A fault detection technique for dynamical systems

This paper shows how a functional observer can be utilized to detect faults in LTI MIMO systems. The fault detection technique is designed so that the functional observer based fault indicator asymptotically converges to a fault indicator that can be derived based on the nominal system. The asymptotic value of the proposed fault indicator is not dependent on the functional observer parameters; moreover, by choosing appropriate functional observer parameters the convergence rate of the fault indicator can be altered. Observability of the system is not a requirement for the design of the fault detection scheme.

Fault detection for dynamical systems using first-order functional observers

This paper reports a new result on the fault detection of dynamical systems by employing only first-order functional observers. Indeed, we show that fault detection can be achieved by utilizing first-order functional observers. The advantages for having such simple structured observers are obvious from the economical and practical points of view as significant cost saving can be achieved. We derive existence conditions and an algorithm for the generation of residual signals to detect faults using firstorder functional observers. Two numerical examples are given to illustrate the proposed fault detection scheme. In one of the examples, a two-area interconnected power system with reheat thermal turbines is considered where only a first-order functional observer is designed to detect faults in the power system.

Online motor fault detection and diagnosis using a hybrid FMM-CART model

In this brief, a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for online motor detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. To evaluate the applicability of the proposed FMM-CART model, an evaluation with a benchmark data set pertaining to electrical motor bearing faults is first conducted. The results obtained are equivalent to those reported in the literature. Then, a laboratory experiment for detecting and diagnosing eccentricity faults in an induction motor is performed. In addition to producing accurate results, useful rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. The experimental outcome positively shows the potential of FMM-CART in undertaking online motor fault detection and diagnosis tasks.

Approach to fault detection of time-delay systems using functional observers

A new methodology is reported for designing functional observers to detect actuator faults of a class of time-delay systems where the matrix pair (A, C) is not observable. First, a generalised state transformation is used to transform the system into new coordinates where the delay term associated with the state vector is injected into the system's output and input. Then, a minimum-order functional observer is designed to construct a residual function that can trigger system faults. The finding is significant as it is now possible to detect faults of time-delay systems where the pair (A, C) is not required to be observable. A numerical example is given to illustrate the effectiveness of the proposed design approach. © The Institution of Engineering and Technology 2014.

Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning

In this paper, a hybrid online learning model that combines the fuzzy min-max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, incorporates the advantages of both FMM and CART for undertaking data classification (with FMM) and rule extraction (with CART) problems. In particular, the CART model is enhanced with an importance predictor-based feature selection measure. To evaluate the effectiveness of the proposed online FMM-CART model, a series of experiments using publicly available data sets containing motor bearing faults is first conducted. The results (primarily prediction accuracy and model complexity) are analyzed and compared with those reported in the literature. Then, an experimental study on detecting imbalanced voltage supply of an induction motor using a laboratory-scale test rig is performed. In addition to producing accurate results, a set of rules in the form of a decision tree is extracted from FMM-CART to provide explanations for its predictions. The results positively demonstrate the usefulness of FMM-CART with online learning capabilities in tackling real-world motor fault detection and diagnosis tasks. © 2014 Springer Science+Business Media New York.

A functional observer based fault detection technique for dynamical systems

This paper presents a functional observer based fault detection method. The fault detection is achieved using a functional observer based fault indicator that asymptotically converges to a fault indicator that can be derived based on the nominal system. The asymptotic value of the proposed fault indicator is independent of the functional observer parameters and also the convergence rate of the fault indicator can be altered by choosing appropriate functional observer parameters. The advantage of using this new method is that the observed system is not necessarily needed to be observable; therefore, the proposed fault detection technique is also applicable for systems where state observers cannot be designed; moreover, the functional observer fault detection scheme is always of reduced order in comparison to a state observer based scheme.

A fault detection scheme for time-delay systems using minimum-order functional observers

This paper presents a method for designing residual generators using minimum-order functional observers to detect actuator and component faults in time-delay systems. Existence conditions of the residual generators and functional observers are first derived, and then based on a parametric approach to the solution of a generalized Sylvester matrix equation, we develop systematic procedures for designing minimum-order functional observers to detect faults in the system. The advantages of having minimum-order observers are obvious from the economical and practical points of view as cost saving and simplicity can be achieved, particularly when dealing with high-order complex systems. Extensive numerical examples are given to illustrate the proposed fault detection scheme. In all the numerical examples, we design minimum-order residual generators and functional observers to detect faults in the system.

Application of rotor response analysis to fault detection in hydro powerplants.

In this article some considerations obtained during the utilization of rotor response analysis techniques in hydraulic powerplants are discussed. An applied research work was carried out in two hydraulic turbines analysing the rotor response both theoretically and experimentally. A developed mathematical model was used to simulate the rotordynamic behaviour of Francis and Kaplan turbines. The main dynamical effects that appear during the operation of the machines are discussed too. A series of measurements were carried out in the turbines using impact hammers to determine the modal behaviour of the units. The tests were carried out with the machine still and in operation. Some results and the comparison with the theory is presented in this article. The improved theoretical model was used for a sensitivity analysis of the different bearings to the main excitations that fake place during the machine operation. From this analysis, the best measuring points for condition monitoring were determined.

Automation in fault detection using neural network and model updating

In this article, an implementation of structural health monitoring process automation based on vibration measurements is proposed. The work presents an alternative approach which intent is to exploit the capability of model updating techniques associated to neural networks to be used in a process of automation of fault detection. The updating procedure supplies a reliable model which permits to simulate any damage condition in order to establish direct correlation between faults and deviation in the response of the model. The ability of the neural networks to recognize, at known signature, changes in the actual data of a model in real time are explored to investigate changes of the actual operation conditions of the system. The learning of the network is performed using a compressed spectrum signal created for each specific type of fault. Different fault conditions for a frame structure are evaluated using simulated data as well as measured experimental data.

Design of a state observer using decay rate LMI constraints for fault detection in mechanical systems

Nowadays, one of the most important concerns for many companies is to maintain the operation of their systems without sudden equipment break down. Because of this, new techniques for fault detection and location in mechanical systems subject to dynamic loads have been developed. This paper studies of the influence of the decay rate in the design of state observers using LMI for fault detection in mechanical systems. This influence is analyzed by the performance index proposed by Huh and Stein for the condition of a state observer. An example is presented to illustrate the methodology discussed.

Fault detection in rotating machines by vibration signal processing techniques

Machines with moving parts give rise to vibrations and consequently noise. The setting up and the status of each machine yield to a peculiar vibration signature. Therefore, a change in the vibration signature, due to a change in the machine state, can be used to detect incipient defects before they become critical. This is the goal of condition monitoring, in which the informations obtained from a machine signature are used in order to detect faults at an early stage. There are a large number of signal processing techniques that can be used in order to extract interesting information from a measured vibration signal. This study seeks to detect rotating machine defects using a range of techniques including synchronous time averaging, Hilbert transform-based demodulation, continuous wavelet transform, Wigner-Ville distribution and spectral correlation density function. The detection and the diagnostic capability of these techniques are discussed and compared on the basis of experimental results concerning gear tooth faults, i.e. fatigue crack at the tooth root and tooth spalls of different sizes, as well as assembly faults in diesel engine. Moreover, the sensitivity to fault severity is assessed by the application of these signal processing techniques to gear tooth faults of different sizes.

Diagnosis and Fault detection in Electrical Machines and Drives based on Advanced Signal Processing Techniques

In the present thesis, a new methodology of diagnosis based on advanced use of time-frequency technique analysis is presented. More precisely, a new fault index that allows tracking individual fault components in a single frequency band is defined. More in detail, a frequency sliding is applied to the signals being analyzed (currents, voltages, vibration signals), so that each single fault frequency component is shifted into a prefixed single frequency band. Then, the discrete Wavelet Transform is applied to the resulting signal to extract the fault signature in the frequency band that has been chosen. Once the state of the machine has been qualitatively diagnosed, a quantitative evaluation of the fault degree is necessary. For this purpose, a fault index based on the energy calculation of approximation and/or detail signals resulting from wavelet decomposition has been introduced to quantify the fault extend. The main advantages of the developed new method over existing Diagnosis techniques are the following: - Capability of monitoring the fault evolution continuously over time under any transient operating condition; - Speed/slip measurement or estimation is not required; - Higher accuracy in filtering frequency components around the fundamental in case of rotor faults; - Reduction in the likelihood of false indications by avoiding confusion with other fault harmonics (the contribution of the most relevant fault frequency components under speed-varying conditions are clamped in a single frequency band); - Low memory requirement due to low sampling frequency; - Reduction in the latency of time processing (no requirement of repeated sampling operation).

Fault detection, diagnosis and active fault tolerant control for a satellite attitude control system

Modern control systems are becoming more and more complex and control algorithms more and more sophisticated. Consequently, Fault Detection and Diagnosis (FDD) and Fault Tolerant Control (FTC) have gained central importance over the past decades, due to the increasing requirements of availability, cost efficiency, reliability and operating safety. This thesis deals with the FDD and FTC problems in a spacecraft Attitude Determination and Control System (ADCS). Firstly, the detailed nonlinear models of the spacecraft attitude dynamics and kinematics are described, along with the dynamic models of the actuators and main external disturbance sources. The considered ADCS is composed of an array of four redundant reaction wheels. A set of sensors provides satellite angular velocity, attitude and flywheel spin rate information. Then, general overviews of the Fault Detection and Isolation (FDI), Fault Estimation (FE) and Fault Tolerant Control (FTC) problems are presented, and the design and implementation of a novel diagnosis system is described. The system consists of a FDI module composed of properly organized model-based residual filters, exploiting the available input and output information for the detection and localization of an occurred fault. A proper fault mapping procedure and the nonlinear geometric approach are exploited to design residual filters explicitly decoupled from the external aerodynamic disturbance and sensitive to specific sets of faults. The subsequent use of suitable adaptive FE algorithms, based on the exploitation of radial basis function neural networks, allows to obtain accurate fault estimations. Finally, this estimation is actively exploited in a FTC scheme to achieve a suitable fault accommodation and guarantee the desired control performances. A standard sliding mode controller is implemented for attitude stabilization and control. Several simulation results are given to highlight the performances of the overall designed system in case of different types of faults affecting the ADCS actuators and sensors.

Design di un sistema di fault detection per un'architettura fail-safe di attuazione delle superfici di controllo di un UAV

In questo lavoro di tesi si affronta una delle problematiche che si presentano oggi nell'impiego degli APR (Aeromobili a Pilotaggio Remoto): la gestione della safety. Non si può più, in altri termini, negare che tali oggetti siano parte integrante dello spazio aereo civile. Proprio su questo tema recentemente gli enti regolatori dello spazio aereo stanno proiettando i loro sforzi al fine di stabilire una serie di regolamenti che disciplinino da una parte le modalità con cui questi oggetti si interfacciano con le altre categorie di velivoli e dall'altra i criteri di idoneità perché anche essi possano operare nello spazio aereo in maniera sicura. Si rende quindi necessario, in tal senso, dotare essi stessi di un sufficiente grado di sicurezza che permetta di evitare eventi disastrosi nel momento in cui si presenta un guasto nel sistema; è questa la definizione di un sistema fail-safe. Lo studio e lo sviluppo di questa tipologia di sistemi può aiutare il costruttore a superare la barriera oggi rappresentata dal regolamento che spesso e volentieri rappresenta l'unico ostacolo non fisico per la categoria dei velivoli unmanned tra la terra e il cielo. D'altro canto, al fine di garantire a chi opera a distanza su questi oggetti di avere, per tutta la durata della missione, la chiara percezione dello stato di funzionamento attuale del sistema e di come esso può (o potrebbe) interagire con l'ambiente che lo circonda (situational awarness), è necessario dotare il velivolo di apparecchiature che permettano di poter rilevare, all'occorrenza, il malfunzionamento: è questo il caso dei sistemi di fault detection. Questi due fondamentali aspetti sono la base fondante del presente lavoro che verte sul design di un ridotto ma preponderante sottosistema dell'UAV: il sistema di attuazione delle superfici di controllo. Esse sono, infatti, l'unico mezzo disponibile all'operatore per governare il mezzo nelle normali condizioni di funzionamento ma anche l'ultima possibilità per tentare di evitare l'evento disastroso nel caso altri sottosistemi siano chiaramente fuori dalle condizioni di normale funzionamento dell'oggetto.