This survey of model-based fault diagnosis focuses on those methods that are applicable to aerospace systems. To highlight the characteristics of aerospace models, generic non-linear dynamical modelling from flight mechanics is recalled and a unifying representation of sensor and actuator faults is presented. An extensive bibliographical review supports a description of the key points of fault detection methods that rely on analytical redundancy. The approaches that best suit the constraints of the field are emphasized and recommendations for future developments in in-flight fault diagnosis are provided.

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Model-based fault diagnosis for aerospace systems: a survey:

Execution monitoring in robotics: A survey

https://grvc.us.es/aware/papers/aware_paper_10.pdf

Sensor and actuator fault detection in small autonomous helicopters

Soft Computing Approaches to Fault Diagnosis for Dynamic Systems: A Survey

Fault diagnosis of Tennessee Eastman process with multi- scale PCA and ANFIS

Fault diagnosis of control engineering systems can be based upon the generation of signals which reflect inconsistencies between the fault-free and faulty system operation-so-called residual signals. This paper outlines some recent approaches to the generation of residual signals using methods of integrating quantitative and qualitative system knowledge, based upon AI techniques.

Artificial intelligence approaches to fault diagnosis

Artificial Intelligence Approaches to Fault Diagnosis for Dynamic Systems

Multiple-Model Fault-Tolerant Control of an Induction Motor in the Presence of Uncertainty

This paper investigates the development of a Supervisory Control System with qualitative tasks at the upper level and simple quantitative models at the lower level to control complex non-linear systems. A new quantitative approach for the stability of non-linear fuzzy inference systems using Takagi-Sugeno (T-S) fuzzy models is presented. The necessary conditions for the assignability of eigenvalues to a region in the s-plane and the necessary conditions to guarantee the stability of fuzzy models for both T-S fuzzy observers and T-S fuzzy controllers together are derived. The problem is solved via the Linear Matrix Inequality (LMI) method. The paper demonstrates the feasibility of this system architecture through application to a fault-tolerant design for a railway traction system using DSP in a hardware test-rig.

C. J. Lopez-Toribio

Papers

Soft Computing Approaches to Fault Diagnosis for Dynamic Systems: A Survey

Artificial intelligence approaches to fault diagnosis

Artificial Intelligence Approaches to Fault Diagnosis for Dynamic Systems

Multiple-Model Fault-Tolerant Control of an Induction Motor in the Presence of Uncertainty

A multiple model approach to fault-tolerant control using Takagi-Sugeno fuzzy modelling: Real application to an induction motor drive system