A Simplified Instrument Failure Detection Scheme

An alternative group of methods has emerged which do not require the use of an explicit model. This is the key basic construct for the data-driven paradigm. Model-free and non-parametric methods for fault detection, process optimisation and control design are currently at a particularly exciting stage of development.This new advanced textbook by Chiang, Russell and Braatz primarily tackles the data-driven routes to Fault Detection and Diagnosis. It is an outgrowth of a prior Advances in Industrial Control monograph; Russell, Chiang and Braatz. Data-driven Techniques for Fault Detection and Diagnosis in ChemicalProcesses, 2000, ISBN 1-85233-258-1. The new textbook expands the material of the monograph and gives a fuller presentation of some of the alternative modelbased methods, the analytical methods, and of the knowledge-based techniques. vi Series Editors' ForewordThis allows the reader to compare and contrast the different approaches to the problem of fault detection and diagnosis. Thus the text is suitable for advanced courses for process, chemical and control engineers.

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“…Hint: see [226,227]. 23 [53,88,139,151,154,155,178,238,249,311,312,331,332]. Write a summary report.…”

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confidence: 99%

Fault Detection and Diagnosis in Industrial Systems

Chiang

Russell

Braatz

2001

Advanced Textbooks in Control and Signal Processing

1,160

811

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“…• Simplified detection scheme (Clark, 1978): this scheme is a particular case of the DOS, and makes use of only one observer driven by only one measurement. Therefore, if any other measurement is faulty, the corresponding residual is non-zero.…”

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confidence: 99%

An SFDI Observer–Based Scheme for a General Aviation Aircraft

Ariola

Mattei

Notaro

et al. 2015

International Journal of Applied Mathematics and Computer Science

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The problem of detecting and isolating sensor faults (sensor fault detection and isolation-SFDI) on a general aviation aircraft, in the presence of external disturbances, is considered. The proposed approach consists of an extended Kalman observer applied to an augmented aircraft plant, where some integrators are added to the output variables subject to faults. The output of the integrators should be ideally zero in the absence of model uncertainties, external disturbances and sensor faults. A threshold-based decision making system is adopted where the residuals are weighted with gains coming from the solution to an optimization problem. The proposed nonlinear observer was tested both numerically on a large database of simulations in the presence of disturbances and model uncertainties and on input-output data recorded during real flights. In this case, the possibility of successfully applying the proposed technique to detect and isolate faults on inertial and air data sensors, modelled as step or ramp signals artificially added to the real measurements, is shown.

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“…For estimating virtual sensor outputs and for generating the residuals, several techniques have been proposed in related disciplines. Widely used and 7/43 well accepted approaches include, e.g., estimation filters [31,32], band-limiting filters [33] as well as innovation testing based on Kalman filters [34], threshold logic [35], and generalized likelihood ratio testing [36]. Among the most efficient approaches for estimating virtual sensor outputs is the application of artificial neural networks, because neural networks are capable to accurately model non-linear and dynamic decentralized systems (such as wireless SHM systems) without the need for first-principle models or a priori knowledge about the complex internal structures of the system observed [37].…”

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confidence: 99%

Decentralized fault detection and isolation in wireless structural health monitoring systems using analytical redundancy

Smarsly

Law

2014

Advances in Engineering Software

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One of the most critical issues when deploying wireless sensor networks for long-term structural health monitoring (SHM) is the correct and reliable operation of sensors. Sensor faults may reduce the quality of monitoring and, if remaining undetected, might cause significant economic loss due to inaccurate or missing sensor data required for structural assessment and life-cycle management of the monitored structure. This paper presents a fully decentralized approach towards autonomous sensor fault detection and isolation in wireless SHM systems. Instead of physically installing multiple redundant sensors in the monitored structure ("physical redundancy"), which would involve substantial penalties in cost and maintainability, the information inherent in the SHM system is used for fault detection and isolation ("analytical redundancy"). Unlike traditional centralized approaches, the analytical redundancy approach is implemented distributively: Partial models of the wireless SHM system, implemented in terms of artificial neural networks in an object-oriented fashion, are embedded into the wireless sensor nodes deployed for monitoring. In this paper, the design and the prototype implementation of a wireless SHM system capable of autonomously detecting and isolating various types of sensor faults are shown. In laboratory experiments, the prototype SHM system is validated by injecting faults into the wireless sensor nodes while being deployed on a test structure. The paper concludes with a discussion of the results and an outlook on possible future research directions.

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A Simplified Instrument Failure Detection Scheme

Cited by 108 publications

References 2 publications

Fault Detection and Diagnosis in Industrial Systems

Fault Detection and Diagnosis in Industrial Systems

An SFDI Observer–Based Scheme for a General Aviation Aircraft

Decentralized fault detection and isolation in wireless structural health monitoring systems using analytical redundancy

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