Types Of Faults Research Articles

Study on Fault Monitoring Technology of Photovoltaic Panel Based on Thermal Infrared and Optical Remote Sensing

Abstract. Rapid access to the operating status of Photovoltaic (PV) panels and troubleshooting can save management and maintenance costs for the development of PV power plants, which is important for PV power plant management and power generation capacity assurance. The use of remote sensing technology to identify the faults of photovoltaic panels has developed rapidly, however, current research usually relies only on a single optical data source to identify and count the area of PV panels in a PV electric field, although there are literature on PV panel fault detection, only the surface fault identification of PV panels is tested, while the internal faults (such as panel bad points or bad lines) cannot be identified because of the limitations of optical remote sensing. In this paper, a photovoltaic panel fault monitoring technology based on multi-source remote sensing is proposed. The optical and thermal infrared hybrid data combined with deep learning technology are used to achieve rapid and accurate fault identification and localization of PV panel arrays. It can not only automatically identify PV panels that are obscured by dust and foreign objects, but also locate PV panels that have bad dots or bad lines, which greatly improves the ability and effectiveness of remote sensing PV panel fault monitoring. The high-resolution unmanned air vehicle (UAV) optical image and thermal infrared image are applied in this experiment. The Mask RCNN algorithm is used to accurately locate and number the photovoltaic panel of the optical image. Then, the fault scene classification model is established for the multi-type fault characteristics of the optical image and thermal infrared image within the panel range, so as to identify five types of faults, such as dust cover, branch cover, bird droppings cover, internal bad points and bad lines of PV panel, which effectively solves the problem that the single optical remote sensing image cannot identify the internal component faults of the photovoltaic panel under normal conditions.

Research on fault detection and remote monitoring system of variable speed constant frequency wind turbine based on Internet of Things

In order to study the operating characteristics of variable speed constant frequency wind turbine under different working conditions and the monitoring system of wind turbine. In this paper, the simulation model of each component system of wind turbine is established by MATLAB/Simulink module, and the influence law of different wind speed and ground fault types on the output power of wind turbine is studied. The active power of wind turbines under different short-circuit fault types is compared. At the same time, in order to realize real-time monitoring of wind turbine speed and output power, an online monitoring system for wind turbine operation based on industrial Internet of Things is proposed, and the composition and operation characteristics of this remote monitoring system are given. The practical application shows that the on-line monitoring system can accurately and remotely monitor the running status of the wind turbine and avoid the unstable running of the wind turbine. The research conclusions of this paper can provide reference for the design and construction of wind turbines and the operation of connecting to the power grid.

A novel decoupling method of compound faults with incomplete dataset of rotating machinery

Abstract The occurrence of fault in rotating machinery is random and complex, and the diagnosis of the compound faults has been a challenge in industrial production. Accurate diagnosis of the compound faults can be of significant help to practical maintenance and management. However, most existing intelligent diagnostic methods typically require abundant data for training, which is often difficult to collect for compound faults. In this paper, a novel method called impact feature-based decoupling capsule network (IFDCN) is proposed for diagnosing compound faults with incomplete datasets. In this model, an improved Laplace wavelet kernel capsule neural network is proposed to extract and enhance the impact features of vibration signal. A decoupling classifier is designed to decouple the compound faults in the diagnostic process so as to identify the sub-faults contained in the compound faults. In using this proposed model for incomplete datasets, the compound fault data is not trained and is not labeled. Through training on single-fault data, the proposed model is capable of classifying and decoupling the fault types. The feature extraction capability of the network is visualized by heat maps, and the physical significance of feature extraction is explained by deep learning network. The effectiveness of IFDCN is verified through different experimental of gear and bearing and the experiment results indicate that the proposed model has higher identifying precision and can accurately decouple the compound faults without compound fault samples.

Voltage Error Estimation-Based Fault Diagnosis Method for Three- phase PWM-inverter in Grid-connected Photovoltaic Power System Without any Over-rating

Inverter is an essential component of a grid connected PV system. In fact, an open-switch fault in this power converter could result an important system malfunction and consequently leads to system disconnection. In this context, this paper focuses on the fault-tolerant control of PWM-inverter with redundancy leg for grid-connected PV system. In addition, a fast method of fault detection and compensation is used to maintain the continuous operation of the converter when there is a half-leg open-circuit fault (IGBT+ anti-parallel diode) on one of PWM-inverter legs. Therefore, it can detect this type of fault and compensate it in less than 50 μs by using a time criterion instead of voltage criterion. Also, the fuzzy logic technique is employed to control the active and reactive power injected into the network without exceeding the whole system power capacity limits. Note that, in this study, the active and reactive power references of the chosen operating point (the reactive-to-active power ratio: tgφ = Q/P = 0.19) are based on experimental tests. Simulation results show the feasibility and the effectiveness of the proposed fault diagnosis method in terms of fault detection and service continuity in presence of a half-leg open-circuit fault in PWM-inverter without any over-rating of the PV system components or instability.

Multiple model fault diagnosis and fault tolerant control for the launch vehicle’s attitude control system

Abstract For the launch vehicle attitude control problem, traditional methods can seldom accurately identify the fault types, making the control method lack of pertinence, which largely affects the effect of attitude control. This paper proposes an active fault tolerant control strategy, which mainly includes fault diagnosis and fault tolerant control. In the fault diagnosis part, a small deviation attitude dynamics model of the launch vehicle is established, Kalman filters with different structures are designed to detect and isolate faults through residual changes, and the fault quantity of the actuator is further estimated. In the fault tolerant control part, the following control scheme is adopted according to the above diagnostic information: when the sensor fault is detected, the sensor measurement data is reconstructed; when the actuator fault is identified, the control allocation matrix is reconstructed. Simulation results show that the proposed method can effectively diagnose sensor fault and actuator faults, and significantly improve attitude tracking accuracy and control adjustment time.

Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model

Ensuring a consistently reliable power supply is paramount in power systems. Researchers are engaged in the pursuit of categorizing transmission line failures to design countermeasures for mitigating the associated financial losses. Our study employs a machine learning-based methodology, specifically the Conformer Convolution-Augmented Transformer model, to classify transmission line fault types. This model processes time series input data directly, eliminating the need for expert feature extraction. The training and validation datasets are generated through simulations conducted on a two-terminal transmission line, while testing is conducted on historical data consisting of 108 events that occurred in the Taiwan power system. Due to the limited availability of historical data, they are utilized solely for inference purposes. Our simulations are meticulously designed to encompass potential faults based on an analysis of historical data. A significant aspect of our investigation focuses on the impact of the sampling rate on input data, establishing that a rate of four samples per cycle is sufficient. This suggests that, for our specific classification tasks, relying on lower frequency data might be adequate, thereby challenging the conventional emphasis on high-frequency analysis. Eventually, our methodology achieves a validation accuracy of 100%, although the testing accuracy is lower at 88.88%. The discrepancy in testing accuracy can be attributed to the limited information and the small number of historical events, which pose challenges in bridging the gap between simulated data and real-world measurements. Furthermore, we benchmarked our method against the ELM model proposed in 2023, demonstrating significant improvements in testing accuracy.

An adaptive Morlet wavelet-based iterative filtering method for locating informative frequency band

Locating the informative frequency band of rolling bearing fault signals is of great significance for feature extraction and fault diagnosis. Benefiting from the adjustable center frequency and bandwidth as well as the similarity to impulse-like characteristics induced by bearing failures, Morlet wavelets are commonly used in resonance demodulation. However, fault impulses are highly susceptible to contamination by strong noise, which impedes the efficacy of existing wavelet parameter selection strategies and frequency band optimization methods. In this paper, an adaptive Morlet wavelet-based iterative filtering (AMIF) method is proposed for frequency band optimization under strong noise. The resonance frequency band is pinpointed based on adaptive Morlet wavelet filter banks, with off-band noise being canceled and fault features being refined during the level-by-level filtering process. Additional iterative operations are leveraged to enhance fault features of in-band signals to facilitate the optimization of the filtering parameters. Effectiveness of the proposed AMIF method and its superiority over the wavelet packet transform-based kurtogram and minimum entropy deconvolution are verified through simulation and experimental analysis. The results demonstrate that AMIF can accurately localize the informative frequency band, thereby extracting high-quality fault features, making it suitable for bearing fault diagnosis under strong noise condition with different fault types.

Unsupervised Feature-Construction-Based Motor Fault Diagnosis.

Any bearing faults are a leading cause of motor damage and bring economic losses. Fast and accurate identification of bearing faults is valuable for preventing damaging the whole equipment and continuously running industrial processes without interruption. Vibration signals from a running motor can be utilized to diagnose a bearing health condition. This study proposes a detection method for bearing faults based on two types of neural networks from motor vibration data. The proposed method uses an autoencoder neural network for constructing a new motor vibration feature and a feed-forward neural network for the final detection. The constructed signal feature enhances the prediction performance by focusing more on a fault type that is difficult to detect. We conducted experiments on the CWRU bearing datasets. The experimental study shows that the proposed method improves the performance of the feed-forward neural network and outperforms the other machine learning algorithms.

Entropy-Aided Meshing-Order Modulation Analysis for Wind Turbine Planetary Gear Weak Fault Detection under Variable Rotational Speed.

As one of the most vital energy conversation systems, the safe operation of wind turbines is very important; however, weak fault and time-varying speed may challenge the conventional monitoring strategies. Thus, an entropy-aided meshing-order modulation method is proposed for detecting the optimal frequency band, which contains the weak fault-related information. Specifically, the variable rotational frequency trend is first identified and extracted based on the time-frequency representation of the raw signal by constructing a novel scaling-basis local reassigning chirplet transform (SLRCT). A new entropy-aided meshing-order modulation (EMOM) indicator is then constructed to locate the most sensitive modulation frequency area according to the extracted fine speed trend with the help of order tracking technique. Finally, the raw vibration signal is bandpass filtered via the corresponding optimal frequency band with the highest EMOM indicator. The order components resulting from the weak fault can be highlighted to accomplish weak fault detection. The effectiveness of the proposed EMOM analysis-based method has been tested using the experimental data of three different gear fault types of different fault levels from a planetary test rig.

Grounding Fault Diagnosis of Running Rails Based on a Multi-scale One-Dimensional Convolutional Neural Network in a DC Subway System

Running rails are the return conductors of the traction current in DC subway systems, which should be insulated from the earth. Due to the large length of the line, as well as the humidity and metal dust in the tunnel, grounding fault of the running rails usually occurs, which will increase stray current (SC) leakage and endanger power supply safety. In this paper, a method of grounding fault diagnosis of running rails based on a multi-scale one-dimensional convolutional neural network (MS-1DCNN) is proposed. Firstly, a platform for the dynamic distribution of SC and rail potential (RP) with grounding faults existing in the running rails is established, which generates the dynamic RP data with various grounding faults. Secondly, a grounding fault diagnostic method of running rails based on MS-1DCNN is proposed, so as to realize the effective identification of ground fault types in subway systems. Thirdly, with the proposed diagnostic method, the datasets under two operating conditions of a single train and two trains are tested; a comparison test between MS-1DCNN and the 1D convolutional neural network (1DCNN) is carried out, and the effectiveness of the proposed method is verified. Results demonstrate that the proposed model can significantly improve the ground fault diagnostic accuracy of running rails. The dynamic RP simulation platform for trains established in this paper lays a theoretical foundation for the grounding fault research of running rail. Moreover, the deep learning method is proposed for the first time to diagnose the grounding fault of running rail, and the high diagnostic accuracy is obtained, which is of great significance for the safe and stable operation of the subway line.

Dual effect of roughness during earthquake rupture sequences on faults with strongly rate-weakening friction

We numerically model earthquake rupture cycles on non-planar faults governed by rate and state friction with an enhanced dynamic weakening in the form of flash heating, investigating quantitatively the relationship between the fault geometry, stresses from the preceding earthquakes, and the rupture characteristics. On planar faults, earthquakes with similar magnitudes and stress drops periodically rupture the entire fault, propagating under a low background shear-to-normal stress ratio as self-healing slip pulses due to the large friction reduction, with a sub-Rayleigh rupture speed. Faults with low roughness levels generally show a similar pattern. Prior to some events, the stress ratio along the fault slightly increases, leading to ruptures with secondary slip pulses. As roughness increases, the stresses become more heterogeneous, leading to a more complex cycle with part of the ruptures arresting at restraining bends with a low stress ratio. Sequences of several partial ruptures lead to a significant stress accumulation on the locked fault segments. These are eventually released by crack-like earthquake ruptures with supershear propagation speed and significantly larger magnitudes, stress drops, and radiated energy than typical planar fault events. Therefore, while fault roughness provides a mechanism for rupture arrest, consistent with previous studies, it can also result in a substantial increase in earthquake magnitudes, which should be considered in hazard assessments.

Research on Fault Identification of Hybrid Multi-Feed High-Voltage Direct Current System Based on Line Commutated Converter and Voltage Source Converter

With the rapid development of voltage source converter (VSC) and line commutated converter (LCC) technology and the relative concentration of power and load, the inverter station of the flexible DC system is fed into the same AC bus with the conventional DC rectifier station, and the high-voltage direct current (HVDC) parallel hybrid feed system is formed in structure. As the electrical distance between the converter stations is very close, when a fault occurs in the near area, the current on the AC wiring on the VSC side will fluctuate greatly, resulting in the misoperation of the AC wiring protection. For this reason, this paper proposes a fault identification method based on VSC/LCC hybrid multi-fed HVDC system, which discriminates the fault and outputs the protection signal according to the protection criterion, and logically judges the combination of the output protection signal to identify the fault type. The simulation results show that the method can identify all kinds of faults of hybrid multi-feed DC system and solve the problem of protection misoperation of the hybrid multi-feed DC system.

Research on fault location of DC line in UHV converter station based on digital twin technology

The technologies such as data, information and intelligence are gradually applied to the power industry, and the power industry is gradually realizing digital transformation to meet the requirements of more reliable, stable, intelligent and safe power supply. UHV converter station is the heart of power grid equipment, which mainly ensures the conversion between AC and DC to ensure the stable operation of the power system. Facing the situation that the fault location is difficult and the loss is too large, this paper introduces the Digital Twin Technology(DTT), which completes the real-time mapping of the data state between the physical body and the virtual body of the converter station line through the digital modeling of the physical intelligent equipment. The recognition rate of CNN-BiGRU model for line fault and type reaches 99.78 % and 98.56 %. The CNN-BiGRU model has the highest recognition rate and the best effect in terms of the line recognition rate and the fault type recognition rate. The maximum Recall and Precision values of CNN-BiGRU model are 99.67 % and 97.96 %. The closer the F1 is to 1, the better the performance of the model is, and the value of CNN-BiGRU reaches 0. 94, which shows that the model has good performance.

First-principles exploration of N- and Pd-doped graphene as CO and NO2 gas sensor for operation status evaluation in AIS

To evaluate the operation status of air insulated switchgears (AIS), this work purposes N- and Pd- embedded graphene (N- and Pd-graphene) as potential gas sensors upon two typical faults gases (CO and NO2) from the first-principles simulations. It is found that the N and Pd atoms can be stably trapped on the C-vacancy of the C-defected graphene with the formation energy of −12.17 and −5.12 eV, respectively. N-graphene behaves physisorption towards CO and NO2 molecules while Pd-graphene behaves chemisorption instead. The resistance-type and work function (WF)-based sensing mechanisms of N- and Pd-graphene upon such two gas species are illustrated and uncovered by analyzing their deformations of electronic property and WF in the gas adsorption systems, which reveals the potential of Pd-graphene as a resistive CO and NO2 sensor, N-graphene as a resistive NO2 sensor, as well as the N- and Pd-graphene as WF-based gas sensor for NO2 detection. This work highlights the comparison of adsorption and sensing performances between N- and Pd-graphene upon two typical gas sensors in AIS, which would be meaningful to explore novel graphene-based sensing materials facilitating their investigations and applications in the power system.

Variational Attention-Based Interpretable Transformer Network for Rotary Machine Fault Diagnosis.

Deep learning technology provides a promising approach for rotary machine fault diagnosis (RMFD), where vibration signals are commonly utilized as input of a deep network model to reveal the internal state of machinery. However, most existing methods fail to mine association relationships within signals. Unlike deep neural networks, transformer networks are capable of capturing association relationships through the global self-attention mechanism to enhance feature representations from vibration signals. Despite this, transformer networks cannot explicitly establish the causal association between signal patterns and fault types, resulting in poor interpretability. To tackle these problems, an interpretable deep learning model named the variational attention-based transformer network (VATN) is proposed for RMFD. VATN is improved from transformer encoder to mine the association relationships within signals. To embed the prior knowledge of the fault type, which can be recognized based on several key features of vibration signals, a sparse constraint is designed for attention weights. Variational inference is employed to force attention weights to samples from Dirichlet distributions, and Laplace approximation is applied to realize reparameterization. Finally, two experimental studies conducted on bevel gear and bearing datasets demonstrate the effectiveness of VATN to other comparison methods, and the heat map of attention weights illustrates the causal association between fault types and signal patterns.

Transformer fault diagnosis based on adversarial generative networks and deep stacked autoencoder

Establishing a deep learning model for transformer fault diagnosis using transformer oil chromatogram data requires a large number of fault samples. The lack and imbalance of oil chromatogram data can lead to overfitting, lack of representativeness of the model, and unsatisfactory prediction results on test set data, making it difficult to accurately diagnose transformer faults. A conditional Wasserstein generative adversarial network with gradient penalty optimization (CWGAN-GP) is adopted in this paper, which based on gradient penalty optimization and expand the oil chromatography fault samples of 500 sets of transformer oil chromatography data with 5 types of faults. The proposed method is used to classify transformer faults using a deep autoencoder, and the sample quality of the neural network model proposed in this paper is compared with several other variants of generative adversarial neural network models. The research results show that after using the method proposed in this paper for sample expansion, the overall accuracy of fault diagnosis can reach 93.2 %, which is 4.98 % higher than the original imbalanced samples. Compared with other sample expansion methods, the accuracy of fault diagnosis of the algorithm in this paper is improved by 1.70 %–3.05 %.

Rapid determination of source parameters of the M6.2 Jishishan earthquake in Gansu Province and its application in emergency response

In this study, we swiftly determined the focal parameters (focal mechanism, seismic imaging process, magnitude) of the Jishishan earthquake, leveraging a solved fault model to assess the intensity field and casualties promptly. The investigation began by retrieving the source mechanism through the P-wave initial motion and W-phase method. This enabled us to chart the spatial and temporal distribution of energy release in the source area via the back-projection technique. Following this, we estimated the earthquake's intensity field by merging the source inversion findings with the ground motion prediction equation. This analysis facilitated the evaluation of earthquake casualties, utilizing the theoretical intensity field and a casualty assessment model. Our findings indicate that the fault type is a thrust fault, characterized by a unilateral rupture in the direction of NW, with a rupture length spanning approximately 10–15 ​km and a duration ranging between 8 and 10 ​s. The earthquake's magnitude varied from M 5.9 to M 6.2. The demarcated high-intensity areas, as per our intensity assessment, align closely with the actual survey results. Furthermore, the predicted total casualties and identified critical rescue zones closely match the real-world casualty figures. These insights offer crucial technical support for governmental emergency command and rescue operations.

A high-accuracy intelligent fault diagnosis method for aero-engine bearings with limited samples

As a crucial component supporting aero-engine functionality, effective fault diagnosis of bearings is essential to ensure the engine's reliability and sustained airworthiness. However, practical limitations prevail due to the scarcity of aero-engine bearing fault data, hampering the implementation of intelligent diagnosis techniques. This paper presents a specialized method for aero-engine bearing fault diagnosis under conditions of limited sample availability. Initially, the proposed method employs the refined composite multiscale phase entropy (RCMPhE) to extract entropy features capable of characterizing the transient signal dynamics of aero-engine bearings. Based on the signal amplitude information, the composite multiscale decomposition sequence is formulated, followed by the creation of scatter diagrams for each sub-sequence. These diagrams are partitioned into segments, enabling individualized probability distribution computation within each sector, culminating in refined entropy value operations. Thus, the RCMPhE addresses issues prevalent in existing entropy theories such as deviation and instability. Subsequently, the bonobo optimization support vector machine is introduced to establish a mapping correlation between entropy domain features and fault types, enhancing its fault identification capabilities in aero-engine bearings. Experimental validation conducted on drivetrain system bearing data, actual aero-engine bearing data, and actual aerospace bearing data demonstrate remarkable fault diagnosis accuracy rates of 99.83%, 100%, and 100%, respectively, with merely 5 training samples per state. Additionally, when compared to the existing eight fault diagnosis methods, the proposed method demonstrates an enhanced recognition accuracy by up to 28.97%. This substantiates its effectiveness and potential in addressing small sample limitations in aero-engine bearing fault diagnosis.

A grid fault diagnosis framework based on adaptive integrated decomposition and cross-modal attention fusion

In large-scale power systems, accurately detecting and diagnosing the type of faults when they occur in the grid is a challenging problem. The classification performance of most existing grid fault diagnosis methods depends on the richness and reliability of the data, in addition, it is difficult to obtain sufficient feature information from unimodal circuit signals. To address these issues, we propose a deep residual convolutional neural network (DRCNN)-based framework for grid fault diagnosis. First, we design a comprehensive information entropy value (CIEV) evaluation metric that combines fuzzy entropy (FuzEn) and mutual approximation entropy (MutEn) to integrate multiple decomposition subsequences. Then, DRCNN and heterogeneous graph transformer (HGT) are constructed for extracting multimodal features and considering modal variability. In addition, to obtain the implicit information of multimodal features and control the degree of their performance, we propose to incorporate the cross-modal attention fusion (CMAF) mechanism in the synthesis framework. We validate the proposed method on the three-phase transmission line dataset and VSB power line dataset with accuracies of 99.4 % and 99.0 %, respectively. The proposed method also achieves superior performance compared to classical and state-of-the-art methods.

Fault diagnosis study of ship solar photovoltaic power generation system

In this paper, a fault diagnosis method of ship photovoltaic (PV) power generation system based on a convolutional neural network (CNN) is proposed to ensure the smooth navigation of ships. Firstly, to understand the normal operation and fault state of the generation system, a simulation model of the photovoltaic power generation system is constructed using MATLAB / Simulink. Then, the model is simulated to extract the fault data under different fault conditions, and the eigenvalue extraction of the fault data is carried out by using the fast Fourier transform method. Following that, a fault diagnosis model is built using the convolutional neural network and trained accordingly. The final diagnostic results show that the established simulation model and convolutional neural network can provide support for the fault diagnosis of ship photovoltaic power generation system, and can autonomously detect the type of faults and locate the location of the faults after the faults occur in the generation system, to ensure the stable and safe operation of ship power generation system.