Fault Detection And Identification Research Articles

Spacecraft fault detection and identification techniques using artificial intelligence

The complexity of spacecraft systems and their missions is increasing, requiring higher levels of performance and innovative solutions. It is essential to have onboard autonomy with minimal faults to ensure reliability, availability, and safety. Fault Detection and Identification (FDI) is critical in identifying spacecraft faults before they cause major failures. However, FDI design and application are challenging due to the space environment and the reliance on system information. To improve accuracy, speed, and noise robustness, modern FDI methods based on Artificial Intelligence (AI) techniques have been developed. This paper investigates the latest FDI techniques in the spacecraft attitude determination and control subsystem (ADCS) and electrical power subsystem (EPS). The article discusses various FDI methodologies and frameworks, highlighting their advantages, drawbacks, and the significance of AI implementation. Additionally, the paper presents a thorough analysis and comparison of the different methods.

Observer-Based Fault Estimation Applied to a Point Absorber Wave Energy Converter

The economic viability of wave energy conversion systems is one of the open points among the research community. To lower the energy cost, the devices in charge of extracting the wave power, called wave energy converters (WEC), are often controlled by means of optimal control (OC) strategies. Such OC systems, which have proven their effectiveness in wave energy applications, often rely on a mathematical model of the device (and, in some cases, on the wave excitation force) to optimize the control action provided to the system. Nevertheless, the marine environment results hostile for general device safe operations, potentially triggering a variety of faults in the WEC system. Such condition (for example, a sensor failure or additional friction inside a gearing) directly affects the system dynamics. If this deviation is not considered by the control algorithm, the energy production performance can degrade considerably, or the control action itself can cause a more serious fault. A possible solution is that of designing an algorithm capable of compensating for eventual faults in the system, while still respecting the initial design performance or, when not possible, preserving the main device functionalities. Such a control strategies belong to the family of Fault Tolerant Control (FTC) techniques, which can be divided into two macro-categories: Passive (PFTC) and active (AFTC) algorithms. While PFTC systems are designed offline and can account only for a predefined set of system faults, AFTC algorithms are more suitable to tackle significant system deviations from the nominal model. For this purpose, such algorithms may require some routine to detect, isolate and eventually estimate the specific fault. This task is accomplished by Fault Detection and Identification (FDI) routines. According to the AFTC algorithm, the FDI module must accomplish different tasks. Furthermore, the FDI module accuracy plays a crucial role in some AFTC strategies, since the poor estimation of a faulty signal can induce the controller to behave incorrectly. This paper presents an FDI algorithm applied to a point-absorber wave energy converter (WEC). The proposed structure consists of an observer-based strategy in charge of detecting, isolating, and tracking effectively faulty signals occurring in numerical simulations. The results demonstrate the proposed observer effectiveness for a predefined set of actuator and sensor faults, both in the case of independent, and simultaneous fault occurrence.

Bumpless‐transfer‐based fault tolerant control for the quadcopter: Piecewise homogeneous emission probability approach

SummaryThis paper investigates the fault‐tolerant control for quadcopter systems with the condition of reducing the bumps of system states. Allowing for the unavailable information of fault detection and identification (FDI) process in reality, a novel fault‐tolerant control scheme integrated with semi‐Markov stochastic process is considered, which stands in marked contrast to previous researches with the assumption of accurate FDI information. The emission probability satisfying piecewise homogeneous property is introduced to express the relationship between the obtained FDI information and real one, which covers more practical scenarios compared with existing ones. Afterwards, sufficient conditions on existence of the bumpless transfer controller reaching prescribed performance are obtained insuring that the quadcopter system is stable in the sense of mean square stability (MSS) and capable of dealing with the unavoidable bumps of states caused by stochastic switching dynamics. Eventually, simulations and comparative studies are performed to illustrate the advantages of the proposed strategy.

Permanent Magnet Synchronous Motor Driving Mechanical Transmission Fault Detection and Identification: A Model-Based Diagnosis Approach

This paper presents a model-based scheme for permanent magnet synchronous motor (PMSM) driving transmission fault detection and identification (FDI) in a steady-state condition. The proposed framework utilizes a PMSM state-space model and an approximated transmission model to construct the regression models for parameter estimation using the Recursive Least-Square (RLS) algorithm. The FDI are accomplished by the residual current spectrum thresholding method to assess the fault characteristic frequency magnitude and also by parameter clustering. Two types of mechanical transmission with three different fault conditions are tested in the experiments. As a preliminary effort in the condition monitoring of PMSM driving transmission, the study results demonstrate a promising approach by considering both residual current spectrum and parameter cluster, which achieved a satisfactory decision making in detecting and identifying the faulty condition.

Finite-time active fault-tolerant attitude control for flexible spacecraft with vibration suppression and anti-unwinding

In this paper, a finite-time active fault-tolerant control scheme is designed for a flexible spacecraft’s attitude control experiencing inertial parametric variations, external disturbances, multiple actuator faults, and estimation errors while suppressing the flexible appendages’ vibrations without using smart vibration suppression actuators. First, relative attitude dynamics of a flexible spacecraft with multiple actuator faults are outlined, and a sliding mode observer is designed to estimate flexible appendages-related vibrations. The proposed fault detection and identification (FDI) strategy can efficiently detect actuator faults, avoiding the false alarms caused by uncertainties and disturbances, and accurately estimate the cumulative fault effects on the spacecraft via Chebyshev neural network (CNN) based estimator. Based on a novel fast nonsingular terminal sliding mode surface, a finite-time, unwinding-free, and adaptive fault-tolerant attitude controller is designed to acclimatize the detected faults and uncertainties effectively, also heeding the errors in the estimation of flexible modes and faults. The spacecraft can carry out the coveted control objective in a definable time, and the stability of the proposed controller is corroborated via Lyapunov techniques. Finally, a comparative simulation analysis with the existing results elucidated the proposed scheme’s efficacy.

A Novel Fault Detection and Identification Framework for Rotating Machinery Using Residual Current Spectrum.

A novel framework of model-based fault detection and identification (MFDI) for induction motor (IM)-driven rotating machinery (RM) is proposed in this study. A data-driven subspace identification (SID) algorithm is employed to obtain the IM state-space model from the voltage and current signals in a quasi-steady-state condition. This study aims to improve the frequency–domain fault detection and identification (FDI) by replacing the current signal with a residual signal where a thresholding method is applied to the residual signal. Through the residual spectrum and threshold comparison, a binary decision is made to find fault signatures in the spectrum. The statistical Q-function is used to generate the fault frequency band to distinguish between the fault signature and the noise signature. The experiment in this study is performed on a wastewater pump in an existing industrial facility to verify the proposed FDI. Two faulty conditions with mathematically known and mathematically unknown faulty signatures are experimented with and diagnosed. The study results present that the residual spectrum demonstrated to be more sensitive to fault signatures compare to the current spectrum. The proposed FDI has successfully shown to identify the fault signatures even for the mathematically unknown faulty signatures.

Computational framework for real-time diagnostics and prognostics of aircraft actuation systems

Prognostics and health management (PHM) are emerging approaches to product life cycle that will maintain system safety and improve reliability, while reducing operating and maintenance costs. This is particularly relevant for aerospace systems, where high levels of integrity and high performances are required at the same time. We propose a novel strategy for the nearly real-time fault detection and identification (FDI) of a dynamical assembly, and for the estimation of remaining useful life (RUL) of the system. The availability of a timely estimate of the health status of the system will allow for an informed adaptive planning of maintenance and a dynamical reconfiguration of the mission profile, reducing operating costs and improving reliability. This work addresses the three phases of the prognostic flow – namely (1) signal acquisition, (2) fault detection and identification, and (3) remaining useful life estimation – and introduces a computationally efficient procedure suitable for real-time, on-board execution. To achieve this goal, we propose to combine information from physical models of different fidelity with machine learning techniques to obtain efficient representations (surrogate models) suitable for nearly real-time applications. Additionally, we propose an importance sampling strategy and a novel approach to model damage propagation for dynamical systems. The methodology is assessed for the FDI and RUL estimation of an aircraft electromechanical actuator (EMA) for secondary flight controls. The results show that the proposed method allows for a high precision in the evaluation of the system RUL, while outperforming common model-based techniques in terms of computational time.

Fault identification and fault-tolerant control for unmanned autonomous helicopter with global neural finite-time convergence

In this paper, the issue of global neural finite-time fault-tolerant control (FTC) is investigated for the medium-scale unmanned autonomous helicopter (UAH). To recognize the actuator bias and loss of effectiveness (LOE) faults, a novel fault detection and identification (FDI) strategy is proposed, which consists of a fault detection observer, two adaptive fault observers and a decision-making algorithm. The neural network (NN) technique is employed to deal with the unknown system uncertainty. In view of the backstepping approach and Lyapunov theory, a finite-time FTC scheme is developed to assure that all closed-loop system tracking errors converge to a small range of zero after a limited amount of time. Meanwhile, by integrating a switching mechanism into the control design, the traditional semi-globally uniformly ultimately bounded (SGUUB) stability is extended to globally uniformly ultimately bounded (GUUB) stability, such that the constraints on initial conditions of the NN controller is moderated. Simulation studies are implemented to demonstrate the usefulness of the presented controller.

Statistical residual-based time series methods for multicopter fault detection and identification

This paper presents the introduction, investigation, and critical assessment of three data-driven statistical residual-based time series methods for rotor fault detection and identification (FDI) in multicopters. A concise overview of statistical residual-based FDI methods is provided based on scalar (univariate) and vector (multivariate) stochastic time series models. The FDI methods employed in this study are based on identified response-only parametric scalar (univariate) and vector (multivariate) autoregressive (AR) representations of multicopter attitudes (time series), as the external excitation is non-observable, and their corresponding model residuals obtained under the considered healthy and faulty multicopter states. The comparative assessment of the effectiveness of three residual-based statistical FDI methods is presented in the face of external disturbances, namely three different levels of turbulence, and for different rotor fault scenarios. To the authors' best of knowledge, this is the first time that residual-based statistical time series methods are investigated and evaluated with respect to multicopter FDI. The unique characteristics of the presented methods are: (i) the data-driven stochastic identification of the multicopter dynamics under healthy and faulty rotor conditions is based only on the use of multicopter attitude signals, i.e. roll, pitch, and yaw, without the need to use additional aircraft states; (ii) there is no analytical modeling involved and the subsequent development of a stochastic multicopter FDI framework does not require knowledge of the controller effort to detect and classify rotor faults. The fault detection methods based on Vector AutoRegressive (VAR) models exhibit improved performance compared to their scalar counterparts, as indicated by lower false alarms and missed fault rates. In the case of rotor fault identification (classification), the methods that are based on scalar AR models exhibit reduced rotor fault classification accuracy, while the VAR-based methods outperform their scalar counterparts and can achieve a fault identification accuracy of up to 99.6%.

Identification of Multi-Faults in GNSS Signals using RSIVIA under Dual Constellation

This publication presents the development of integrity monitoring and fault detection and exclusion (FDE) of pseudorange measurements, which are used to aid a tightly-coupled navigation filter. This filter is based on an inertial measurement unit (IMU) and is aided by signals of global navigation satellite system (GNSS). Particularly, the GNSS signals include global positioning system (GPS) and Galileo. By using GNSS signals, navigation systems suffer from signal interferences resulting in large pseudorange errors. Further, a higher number of satellites with dual-constellation increases the possibility that satellite observations contain multiple faults. In order to ensure integrity and accuracy of the filter solution, it is crucial to provide sufficient fault-free GNSS measurements for the navigation filter. For this purpose, a new hybrid strategy is applied, combining conventional receiver autonomous integrity monitoring (RAIM) and innovative robust set inversion via interval analysis (RSIVIA). To further improve the performance, as well as the computational efficiency of the algorithm, the estimated velocity and its variance from the navigation filter is used to reduce the size of the RSIVIA initial box. The designed approach is evaluated with recorded data from an extensive real-world measurement campaign, which has been carried out in GATE Berchtesgaden, Germany. In GATE, up to six Galileo satellites in orbit can be simulated. Further, the signals of simulated Galileo satellites can be manipulated to provide faulty GNSS measurements, such that the fault detection and identification (FDI) capability can be validated. The results show that the designed approach is able to identify the generated faulty GNSS observables correctly and improve the accuracy of the navigation solution. Compared with traditional RSIVIA, the designed new approach provides a more timely fault identification and is more computational efficient.

Fault Detection and Identification Method for Quadcopter Based on Airframe Vibration Signals.

Quadcopters are widely used in a variety of military and civilian mission scenarios. Real-time online detection of the abnormal state of the quadcopter is vital to the safety of aircraft. Existing data-driven fault detection methods generally usually require numerous sensors to collect data. However, quadcopter airframe space is limited. A large number of sensors cannot be loaded, meaning that it is difficult to use additional sensors to capture fault signals for quadcopters. In this paper, without additional sensors, a Fault Detection and Identification (FDI) method for quadcopter blades based on airframe vibration signals is proposed using the airborne acceleration sensor. This method integrates multi-axis data information and effectively detects and identifies quadcopter blade faults through Long and Short-Term Memory (LSTM) network models. Through flight experiments, the quadcopter triaxial accelerometer data are collected for airframe vibration signals at first. Then, the wavelet packet decomposition method is employed to extract data features, and the standard deviations of the wavelet packet coefficients are employed to form the feature vector. Finally, the LSTM-based FDI model is constructed for quadcopter blade FDI. The results show that the method can effectively detect and identify quadcopter blade faults with a better FDI performance and a higher model accuracy compared with the Back Propagation (BP) neural network-based FDI model.

Robust Three Stage Central Difference Kalman Filter for Helicopter Unmanned Aerial Vehicle Actuators Fault Estimation

This paper proposes state and fault estimations for uncertain time-varying nonlinear stochastic systems with unknown inputs. we suppose, the information about the fault and unknown inputs is not perfectly known. For this purpose, in this manuscript, we developed a robust three-stage central difference Kalman filter (RThSCDKF). We used RThSCDKF for model-based fault detection and identification (FDI) in nonlinear hover mode of helicopter unmanned aerial vehicle (HUAV) in the presence of external disturbance. In this system, actuator faults are affected by each other. The proposed method estimates and decouples actuator faults in the presence of external disturbances. This model can detect stuck and floating faults that are important to detect. At the end, this method is compared with the three-stage extended Kalman filter (ThSEKF). Simulation results show the effectiveness of the proposed robust method for detection and isolation of various actuator faults and also this shows more accuracy with respect to ThSEKF.

Application of interval type-2 fuzzy logic systems to gas turbine fault diagnosis

Several approaches have been employed for gas turbine Fault Detection and Identification (FDI), since a reliable FDI system with minimum false alarm rate can effectively reduce maintenance cost and downtime. This paper introduces the application of Interval Type-2 Fuzzy Logic Systems (IT2FLSs) to gas turbine fault diagnosis for the first time. The proposed FDI system is composed of a bank of IT2FLSs, trained for state detection and health assessment of an industrial gas turbine at various operating conditions. For this purpose, train and test data are generated by applying mechanical fault signatures to gas turbine’s mathematical model. Fuzzy Rule Base is then developed by means of Interval Type-2 Fuzzy C-Means (IT2FCM) clustering, and parameters of the IT2FLSs are optimized using a metaheuristic algorithm. Finally, the performance of the IT2FL based FDI system is compared to several classification techniques. It is concluded that as a compromise among the objectives of online applicability, accuracy, reliability against measurement uncertainty, incipient fault diagnosis, robustness against abrupt sensor failure and generalization capacity, the proposed method demonstrates a promising performance.

Fault tolerant sliding mode intelligent control based on fault hiding for a nonlinear induction furnace system

In this article, the design of a fault tolerant controller for an induction furnace is presented. The fault studied was that of an actuator fault in the furnace. The design is comprised of a fault detection and identification (FDI) unit as well as a reconfiguration block. The induction furnace is modeled using Radial Basis Function (RBF) neural networks so that when an actuator fault happens, the FDI block detects and estimates the magnitude of the fault that occurred. Then based on a fault hiding strategy, the reconfiguration mechanism is activated to accommodate the destructive effects of the fault in a way that from the controller’s point of view the actuator fault is hidden. Utilization of the RBF neural networks model in the system also gives training ability to the system and raises its intelligence. In order to make the system robust against uncertainties caused by the fault, a sliding mode scheme is implemented as well. Using Lyapunov’s stability approach, the stability of the proposed method is verified. Finally, the simulation result of this method on the induction furnace system validates the effectiveness of the proposed algorithm in the presence of an actuator fault.

An active fault-tolerant control method based on moving window hidden Markov model

This article proposes a new active fault-tolerant control (AFTC) method based on moving window hidden Markov models (MWHMM) for nonlinear processes. The AFTC approach could be divided into two steps, fault detection and identification (FDI) and fault accommodation. The information obtained from the FDI step is used for fault accommodation, under the framework of model predictive control (MPC). Firstly, MWHMM is utilized for online fault detection and identification. Moreover, to improve the discernibility of the prediction status probability density function (PDF) for identification between the normal and faulty modes, the Hellinger distance, which could measure the similarity between probability distributions, is introduced. As the separability between the normal and faulty modes is increased, the accuracy and reliability of fault identification by MWHMM is greatly improved. At last, not only the occurrence of a single fault but also the occurrence of a hybrid fault, which means that multiple faults occur simultaneously, is considered in this article. The validity of the suggested method is verified by the simulation results on a three-tank water system.

Model reduction and dynamic matrices extraction from state-space representation applied to rotating machines

Model reduction is a relevant subject within the field of rotordynamics since low order models are fundamental for control strategies design and implementation, health monitoring, behavior prediction, Fault Detection and Identification (FDI) and stochastic analyses. In this context, this article proposes a complete review of three widely used reduction methods: static or Guyan technique, the System Equivalent Reduction Expansion Process (SEREP) and the modified SEREP. Regarding SEREP, a new approach is presented in which right and left eigenvectors from the undamped original system (with mass and stiffness matrices not symmetric) are used to transform all original system dynamic matrices. To modified SEREP (that contemplates all original system characteristics, including frequency dependent damping and gyroscopic effect) an extraction from the reduced state-space representation is achieved to build rotor and bearings reduced dynamic matrices with physical interpretability. A set of practical recommendations is presented, highlighting key aspects to increase reduction success chances. The methods are applied to two different rotors and results show satisfactory agreement between reduced and complete model responses when analyzing Frequency Response Functions (FRFs) and Campbell diagrams. The computational costs of processing each reduced model and running common rotordynamic analyses with reduced and complete models are compared.

Active Fault-Tolerant Control With Imperfect Fault Detection Information: Applications to UAVs

A major issue in active fault-tolerant control (FTC) is having to rely on uncertain information provided by the fault detection and identification (FDI) algorithm. To achieve a reliable FTC, the controller needs to be robust against FDI uncertainties, namely missed “small” faults and fault detection delays. In this article, the FDI system only needs to indicate which actuator is faulty after a predefined amount of time, but does not need to estimate the actuator fault value or its faulty position. The goal is to design a FTC, which is robust against potentially undetected “small” actuator faults and which guarantees boundedness of the unstable poles for all possible actuator fault types. An adapted μ analysis is developed to analyze the unstable system poles, due to the presence of a fault. It is integrated within a DK-iteration approach to synthesize a controller in a robust control framework with H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -design objectives. The synthesis is shown on a fixed-wing UAV, where fault-tolerance against single-actuator faults is achieved. Real flight experiments of a UAV with aileron and flap faults show the validity of the approach.

A novel gas turbine fault detection and identification strategy based on hybrid dimensionality reduction and uncertain rule-based fuzzy logic

Persistent health monitoring of a gas turbine engine preserves the operational efficiency and lifetime of its components. An online monitoring system should meet requirements such as high accuracy, reliability against measurement uncertainty, and ease of implementation. Factors such as high dimensions of data, variety of deteriorations, and similarity of fault patterns are also challenging to the Fault Detection and Identification (FDI) process. In this paper, a new gas turbine health assessment strategy based on hybrid dimensionality reduction and Interval Type-2 Fuzzy Logic (IT2FL) is developed, and it is demonstrated that the proposed pre-processing method not only reduces the computational burden, but also preserves the part of information that is necessary for maintaining the reliability of the FDI procedure. In this approach, raw data are projected to a nonlinear self-organized feature space, and optimum feature subsets are selected using multi-objective optimization. Pre-processed data are then utilized for training a bank of IT2FL Systems (IT2FLSs), which are developed to estimate the gas turbine’s health status at various ambient and working conditions. The proficiency of the proposed approach is compared to other feature extraction methods, and it is indicated that the Self-Organizing Map (SOM) based FDI system outstands in terms of incipient fault diagnosis, reliability, and robustness against measurement uncertainty.

Data-Driven Approaches for Diagnosis of Incipient Faults in DC Motors

Fault detection and identification (FDI) of electrical motors is crucial to ensuring smooth operation of several industrial systems. Detection and diagnosis of faults at the incipient stage allows corrective actions to be adopted so as to curb the severity of faults. However, FDI of incipient faults has proved to be elusive to traditional methods of fault diagnosis. With recent developments in statistical machine learning, new methods are proposed that can be used for FDI. In this paper, we adopt three tools (support vector machine, convolutional, and recurrent networks) from machine learning to address the challenge of FDI of incipient faults. We perform FDI of a dc motor with the most commonly and readily measured current data. Results from experimental data reveal that the convolutional network performs the best of the three methods. A comparative study of the performance of the three methods under different types of operating conditions is provided. Sensitivity of the techniques to noise in measurements is also studied. The proposed approach serves as a reliable tool for FDI of dc motor under different types of loading conditions.

Inflight Aerodynamic Parameter Estimation for Fixed Wing Unmanned Aerial Vehicle

This paper presents the frame work for aerodynamic parameter estimation for small fixed wing unmanned aerial vehicle (UAV). The recent development in autopilot hardware for small UAV enables the in-flight data collection of flight characteristics. A methodology is outlined to collect, process and arrive at a conclusion from the in-flight data using commercial flight controller of under 2kg (micro) fixed wing aircraft, ‘VAF01’ for which a Fault Detection and Identification (FDI) system is under development. As a part of the FDI, the linear longitudinal (3 DOF) aerodynamic model is developed and in-flight experimental data is used to estimate the longitudinal aerodynamic parameters. The Flight Path Reconstruction is completed with the acquired parameters from in-flight experiments and results are discussed for further utilization of them.