

In measurement systems, environmental conditions are measured based on predefined scenarios. Measured data are then processed in either a decentralized or centralized manner. In advanced systems (especially for distributed data processing), taking artificial intelligence features into consideration could improve measurement performance and reliability. It is assumed as autonomy in measurement system which leads to distributed "intelligent data measurement and processing". In this paper, two different methodologies for "temperature prediction" are compared. A discussion concerning the classification of recorded data is then presented. Both a mathematical approach, the so-called "least squares" approach, and a model-free approach, called back-propagation, are applied and compared for temperature approximation. After approximation, the predicted temperature values are compared with real temperature records for classification purposes. The "classification mechanism" includes signal processing features for improving performance.

show abstract

Section: Introductionmentioning

confidence: 99%

Application of data approximation and classification in measurement systems - comparison of “neural network” and “Least Squares” approximation

Jabbari

Jedermann

Lang

2008

2008 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications

View full text Add to dashboard Cite

show abstract

“…The existing sensor fault diagnosis methods include hardware redundancy [3], analytical redundancy [4], neural network method [5], model-based LES method [6], and wavelet analysis [7]. Among them, wavelet analysis not only overcomes the defects of Fourier transform without localization in the time domain, but also makes up the shortage of the short time Fourier transform in fixed resolution.…”

Section: Introductionmentioning

confidence: 99%

Energy Analysis Method Based on Wavelet Transform for Sensor Fault Diagnosis

Qian

Liu

Zhang

et al. 2011

AEF

View full text Add to dashboard Cite

Abstract. Wavelet analysis with its unique features is very suitable for analyzing non-stationary signal, and it can also be used as an ideal tool for signal processing in fault diagnosis. The characteristics of the faults and the necessary information on the diagnosis can be constructed and extracted respectively by wavelet analysis. Though wavelet analysis is specialized in characteristics extraction, it can not determine the fault type. So this paper has proposed an energy analysis method based on wavelet transform. Experiment results show the method is very effective for sensor fault diagnosis, because it can not only detect the sensor faults, but also determine the fault type.

show abstract

Model based fault detection of vehicle suspension and hydraulic brake systems

et al. 2002

View full text Add to dashboard Cite

Detection and isolation of sensor faults on nonlinear processes based on local linear models

Cited by 10 publications

References 12 publications

Application of data approximation and classification in measurement systems - comparison of “neural network” and “Least Squares” approximation

Application of data approximation and classification in measurement systems - comparison of “neural network” and “Least Squares” approximation

Energy Analysis Method Based on Wavelet Transform for Sensor Fault Diagnosis

Model based fault detection of vehicle suspension and hydraulic brake systems

Contact Info

Product

Resources

About

Detection and isolation of sensor faults on nonlinear processes based on local linear models

Cited by 10 publications

References 12 publications

Application of data approximation and classification in measurement systems - comparison of &#x201C;neural network&#x201D; and &#x201C;Least Squares&#x201D; approximation

Application of data approximation and classification in measurement systems - comparison of &#x201C;neural network&#x201D; and &#x201C;Least Squares&#x201D; approximation

Energy Analysis Method Based on Wavelet Transform for Sensor Fault Diagnosis

Model based fault detection of vehicle suspension and hydraulic brake systems

Contact Info

Product

Resources

About

Application of data approximation and classification in measurement systems - comparison of “neural network” and “Least Squares” approximation

Application of data approximation and classification in measurement systems - comparison of “neural network” and “Least Squares” approximation