Fault Isolation Method Research Articles

Motor Fault Detection and Isolation for Multi-Rotor UAVs Based on External Wrench Estimation and Recurrent Deep Neural Network

AbstractFast detection of motor failures is crucial for multi-rotor unmanned aerial vehicle (UAV) safety. It is well established in the literature that UAVs can adopt fault-tolerant control strategies to fly even when losing one or more rotors. We present a motor fault detection and isolation (FDI) method for multi-rotor UAVs based on an external wrench estimator and a recurrent neural network composed of long short-term memory nodes. The proposed approach considers the partial or total motor fault as an external disturbance acting on the UAV. Hence, the devised external wrench estimator trains the network to promptly understand whether the estimated wrench comes from a motor fault (also identifying the motor) or from unmodelled dynamics or external effects (i.e., wind, contacts, etc.). Training and testing have been performed in a simulation environment endowed with a physic engine, considering different UAV models operating under unknown external disturbances and unexpected motor faults. To further assess this approach’s effectiveness, we compare our method’s performance with a classical model-based technique. The collected results demonstrate the effectiveness of the proposed FDI approach.

Fault isolation considering real mechanical coupling effects with application to liquid oxygen/kerosene staged combustion rocket engines

Liquid rocket engines are crucial for space exploration, necessitating reliable fault diagnosis systems. This paper focuses on fault isolation in liquid oxygen (LOX)/kerosene staged combustion rocket engines. Addressing deficiencies in current methods, we first propose a theorem to evaluate the adaptability between the analytical model and fault factors based on available sensors. Through the analysis of fault mechanisms, this paper defines fault modes using an innovative multi-factor coupling approach based on engine operational principles. Moreover, a fault isolation method with two steps is established, incorporating the optimal selection of sensors to accommodate robust requirements. This method features a clear process, low computational complexity, and comprehensive fault coverage, and it can also be applied to systems with similar complex operational mechanisms.

A fault detection and isolation method for independent metering control system based on adaptive robust observer

The independent metering control system (IMCS) demonstrates high control precision and energy efficiency for advanced electrohydraulic applications but tends to be faulty. Current model-based fault diagnosis methods face challenges in robustness and adaptability to high uncertainties and high nonlinearities of the IMCS. To enhance the safety and reliability of the IMCS, a novel fault detection and isolation method based on adaptive robust observer (ARO) is proposed. To reduce estimation errors, the designed ARO combines the advantages of the robust filter and parameter adaption, which can overcome the impact of uncertainties and nonlinearities. To detect the fault robustly and sensitively, three types of adaptive thresholds are designed according to the spool displacement residuals. To isolate the fault, a fault space is established, and spatial fault points are calculated. The verification experiments show that the proposed method can well estimate the spool displacement while accurately detecting and isolating faults of the IMCS.

An enhanced digital twin-driven fault detection and isolation method based on sensor series imaging mechanism for gas turbine engine

Condition based maintenance plays a crucial role in ensuring the safety and reliability of gas turbine engine. Specifically, fault detection and isolation (FDI) can improve efficiency and reduce costs during maintenance. The traditional FDI methods, which developed only using physical engine data, lack a comprehensive consideration of high accuracy and strong applicability across different engine individuals. In this paper, an enhanced digital twin-driven FDI method is proposed, which combines a digital engine, sensor series imaging mechanism and a convolutional neural network. The digital engine is constructed to mirror the operational status of the physical engine. The output residuals between the physical and digital engines are transformed into multi-channel images to train a convolutional neural network for FDI of the physical engine. The network is then fine-tuned using fault-free data from the target engine to further strengthen diagnostic performance across different engine individuals. The effectiveness of the digital engine is verified using actual ground test data, and the performance of the proposed method is demonstrated through comparative simulations. The network, trained on operational data from Engine A and fine-tuned with fault-free data from other engines, exhibits the highest diagnostic accuracy and fault isolation rate. Compared with the physical engine data-based method, the accuracy is increased from 68.25% to 97.95%, the fault isolation rate is improved from 85.91% to 99.26%, and the false alarm rate is decreased from 4.41% to 0.34%. The results show that the proposed method has better FDI capabilities across different engine individuals.

Steady-State Fault Propagation Characteristics and Fault Isolation in Cascade Electro-Hydraulic Control System

Model-based fault diagnosis serves as a powerful technique for addressing fault detection and isolation issues in control systems. However, diagnosing faults in closed-loop control systems is more challenging due to their inherent robustness. This paper aims to detect and isolate actuator and sensor faults in the cascade electro-hydraulic control system of a turbofan engine. Based on the fault characteristics, we design a robust unknown perturbation decoupling residual generator and an optimal fault observer specifically for the inner and outer control loops to detect potential faults. To locate the faults, we analyze the steady-state propagation laws of actuator and sensor faults within the loops using the final value theorem. Based on this, we establish the minimal-dimensional fault influence distribution matrix specific to the cascade turbofan engine control system. Subsequently, we construct the normalized residual vectors and monitor its vector angles against each row of the fault influence distribution matrix to isolate faults. Experiments conducted on an electro-hydraulic test bench demonstrate that our proposed method can accurately locate four typical faults of actuators and sensors within the cascade electro-hydraulic control system. This study enriches the existing fault isolation methods for complex dynamic systems and lays the foundation for guiding component repair and maintenance.

Event-Triggered Interval Observer Fault Detection and Isolation for Multiagent Systems.

This article investigates an event-triggered interval observer (ETIO) fault detection and isolation method for multiagent systems. First, an event-triggered mechanism is developed to reduce unnecessary communication transmission. Then, a distributed ETIO is designed by combining an interval observer and the proposed event-triggered mechanism. Furthermore, for achieving the desired tradeoff between the robustness to disturbances and the sensitivity to faults, the ETIO is formulated as a multiobjective optimization with l1 / H∞ performance. Second, a bank of ETIOs are interpreted to isolate the faulty agent on a local agent using only the output information from itself and its neighbors. Comparison result with the existing method is given to highlight the superiority of our methodology. Finally, the multiunmanned aerial vehicles system is utilized as the case research, and specific simulation results are presented.

A fault isolation strategy for industrial processes using outlier-degree-based variable contributions

In industrial process monitoring, it is always a challenging and practical problem to analyze the causes of the system fault by isolating true fault variables from vast amounts of process data. However, the phenomenon of smearing effect occurs by using the traditional contribution analysis-based isolation methods since the defined isolation indices of different variables affect each other. In this paper, a new fault isolation method is proposed based on local outlier factor and improved k-nearest neighbor rule aiming to improve the isolation accuracy. Firstly, the nearest neighbors of each sample are obtained along the direction of a specific variable. Based on the nearest neighbors, the outlier-degree value of the variable is calculated and regarded as the contribution of the variable. Then, the contribution of the variable in all samples are obtained in the same way, among which the maximum one is selected as the isolation threshold value of this variable. During the online monitoring, the contribution of the variable in the newly collected sample is calculated in real time. Once the contribution is greater than the threshold, the variable is judged to be the dominant factor causing the system fault. Two cases on numerical example and Tennessee Eastman process are conducted to evaluate the effectiveness of the proposed method.

Prior knowledge-infused Self-Supervised Learning and explainable AI for Fault Detection and Isolation in PEM electrolyzers

In this paper, a novel Fault Detection and Isolation (FDI) method for Proton Exchange Membrane (PEM) electrolyzers is presented. The challenge of limited availability of labeled fault data is addressed through the utilization of Bond Graphs (BG) and Self-Supervised Learning (SSL). The Linear Fractional Transformation-Bond Graph (LFT-BG) model is employed to generate uncertain residuals and pseudo labels, enabling the training of a deep neural network through self-supervised learning. Additionally, the BG-XAI method is introduced, leveraging eXplainable AI (XAI) and structural equations from Bond Graphs to provide explanations for the decisions made by the deep learning model. The superior performance of the proposed approach, particularly in dealing with limited labeled data, is demonstrated through comparative assessments against various state-of-the-art SSL methods. The demo code of the proposed method is available in this repository: https://github.com/mohan696matlab/SSL_based_Hybrid_FDI.

Design of Series-Connected Novel Large-Scale Offshore Wind Power All-DC System with Fault Blocking Capability

The utilization of wind power all-DC systems with DC collection and transmission is an effective solution for the extensive development of wind power in deep-sea areas. However, in the event of faults occurring in wind power all-DC systems, the fault propagation speed is extremely rapid, with a wide-ranging impact, and to date, there are no complete DC engineering references available. It is crucial to research the topology and fault isolation methods applicable to large-scale offshore wind power all-DC systems in deep-sea areas. This paper proposes a novel series-connected all-DC system topology and presents corresponding fault isolation methods for internal faults in wind turbine units and faults in high-voltage DC transmission lines. The system simulation model was constructed using PSCAD/EMTDC (v4.6.3), and simulations were conducted for internal faults in the wind turbine units and DC transmission line short-circuit faults. The simulation results demonstrate that the proposed system can isolate various DC faults while maintaining stable operation, thereby validating the effectiveness of the control strategies and fault isolation methods proposed in this paper.

Structured collaborative sparse dictionary learning for monitoring of multimode processes

In this paper, a novel structured collaborative sparse dictionary learning approach is proposed to improve the monitoring performance of discriminative dictionary learning for multimode processes. The mode discriminability and data reconstruction are first balanced by decomposing the dictionary coefficients into between- and within-class parts and introducing a within-class self-expression regularization term. A weight vector of between-class coefficients is subsequently exploited for accurate mode identification of data that falls into the overlapping regions between different class distributions. Moreover, in order to pinpoint the fault variables, a scalable fault isolation method is developed which imposes a constraint of statistical control limit and introduces the ℓ1/ℓ2,0-structured sparsity regularization terms. The mode identification capability of the proposed method is proved theoretically by Theorems 1 and 2, and a lower-bound magnitude is provided by Theorem 3 for fault isolation. Finally, extensive experiments conducted in the numerical and industrial process demonstrate that our proposed method outperforms some state-of-the-art methods.

Wind turbine fault detection and isolation robust against data imbalance using KNN

AbstractDue to the difficulties of system modeling, nonlinearity effects, uncertainties, and the availability of Wind Turbines (WTs) SCADA system data, data‐driven Fault Detection and Isolation (FDI) methods for WTs have received increasing attention. In this paper, using the wind turbine SCADA data, an effective FDI scheme is proposed using the K‐Nearest Neighbors (KNN) classifier. The operational data set is labeled by the status and warning data sets, and the labeled operational data set, after eliminating invalid data, feature selection, and standardization, is used for training and validation of the FDI model. Data imbalance, which is common in real data sets, does not affect the performance of the proposed method, hence there is no need for data balancing methods in this algorithm and the performance is not deteriorated by occurring false alarms. Therefore, the proposed method has provided impressive performance in FDI compared with previous research on this data set. Also, many of the fault classes addressed in this paper were not considered in previous works on this data set.

A Comprehensive Fault Detection and Isolation Method for DC Microgrids Using Reduced-Order Unknown Input Observers

Fault diagnosis is of critical importance to the safety of power electronic devices in DC microgrids. To detect and isolate different component faults in DC microgrids, this paper introduces a comprehensive protection scheme using reduced-order unknown input observers (ROUIOs). As opposed to conventional protection strategies, the proposed method provides a centralized fault detection and isolation (FDI) solution for DC microgrids that covers multiple faults occurring in different components in a unified process. Moreover, it reduces the complexity of observer model and relaxes the requirements of measurement signals compared with existing observer-based FDI methods for DC microgrids. To this end, the state-space model of a multi-terminal DC microgrid with different faults is first established. On this basis, a bank of ROUIOs are designed with the aim of classifying different component faults in the system. At last, the performance of the proposed FDI method is verified through numerical simulations with MATLAB/Simulink and hardware tests. Test results show that the proposed method can accurately detect and isolate different component faults in DC microgrids in a short response time of 1 ms.

A Fault Detection and Isolation Method via Shared Nearest Neighbor for Circulating Fluidized Bed Boiler

Accurate and timely fault detection and isolation (FDI) improve the availability, safety, and reliability of target systems and enable cost-effective operations. In this study, a shared nearest neighbor (SNN)-based method is proposed to identify the fault variables of a circulating fluidized bed boiler. SNN is a derivative method of the k-nearest neighbor (kNN), which utilizes shared neighbor information. The distance information between these neighbors can be applied to FDI. In particular, the proposed method can effectively detect faults by weighing the distance values based on the number of neighbors they share, thereby readjusting the distance values based on the shared neighbors. Moreover, the data distribution is not constrained; therefore, it can be applied to various processes. Unlike principal component analysis and independent component analysis, which are widely used to identify fault variables, the main advantage of SNN is that it does not suffer from smearing effects, because it calculates the contributions from the original input space. The proposed method is applied to two case studies and to the failure case of a real circulating fluidized bed boiler to confirm its effectiveness. The results show that the proposed method can detect faults earlier (1 h 39 min 46 s) and identify fault variables more effectively than conventional methods.

An Improved Fault Detection and Isolation Method for Airborne Inertial Navigation System/Attitude and Heading Reference System Redundant System

The integrity of airborne inertial navigation systems (INSs) is the key to ensuring the safe flight of civil aircraft. The airborne attitude and heading reference system (AHRS) is introduced into the construction of a redundant inertial navigation system. As a backup system for an airborne INS, the AHRS exhibits a different device performance. A sequential weighted generalized likelihood ratio test (SWGLT) method, based on a principal component parity vector (PPV), is proposed. The PPV method improves the adaptability of the detection threshold to the inertial sensors’ noise and improves the probability of correct detection. At the same time, the multiscale problem of a heterogeneous redundant system error is solved by sequential weighting, and the false alarm rate is reduced. Simulation experiments show that the proposed method can improve fault detection sensitivity, reduce false alarm rates, and ensure the integrity of civil aircraft navigation systems.

FREEDOM: Validated Method for Rapid Assessment of Incipient Faults of Aerospace Systems

Model-based fault detection and isolation (FDI) methods allow to infer the health status of complex aerospace systems through a large quantity of data acquired in flight and evaluations of numerical models of the equipment. This results in an intensive computational procedure that can be addressed only grounding the aircraft. We introduce an original methodology to sensitively accelerate FDI by reducing the computational demand to identify the health status of the aircraft. Our scheme FREEDOM (Fast Reliability Estimate and Incipient Fault Detection of Multiphysics Aerospace Systems) proposes an original combination of a novel two-step compression strategy to compute offline a synthesized representation of the dynamical response of the system and uses an inverse Bayesian optimization approach to infer online the level of damage determined by multiple fault modes affecting the equipment. We demonstrate and validate FREEDOM against numerical and physical experiments for the case of an electromechanical actuator employed for secondary flight controls. Particular attention is dedicated to simultaneous incipient mechanical and electrical faults considering different experimental settings. The outcomes validate our FDI strategy, which permits to achieve the accurate identification of complex damages outperforming the computational time of state-of-the-art algorithms by two orders of magnitude.

Multiple fault isolation method for micro thrusters of drag-free systems

This paper proposes a multiple fault isolation framework for micro thrusters of drag-free systems, based on the minimum number of isolation filters. Based on the consideration of fault isolatability, the original system is reasonably divided into two completely isolated subsystems, and then two sets of fault isolation filters are designed based on the principles of duality design for state feedback decoupling, which achieves residual decoupling, and therefore, can detect and isolate multiple faults occurred on the micro thrusters. In particular, this method has low computational burden (a feature of practical importance in reducing the usage of onboard resources) and short isolation time, making it suitable for on-board fast and precise fault isolation. The simulation results demonstrate the efficacy of the proposed method when utilized for a drag-free system with 12 field emission electric propulsion (FEEP) thrusters.

Koopman-based deep iISS bilinear parity approach for data-driven fault diagnosis: Experimental demonstration using three-tank system

Precise and timely fault diagnosis is crucial in many practical systems and control processes. Particularly due to the increasing amount of available data collected by sensors, data-driven fault diagnosis has been a hot research topic in the prognosis and health management of industrial systems. In this paper, a reliable, accurate, and interpretable data-driven fault detection and isolation method is proposed for the general class of nonlinear systems. The proposed approach is based on the integration of the bilinear Koopman model realization, deep learning, and a bilinear parity-space framework. This work leverages the potential of neural networks to investigate lifting functions and bilinear Koopman realization simultaneously. Furthermore, to enhance the stability of the realized model, the input-to-state stability constraint is enforced on the training algorithm, ensuring the realized model is integral input-to-state stable. This method does not require any prior knowledge regarding the system dynamics and only uses the data collected from the normal operation of the system. In addition, it is capable of diagnosing all types of sensor and actuator faults, whether they are additive or multiplicative. The effectiveness of the proposed method is demonstrated experimentally using a laboratory setup of the three-tank system. Lastly, a comparison is conducted to demonstrate the advantages of the proposed method over another recent data-driven fault diagnosis method.

Data-driven distributed collaborative fault detection and isolation for large-scale dynamic processes in simultaneous-fault cases

This paper proposes a data-driven distributed collaborative fault detection and isolation scheme for large-scale dynamic processes, interconnected by heterogeneous subsystems with both actuator and sensor faults. The residual generator in each subsystem is directly constructed with both local and neighboring process data based on subspace identification. Utilizing the rank deficiency property, the healthy mode residual covariance matrix is estimated with noisy data, and thus the test statistic and detection threshold are obtained. Then, a collaborative simultaneous fault isolation method is developed, where the local residual is decoupled with neighboring actuator fault’s effects and a series of robust residual generators are constructed by solving corresponding optimization problems. In each subsystem, both local fault isolation and neighboring sensor fault isolation are realized. Thus, sensor fault detection and isolation for the whole process can be achieved with a reduced number of monitoring subsystems. Finally, case studies on the Tennessee Eastman (TE) process and a real hot strip mill process (HSMP) verify the feasibility and good performance of the proposed scheme.

Hybrid robust fault detection and isolation of satellite reaction wheel actuators

In this paper, a combined robust fault detection and isolation scheme is studied for satellite system subject to actuator faults, external disturbances, and parametric uncertainties. The proposed methodology incorporates a residual generation module, including a bank of filters, into an intelligent residual evaluation module. First, residual filters are designed based on an improved nonlinear differential algebraic approach so that they are not affected by external disturbances. The residual evaluation module is developed based on the suggested series and parallel forms. Further, a new ensemble classification scheme defined as blended learning integrates heterogeneous classifiers to enhance the performance. A wide range of simulations is carried out in a high-fidelity satellite simulator subject to the constant and time-varying actuator faults in the presence of disturbances, manoeuvres, uncertainties, and noises. The obtained results demonstrate the effectiveness of the proposed robust fault detection and isolation method compared to the traditional nonlinear differential algebraic approach.

Graph Complexity Reduction of Exergy-Based FDI—A Tennessee Eastman Process Case Study

When applying graph-based fault detection and isolation (FDI) methods to the attributed graph data of large and complex industrial processes, the computational abilities and speed of these methods are adversely affected by the increased complexity. This paper proposes and evaluates five reduction techniques for the exergy-graph-based FDI method. Unlike the graph reduction techniques available in literature, the reduction techniques proposed in this paper can easily be applied to the type of attributed graph used by graph-based FDI methods. The attributed graph data of the Tennessee Eastman process are used in this paper since it is a popular process to use for the evaluation of fault diagnostic methods and is both large and complex. To evaluate the proposed reduction techniques, three FDI methods are applied to the original attributed graph data of the process and the performance of these FDI methods used as control data. Each proposed reduction technique is applied to the attributed graph data of the process, after which all three FDI methods are applied to the reduced graph data to evaluate their performance. The FDI performance obtained with reduced graph data is compared to the FDI performance using the control data. This paper shows that, using the proposed graph reduction techniques, it is possible to significantly reduce the size and complexity of the attributed graph of a system while maintaining a level of FDI performance similar to that achieved prior to any graph reduction.