Sebastian Tornil-Sin

Robust Fault Diagnosis of Nonlinear Systems Using Interval Constraint Satisfaction and Analytical Redundancy Relations

In this paper, a robust fault diagnosis problem for nonlinear systems considering both bounded parametric modeling errors and noise is addressed using parity-equation-based analytical redundancy relations (ARR) and interval constraint satisfaction techniques. Fault detection, isolation, and estimation tasks are considered. Moreover, the problem of quantifying the uncertainty in the ARR parameters is also addressed. To illustrate the usefulness of the proposed approach, a case study based on the well-known wind turbine benchmark is used.

Robust fault detection of non-linear systems using set-membership state estimation based on constraint satisfaction

In this paper, the robust fault detection problem for non-linear systems considering both bounded parametric modelling errors and measurement noises is addressed. The non-linear system is monitored by using a state estimator with bounded modelling uncertainty and bounded process and measurement noises. Additionally, time-variant and time-invariant system models are taken into account. Fault detection is formulated as a set-membership state estimation problem, which is implemented by means of constraint satisfaction techniques. Two solutions are presented: the first one solves the general case while the second solves the time-variant case, being this latter a relaxed solution of the first one. The performance of the time-variant approach is tested in two applications: the well-known quadruple-tank benchmark and the dynamic model of a representative portion of the Barcelonas sewer network. In both applications, different scenarios are presented: a faultless situation and some faulty situations. All considered scenarios are intended to show the effectiveness of the presented approach.

Fault-Tolerant Control design using LPV Admissible Model Matching: Application to a two-degree of freedom helicopter

In this paper, a new approach to design a Fault-Tolerant Control (FTC) based on Linear Parameter Varying (LPV) Admissible Model Matching (AMM) is proposed. The suggested strategy is an active technique that requires the fault to be detected and estimated by the FDI scheme. Then, the controller is redesigned accordingly. In this work, faults are expressed as a change in the system dynamics (in particular, in the model parameters). The main contribution of the proposed approach is to consider the fault as a scheduling variable in the LPV model that allows the controller reconfiguration. The range of the scheduling variables (fault magnitude and system operating point) allow to specify the set of admissible behaviors. The effectiveness and performances of the method have been illustrated in a two degree of freedom helicopter.

Set-membership identification and fault detection using a Bayesian framework

This paper deals with the problem of set-membership identification and fault detection using a Bayesian framework. The paper presents how the set-membership model estimation problem can be reformulated from a Bayesian viewpoint in order to determine the feasible parameter set and, in a posterior fault detection stage, to check the consistency between data and the model. The paper shows that, assuming uniform distributed measurement noise and flat model prior probability distribution, the Bayesian approach leads to the same feasible parameter set than the set-membership strips technique and, additionally, can deal with models nonlinear in the parameters. The procedure and results are illustrated by means of the application to a quadruple tank process.

Nonlinear set-membership identification and fault detection using a Bayesian framework: Application to the wind turbine benchmark

This paper deals with the problem of nonlinear set-membership identification and fault detection using a Bayesian framework. The paper presents how the set-membership model estimation can be reformulated from a Bayesian viewpoint in order to determine the feasible parameter set and, in a posterior fault detection stage, to check the consistency between the model and the data. The paper shows that the Bayesian approach, assuming uniform distributed measurement noise and flat model prior probability distribution, leads to the same feasible parameter set as the set-membership technique. To illustrate this point a comparison with the subpavings approach is included. Finally, by means of the application to the wind turbine benchmark problem, it is shown how the Bayesian fault detection test works successfully.

Set computations with subpavings in MATLAB: The SCS Toolbox

This paper presents a MATLAB Toolbox that allows to represent and manipulate subpavings, i.e. unions of non-overlapped boxes. The main property of subpavings is their ability to approximate compact sets in Rn with arbitrary precision.

Fault detection and isolation for a wind turbine benchmark using a mixed Bayesian/set-membership approach

This paper addresses the problem of fault detection and isolation of wind turbines using a mixed Bayesian/Set-membership approach. Modeling errors are assumed to be unknown but bounded, following the set-membership approach. On the other hand, measurement noise is also assumed to be bounded, but following a statistical distribution inside the bounds. To avoid false alarms, the fault detection problem is formulated in a set-membership context. Regarding fault isolation, a new fault isolation scheme that is inspired on the Bayesian fault isolation framework is developed. Faults are isolated by matching the fault detection test results, enhanced by a complementary consistency index that measures the certainty of not being in a fault situation, with the structural information about the faults stored in the theoretical fault signature matrix. The main difference with respect to the classical Bayesian approach is that only models of fault-free behavior are used. Finally, the proposed FDI method is assessed against the wind turbine FDI benchmark proposed in the literature, where a set of realistic fault scenarios in wind turbines are proposed.

Robust Fault Diagnosis of Non-linear Systems using Constraints Satisfaction

Abstract In this paper, the robust fault diagnosis problem for non-linear systems considering both bounded parametric modelling errors and noises is addressed using constraints satisfaction. Combining available measurements with the model of the monitored system, a set of analytical redundancy relations (ARR), relating only known variables, can be derived. These relations will be used in the fault diagnosis procedure to check the consistency between the observed and the predicted system behaviour. When some inconsistency is detected, the fault isolation mechanism will be activated in order to provide an explanation of the possible cause. Finally, a fault estimation procedure is used to estimate the fault size. To illustrate the usefulness of the proposed approach, a case study based on a well-known four tanks system benchmark is used.

Fault-Tolerant Control design using LPV Admissible Model Matching with H 2 /H ∞ performance: Application to a two-degree of freedom helicopter

In this paper, an approach to design an Admissible Model Matching (AMM) Fault Tolerant Control (FTC) based on Linear Parameter Varying (LPV) techniques is proposed. The main contribution of the proposed approach consists in the on-line reconfiguration of the controller based on the use of LPV gain-scheduling techniques that allow to take into account changes in the system parameters due to changes in the operating point and faults. The proposed strategy is an active FTC strategy that requires the fault to be detected, isolated and estimated by the FDI scheme. The formulation of AMM is based on establishing a set of admissible behaviors by specifying the region where the closed-loop should lie through LMIs. To select the best controller, the notion of quadratic H2/H∞ performance is used in the FTC design. The effectiveness and performance of proposed approach have been illustrated using a two degree of freedom helicopter.

Sensor placement for classifier-based leak localization in water distribution networks using hybrid feature selection

This paper presents a sensor placement approach for classifier-based leak localization in water distribution networks. The proposed method is based on a hybrid feature selection algorithm that combines the use of a filter based on relevancy and redundancy with a wrapper based on genetic algorithms. This algorithm is applied to data generated by hydraulic simulation of the considered water distribution network and it determines the optimal location of a prespecified number of pressure sensors to be used by a leak localization method based on pressure models and classifiers proposed in previous works by the authors. The method is applied to a small-size simplified network (Hanoi) to better analyze its computational performance and to a medium-size network (Limassol) to demonstrate its applicability to larger real-size networks.