Online Fault Research Articles

An online fault-tolerant control approach based on policy iteration algorithm for nonlinear time-delay systems

This paper introduces an online fault-tolerant control method for nonlinear time-delay systems with actuator faults using a policy iteration algorithm. A new performance index function is proposed to account for time-delay states, from which control laws are derived. The approach combines policy iteration with a fault compensator, solving the Hamilton-Jacobi-Bellman equation associated with this innovative value function via a critic neural network. Actuator faults are reconstructed for online fault compensation without the need for fault detection and isolation. The Lyapunov functional analysis demonstrates the closed-loop system's asymptotic stability in the presence of actuator faults and time-delay states. Simulation results validate the effectiveness of the proposed fault compensation strategy. The key contribution of this paper is the incorporation of time-delay states into the value function of the policy iteration algorithm, addressing the problem of fault compensation in nonlinear time-delay systems.

Online monitoring and fault early warning prediction method for the operational status of steam turbine sliding pin systems

Abstract In modern power systems, ensuring the safe and reliable operation of the sliding pin system in large steam turbine generator sets is crucial. However, the measurable parameters in the current distributed control system are insufficient for fault early detection of the sliding pin system’s operational state. Additionally, there is a lack of relevant research in this area at present. This paper utilizes a typical 300 MW-class unit as a case study. By analyzing the operational mechanism and fault modes of the sliding pin system, a method for online monitoring of its operational status based on cylinder expansion measurement parameters is proposed. Based on this foundation, taking the advantage of long short-term memory (LSTM) network to effectively extract features from univariate time series, and integrating improved particle swarm optimization (IPSO) for automatic hyperparameter optimization, a multi-step prediction model and fault prediction method for the operational status of sliding pin systems based on IPSO-LSTM is designed. Test results based on various performance evaluation metrics indicate that the IPSO-LSTM algorithm significantly enhances the prediction model’s accuracy. Specifically, the TVIWAC-PSO model, which varies the inertia weight (TVIW) and acceleration coefficients simultaneously in the PSO algorithm, optimizes by enhancing global search in the early stages and emphasizing local search in the later stages of iteration. Furthermore, TVIWAC-PSO demonstrates superior performance in optimizing the hyperparameters of the LSTM algorithm. Finally, based on the gap standard between sliding pins and keyway in the actual operating procedures of the unit, combined with the low-pressure cylinder and rotor expansion difference operation standard, thresholds for anomaly detection and early fault prediction of the sliding pin system’s operational status are provided. This study holds significant engineering application value.

Troubleshooting of wind turbine gearboxes

Abstract Qinghai Province is rich in wind power resources, actively building wind power generation clean energy highland, large installed capacity, and in the rapid expansion, how to achieve the safe and stable operation of the wind power base is particularly critical. Wind turbine gearboxes operate 24 hours a day under poor working conditions, and key components are prone to failure, accounting for the highest proportion of maintenance costs, so it is of great significance to research intelligent assessment methods and key technologies for the health status of wind turbine gearboxes. A dynamic model of wind-machine-network coupling in wind turbine transmission chain is established to investigate the failure mechanism and dynamic evolution of transmission chain gearboxes under variable wind load. A time-frequency energy concentration algorithm based on a synchronous extraction operator is found to explore the mapping relationship between fault sources and signs. Using a multi-source signal fusion strategy and migration learning method for order spectral analysis, a system fault characteristic parameter identification model is established to implement online monitoring and fault prediction of the wind turbine operation condition, and to improve its operation reliability.

Manifold learning-empowered lightweight unsupervised deep representation for edge-side fault detection

This paper addresses the problem of fault diagnosis in rotating systems by monitoring and analysis of their vibration signals. To tackle the prevalent challenges posed by the scarcity of high-quality labelled data and the complexity of models in the context of intelligent fault diagnosis for edge computing deployments, this research introduces a lightweight unsupervised deep representation method for edge-side fault detection. By leveraging manifold learning, this approach places a strong emphasis on unsupervised learning and model compactness. Firstly, the approach utilises the non-linear fitting capabilities of deep learning to implement manifold learning's dimensionality reduction, which can efficiently extract structured relationships from high-dimensional data, enabling rapid mapping of unlabelled samples to a lower-dimensional space. Then, the proposed method includes a clustering algorithm to refine the model's accuracy by correcting pseudo-labels near category centres, reducing intra-class distances and enhancing low-dimensional clustering. Finally, the model employs the isolation forest (iForest) algorithm for the rapid identification of both known and novel fault types at the edge, an innovative step in fault detection technology. Experiments on an open-source dataset and a real-world fault dataset collected by a customised test-rig indicate that the model proves effective in complex scenarios involving unlabelled data, sample imbalances and emerging fault categories. Its success underscores its significant potential for real-time online fault diagnosis in industrial environments, marking a substantial advancement in the field of intelligent fault identification.

Fault-Tolerant Closed-Loop Controller Using Online Fault Detection by Neural Networks

This paper presents an online model-free sensor fault-tolerant control scheme capable of tolerating the most common faults affecting an induction motor. This approach involves using neural networks for fault detection to provide the controller with sufficient information to counteract adverse consequences due to sensor faults, such as degradation in performance, reliability, and even failures in the control system. The proposed approach does not consider the knowledge of the nominal model of the system or when the fault may occur. Therefore, a high-order recurrent neural network trained online by the Extended Kalman Filter is used to obtain a mathematical model of the system. The obtained model is used to synthesize a discrete-time sliding mode control. Then, the fault-detection and -isolation stage is performed by independent neural networks, which have as input the signal from the current sensor and the position sensor, respectively. In this way, the neural classifiers continuously monitor the sensors, showing the ability to know the sensor status. The combination of controller and fault detection maintains the operation of the motor during the time of the fault occurrence, whether due to sensor disconnection, degradation, or connection failure. In fact, the MLP neural network achieves an accuracy between 95% and 99% and shows an AUC of 97% to 99%, and this neural network correctly classifies true positives with acceptable performance. The Recall value is high, between 97% and 99%, and the F1 score confirms a good performance. In contrast, the CNN shows a higher accuracy, between 96% and 99% in accuracy and 98% to 99% in AUC. In addition, its Recall and F1 reflect a better balance and capacity to handle complex data, demonstrating its superiority to MLP in fault classification. Therefore, neural networks are a promising approach in areas such as fault-tolerant control.

A Real-Time Fault Diagnosis Method for Multi-Source Heterogeneous Information Fusion Based on Two-Level Transfer Learning.

A convolutional neural network can extract features from high-dimensional data, but the convolution operation has a high time complexity and requires a large amount of computation. For equipment with a high sampling frequency, fault diagnosis methods based on convolutional neural networks cannot meet the requirements of online fault diagnosis. To solve this problem, this study proposes a fault diagnosis method for multi-source heterogeneous information fusion based on two-level transfer learning. This method aims to fully utilize multi-source heterogeneous information and external domain data, construct a two-level transfer mechanism to fuse multi-source heterogeneous information, avoid convolutional operations, and achieve real-time fault diagnosis. Its main work is to build a feature extraction network model of screenshots, design a mechanism for transfer from the feature extraction model using screenshots to the deep learning model using one-dimensional sequence signals, and complete the transfer from a convolutional neural network to a deep neural network. After two-level transfer, the fault diagnosis model not only integrates the characteristics of one-dimensional sequence signals and screenshots but also avoids convolution operations and has a low time complexity. The effectiveness of the proposed method is verified using a gearbox dataset and a bearing dataset.

Fault diagnosis for multi-axis carving machine systems with Gaussian mixture hidden Markov models: A data-model interactive perspective

This paper is concerned with sensor fault diagnosis problems for multi-axis carving machine systems (MACMSs) with repetitive machining tasks. A novel fault diagnosis method that combines the multi-feature fusion technology and Gaussian mixture hidden Markov models (GMHMMs) is proposed, which is inspired by a data- and model-driven collaborative perspective. With fault-sensitive features first extracted from both the time domain and time–frequency domain, the composite health index (CHI) is constructed to facilitate the understanding of the time-varying evolution. Then, GMHMMs are established to characterize the probabilistic relationship between the hidden states and CHI. To achieve high-precision fault classification, a well-designed global objective function is adopted to dynamically optimize both the CHI construction and classifier model training in a closed-loop feedback mechanism. Specifically, the fusion coefficients with range and equality constraints are integrated as part of the model parameters into the global optimization objective function, thereby reducing the search range and improving convergence speed. Besides, the well-trained GMHMMs interact with each other to capture the correlation information between different faults, and are utilized for online fault diagnosis. Finally, experiments are conducted on a self-developed multi-axis carving machine platform. The results exhibit outstanding performance in comparison with existing methods, particularly attaining a diagnostic accuracy of 95.37%.

An online open circuit faults diagnosis method for converter using the lightweight two-channel deep network

Online diagnostic technology is crucial for ensuring the reliable operation of power converters in industrial applications. However, quickly diagnosing the faults of a power converter using intelligent algorithms is challenging due to the redundancy and noise of the sensor measurement signal. Hence, this paper proposes a lightweight two-channel deep network (LTCDN) to overcome it and achieve rapid fault diagnosis. It consists of a quadratic mean preprocessing layer (QMPL), a two-channel convolutional layer (TCCL), and a bidirectional gated recurrent unit (BGRU). The QMPL is designed to achieve denoising and lightening of output currents. Then, the TCCL is proposed to extract edge features and internal key information for improving diagnostic accuracy. Finally, the BGRU classifier completes the online fault diagnosis with a fast detection speed and high precision. It is evaluated through experiments with real-time open circuit faults for multiple switches of a four-level active neutral point clamped (4L-ANPC) converter, demonstrating superior performance with a false diagnosis rate of less than 1% and a diagnosis time of under 8ms. The method combines a novel data preprocessing layer with an efficient neural network to provide an effective fault detection method for a 4L-ANPC converter, which not only has high accuracy but also has significant computational efficiency. Its scalability for more complex multilevel converters is also verified.

Online and Offline Fault Detection and Diagnostics in a Nuclear Power Plant Condenser

Nuclear power plants (NPPs) face significant financial pressures due to operational and maintenance costs. This research investigates Fault Detection and Diagnostics (FDD) techniques to optimize maintenance schedules and reduce expenses. The NPP condenser plays a critical role in converting turbine exhaust steam back into water for reuse. Condenser tube fouling, a prevalent fault mode, impedes heat transfer efficiency and can lead to decreased plant efficiency and safety risks. This study proposes an FDD framework that leverages raw signal analyses from temperature and pressure monitoring to detect and diagnose condenser tube fouling in both online and offline settings. The online approach facilitates close-to-real-time predictions, enabling proactive maintenance strategies. Additionally, the framework explores incorporating a condenser’s maintenance history for enhanced diagnostics. We employ a dataset obtained from a simulated nuclear power plant condenser using the Asherah Nuclear Power Plant Simulator (ANS). ANS replicates the operational dynamics of a pressurized water reactor (PWR) type NPP. The proposed methodology utilizes an encoder-decoder (E-D) structured 1DCNN model to analyze the raw signals. The research demonstrates consistent and accurate fault detection and diagnostics for condenser tube fouling in both online and offline scenarios. A high potential for generalization to unseen conditions was observed. However, online detection using small data windows necessitates caution due to potential false alarms around the transition points. Our findings pave the way for further exploration of robust diagnostics by accommodating a wider spectrum of fouling rates within degradation classes using ANS. This combined online and offline FDD approach offers a promising solution for promoting operational safety, efficiency, and cost-effectiveness in NPP condensers.

High-Frequency Test of Electric Locomotive Arrester and Its PHM Design

The zinc oxide (ZnO) arrester is positioned on the locomotive roof, preventing other onboard electrical equipment from being harmed by external and internal overvoltages. A number of locomotive arrester burnout and explosion accidents occur during traction supply network harmonic resonance, causing train stoppages and power outages in the network. This study first carries out high-frequency tests to investigate the wide frequency band electrical characteristics of the locomotive arrester and prove that the arrester is prone to fault during resonance. Consequently, a prognostics and health management (PHM) methodology for locomotive arresters is proposed, providing online monitoring, fault warning, and health assessment, which is beneficial to the operation and maintenance of arresters in the future.

Feature-weighted Random Forest with Boruta for Fault Diagnosis of Satellite Attitude Control Systems

The performance of random forest (RF) based satellite attitude control system (ACS) fault diagnosis methods is limited by uninformative features in high-dimensional data. To solve this problem, we proposed a feature-weighted random forest with Boruta (FWRFB) based fault diagnosis method is proposed for fault diagnosis of ACSs. Firstly, a Boruta feature selection algorithm is used to obtain a feature set and determine significant feature weights. Subsequently, a novel feature-weighted random forest (FWRF) algorithm is designed, which utilizes feature-weighted random sampling instead of simple random sampling to generate feature subsets in the RF. The FWRFB effectively utilizes the feature information while mitigating noise interference. Finally, a FWRFB-based diagnostic module is developed for online fault diagnosis of ACSs. The effectiveness of the proposed method is verified by the ACS data from a semi-physical simulation platform.

Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field data

Gaussian process-based online health monitoring and fault analysis of lithium-ion battery systems from field data

Spatial–Temporal Deviation Analysis for Multivariate Statistical Process Monitoring

ABSTRACTGiven that an effective process monitoring implementation should take both the spatial and temporal variations into account, a novel online process monitoring scheme based on a newly formulated algorithm titled as spatial–temporal deviation analysis (STDA) is proposed. Different from the mainstream process monitoring methods that focus on characterizing the spatial and/or temporal variation in the historical normal samples, the proposed STDA algorithm is designed to adaptively and timely train a pair of projecting vectors to uncover potential deviation in the spatial–temporal variation of online monitored samples, so as to guarantee consistently enhanced monitoring performance. Instead of utilizing a fixed projecting framework trained offline, the STDA algorithm is repeatedly executed once a newly measured sample become available for online monitoring. Therefore, the proposed STDA‐based method could consistently ensure its effectiveness for online fault detection, because a projecting framework targeted to revealing deviation in spatial–temporal variation is dynamically determined for different online monitoring samples in a timely manner. Finally, the salient monitoring performance achieved by the proposed STDA‐based approach is evaluated through comparisons with other counterparts.

ADP-based fault-tolerant consensus control for multiagent systems with irregular state constraints

This paper investigates the consensus control issue for nonlinear multiagent systems (MASs) subject to irregular state constraints and actuator faults using an adaptive dynamic programming (ADP) algorithm. Unlike the regular state constraints considered in previous studies, this paper addresses irregular state constraints that may exhibit asymmetry, time variation, and can emerge or disappear during operation. By developing a system transformation method based on one-to-one state mapping, equivalent unconstrained MASs can be obtained. Subsequently, a finite-time distributed observer is designed to estimate the state information of the leader, and the consensus control problem is transformed into the tracking control problem for each agent to ensure that actuator faults of any agent cannot affect its neighboring agents. Then, a critic-only ADP-based fault tolerant control strategy, which consists of the optimal control policy for nominal system and online fault compensation for time-varying addictive faults, is proposed to achieve optimal tracking control. To enhance the learning efficiency of critic neural networks (NNs), an improved weight learning law utilizing stored historical data is employed, ensuring the convergence of critic NN weights towards ideal values under a finite excitation condition. Finally, a practical example of multiple manipulator systems is presented to demonstrate the effectiveness of the developed control method.

Improved diffusion mapping combined with procrustes analysis for capturing local-global data structures in industrial process monitoring

BackgroundProcess monitoring, by providing early warnings of abnormal operating states resulting from process faults, facilitates the maintenance of normal production and ensures process safety. In the domain of industrial process monitoring, capturing the local-global structural features of data and acquiring an explicit mapping relationship for dimensionality reduction projection holds significant importance for online fault detection in industrial processes. MethodsThis study introduces an Improved Diffusion Mapping and Procrustes analysis (IDM-P) method for this purpose. Initially, considering the multiscale and correlation among industrial data features, the Mahalanobis distance is incorporated to improve the diffusion mapping algorithm. Utilizing this method allows for the concurrent capture of both local and global data structures, leading to a more efficient extraction of data-representative features, which enhances the accuracy of fault detection. Procrustes analysis is then used to obtain an explicit mapping matrix between high-dimensional data and low-dimensional manifolds, improving the efficiency of the key feature extraction of the new samples. Finally, this matrix is utilized to construct process monitoring statistics for fault detection. Significant FindingsThe method's effectiveness was validated through experiments on the TEP dataset and actual industrial data, demonstrating that IDM-P maintains higher accuracy and achieves optimal fault detection compared to other methods.

Infrared Image Object Detection Algorithm for Substation Equipment Based on Improved YOLOv8

Substations play a crucial role in the proper operation of power systems. Online fault diagnosis of substation equipment is critical for improving the safety and intelligence of power systems. Detecting the target equipment from an infrared image of substation equipment constitutes a pivotal step in online fault diagnosis. To address the challenges of missed detection, false detection, and low detection accuracy in the infrared image object detection in substation equipment, this paper proposes an infrared image object detection algorithm for substation equipment based on an improved YOLOv8n. Firstly, the DCNC2f module is built by combining deformable convolution with the C2f module, and the C2f module in the backbone is replaced by the DCNC2f module to enhance the ability of the model to extract relevant equipment features. Subsequently, the multi-scale convolutional attention module is introduced to improve the ability of the model to capture multi-scale information and enhance detection accuracy. The experimental results on the infrared image dataset of the substation equipment demonstrate that the improved YOLOv8n model achieves mAP@0.5 and mAP@0.5:0.95 of 92.7% and 68.5%, respectively, representing a 2.6% and 3.9% improvement over the baseline model. The improved model significantly enhances object detection accuracy and exhibits superior performance in infrared image object detection in substation equipment.

Fault diagnosis of photovoltaic array with multi-module fusion under hyperparameter optimization

Photovoltaic (PV) arrays’ random and intermittent output characteristics impact power system safety. To improve the performance of the PV array fault diagnosis model, a novel online fault monitoring technique is introduced. (1) Fault diagnostic model construction: Significant differences in PV arrays’ I-V and P-V curves under various fault conditions led to constructing a 3D channel feature map based on I, V, and P features. (2) Multi-source information fusion network (MSIFN): this multi-module fusion model includes a time–frequency domain fusion module (TDFM), a multi-feature shuffle expansion convolution module (MSECM), a parameter-free parallel hybrid attention enhancement module, and a multi-scale mixed pooling fusion classification module (MMPCM). (3) Multi-strategy fusion whale optimization algorithm (MSFWOA): addressing the original WOA’s deficiencies, we designed time control, parameter modification, and greedy control strategies based on lens imaging to optimize MSIFN’s hyper-parameters. Experimental results show that the MSFWOA-MSIFN model excels in PV array fault diagnosis (Paccuracy=Pprecision=Precall = 99.92 %). In three types of noise experiments with 15 dB, 25 dB, and 30 dB, the average performance index remained above 99 %. In practical experiments, the average performance indices werePaccuracy = 97.53 %, Pprecision = 97.32 %, andPrecall = 97.41 %, further demonstrating its excellent diagnostic performance. This model effectively diagnoses various faults in PV arrays, providing scientific and theoretical support for PV system operations.

Unknown input observer design for uncertain nonlinear systems with time-varying delay: application to online fault estimation

This paper recommends an unknown input observer (UIO) for uncertain nonlinear time-delay dynamics that enables online fault estimation. The main novelty of this paper is designing UIO to fault estimation for nonlinear systems that include time-varying delays in states and time-varying uncertainties in all matrices of the system. The other advantage of the proposed method is that both abrupt and incipient faults are considered. Furthermore, the state and output disturbances, as well as actuator and sensor faults, are considered. The Lyapunov–Krasovskii concept is used to guarantee the stability of the augmented closed-loop system. Calculating observer gains and stabilisation of estimation error dynamics are accomplished by utilising a new linear matrix inequality (LMI). Finally, an uncertain continuous-stirred tank reactor (CSTR) with a time-varying delay is used to evaluate the performance of the suggested technique.

Fast Online Fault Diagnosis for PMSM Based on Adaptation Model

Fast Online Fault Diagnosis for PMSM Based on Adaptation Model

Online and on-grid PV power plant faults detection based on sensitive parameters

Online and on-grid PV power plant faults detection based on sensitive parameters