Active Fault Diagnosis Research Articles

Active Fault Diagnosis for Uncertain LPV Systems: A Zonotopic Set-Membership Approach

Active Fault Diagnosis for Uncertain LPV Systems: A Zonotopic Set-Membership Approach

Integrated Design for Active Fault Diagnosis and Control: A Decomposition-Composition Method

Integrated Design for Active Fault Diagnosis and Control: A Decomposition-Composition Method

Active fault diagnosis for stochastic systems subject to non-convex input constraints

In this paper, the problem of active fault diagnosis (AFD) is investigated for a class of continuous-time stochastic systems subject to non-convex input constraints. The input is designed to minimize the probability of misdiagnosis. Since the misdiagnosis probability cannot be expressed in the closed form, its Chernoff upper bound likely tighter than its widely used Bhattacharyya upper bound is exploited as an alternative. The closed-form expression of the Chernoff distance under Gaussian distributions is correctly deduced. For calculation simplification and diagnosis performance enhancement purposes, a novel input design criterion is constructed for AFD based on the Chernoff distance. The optimization problem for AFD is formulated as a bilevel optimization, where the outer and inner layers optimize the Chernoff distance parameter and the input respectively. A convex relaxation is proposed to relax non-convex input constraints into convex ones, and the optimal solution to the relaxed inner input design problem is proven to be also optimal for the original inner problem whose objective function and input constraints are both non-convex under certain conditions, which is referred to as lossless relaxation. The relaxed inner input design problem under the constructed design criterion can be solved to global optimums, and the relaxed outer optimization is solved by a grid search method. Given the optimal input, faults are diagnosed based on the minimum misdiagnosis probability decision rule. The effectiveness of the proposed AFD approach is demonstrated by utilizing a planetary landing example.

Active Distribution Network Fault Diagnosis Based on Improved Northern Goshawk Search Algorithm

Timely and accurate fault location in active distribution networks is of vital importance to ensure the reliability of power grid operation. However, existing intelligent algorithms applied in fault location of active distribution networks possess slow convergence speed and low accuracy, hindering the construction of new power systems. In this paper, a new regional fault localization method based on an improved northern goshawk search algorithm is proposed. The population quality of the samples was improved by using the chaotic initialization strategy. Meanwhile, the positive cosine strategy and adaptive Gaussian–Cauchy hybrid variational perturbation strategy were introduced to the northern goshawk search algorithm, which adopted the perturbation operation to interfere with the individuals to increase the diversity of the population, contributing to jumping out of the local optimum to strengthen the ability of local escape. Finally, simulation verification was carried out in a multi-branch distribution network containing distributed power sources. Compared with the traditional regional localization models, the new method proposed possesses faster convergence speed and higher location accuracy under different fault locations and different distortion points.

A modified active learning intelligent fault diagnosis method for rolling bearings with unbalanced samples

To obtain excellent classification performance for fault diagnosis, most intelligent fault diagnosis methods based on deep learning require massive labeled samples for training. However, collecting sufficient labeled fault samples is very difficult in practice due to the time-consuming and laborious work, which means the actual available dataset is the unbalanced dataset, i.e., normal data is the vast majority, while the fault samples are very small. To address this problem, a modified active learning intelligent fault diagnosis method is proposed for rolling bearings with unbalanced samples. The proposed method can adeptly employ a limited number of labeled samples to intelligently label the unlabeled samples. Therefore, the proposed method can improve classification performance while simultaneously minimizing the requisite amount of labeled samples during training. First, time and time–frequency features of vibration signals are extracted to obtain their distribution in the feature space. Second, to solve the problem of sample class unbalance, a Gaussian mixture model is constructed to obtain the distribution representation of the samples. The random undersampling method was used in Gaussian sub-model, which can extract some samples from majority classes. These extracted samples have similar distribution to the original sample set, and hence can represent the original dataset and be used to establish balanced labeled sample set. Third, an initial active learning classifier based on density peak clustering is established, utilizing the representative examples to intelligently label the unlabeled samples. To optimize the utilization of unlabeled samples, batch process method is adopted to update the initial classifier. The effectiveness of the proposed method is verified by two rolling bearings fault simulation experiments. The results show that our method can effectively improve fault diagnosis accuracy with unbalanced samples, and the updated classifier needs fewer training data to achieve comparable diagnostic performance.

A new input design framework for asymptotic active fault diagnosis with application to integrated diagnosis and control

This paper proposes a new input design framework for set-based asymptotic active fault diagnosis and then applies it to integrated design of active fault diagnosis and control. First, a new input design method is proposed for asymptotic active fault diagnosis by maximizing a newly-defined excluding degree of the origin from all healthy and faulty residual zonotopes. Second, an input set is constructed based on the mathematical expression of the excluding degree at each time instant such that any input out of the input set can be used for active fault diagnosis. Third, an optimal input is designed out of the input set at each time instant for simultaneous active fault diagnosis and control. Besides, an optimal input can also be designed out of the input set to achieve integrated design of active fault diagnosis and other objectives (e.g., minimal input energy to reduce harm to the system, etc.). At the end of this paper, several examples are used to illustrate the effectiveness of the proposed methods.

Active Diagnosis of Incipient Actuator Faults for Stochastic Systems

In this paper, the active diagnosis problem of incipient actuator faults is investigated for a class of discrete-time stochastic systems. The input is exclusively designed for diagnosis purposes to optimally discriminate among multiple mode hypotheses that correspond to the multiple incipient actuator fault scenarios. Due to infeasibility in evaluating the misdiagnosis probability in the closed form, its new upper bound is derived as an alternative. It is proven that this upper bound is a tighter bound on the misdiagnosis probability for incipient actuator faults, compared with the widely used Bhattacharyya upper bound. For computation complexity reduction and diagnosis performance improvement purposes, a novel input design criterion is constructed for active fault diagnosis (AFD) based on the derived upper bound, under which the input design problem can be solved to global optimality. By injecting the optimal input, to fully utilize the diagnostically relevant information in the output data so as to draw a reliable diagnosis conclusion, the resulting measurement output sequence is exploited to make the AFD decision on the basis of the minimum misdiagnosis probability rule. Finally, experimental results on a four-tank system illustrate the effectiveness and superiority of the proposed AFD approach.

Active Fault Diagnosis Based on Dual-Observer for Leakage Detection and Estimation With Adaptive Auxiliary Signal Mechanism in Industrial Gas Networks

Active Fault Diagnosis Based on Dual-Observer for Leakage Detection and Estimation With Adaptive Auxiliary Signal Mechanism in Industrial Gas Networks

Robust Active Fault Diagnosis for Linear Stochastic Systems Within Bayesian Decision Framework

Robust Active Fault Diagnosis for Linear Stochastic Systems Within Bayesian Decision Framework

Active Fault Diagnosis for Stochastic Systems Under Accuracy-Limited Sensors

Active Fault Diagnosis for Stochastic Systems Under Accuracy-Limited Sensors

Active Fault Diagnosis for LPV Systems Based on Constrained Zonotopes

Active Fault Diagnosis for LPV Systems Based on Constrained Zonotopes

Active fault diagnosis for stochastic large scale systems under non-separable costs

The paper focuses on active fault diagnosis of stochastic large-scale systems decomposed into several coupled subsystems, where the subsystem fault-free and faulty behavior is described in the multiple-model framework. In the active approach, the detector generates optimal excitation input to improve the diagnosis. This paper proposes a solution to the problems with cost functions in a generally non-separable form. Unlike the separable form, the generally non-separable form facilitates penalizing missed detections, false alerts, and incorrect, false identifications involving several subsystems simultaneously. Three approaches are proposed to treat such cost functions in the offline stage of the active fault diagnosis algorithm. Their performance is illustrated using a simple example and an elaborate example involving a power network system.

Active Fault Diagnosis for Stochastic Systems Within Bayesian Minimum Risk Decision Framework

In this paper, the active fault diagnosis (AFD) problem is investigated for a class of discrete-time stochastic systems. We consider multiple fault modes of the system, and the different types of misdiagnoses of these faults lead to different losses. A novel AFD framework is developed based on the Bayesian minimum risk decision framework. The misdiagnosis risk is derived to characterize the total losses caused by the misdiagnoses of all considered faults. The input is specifically designed to improve diagnosis performance by minimizing the risk of misdiagnosis. Since it is intractable to express the misdiagnosis risk in the closed form, we deduce an upper bound on it to establish a novel input design criterion based on the Bhattacharyya distance. The optimal input signal can be obtained for AFD via concave quadratic programming. For diagnosis performance enhancement and computation simplification purposes, we propose two new semi-online implementation manners of the input design for stochastic systems with and without multi-fault modes switching respectively. Given the optimal input signal and the proposed implementation manner, faults are diagnosed based on the minimum misdiagnosis risk decision principle. The effectiveness and superiority of the proposed AFD method are demonstrated by simulation results on a four-tank system.

Robust Active Learning Multiple Fault Diagnosis of PMSM Drives With Sensorless Control Under Dynamic Operations and Imbalanced Datasets

Robust Active Learning Multiple Fault Diagnosis of PMSM Drives With Sensorless Control Under Dynamic Operations and Imbalanced Datasets

Active fault diagnosis for linear stochastic systems subject to chance constraints

In this paper, the problem of active fault diagnosis (AFD) is investigated for a class of linear stochastic systems subject to chance constraints, where the input signals are specifically designed to enhance diagnosis performance by discriminating between multi-fault modes. As the misdiagnosis probability cannot be expressed in the closed form, a reachable upper bound on it is derived to construct a novel input design criterion for AFD based on the Cauchy–Schwarz divergence. A sufficient condition is provided to decompose the multivariate chance constraints into a set of univariate ones. A three-stage optimization approach is proposed to solve the input design problem incorporating chance constraints for AFD. In the upper stage, the best iteration parameter is determined for subsequent iterative optimization to obtain the least conservative risk allocation scheme. In the middle stage, the total risk of violating the multivariate constraints is optimally allocated to a set of univariate ones through iterative optimization, which can improve diagnosis performance. In the lower stage, a set of univariate chance constraints is equivalently converted into expectation constraints under the allocated risk, and then the input design problem under the constructed design criterion is solved to global optimality. The optimal input signals are injected, and the AFD decision is made based on real-time measurement information according to the minimum probability of misdiagnosis decision principle. Finally, simulation results on a four-tank system demonstrate the effectiveness and superiority of the proposed AFD method.

Research on Fault-Tolerant Control of Distributed-Drive Electric Vehicles Based on Fuzzy Fault Diagnosis

This paper addresses the fault problem in distributed-four-wheel-drive electric vehicle drive systems. First, a fault-factor-based active fault diagnosis strategy is proposed. Second, a fault-tolerant controller is designed to reconstruct motor drive torque based on vehicle stability. This controller ensures that the vehicle maintains stability by providing fault-free motor output torque based on fault diagnosis results. To validate the effectiveness of the fault diagnosis and fault-tolerant control, SIL simulation is conducted using MATLAB/Simulink and CarSim. A hardware-in-the-loop (HIL) simulation platform with the highest confidence level is established based on NI PXI and CarSim RT. Through the HIL simulation experiments, it is shown that the proposed control strategy can accurately diagnose the operating state of the motor, rebuild the motor torque based on stability, and demonstrate robust stability when the drive system fails. Under various fault conditions, the maximum error in the vehicle lateral angular velocity is less than 0.017 rad/s and the maximum deviation in the lateral direction is less than 0.7 m. These findings substantiate the highly robust stability of the proposed method.

A novel density ratio‐based batch active learning fault diagnosis method integrated with adaptive Laplacian graph trimming

AbstractIn actual industrial processes, although a large number of original data are easy to obtain, only a few samples are effectively labelled, which is insufficient to construct a supervised fault diagnostic model. Facing the industrial demand of fault diagnosis, in this paper, a novel density ratio (DR)‐based batch active learning (BAL) fault diagnosis method integrated with adaptive Laplacian graph trimming (ALGT) method is proposed. First, under the active learning framework, a new index DR‐based on local reachability density (LRD) is proposed to search the low density and high uncertainty samples, in which the local outliers factor (LOF) is used to search the samples in low density region and the ratio of LRD and intra‐class LRD is calculated to search the samples with high uncertainty. Second, the samples are selected and manually labelled in batches according to the proposed index DR, and the labelled data set and the unlabelled data set are updated and reconstructed. Third, based on the reconstructed labelled dataset and remaining unlabelled dataset, a semi‐supervised classifier ALGT is constructed for fault diagnosis. In ALGT, the Laplacian weighted graph is initialized and iteratively optimized by ALGT. Finally, the proposed DR‐based BAL‐ALGT (DRBAL‐ALGT) fault diagnosis method is verified by the Tennessee Eastman process (TEP) and applied to grid‐connected photovoltaic systems (GPVS). The experimental results show that the proposed DRBAL‐ALGT method can achieve higher accuracy for fault diagnosis.

Active Fault Diagnosis and Control of a Morphing Multirotor Subject to a Stuck Arm

In this paper, we propose a fault tolerant control law for a morphing quadrotor, where the considered morphing ability is that of extendable/telescopic arms. This quite recent class of systems is able to provide a good trade-off between payload capabilities, maneuverability, and space occupancy. However, such degrees of freedom require dedicated servomotors, which in turn implies more possible faults. Thus, the problem of diagnosis for the telescopic servo motors subject to a stuck fault is considered. System symmetries are exploited and used in a residual generator design, which triggers an active fault isolation/identification phase. External disturbances are also taken into account and estimated through a nonlinear disturbance observer. A classical double-loop controller closes the loop, providing an overall control system structure that follows the disturbance observer-based control paradigm. The control scheme is validated through realistic numerical simulations, and the closed-loop performances are analyzed.

Exclusion tendency-based observer design framework for active fault diagnosis

This paper proposes a new closed-loop observer-based active fault diagnosis (AFD) framework using a bank of set-valued observers (SVOs). Each SVO is designed for one healthy or faulty system mode to obtain the corresponding output estimation set (OES). The principle of fault diagnosis is to check whether the considered OES includes the system output. Compared with the existing works that mainly make use of SVOs to obtain accurate estimation results, our proposed method optimizes the observer gains to deform OESs such that the exclusion tendency of the system output in each OES is maximized. With this idea, inconsistent system modes can be removed rapidly, and faults can be diagnosed. The design logic is formulated as a bi-level max–min problem, which is transformed into a linear problem by Lagrangian duality and solved efficiently. At the end of this paper, a quadruple-tank example is used to compare the results of our proposed method with an existing fault diagnosis method to illustrate the effectiveness of the proposed method.

An integrated design method for active fault diagnosis and control

SummaryThe injection of an auxiliary input signal for active fault diagnosis may cause the change of system control performance in closed‐loop operation. This paper presents a novel integrated design method for active fault diagnosis and tracking control in order to facilitate the detection of incipient faults, meanwhile ensuring the normal operation of the closed‐loop system. First, a zonotopic filter is generated based on radius minimization method to estimate the uncertain bound set of systems states. Then, an auxiliary input signal is designed to separate the reachable sets of the system in healthy and faulty operation situations. Generally, the auxiliary input signal is required to be designed minimizing the system closed‐loop performance degradation. In this paper, a tracking controller is designed altogether with the auxiliary input signal to make the system output track a given reference. An optimal index is proposed to minimize simultaneously the energy of the auxiliary input signal and the output tracking error. Next, a fault detection logic is provided based on checking whether the closed‐loop system state belongs to healthy zonotope. Finally, the feasibility and advantages of this work are verified by means of a numerical example.