Analytical Redundancy Relations Research Articles

A Distributed Architecture for HVAC Sensor Fault Detection and Isolation

This paper presents a design and analysis methodology for detecting and isolating multiple sensor faults in heating, ventilation, and air-conditioning (HVAC) systems. The proposed methodology is developed in a distributed framework, considering a multizone HVAC system as a set of interconnected nonlinear subsystems. A dedicated local sensor fault diagnosis (LSFD) agent is designed for each subsystem, while it may exchange information with other LSFD agents. Distributed sensor fault detection is conducted using robust analytical redundancy relations of estimation-based residuals and adaptive thresholds. The distributed sensor fault isolation procedure is carried out by combining the decisions of the LSFD agents and applying a reasoning-based decision logic. The performance of the proposed methodology is analyzed with respect to robustness, sensor fault detectability, and isolability. Simulation results are used for illustrating the effectiveness of the proposed methodology applied to an eight-zone HVAC system.

Decentralized Isolation of Multiple Sensor Faults in Large-Scale Interconnected Nonlinear Systems

This paper presents the design and analysis of a methodology for detecting and isolating multiple sensor faults in large-scale interconnected nonlinear systems. The backbone of the proposed decentralized methodology is the design of a local sensor fault diagnosis agent dedicated to each interconnected subsystem, without the need to communicate with neighboring agents. Each local sensor fault diagnosis agent is responsible for detecting and isolating multiple faults in the local set of sensors. The local sensor fault diagnosis agent consists of a bank of modules that monitor smaller groups of sensors in the corresponding local sensor set. The detection of faults in each of the sensor groups is conducted using robust analytical redundancy relations, formulated by structured residuals and adaptive thresholds. The multiple sensor fault isolation in each local sensor fault diagnosis agent is realized by aggregating the decisions of the modules and applying a diagnostic reasoning-based decision logic. The performance of the proposed diagnostic scheme is analyzed with respect to sensor fault detectability and multiple sensor fault isolability. A simulation example of two interconnected robot manipulators is used to illustrate the application of the multiple sensor fault detection and isolation methodology.

Incident detection and isolation in drilling using analytical redundancy relations

Early diagnosis of incidents that could delay or endanger a drilling operation for oil or gas is essential to limit field development costs. Warnings about downhole incidents should come early enough to allow intervention before it develops to a threat, but this is difficult, since false alarms must be avoided. This paper employs model-based diagnosis using analytical redundancy relations to obtain residuals which are affected differently by the different incidents. Residuals are found to be non-Gaussian – they follow a multivariate t-distribution – hence, a dedicated generalized likelihood ratio test is applied for change detection. Data from a 1400 m horizontal flow loop test facility is used to assess the diagnosis method. Diagnosis properties of the method are investigated assuming either with available downhole pressure sensors through wired drill pipe or with only topside measurements available. In the latter case, isolation capability is shown to be reduced to group-wise isolation, but the method would still detect all serious events with the prescribed false alarm probability.

Improved diagnosis of hybrid systems using instantaneous sensitivity matrices

One approach to quantitative model-based fault detection and isolation (FDI) is based on analytical redundancy relations (ARRs) and fault signatures. Numerical evaluation of ARRs creates residuals, which then, provide online information of consistency between the system and its nominal model. An inconsistency is represented by a signature. Traditionally in the quantitative approach, these signatures are binary vectors, where the term 0 means a residual is consistent and 1 means inconsistent. In this paper, the measured trend of residuals is utilized for FDI by a different signature type, called sensitivity signature. In this signature, the consistency of ARRs is represented by three terms; the term +1 indicates a residual is crossing an upper threshold, the term −1 indicates a residual is crossing a lower threshold and 0 means otherwise. The expected sensitivity signature related to a certain fault or to a mode change is taken from partial derivative of residuals. Since consistency, in the sensitivity approach, is represented by three terms (instead of two), more distinguished signatures are generated and improved fault and mode change isolation abilities are achieved. Issues related to practical implementation of the proposed diagnosis method are extensively discussed and experimental results are presented.

Fault Diagnosis of Advanced Wind Turbine Benchmark using Interval-based ARRs and Observers

This paper proposes a model-based fault diagnosis (FD) approach for wind turbines and its application to a realistic wind turbine FD benchmark. The proposed FD approach combines the use of analytical redundancy relations (ARRs) and interval observers. Interval observers consider an unknown but bounded description of the model parametric uncertainty and noise using the the so-called set-membership approach. This approach leads to formulate the fault detection test by means of checking if the measurements fall inside the estimated output interval, obtained from the mathematical model of the wind turbine and noise/parameter uncertainty bounds. Fault isolation is based on considering a set of ARRs obtained from the structural analysis of the wind turbine model and a fault signature matrix that considers the relation of ARRs and faults. The proposed FD approach has been validated on a 5-MW wind turbine using the National Renewable Energy Laboratory FAST simulator. The obtained results are presented and compared with that of other approaches proposed in the literature.

Design of distributed Fault Detection and Isolation systems

Safety operating of controlled systems requires the validation of the data produced by the sensors. The objective of this validation is to detect erroneous data and then not to transmit these data to the control or other algorithms. One possibility to detect and isolate faulty data rests on the online evaluation of Analytical Redundancy Relations (ARRs), expressing the links between the different variables. In this paper a method to distribute the ARRs on the existing controllers, according the producing places of the measures, while minimizing the network exchanges is proposed. The proposed approach rests on an adequate arrangement of the rows and columns of a fault signature matrix used by fault detection and isolation algorithms in order to make appear more or less independent blocks.

Distributed Adaptive Estimation Scheme for Isolation of Sensor Faults in Multi-zone HVAC Systems

This paper presents a model-based distributed scheme with emphasis on the isolation of sensor faults in multi-zone heating, ventilating and air-conditioning (HVAC) systems. A bank of local sensor fault detection and isolation agents are designed to diagnose sensor faults in a HVAC system, modeled as a set of interconnected, nonlinear subsystems. Each agent consists of the local sensor fault detection and adaptive estimation scheme for isolation of sensor faults. Detection and isolation signals are generated based on analytical redundancy relations. These signals are provided to a local decision logic in order to distinguish between local and propagated sensor faults. Simulation results are used to illustrate the effectiveness of the proposed methodology applied to a four-zone HVAC system.

Structural Detectability Analysis of a Distributed Sensor Fault Diagnosis Scheme for a Class of Nonlinear Systems

The objective of this work is to analyze the performance of the local monitoring modules of a distributed diagnosis scheme tailored to detect multiple sensor faults in a class of nonlinear systems. The local modules monitor the healthy operation of subsets of sensors (local sensor sets). Every module is designed to detect the occurrence of faults in the local sensor sets when some analytical redundancy relations (ARRs) are violated. The set of ARRs is formulated using structured residuals and adaptive thresholds based on a nonlinear observer. In order to characterize the sensitivity of every monitoring module to local sensor faults, we obtain structural fault detectability conditions based on adaptive thresholds, and strong fault detectability conditions based on ultimate robust positively invariant sets. These conditions correspond to explicit relationships between the local sensor faults, the worst-case bounds on modeling uncertainties and the design parameters of the local monitoring module.

Improved Implementation of Nonlinear Analytical Redundancy Relations: Application in Robotics

This paper copes with the problem of detecting actuators’ faults in a two-degree of freedom robot manipulator by using nonlinear analytical redundancy relations composed by derivatives of known signals. To generate the residuals, the redundancy relations are handled with two procedures. The first one allows the determination of a transformation of the base relations to reduce by one the order of the involved derivatives. The second one directly estimates the derivatives of the measurements by using a uniform robust exact differentiator. Simulation results are presented to show the robustness of the nonlinear analytical redundancy relations by using the transformation procedure even in the case of noisy measurements.

An approach for diagnosability analysis and sensor placement for continuous processes based on evolutionary algorithms and analytical redundancy

In this work we propose to use an approach based on genetic algorithms to obtain analytical redundancy relations to study the diagnosability property on a given continuous system, and if this not fulfill, our approach allows studying the sensor placement problem in order to fulfill it. The redundancy relations are based on the minimal test equation support and in a structural analysis over a bipartite graph. The faults analysis is studied using a multi-objective fitness function in two genetic algorithms which describe the different constraints to be covered in order to reach the diagnosability property on the system. Additionally, our approach allows studying the sensors placement problem on systems that do not fulfill the detectability or isolability properties, using another genetic algorithm. 2126 Ruben Leal et al.

An integrated approach for sensor placement in linear dynamic systems

This paper investigates the sensor placement in linear dynamic systems for fault detectability and isolability. A linear system is modelled by a bond graph (BG) that can be associated in a natural way with a set of linear differential-algebraic equations (DAEs). Simultaneously, possible sensor locations are modelled as junctions on the BG. Causal paths capture cause–effect relationships of a linear system and provide a means to analyze what subset of junctions contributes to fault detectability and isolability. Furthermore, this paper exploits DAEs associated with a BG and proves a necessary and sufficient condition of sensor placement to fulfill fault detectability. Based on the fault detectability condition, a necessary condition of sensor placement to achieve two-fault distinguishability is developed in the DAEs model and serves as the basis of formulating the sensor placement problem with regard to a fault set F. For efficiency, a dedicated dynamic programming (DP) algorithm is devised to attain the optimal set of junctions for fault isolability. The two-tank system is employed to illustrate the sensor placement steps in a linear system. The optimal set(s), computed by the proposed sensor placement methodology, is/are validated by deriving primary analytical redundancy relations (ARRs) of the two-tank system.

Integrated Diagnosis and Prognosis of Uncertain Systems: A Bond Graph Approach

Bond Graph (BG) methodology is used to model the dynamic uncertain systems. Uncertainty is considered on the system parameters in form of intervals. The uncertain parameters are allowed to deviate within their prescribed interval limits. Single fault hypothesis is considered in this work such that the parameter undergoing degradation is known a priori. A new method for generation of interval valued thresholds is briefly described in the framework of BG models in Linear Fractional Transformation form. The diagnostic module is formed using such thresholds which detect the beginning of degradation of a parameter in the real system. The new concept of Interval Extension of Analytical Redundancy Relations (IE-ARRs) is introduced which consider the parametric uncertainties and the evolution of degrading parameter in real time. Then, the Centre and Range method for fitting linear regression models to interval symbolic data is adapted to fit piece wise linear models to the interval valued times series data of IE-ARRs. Further, the new concept of generation of failure thresholds from a nominal system model is introduced and developed. Finally, the fitted linear model is used to estimate the remaining useful life of the parameter under degradation. Simulations are carried out on an example DC motor model. Linear and non-linear parametric degradations are considered. Results are presented in form of simulations.

Model-Based Health Monitoring for a Vehicle Steering System With Multiple Faults of Unknown Types

This paper presents a model-based fault diagnosis and prognosis scheme for a vehicle steering system. The steering system is modeled as a hybrid system with continuous dynamics and discrete modes using the hybrid bond graph tool. Multiple faults of different types, i.e., abrupt fault, incipient fault, and intermittent fault, are considered using the concept of Augmented Global Analytical Redundancy Relations (AGARRs). A fault discriminator is constructed to distinguish the type of faults once they are detected. After that, a fault identification scheme is proposed to estimate the magnitude of abrupt faults, the characteristic of intermittent faults, and the degradation behavior of incipient faults. The fault identification is realized by using a new adaptive hybrid differential evolution (AHDE) algorithm with less control parameters. Based on the identified degradation behavior of incipient faults, prognosis is carried out to predict the remaining useful life of faulty components. The proposed algorithm is verified experimentally on the steering system of a CyCab electric vehicle.

Bond Graph Algorithms for Fault Detection and Isolation in Wind Energy Conversion

The use of wind energy has increased during the last years; however, wind power varies greatly throughout the day creating important intermittence problems. This paper deals with the modeling, fault detection and isolation of wind turbine generation systems by bond graph approach. The modeling of the wind phenomenon, the turbine mechanical system and the electrical machine, along with the corresponding converter and electrical grid are described, and the problem of fault diagnosis in wind energy conversion is addressed. One of the original points in this work is the use of a new fault detection and isolation method. The proposed method avoids the exploration of all the combinations for its application to the diagnostic of this system operation. The causal paths are used to generate the analytical redundancy relations at each computation step based on the constitutive and structural junction relations. This is shown through an algorithm for monitoring the system by sensor placements on the corresponding bond graph model. The performance of the developed algorithm is evaluated on a model of a commercial sized 4.8 MW wind turbine.

Bond graph modeling for fault diagnosis: the continuous stirred tank reactor case study

Supervision of chemical and biochemical processes is difficult due to various factors, for example the evolution of nonlinear state variables, uncertainties in parameters and disturbances, and system operating conditions. The complexities arise out of the energetic couplings in the model. In this work, the bond graph representation, as a unified and multidisciplinary tool, is used for dynamic modeling and diagnosis for process engineering, incorporating the chemical kinetics of an equilibrated reaction, heat and mass transport phenomena. A pseudo bond graph model technique is applied to a pilot reactor fitted with a mono-fluid heating/cooling system. A modeling structure has been implemented through process sub-units and validated through experimental observations. This technique is proposed to indicate physico-chemical failures, for example matter leakage, caloric isolation, or the occurrence of a secondary reaction or temperature runaway when a very exothermic reaction occurs. It provides the computational algorithms to eliminate unknown variables from coupled thermochemical models and thus generate analytical redundancy relations (ARRs) in terms of measurements and parameters for online diagnosis.

Extension of the bond graph causality inversion method for fault detection and isolation

Controlled systems can be subjected to faults that may affect the performance of the system, and unable its objectives to be achieved. Fault detection and isolation algorithms are then used to study these faults. The bond graph tool can be used for modeling purposes and then its structural, and causal properties can be exploited for automatic generation of analytical redundancy relations (ARRs) through a procedure named causality inversion method, which are used for diagnosis applications. These ARRs are mathematical constraints that are used to verify the coherence between the process measurements and the system model. This paper proposes an extension of the causality inversion method by different versions of the same ARR. The goal is to increase the number of isolable faults. Moreover, structural conditions are given in order to avoid the generation of redundant ARRs. To validate the obtained structural procedure, a fault is imposed in a traction of an omnidirectional mobile robot.

Bond graph model-based system mode identification and mode-dependent fault thresholds for hybrid systems

Hybrid system models exploit the modelling abstraction that fast state transitions take place instantaneously so that they encompass discrete events and the continuous time behaviour for the while of a system mode. If a system is in a certain mode, e.g. two rigid bodies stick together, then residuals of analytical redundancy relations (ARRs) within certain small bounds indicate that the system is healthy. An unobserved mode change, however, invalidates the current model for the dynamic behaviour. As a result, ARR residuals may exceed current thresholds indicating faults in system components that have not happened. The paper shows that ARR residuals derived from a bond graph cannot only serve as fault indicators but may also be used for bond graph model-based system mode identification. ARR residuals are numerically computed in an off-line simulation by coupling a bond graph of the faulty system to a non-faulty system bond graph through residual sinks. In real-time simulation, the faulty system model is to be replaced by measurements from the real system. As parameter values are uncertain, it is important to determine adaptive ARR thresholds that, given uncertain parameters, allow to decide whether the dynamic behaviour in a current system mode is the one of the healthy system so that false alarms or overlooking of true faults can be avoided. The paper shows how incremental bond graphs can be used to determine adaptive mode-dependent ARR thresholds for switched linear time-invariant systems with uncertain parameters in order to support robust fault detection. Bond graph-based hybrid system mode identification as well as the determination of adaptive fault thresholds is illustrated by application to a power electronic system easy to survey. Some simulation results have been analytically validated.

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.

Integration of Fault Diagnosis and Fault-Tolerant Control for Health Monitoring of a Class of MIMO Intelligent Autonomous Vehicles

This paper deals with the integration of robust bondgraph-model-based fault diagnosis (FD) with structural recoverability analysis and fault-tolerant control (FTC) of an intelligent heavy-size and autonomous vehicle, used for loading and routing 20- and 40-ft containers inside port terminals. The overactuated vehicle is an omnidirectional mobile platform with redundant actuators such as four independent driven wheels, four independent braking wheels, and four-wheel steering systems. The supervision system is able to monitor the health condition of the vehicle and to study the fault recoverability possibilities to reconfigure the control input when a fault occurs. For FD, analytical redundancy relations (ARRs) are derived from the bond graph model. The latter is constraint relations describing the nominal system behavior and is written in terms of the measured system variables. To perform robust FD, adaptive thresholds are generated. They consider modeling and measurement uncertainties. Once a fault is detected, the structural recoverability algorithm analyzes the redundancy presented on the system, and an appropriate control strategy is applied. The designed procedure of FD and FTC is validated by considering a multiple-fault scenario on the vehicle and comparing its results with the nominal case.

Synchronous Generator Off-line Diagnosis Approach Including Fault Detection and Estimation of Failures on Machine Parameters

Fault diagnosis involves the detection, location, and identification of faults. The objective of this work is twofold: first, fault detection in a synchronous generator using the parity space approach, a method based on building the fault indicators using analytical redundancy relationships, a generator model, and actual input and output data; second, computation of defaulting synchronous generator parameters using the maximum likelihood estimation algorithm, which combines the generalized least-squares estimator, the linear Kalman filter, and the Newton-type finite-difference optimization algorithm. In the first step, the proposed technique is successfully applied for external faults diagnosis of two generators: (1) a 4-pole, 60-Hz, 208-V, 1.5-kVA loaded laboratory synchronous generator using a 3-phase, sudden short-circuit fault at 10% rated voltage, and (2) an unloaded 20-pole, 60-Hz, 13.2-kV, 13.75-MVA large generator at Hydro-Québec's Rapides-des-Quinzes plant following a scenario of a 10% line-to-line short-circuit fault. For external faults, failures on parameters do not exist. The proposed scheme could therefore aid in building an automatic protective relay for the synchronous generators. In the second step, the diagnosis process is applied for the fault detection and determination of failures on parameters occurring on the 1.5-kVA generator following broken damper rotor's bars using finite-element simulated data.