Prior Fault Research Articles

Information enhanced slow feature analysis integrated with prior fault data for sensitive monitoring of chemical processes

As an effective feature extraction method, slow feature analysis (SFA) has been successfully applied in the domain of chemical process monitoring. However, as an unsupervised method, the basic SFA ignores the key information carried by prior fault data so that many complicated faults cannot be sensitively detected. To address this issue, an enhanced SFA method, termed Information Enhanced SFA (IE-SFA), is proposed by integrating available fault discriminant information to achieve more sensitive detection of complicated chemical process faults. The proposed method builds a primary-auxiliary SFA modeling framework. On the one hand, the normal training data are analyzed to establish the primary SFA model. On the other, prior fault data are integrated to develop an auxiliary monitoring model for providing extra fault-sensitive information. In the auxiliary model, a sample-variable weighted Fisher discriminant analysis method is performed on normal data, fault data, and their respective slow feature components, thereby extracting fault-sensitive features for enhancing the fault detection capability. For full-view fault monitoring, the Bayesian fusion strategy is used to fuse the outcomes from both primary and auxiliary models. The applications on a benchmark Tennessee Eastman chemical process and a three-phase flow process demonstrate that the proposed IE-SFA method provides superior fault detection performance compared to the basic SFA method.

Advancements In Machine Learning Algorithms for Enhanced Fault Analysis and Categorization in Power Systems

With the growing number of sources, complexity, and dynamic elements of power systems that are prone to disturbance and electrical faults, conventional methods of fault detection are lagging in power system protection. Major faults are exposed to transmission lines due to several factors, such as climate change, heat, lightning strokes, sudden spikes of current and voltage etc. Protecting these lines from faults is cumbersome using traditional methods. However, with the increasing complexity of transmission lines in power systems, advanced technologies such as machine learning come into play for prior fault detection and identification. ML is becoming a popular and intelligent technique for monitoring and predicting health and diagnosing faults in transmission lines of power systems. Since ML is evolving at a faster pace and given the need for and growth of advanced technologies like ML and artificial intelligence, this paper emphasizes comparative analysis of various ML algorithms for transmission line fault analysis. Comparisons for various line faults (LL, LLG, LG, LLL, LLLG, no fault) in power systems using machine learning techniques have been done in terms of accuracy, precision, and other performance measures. The results of the accuracy of various algorithms are represented as tabulated facts in this paper. The Python programming language has been utilized over a dataset to calculate results. For each algorithm, the confusion matrix shows the accuracy of predicting faults over the testing data after training the model or algorithm on the training data. Upon completion of analysis and comparison of MLT's like LR, SVM, DT, RF, K-NN, and gradient descent for analysis of transmission line fault detection, RF and DT machine learning algorithms provided the best accuracy and results. Finally, this paper gives a brief overview of various line faults, ML algorithms accuracy for each transmission fault in a confusion matrix and tabular format and concluded the best machine learning algorithms for power system transmission line fault identification.

Multi-Node Feature Learning Network Based on Maximum Spectral Harmonics-to-Noise Ratio Deconvolution for Machine Condition Monitoring

Since the cyclostationarity in vibration signals is the key to judge the rotating machine health state, spectral harmonics-to-interference ratio (SHIR) has been used to construct single node feature learning network (SHIR-based blind deconvolution, BD-SHIR) to realize condition monitoring of machine. However, BD-SHIR still has several obvious limitations, including the need for prior fault information and the tendency to fall into local optimal solutions which will affect its feature learning and state monitoring performance. Therefore, this paper proposes a multi-node feature learning network based on BDSHIR, which does not rely on prior information, multi-node adaptive BDSHIR (MABD-SHIR). It uses each filter in the 1/3 binary tree filter bank to design each initial node (filter) in the learning network correspondingly, and introduces an adaptive fault characteristic frequency (FCF) detection process. Since this initialization can give some filters close to the fault resonant band, iterations starting from these nodes are more likely to detect the precise FCF and converge to the optimal solution. Finally, MABD-SHIR can obtain multiple local optimal solutions, among which the one with the largest SHIR is generally closer to the global optimal solution than the one obtained by BD-SHIR. Simulation and experimental data of defective rotating machinery verify the effectiveness of MABD-SHIR in machinery condition monitoring and fault feature learning. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the problems that the rotating machine condition monitoring based on BDSHIR is easy to prematurely fall into local optimal solution and requires prior fault information. These problems can be effectively solved by extending BDSHIR from single node structure to multi-node structure and introducing adaptive period detection technique (A-DPT). The core idea is to start iterations from multiple initial filters and to detect the FCF adaptively during each iteration. Multiple features conforming to filtered signals’ SHIR maximization can be finally learned. Therefore, in this paper, MABD-SHIR is proposed, which is equivalent to a multi-node feature learning network. Since the objective function of BD is usually non-convex, MABD-SHIR can obtain multiple local optimal solutions without requiring prior FCFs. This is more likely to achieve a global optimal solution than BDSHIR, which starts iteration from only one initial filter and ends up with one local optimal solution. In addition, the objective function of MABD-SHIR has lower mathematical complexity, which reduces the difficulty of optimization.

Multi-Objective Sparsity Maximum Mode De-Composition: A New Method for Rotating Machine Fault Diagnosis on High-Speed Train Axle Box

Signal decomposition techniques generally have two limitations. First, for adaptive correlated kurtogram (ACK) and variational mode decomposition (VMD), etc., the definition of the decomposition target has no direct relationship with the features of fault impacts, i.e., the decomposition is not guided by the health indices used to characterize bearing faults, resulting in fault impacts cannot be completely separated from vibration data. Second, it is hard to accurately determine the decomposition stop parameter (the number of final output modes), resulting in frequent over- or under-decomposition. All these are not conducive to bearing fault detection in engineering practice. In this paper, a new decomposition method, multi-objective sparse maximum mode decomposition (MOSMMD), is proposed. Firstly, by analyzing the Fourier spectrum of vibration data, MOSMMD constructs multiple finite impulse response filters covering different frequency bands to obtain initial candidate modes. Then, aiming at maximizing <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L2</i> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L1</i> norm of envelope spectrum (ES), the upper and lower cutoff frequencies of each filter are adjusted adaptively through iterative optimization, and the frequency bands covered by each mode are adjusted accordingly. Finally, by using that fault feature harmonics have periodic natures in ES, the redundant modes are eliminated and only the modes with obvious fault features are output. Compared with VMD and ACK, the decomposition target of MO-SMMD is directly related to the fault features, i.e., cyclostationarity of fault impacts, which makes the extraction of fault impacts more accurate. Moreover, the number of final output modes of MOSMMD is determined adaptively and does not need to be set in advance as an input parameter. Also, MOSMMD can decompose fault modes from vibration data without prior fault information, even when processing concurrent fault signals. Simulation and experiments demonstrate the superiority of the proposed MOSMMD.

A coarse-to-fine demodulation frequency band selection strategy for multi-fault detection of rotating machinery

The most critical issue of envelope demodulation analysis for rotating machinery fault diagnosis is to identify the fault-induced informative frequency bands. However, the majority of currently used blind or targeted demodulation methods either only focus on selecting a single frequency band as the best (which may result in unexpected missed diagnosis in the case of multiple faults) or require prior fault knowledge (this is unfeasible for most blind cases). To address this issue, this study proposes a coarse-to-fine demodulation frequency band selection strategy for multi-fault detection of rotating machines. A reweighted Variational Mode Decomposition algorithm, aided by a new evaluation indicator coined fault information correlation index (CFII), is first proposed to coarsely select the useful frequency bands for signal initial denoising and reconstruction. Then, Cyclic Spectral Coherence (CSCoh), a powerful cyclic spectral analysis tool, is implemented to further process the reconstructed signal for fine demodulation analysis. To solve the problem of selecting the spectral frequency band of CSCoh, a practical guideline is established together with the cyclic-frequency-domain fault information index (αFII) for the automatic determination of informative frequency bands to be integrated. Benefiting from the above two stages and developed indicators, the proposed method can effectively detect features of incipient and multiple faults under complex interferences without requiring any prior candidate fault period. Its effectiveness and advancements over several commonly used demodulation techniques are validated using simulation and experimental scenarios involving multi-fault gears and bearings.

Implementation and analysis of fault grouping for multi-constellation advanced RAIM

The aviation community is pursuing multi-constellation Advanced Receiver Autonomous Integrity Monitoring (ARAIM) to offer safe aircraft navigation services. The computational issue of ARAIM becomes critical when multiple constellations are involved and Fault Exclusion (FE) is enabled. This paper proposes an implementation of fault grouping to improve the efficiency of multi-constellation ARAIM and to benefit navigation performance. To potentially accommodate most ARAIM services, we consider both ARAIM Fault Detection (FD) and ARAIM Fault Detection and Exclusion (FDE) scenarios. In addition, given the difference between Vertical ARAIM (V-ARAIM) and Horizontal ARAIM (H-ARAIM), the implementation steps of fault grouping for V-ARAIM and H-ARAIM are respectively described. More importantly, unlike most of the prior approaches that were limited to dual-constellation scenarios, our implementation can support up to four constellations. The implementation of fault grouping is evaluated using multiple sets of simulations, which are carried out as a function of the number of constellations, prior fault probabilities, error models, and operational services. The results suggest that in most cases, the proposed implementation of fault grouping can effectively reduce the ARAIM computational load while benefiting or maintaining the navigation performance.

Data-driven fault detection and isolation in DC microgrids without prior fault data: A transfer learning approach

The lack of fault data is the major constraint on data-driven fault detection and isolation schemes for DC microgrids. To solve this problem, this paper develops an adversarial-based deep transfer learning model that can detect and classify short-circuit faults in DC microgrids without using historical fault data. In this transfer learning framework, the knowledge of faults is extracted from the transient features of line currents during normal operating disturbances, which is adversarially augmented and then transferred to a target domain as the labels of faults. With the transferred knowledge, a deep learning model combining convolutional neural network and attention-based bidirectional long short-term memory is trained, which is strengthened by attention and soft-voting ensemble mechanisms. In verification tests, this model reaches a high accuracy of over 90% in classifying various short-circuit faults in a multi-terminal DC microgrid model within a short response time of less than 1 ms. Moreover, it is robust against measurement noises and adaptive to system configuration changes. The test results prove the effectiveness of the proposed method in the protection of DC microgrids without prior knowledge of faults.

Preliminary Analysis of BDS-3 Performance for ARAIM

<h3>Abstract</h3> To support the operation of advanced receiver autonomous integrity monitoring (ARAIM), an integrity support message indicating a minimum performance level of satellite constellation is required for aircraft navigation. With BDS-3 providing worldwide service since July 2020, it is desirable to undertake a detailed study on its signal-in-space range error characteristics and prior fault probabilities for ARAIM. The latest accuracy criteria released by the China Satellite Navigation Office in May 2021 is validated by the 27 MEO and IGSO satellites in orbit from July 2020 to June 2021, in which 10 single satellite faults were identified and analyzed in detail with no constellation fault found. Based on this one-year data, the probability of single satellite faults and constellation faults can be initially set as 8 × 10⁻⁵ and 1 × 10⁻³, respectively, for BDS-3 in ARAIM.

Associated Fault Diagnosis of Power Supply Systems Based on Graph Matching: A Knowledge and Data Fusion Approach

With the rapid development of more-electric and all-electric aircraft, the role of power supply systems in aircraft is becoming increasingly prominent. However, due to the complex coupling within the power supply system, a fault in one component often leads to parameter abnormalities in multiple components within the system, which are termed associated faults. Compared with conventional faults, the diagnosis of associated faults is difficult because the fault source is hard to trace and the fault mode is difficult to identify accurately. To this end, this paper proposes a graph-matching approach for the associated fault diagnosis of power supply systems based on a deep residual shrinkage network. The core of the proposed approach involves supplementing the incomplete prior fault knowledge with monitoring data to obtain a complete cluster of associated fault graphs. The association graph model of the power supply system is first constructed based on a topology with characteristic signal propagation and the associated measurements of typical components. Furthermore, fault propagation paths are backtracked based on the Warshall algorithm, and abnormal components are set to update and enhance the association relationship, establishing a complete cluster of typical associated fault mode graphs and realizing the organic combination and structured storage of knowledge and data. Finally, a deep residual shrinkage network is used to diagnose the associated faults via graph matching between the current state graph and the historical graph cluster. The comparative experiments conducted on the simulation model of an aircraft power supply system demonstrate that the proposed method can achieve high-precision associated fault diagnosis, even under circumstances where there are an insufficient number of samples and missing parameters.

Bayesian fault-tolerant protection level for multi-constellation navigation from integrity perspective

The multi-constellation is anticipated to enhance navigation performance by providing abundant satellites in view. However, with this comes the necessity of considering simultaneous fault events in integrity risk assessment when applying the receiver autonomous integrity monitoring. Meanwhile, the integrity monitoring efficacy remains vulnerable to the uncertainty of prior fault probability in multi-constellation navigation. In this regard, this paper proposes a Bayesian approach for the multi-constellation navigation at the user segment to strengthen the evaluation robustness of integrity risk. We bound the Bayesian posterior probability of fault hypotheses that comprise multiple concurrent faults (satellite or constellation faults) without assuming an optimistic prior fault distribution. This posterior probability bound leads to an associated Bayesian integrity risk. Following that, a fault-tolerant estimate accounting for multiple faults and nominal biases is derived to minimize the Bayesian integrity risk. Finally, we establish a Bayesian fault-tolerant protection level to bound the position estimate error and assess the integrity availability, resisting the uncertainty in the prior fault probability. Results validate the robustness improvement the proposed Bayesian approach fulfills compared with the advanced receiver autonomous integrity monitoring in four cases. In addition, the Bayesian approach can lower the protection level at the cost of an offset from the least-square estimate while maintaining the same position accuracy.

Time domain graph-based anomaly detection approach applied to a real industrial problem

Detecting anomalies in industrial processes is a critical task. Prior fault detection can reduce company costs, and most importantly, may prevent accidents and environmental damage. Anomaly detection can be treated as drift detection, as both aims at identifying changes in data that happen unexpectedly over time. In this paper, a graph-based approach to anomaly detection is presented. Based on graph theory and set coverage, the method brings results similar to deep autoencoders, without the need to extensively set parameters.

Gini Indices II and III: Two new Sparsity Measures and Their Applications to Machine Condition Monitoring

Machine condition monitoring (MCM) uses signal processing and machine learning methods to analyze monitoring data and perform timely condition-based maintenance. Since monitoring data usually have a sparsity property, sparsity measures (SMs) are naturally considered to quantify the sparsity of signals and they serve as the objective functions of many signal processing and machine learning methods. Although Gini index, kurtosis, smoothness index, negative entropy, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lp</i> / <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lq</i> norm have been considerably investigated for MCM, the design of new SMs for enhancing MCM is rarely reported. In this article, based on the ratio of different quasi-arithmetic means (RQAM), two new SMs, coined as Gini index Ⅱ (GI2) and Gini index Ⅲ (GI3), are designed. New proofs show that the GI2 and GI3 satisfy all six sparsity attributes. Subsequently, the GI2 and GI3 of the square envelope of Gaussian white noise are theoretically investigated and their theoretical values are, respectively, equal to 2/3 and 1/3, which can be used as baselines for machine abnormality detection. Once GI2 and GI3 exceed the baselines, abnormal health conditions can be detected without needing historical data and prior fault knowledge. Finally, simulated and experimental case studies showed that the proposed GI2 and GI3 have competitive performance with Gini index and that they are better than kurtosis, negative entropy, and smoothness index, in characterizing the sparsity of signals. This article demonstrates that the RQAM is a potential framework to design new SMs.

Performance-Driven Component Selection in the Framework of PCA for Process Monitoring: A Dynamic Selection Approach

Principal component analysis (PCA) and its variants have been widely used for process monitoring and quality control. A key issue of PCA-related methods is to select an appropriate number of principal components (PCs). However, few approaches for component selection consider the monitoring performance, and they usually rely on prior fault information. This article develops an effective algorithm for dynamic component selection, which selects components for each sample based on a detection performance index, without need for prior fault information. Component selection is transformed into a stochastic optimization problem, whose optimal solution is derived analytically. Then, a subset of components that are sensitive to faults is obtained. The proposed method reduces the requirement for the detectable fault amplitude, which leads to better performance. Furthermore, the differences between PCA and the proposed method are discussed, and online computational complexity is analyzed. Case studies on a continuous stirred tank heater (CSTH), the Tennessee Eastman process (TEP), and a real ultra-supercritical power plant demonstrate that the proposed method has better monitoring performance than PCA and some of its variants.

Incipient fault detection for dynamic chemical processes based on enhanced CVDA integrated with probability information and fault-sensitive features

Recently, canonical variate dissimilarity analysis (CVDA) has emerged as an efficient incipient fault monitoring method for dynamic chemical processes. Nevertheless, the basic CVDA method omits the mining of the probability information hidden in the canonical variate vectors and does not make full use of the fault-sensitive features provided by the prior fault data. Aiming at these two limitations, this paper presents an enhanced CVDA method, called fault-sensitive probability-related CVDA (FSPRCVDA), for better monitoring incipient faults. Firstly, the traditional CVDA is improved to the probability-related CVDA (PRCVDA) by applying the Kullback–Leibler divergence to measure the probability distribution changes of canonical variates. Secondly, fault-sensitive features are extracted by local Fisher discriminant analysis on the normal and prior fault data. The extracted fault-sensitive features are used to construct the auxiliary model for assisting the primary PRCVDA model, which leads to the holistic FSPRCVDA model. The case study on a continuous stirred tank reactor system shows that the proposed FSPRCVDA method has higher incipient fault detection rate than the basic CVDA method.

Initial Fault Diagnosis of Rolling Bearing Based on Second-Order Cyclic Autocorrelation and DCAE Combined With Transfer Learning

Under actual working conditions, the initial faults of rolling bearings are difficult to be predicted effectively because of the lack of prior fault knowledge, weak fault information, and strong noise interference. How to enhance the fault characteristics while removing the vibration signal noise of rolling bearings has become the key challenge to the field of early fault diagnosis of rolling bearings. Based on the above problems, a rolling bearing initial fault diagnosis model based on second-order cyclic autocorrelation (Soca) and deep auto-encoder (AE) combined with transfer learning (TL) was proposed to improve the overall performance of rolling bearing fault identification. First, the Soca is used to estimate the second-order cyclic statistics of the vibration signal, which can better highlight the periodic pulse characteristics of rolling bearings and suppress random noise to a certain extent. After that, the dataset containing the valuable health state information of the rolling bearing is input into the denoising and contraction auto-encoder (DCAE). The advantages of denoising AEs (DAEs) and contraction AEs are fully utilized to extract the initial fault characteristics of rolling bearings. Finally, the balanced distribution adaptation (BDA) is used to reduce the distribution difference and class spacing of the transferable features extracted from the original datasets and the target datasets, and the initial fault diagnosis of the target rolling bearing is diagnosed according to the feature knowledge of the source rolling bearing. In three experimental datasets, six TL experiments were carried out to verify that the Soca-Denoising auto-encoder, Contraction auto-encoder and Transfer learning (DCT) model can effectively enhance the initial fault characteristics of rolling bearings, and has higher diagnostic accuracy and robustness compared with the existing initial fault diagnosis methods of rolling bearings. This method has a good application prospect in the field of early fault diagnosis of rolling bearings.

A Novel Blind Deconvolution Method with Adaptive Period Estimation Technique and Its Application to Fault Feature Enhancement of Bearing

Minimum correlated generalized Lp/Lq deconvolution (MCG-Lp/Lq-D) is an important tool to detect periodic impulses in vibration mixture. It is proved to be a more stable technique than maximum correlated kurtosis deconvolution (MCKD) to recover the fault impulse under strong noise conditions. However, MCG-Lp/Lq-D still has limitations. One of the necessary conditions for the success of MCG-Lp/Lq-D is to provide a precise period of fault. An imprecise prior period will lead to performance degradation or even failure of the method. Therefore, in this paper, a MCG-Lp/Lq-D with adaptive fault period estimation capability is proposed, adaptive minimum correlated generalized Lp/Lq deconvolution (AMCG-Lp/Lq-D). The proposed method uses the autocorrelation function of envelope signal to estimate the fault period adaptively in each iteration and then takes the estimated period as the input parameter of MCG-Lp/Lq-D for the next iteration optimization. The proposed method does not require precise prior fault period input, which greatly improves the fault recovery accuracy and application range of MCG-Lp/Lq-D. Eventually, simulated and experimental data verify the effectiveness and superiority of AMCG-Lp/Lq-D.

An integrated methodology for fault detection, root cause diagnosis, and propagation pathway analysis in chemical process systems

Although the current research on fault detection and diagnosis (FDD) has made significant developments, there exists some challenges such as earlier fault detection with lower false alarms, accurate fault diagnosis without prior fault information, and construction of fault propagation pathway. Many articles have contributed to address one or more aforementioned challenges. However, very few of them has attempted to capture these research gaps in a unified framework. Hence, the current article presents an integrated method for process FDD and propagation pathway identification. The methodology is developed combining the early fault detection capacity of the multivariate exponentially weighted moving average principal component analysis (MEMWA-PCA) and robust diagnosis and causality representability of the Bayesian network (BN). MEWMA-PCA can generally detect the fault earlier than PCA with a lower number of false alarms. Multiple likelihood evidence-based BN updating is utilized to ensure accurate diagnosis without in-depth fault information; this BN updating mechanism is found to be superior to the conventional hard evidence-based updating technique. Additionally, for taking corrective actions effectively, fault propagation pathway can be generated using BN. The proposed method is applied to a total of ten fault cases in the benchmark Tennessee Eastman chemical process. It is observed that the developed method is robust and comprehensive compared to the existing PCA and PCA-BN-based hybrid methods.

Characterizing BDS signal‐in‐space performance from integrity perspective

The full deployment of China’s BeiDou navigation satellite system (BDS) was finalized in June 2020. To support safety-critical applications, the system must provide assured signal-in-space (SIS) performance. As one of the key steps forward for BDS, this paper characterizes the SIS range errors (SISREs) for both the regional (BDS-2) and the global (BDS-3) systems from the integrity perspective. Following the safety standards in aviation, a data-driven SISRE evaluation scheme is presented in this work. This scheme evaluates the overbounding user range accuracy (URA) and the prior fault probability to respectively capture the nominal and anomalous SIS behaviors. By processing the 4.5-year ephemerides starting from 2016 for BDS-2 and the recent 1.5-year data from2019 for BDS-3, we preliminarily provide an overall picture of the BDS SIS characteristics and reveal the significant performance variation among different satellites.

A practical methodology for enhancement and detection of transient faults in a gearbox without prior fault feature information

Gear fault diagnosis has been the focus of research in both academia and industry in the past. Due to the complicated structures of gearboxes, the vibration signals collected from the gearbox casings are comprised of multiple sources, in which the fault-related components may be easily masked by the Gaussian and non-Gaussian noises (e.g. random external shocks and gear meshing harmonics). Moreover, fault feature frequencies of interest cannot always be obtained in advance in practical applications. Therefore, it is necessary to choose an appropriate signal processing method for gear fault diagnosis. In this study, a new practical methodology is proposed to extract the transient fault features from a gearbox vibration signal corrupted by complicated interference without prior fault feature information. In the proposed method, a modified self-adaptive noise cancellation (MSANC) algorithm is first developed for removing the interferences of gear meshing harmonics adaptively, which can overcome the shortcomings of the traditional SANC in parameter selection and convergence performance. Based on the de-noised signal, a cyclic spectral analysis tool called the fast spectral correlation and assisted by the multipoint optimal minimum entropy deconvolution adjusted algorithm is then applied to enhance and detect the certain and potential gear faults. The effectiveness of the proposed method is demonstrated by a numerical simulation and two experimental scenarios that are compared to an advanced blind optimal demodulation band selection technique. The results reveal that the proposed methodology has good capability to detect mono- and multiple gear faults under complicated interferences, and can be regarded as an efficient method for practical gear fault diagnosis, especially for applications where the fault feature frequencies are unknown in advance.

Hybrid Robust H2/H∞ Fault-Tolerant Control with Random Delay NCS

In this paper, a hybrid fault-tolerant control method with off-line design and online scheduling is proposed for NCS with actuator faults, random delay, and external finite energy disturbance. The problem of less conservatism of robust generalized H2/H∞ hybrid fault-tolerant control is studied. Firstly, a closed-loop fault model of the system with random delay parameters was established according to the Bernoulli 0-1 distribution; all possible prior faults are divided into a few intervals according to certain rules, and then an interval fault-tolerant controller is designed off-line according to the prior faults of each interval. Secondly, when the fault is estimated online, the corresponding interval fault-tolerant controller is called through the scheduling mechanism to achieve rapid fault tolerance of prior faults within the interval and mitigate the impact of other faults within the interval, which provides a guarantee for subsequent safe reconstruction control. Finally, the effectiveness of the proposed method is verified by Matlab simulation.