Multiple Faults Research Articles

Disturbance observer based fixed-time control of stochastic systems

In this paper, the novel fixed-time anti-disturbance control scheme is proposed for a class of stochastic systems subjected to multiple disturbances and faults, and the disturbances consist of derivative-bounded disturbances and multiply noise. Based on the pole placement method, the fixed-time disturbance observer (Fixed-time DO) is devised to estimate derivative-bounded disturbances and an adaptive law is employed to approach the time-varying fault. Then a composite fixed-time controller is constructed to make the system converge to equilibrium position within a pre-specified time. At the same time, simulation examples illustrate that the proposed controller is effective and the convergence time is not related to the initial states of the system.

Impact of Burial Dissolution on the Development of Ultra‐deep Fault‐controlled Carbonate Reservoirs: Insights from High‐temperature and High‐pressure Dissolution Kinetic Simulation

AbstractBurial dissolution is a critical diagenetic process influencing ultra‐deep carbonate reservoir development and preservation. Artificial carbonate samples with different internal structures were prepared, and high‐temperature and high‐pressure dissolution kinetic simulations were conducted. The results demonstrate that the intensity of burial dissolution is controlled by temperature and pressure, while tectonic‐fluid activity influences the development pattern of burial dissolution, ultimately determining the direction of its differential modification. Extensive burial dissolution is likely to occur primarily at relatively shallow depths, significantly influencing reservoir formation, preservation, modification, and adjustment. The development of faults facilitates the maintenance of the intensity of burial dissolution. The maximum intensity of burial dissolution occurs at the tips and overlap zones of faults and intersections of multiple faults. The larger the scale of the faults, the more conducive it is to the development of burial dissolution. Burial dissolution fosters the formation of fault networks characterized by enhanced reservoir capacity and permeability. Burial dissolution controlled by episodic tectonic‐fluid activity is a plausible explanation for forming the Tarim Basin's ultra‐deep fault‐controlled “string‐bead‐like” reservoirs.

Remaining Useful Life Prediction for Aircraft Engines under High-Pressure Compressor Degradation Faults Based on FC-AMSLSTM

The healthy operation of aircraft engines is crucial for flight safety, and accurate Remaining Useful Life prediction is one of the core technologies involved in aircraft engine prognosis and health management. In recent years, deep learning-based predictive methods within data-driven approaches have shown promising performance. However, for engines experiencing a single fault, such as a High-Pressure Compressor fault, existing deep learning-based predictive methods often face accuracy challenges due to the coupling relationship between different fault modes in the training dataset that includes a mixture of multiple fault modes. In this paper, we propose the FC-AMSLSTM method, a novel approach for Remaining Useful Life prediction specifically targeting High-Pressure Compressor degradation faults. The proposed method effectively addresses the limitations of previous approaches by fault classification and decoupling fault modes from multiple operating conditions using a decline index. Then, attention mechanisms and multi-scale convolutional neural networks are employed to extract spatiotemporal features. The long short-term memory network is then utilized to model RUL estimation. The experiments are conducted using the Commercial Modular Aero-Propulsion System Simulation dataset provided by NASA. The results demonstrate that compared to other prediction models, the FC-AMSLSTM method effectively reduces RUL prediction error for HPC degradation faults under multiple operating conditions.

Non-stationary signal analysis utilizing VMD and EMD algorithm for multiple fault classification for industrial applications

The Hilbert spectrum images of intrinsic mode functions (IMF) of empirical mode decomposition (EMD) analysis and variational mode decomposition (VMD) analysis of faulty machine vibration signals are used in deep convolutional neural network (DCNN) for machine fault classification in which the DCNN automatically learns the features from spectral images using convolution layer. Though both EMD and VMD analysis suit well for non-stationary signal analysis, VMD has the merit of aliasing free IMFs. In this paper, the performance improvement of DCNN classification for a non-stationary vibration signal dataset using VMD is brought out. The numerical experiment uses the Hilbert spectrum images of 4 EMD-IMFs and 4 VMD-IMFs in DCNN to classify 10 different faults of the Case Western Reserve University (CWRU) bearing dataset. The confusion matrices are obtained and the plot of model accuracies in terms of epochs for the DCNN is analysed. It is shown that the spectrum images of one of the four EMD-IMFs, IMF0, give a validation accuracy of 100% and in the case of VMD the spectrum images of two of the four VMD-IMFs, IMF0, and IMF1 give a validation accuracy of 100%. This reveals that non-aliasing IMFs of VMD are better at classifying bearing faults. Further to bring out the merits of VMD analysis for non-stationary signals the numerical experiment is conducted using VMD analysis for binary fault classification of the milling dataset which is more non-stationary than the bearing dataset which is proved by plotting the statistical parameters of both datasets against time. It is found that the DCNN classification is 100% accurate for IMF3 of VMD analysis which is much better than the 81% accuracy provided by EMD analysis as per existing literature. The performance comparison highlights the merits of VMD analysis over EMD analysis and other state-of-the-art methods and ensemble learning methods.

Predefined Time and Accuracy Adaptive Fault-Tolerant Control for Nonlinear Systems with Multiple Faults

This work mainly studies the issue of predefined time and accuracy adaptive fault-tolerant control for strict-feedback nonlinear systems with multiple faults. The faults in the controlled system include actuator faults and external system faults. The unknown functions for nonlinear systems are approximated by fuzzy logic systems (FLSs). And then, according to the backstepping technique and the predefined time stability theory, an adaptive fuzzy control algorithm is presented, which can make sure that all closed-loop system signals remain predefined time bound and the tracking error converges to a predefined accuracy within the predefined time. Ultimately, the effectiveness of the presented control algorithm is proved through two simulation examples.

Multiple-failure mode division and condition-based maintenance decision making for systems with multi-indicator performance degradation

In prognostics and health management research, it is typically assumed that the health status of a system can be reflected by a single performance indicator. However, modern industrial systems are complex and often require multiple indicators to represent different aspects of system health. Furthermore, these indicators may be associated with multiple fault types, leading to multiple failure modes. This study focuses on systems with multi-indicator performance degradation. We construct a division model of multiple failure modes, and investigate the optimal condition-based maintenance decision under multiple failure modes. By constructing division models for systems with two-indicator performance degradation and systems with three-indicator performance degradation, a common multiple-failure mode division model for systems with multi-indicator performance degradation is to distinguish different fault types that may occur in such systems. Taking the systems with two-indicator performance degradation as an example, a condition-based maintenance strategy is developed that considers the differences in maintenance types and effectiveness corresponding to different faults types. A joint probability recursive model under the influence of strategies is derived, and a cost-rate model in a finite time horizon is established to determine the optimal inspection cycle and maintenance threshold for each indicator. Taking the steel rolling system as an application case, the correctness and effectiveness of the proposed strategy and model are verified. The results indicate that the division model can represent the complex relationships among multiple indicators and fault types and that the strategy considering multiple maintenance types and effects can reduce the cost rate of the system.

Multi-Fault Diagnosis Based on Hybrid Bio-Inspired Algorithm ACO-GA

The fault detection and isolation (FDI) procedure increases the assurance of quality, reliability, and safety of industrial systems. Several faults may appear simultaneously, and the purpose of multi-fault diagnosis is to identify and locate these multiple faults. This work is particularly interested in the diagnosis based on the structural analysis of the system; residuals can be generated and used as fault indicators by model-based fault detection techniques. The isolation is dependent on the structure of the fault signature matrix. A new fault signature that represents the superposition of the fault is produced by simultaneous fault effects, resulting in an additional column in an extended signature matrix. This remedy is rather combinatorial. This research focuses on two methods to isolate multiple faults: (1) A modified enumerative method; (2) A hybrid ant colony optimization algorithm-genetic algorithm (Hybrid ACO-GA) is adapted to the MFD problem which has the advantage of a better research as well as the hybridization with GA.

Analysis of multiple-faults of high-voltage circuit breakers based on non-negative matrix decomposition

High-voltage circuit breakers are the core equipment in power networks, and to a certain extent, are related to the safe and reliable operation of power systems. However, their core components are prone to mechanical faults. This study proposes a component separation method to detect multiple mechanical faults in circuit breakers that can achieve online real-time monitoring. First, a model and strategy are presented for obtaining mechanical voiceprint signals from circuit breakers. Subsequently, the component separation method was used to decompose the voiceprint signals of multiple faults into individual component signals. Based on this, the recognition of the features of a single-fault voiceprint signal can be achieved. Finally, multiple faults in high-voltage circuit breakers were identified through an experimental simulation and verification of the circuit breaker voiceprint signals collected from the substation site. The research results indicate that the proposed method exhibits excellent performance for multiple mechanical faults, such as spring structures and loose internal components of circuit breakers. In addition, it provides a reference method for the real-time online monitoring of high-voltage circuit breakers.

Diagnosing simultaneous bearing and misalignment faults in an induction motor using sensor fusion

A monitoring system for induction motors (IMs) is essential for most industrial plants. Bearing faults and shaft misalignment are common mechanical defects in induction motors. Since one fault could cause another fault in the system, multiple faults can occur simultaneously and change the vibration (electrical) behaviour of the induction motors from that of a single fault condition. This paper aims to identify two common faults (shaft misalignment and defective bearing) simultaneously in IMs using data fusion of vibration and current measurements. Sensor fusion of accelerometer and Hall-effect sensor signals is used to combine the vibration and current signals. The proposed method is applied via a laboratory test-rig based on data fusion to detect multiple defects simultaneously in induction motors. Then, by extracting the important features using a principal component analysis (PCA) algorithm, the K-nearest neighbours (KNN) classification algorithm is used to detect defects and make decisions. The results show that the fusion of both current and vibration signal analyses significantly improves the efficiency and reliability of multiple fault detection. Also, bispectrum analysis of the current signal is highly sensitive to misalignment and can be an effective method for detecting such faults.

Spectrum-based rule- and item-level localization of faults in context-free grammars

We describe and evaluate spectrum-based methods aimed at finding faults in context-free grammars. In their basic form, they take as input a test suite and a parser for the grammar that is modified to collect grammar spectra (i.e., the sets of grammar elements used in attempts to parse the individual test cases), and return as output a ranked list of suspicious elements. We define grammar spectra suitable for localizing faults on the level of the grammar rules (i.e., rule spectra) and the rules’ individual symbols (i.e., item spectra), respectively. We show how both types of grammar spectra can be collected by both LL and LR parsers, and how the JavaCC, ANTLR, and CUP parser generators can be modified and used to automate the collection of the grammar spectra. We also show how grammar spectra can be synthesized directly from test cases derived from a grammar, and how such synthetic spectra can be used to localize differences between a grammar and a black-box system under test.We first evaluate our approach over a large number of medium-sized single fault grammars, which we constructed by fault seeding from a common origin grammar. At the rule level, it ranks the rules containing the seeded faults within the top five rules in about 40%–70% of the cases, depending on the applied parsing technique, test suite, and ranking metric, and pinpoints them (i.e., correctly identifies them as unique most suspicious rule) in about 10%–30% of the cases, with significantly better results for the synthetic spectra. At the item level, our approach remains remarkably effective despite the larger number of possible locations, provided it is coupled with a simple tie-breaking strategy that prefers items with the right-most designated position over other items from the same rules in a tie. It typically ranks the seeded faults within the top five positions in about 30%–60% of the cases, and pinpoints them in about 15%–40% of the cases. This specialized item-level localization also significantly outperforms a simplistic extension of the rule-level localization, where all positions within a rule are given the same score.We further evaluate our approach over grammars that contain real faults. We show that an iterative method can be used to localize and manually remove one by one multiple faults in grammars submitted by students enrolled in various compiler engineering courses; in most iterations, the top-ranked rule already contains an error, and no error is ranked outside the top five ranked rules. We finally apply our approach to a large open-source SQLite grammar and show where this version deviates from the language accepted by the actual SQLite system.

A Learning-Based Approach for Diagnosis and Diagnosability of Unknown Discrete Event Systems.

This article develops a novel active-learning technique for fault diagnosis of an initially unknown finite-state discrete event system (DES). The proposed method constructs a diagnosis tool (termed diagnoser), which is able to detect and identify occurred faults by tracking the observable behaviors of the system under diagnosis. The proposed algorithm utilizes an active-learning mechanism to incrementally collect the information about the system to construct the diagnoser. This is achieved by completing a series of observation tables in a systematic way, resulting in the construction of the diagnoser. It is proven that the proposed algorithm terminates after a finite number of iterations and returns a correctly conjectured diagnoser. The developed diagnoser is a deterministic finite-state automaton. Furthermore, we have proven that the developed diagnoser consists of a minimum number of states. A sufficient condition for diagnosability of the system under diagnosis is derived, which guarantees the diagnosis of faults within a bounded number of observations. The developed method is applied to two case-studies, illustrating the steps of the proposed algorithm and its capability of diagnosing multiple faults.

Multi-fault diagnosis of district heating system based on PCA_BP neural network

To diagnose multiple faults (blockage and leakage) of a district heating system (MFDHS) in a timely and accurate manner, a new multi-fault diagnosis method is proposed that considers fault diagnosis as a classification problem based on principal component analysis (PCA) and a Back Propagation (BP) neural network. Using this method, a simulation model of heating-network faults is constructed and fault experiments are conducted. Based on the simulation model and experiments, the experimental (ED) and model (MD) datasets are effectively constructed followed by the construction of substitute datasets(SD) that replaces the experimental data with model data. PCA and data standardization are then adopted to process all the datasets. Finally, three BP neural network classification models are trained and tested using the three datasets. To demonstrate the effectiveness of this method, four experimental heating networks, namely a single heat source and branch (SHB), single heat source and single loop (SHSL), double heat source and branch (DHB), and double heat source and double loop (DHDL), are discussed as case studies. The results show that the relative errors of the experimental and simulation data for the pressures and flow rates are less than 5%. The highest prediction accuracy is obtained when the fault to the normal data are 95–5% under training sets to testing sets of 70–30%, while for the four heating networks, the prediction accuracy of the method built with ED is 99.93%, 99.99%, 99.84%, and 99.66% respectively, whereas it reaches 99.99% for MD. The accuracies are 99.99%, 98.13%, 97.4%, and 99.99%, respectively, with MD serving as the training set while another 11400 sets of experimental data serve as the testing set. Accordingly, the accuracy ranges from 95.56% to 88.03%, 97.85–93.29%, 95.65–85.71%, and 100–84.82%, with SD serving as the training set and another 11400 sets of experimental data serving as the testing set.

A concurrent fault diagnosis method for electric isolation valves in nuclear power plants based on rule-based reasoning and data-driven methods

In order to improve the fault diagnosis ability of electric isolation valves in nuclear power plants, especially for the diagnosis of concurrent faults, a novel concurrent fault diagnosis method based on rule reasoning and data-driven methods is proposed in this study. The concurrent fault diagnosis method is divided into two layers: the rule-based reasoning layer and data-driven layer. In order to realize the diagnosis of most concurrent faults, the rule-based reasoning layer is established by a rule inference model according to the failure mode and effects analysis (FMEA) of the electric isolation valve. In order to diagnose concurrent faults with mutual interference of fault features that cannot be diagnosed by rule-based reasoning layer, the data-driven layer is established by variational mode decomposition (VMD) and fast independent component analysis (Fast-ICA) method to separate concurrent fault signals and gate recurrent unit (GRU) method to identify fault patterns. After testing on the electric valve test bench and simulation data, multiple concurrent faults of the electric isolation valve are diagnosed accurately by the proposed concurrent fault diagnosis framework, including complex concurrent faults such as internal leakage and external leakage, and the framework can also work in a single fault diagnosis scenario without changing models. Compared with the concurrent fault diagnosis based on multi-label support vector machine, the accuracy of concurrent fault diagnosis is improved by about 25.3%. The research can provide a new idea for the concurrent fault diagnosis of electric isolation valves and other equipment.

Fault location in high-voltage direct-current grids based on panoramic voltage features and vision transformers

This paper presents a novel fault location method designed for high-voltage direct-current (HVDC) grids equipped with quick-action protections and circuit breakers. The method relies on a very short data window of single-ended voltage signals, which allows the acquisition of necessary measurements before the ultra-fast fault-isolation stage. Through fault analysis of the simulated four-terminal HVDC grid, it is observed that the fault distance induces a unique transient response in the voltage signal. However, due to the complex mathematical representation of loop components and the multiple fault factors involved, a data-driven strategy is adopted for fault distance estimation. Based on this strategy, four signal processing techniques, including Gramian angular field, recurrence plot, Markov transition field, and continuous wavelet transform, are used to extract panoramic voltage features. Additionally, four advanced lightweight variants of vision transformers (ViT), including MobileViT, EfficientFormer, Efficient-Model, and EdgeViT, are considered for constructing the regression model. The optimal combination of panoramic voltage features and regression models for configuring the locator is determined by statistical evaluation. The results of semi-physical experiments based on real-time digital simulators demonstrate that the proposed method, first, can accurately estimate the fault distance, second, possesses robustness against high transition resistance, low sampling frequency, and strong noise interference, and third, is practically feasible.

Sequential Model-Based Diagnosis by Systematic Search (Abstract Reprint)

Model-based diagnosis aims at identifying the real cause of a system's malfunction based on a formal system model and observations of the system behavior. To discriminate between multiple fault hypotheses (diagnoses), sequential diagnosis approaches iteratively pose queries to an oracle to acquire additional knowledge about the diagnosed system. Depending on the system type, queries can capture, e.g., system tests, probes, measurements, or expert questions. As the determination of optimal queries is NP-hard, state-of-the-art sequential diagnosis methods rely on a myopic one-step-lookahead analysis which has proven to constitute a particularly favorable trade-off between computational efficiency and diagnostic effectivity. Yet, this solves only a part of the problem, as various sources of complexity, such as the reliance on costly reasoning services and large numbers of or not explicitly given query candidates, remain. To deal with such issues, existing approaches often make assumptions about the (i) type of diagnosed system, (ii) formalism to describe the system, (iii) inference engine, (iv) type of query to be of interest, (v) query quality criterion to be adopted, or (vi) diagnosis computation algorithm to be employed. Moreover, they (vii) often cannot deal with large or implicit query spaces or with expressive logics, or (viii) require inputs that cannot always be provided. As a remedy, we propose a novel one-step lookahead query computation technique for sequential diagnosis that overcomes the said issues of existing methods. Our approach (1) is based on a solid theory, (2) involves a systematic search for optimal queries, (3) can operate on implicit and huge query spaces, (4) allows for a two-stage optimization of queries (wrt. their number and cost), (5) is designed to reduce expensive logical inferences to a minimum, and (6) is generally applicable. The latter means that it can deal with any type of diagnosis problem as per Reiter's theory, is applicable with any monotonic knowledge representation language, can interact with a multitude of diagnosis engines and logical reasoners, and allows for a quality optimization of queries based on any of the common criteria in the literature. We extensively study the performance of the novel technique using a benchmark of real-world diagnosis problems. Our findings are that our approach enables the computation of optimal queries with hardly any delay, independently of the size and complexity of the considered benchmark problem. Moreover, it proves to be highly scalable, and it outperforms the state-of-the-art method in the domain of our benchmarks by orders of magnitude in terms of computation time while always returning a qualitatively as good or better query.

Analytical and numerical analysis on the mechanical response and damage characteristics of tunnels subjected to multiple normal faulting

Multiple fault planes often coexist within a fault zone during fault dislocation. However, all the previous analytical models assume that there is only one fault plane during a fault dislocation, which is inconsistent with the actual engineering conditions. In this paper, theoretical analysis and numerical simulations are used to investigate the mechanical response and damage characteristics of tunnels subjected to multiple normal faulting. A nonlinear theoretical model is established for analyzing the mechanical response of tunnels subjected to multiple normal faulting. The number of fault planes, the shear effect of the soil and the tunnel, the fault zone width, and the nonlinear soil−tunnel interaction are applied inside the theoretical model, significantly improving the analysis accuracy and applied range. The corresponding numerical simulation based on the Concrete Damaged Plasticity (CDP) Model is carried out to study the damage characteristics of the tunnel. The proposed theoretical model is verified by model tests and numerical simulations, which exhibit consistency in both qualitative and quantitative aspects. A parametric analysis is presented, wherein the impacts of varying numbers of fault planes, fault plane distances (d), fault displacement ratios (ξ), and buried depths (C) on the tunnel response are investigated. The results show that an increasing number of fault planes leads to a reduced peak bending moment (Mmax) and shear force (Vmax). As the number of fault planes increased from one to four, Mmax and Vmax decreased by 1.57 times and 3.31 times, respectively. Expanding d corresponds to a reduction in both Mmax and Vmax. The minimum Vmax within the tunnel materializes at ξ4 (Δfd1 = Δfd2 = Δfd3), and the tunnel's Vmax appears at the fault plane of maximum fault displacement. Moreover, with the augmentation of C, an increase in both Mmax and Vmax was observed. Additionally, upon attaining a normal fault displacement of 0.2 m, the tunnel lining undergoes both tensile and compressive failure at the fault plane. As the normal fault displacement surpasses 0.4 m, the failure range of the tunnel at the fault plane undergoes a precipitous escalation, marked by a maximum increase of 68.8 % in tensile failure and a 29.6 % increase in compressive failure.

A large fault partially reactivated during two contiguous seismic sequences in Central Italy: The role of geometrical and frictional heterogeneities

Earthquakes can rupture multiple fault segments as well as faults with complex geometry, or heterogeneous pre-stress and frictional properties. These observations have been documented mainly for moderate-to-large earthquakes by inverting geodetic and seismic data and by studying the influence of fault orientation and rheology within the regional stress field.In this work we have studied the Gorzano fault, GF, a large normal fault within the active fault system of Central Italy that during the last two largest Italian seismic sequences, L'Aquila (2009) and Amatrice-Visso-Norcia (2016–2017), was reactivated via a series of 5.0 < Mw < 6.0 events. We calculated moment tensor solutions for 134 M > 3 events and evaluated their normalized slip-tendency. Merging these results with high resolution earthquake catalogs, available M > 5 earthquake slip distributions, and frictional properties characterizing the activated fault, we develop a mechanical model and discuss potential earthquake rupture scenarios.The GF is an optimally oriented fault within the regional stress field and from, the reactivation via aftershock or mainshock slip of complementary fault portions from 2009 to 2017 indicates that the fault behaves as a single fault structure. The geometrical and mechanical heterogeneities suggest that the most likely slip behavior of GF is the reactivation of different fault portions with M > 5.0. However, due to favorable initial stress conditions, we suggest that a seismic rupture can produce the complete reactivation of the fault, resulting in a M 6.5–6.6 earthquake as documented in paleoseismological data.

Signed-data reinforced observer-based fault diagnosis for virtually-coupled electric multiple units trains

Virtual coupling has been recognized as a promising yet challenging technology for the next generation of train control systems. As a mean of guaranteeing the safety of closer-running of virtually-coupled electric multiple units (EMU) trains, fault detection and diagnosis play a critical role in perceiving abnormal running conditions. This paper develops a new signed-data reinforced observer-based technique for dealing with actuator faults’ detection and diagnosis for virtually-coupled EMU trains. The introduction of novel signed-data reinforcement technique ensures that unbiased fault diagnosis can be guaranteed with any regressor vector composed of control signals on EMU trains. Fault detection and diagnosis observers, together with fault alarming principle, information transmission coding strategy are elaborated using Lyapunov stability theorem. Simulation results are given to demonstrate the effectiveness of proposed algorithm.

Data-driven approach for the detection of faults in district heating networks

At present, District Heating Networks (DHNs) are required to operate more and more reliably and efficiently in order to further save primary energy and reduce environmental impact. Thus, monitoring and diagnostic approaches are necessary to identify the typical faults that affect this type of systems (e.g., anomalous heat and pressure losses), with the final goal of optimizing DHN operation and management. To this aim, this paper presents a data-driven diagnostic methodology that exploits NARX (nonlinear autoregressive network with exogenous inputs) neural networks to simulate DHN healthy operation and a threshold-based criterion for fault detection and identification. The novel diagnostic methodology is tested for evaluating the health state of the DHN of the campus of the University of Parma (Italy), based on the availability of time series of measurable variables (mass flow rate, pressure, and temperature) for both the power plant and the end-users. Both single and multiple faults of anomalous heat losses and anomalous pressure losses, with different magnitudes and location, were artificially implanted in DHN pipes. The main novel contribution of this paper with respect to state-of-the-art literature relies on the development of a real-time simulation approach aimed to predict DHN future operation and detect abnormal deviations from normal operation. The methodology proves to correctly detect and identify all simulated faults related to heat and pressure losses, by also correctly estimating their magnitude even in the most challenging scenarios.

Actuator and sensor fault isolation in a class of nonlinear dynamical systems

Fault isolation in dynamical systems is a challenging task due to modeling uncertainty and measurement noise, interactive effects of multiple faults and fault propagation. This paper proposes a unified approach for isolation of multiple actuator or sensor faults in a class of nonlinear uncertain dynamical systems. Actuator and sensor fault isolation are accomplished in two independent modules, that monitor the system and are able to isolate the potential faulty actuator(s) or sensor(s). For the sensor fault isolation (SFI) case, a module is designed which monitors the system and utilizes an adaptive isolation threshold on the output residuals computed via a nonlinear estimation scheme that allows the isolation of single/multiple faulty sensor(s). For the actuator fault isolation (AFI) case, a second module is designed, which utilizes a learning-based scheme for adaptive approximation of faulty actuator(s) and, based on a reasoning decision logic and suitably designed AFI thresholds, the faulty actuator(s) set can be determined. The effectiveness of the proposed fault isolation approach developed in this paper is demonstrated through a simulation example.