Fault Detection Technique Research Articles

Research on power plant security issues monitoring and fault detection using attention based LSTM model

OverviewFor Photo Voltaic (PV) arrays and Wind systems to operate as efficiently and effectively as possible, fault detection is essential. It is possible to improve the safety of renewable energy systems and guarantee that the service will continue uninterrupted if problem detection and diagnostics are performed in a timely and accurate manner. In general, wind power is one of the three major renewable energy sources, along with solar power and hydropower. Wind power is well distributed around the world, making it suitable to be exploited in human activities for the general welfare of society.ObjectivesA prototype security situational awareness system applicable to the power data communication network service and traffic model should be developed. This will help to successfully enhance the security and service quality of the power data communication network, effectively cope with network security threats in the new environment, and ensure the security of the power plant network access. The traffic of the main network of the existing data communication network will be combined and analyzed, and NQA traffic management algorithms will be studied and proposed. These actions will improve the SLA hierarchical service capability, the service quality of the core services carried by the backbone network, and strengthen the security capability of the new energy power plant communication network access system.MethodologyFor the purpose of this investigation, an attention-based long short-term memory (Att-LSTM) model was used for the categorization of time series actual data. The approach that has been developed is able to identify defects in photovoltaic arrays and inverters, which offers a dependable option for improving the efficiency and dependability of solar energy systems. For the purpose of evaluating the proposed method, a real-world solar energy dataset is used.ResultsThe findings acquired from this evaluation are compared to the results received from existing detection approaches such as Cryptography, Intrusion Detection System (IDS) methods, and Network Defense Schemes. The results obtained demonstrate that the suggested method surpasses current fault detection techniques, providing greater accuracy and better performance.

A Novel Machine Learning Technique for Fault Detection of Pressure Sensor

Pressure transmitters are widely used in the process industry for pressure measurement. The sensing line, a core component of the pressure sensor in the pressure transmitter, significantly impacts the accuracy of the pressure transmitter’s output. The reliability of pressure transmitters is critical in the nuclear power industry. Blockage is recognized as a common failure in pressure sensing lines; therefore, a novel detection method based on Trend Features in Time–Frequency domain characteristics (TFTF) is proposed in this paper. The dataset of pressure transmitters comprises both fault and normal data. This method innovatively integrates multi-scale time series decomposition algorithms with time-domain and frequency-domain feature extraction techniques. Initially, this dataset is decomposed into multi-scale time series to mitigate periodic component interference in diagnosis. Subsequently, via the sliding window algorithm, both the time-domain features and frequency-domain features of the trend components are extracted, and finally, the XGBoost algorithm is used to detect faults. The experimental results demonstrate that the proposed TFTF algorithm achieves superior fault detection accuracy for diagnosing sensing line blockage faults compared with traditional machine learning classification algorithms.

Concept Drift Early Fault Detection in Wind Turbine Based on Distance Metric: A Systematic Literature Review

The Supervisory Control and Data Acquisition (SCADA) system in wind turbines generates substantial data that remains underutilized in terms of wind farm operation and maintenance (O&M). Numerous fault detection methods leveraging SCADA data are being extensively researched to reduce O&M costs. The detection methods are revolutionizing wind farm O&M strategies, shifting from scheduled passive detection to predictive active detection, with the potential to significantly reduce spare parts and labor costs. This paper presents a systematic review of wind turbine fault detection methods based on concept drift and distance metrics, employing the PRISMA methodology. The selected literature is analyzed from three perspectives: fault components, modeling methods, and data sources. Additionally, this review addresses research questions related to current trends, concept drift applications, and distance metric utilization in wind turbine fault detection. Lastly, it provides valuable insights for researchers and industry practitioners in wind energy engineering to explore future research and development in fault detection techniques for enhancing the reliability and efficiency of wind turbine operations.

A small defect detection technique for industrial product surfaces based on the EA-YOLO model

A small defect detection technique for industrial product surfaces based on the EA-YOLO model

Thermal imaging-based fault detection and energy efficiency analysis in a 1.6 MW photovoltaic system in Bağyurdu OIZ, Türkiye

The present study assesses the influence of thermal imaging defect detection on the energy efficiency of a 1.6 MW solar power facility in the Bağyurdu Organized Industrial Zone (OIZ) in İzmir, Turkey. Thermal imaging has demonstrated efficacy in detecting serious problems in photovoltaic (PV) panels, including hot spots, inoperative modules, faulty connections, and shadowing, which substantially impact system performance. A comprehensive investigation revealed that around 15% of the photovoltaic panels displayed defects, resulting in a 16% decrease in system performance and an estimated yearly energy loss of 0.35 GWh. The study emphasizes the benefits of thermal imaging compared to conventional fault detection techniques, including its capacity for swift and non-invasive identification of localized overheating, which may lead to fires, and its ability to discern fluctuations in energy output due to shading or malfunctioning modules. The results underscore the necessity for routine thermal evaluations and maintenance to guarantee photovoltaic systems’ operational efficacy and dependability. This study enhances the sparse data on large-scale photovoltaic systems in Türkiye and illustrates the effectiveness of thermal imaging as an economical and accurate diagnostic instrument. Future studies should amalgamate thermal imaging with sophisticated diagnostic techniques, like electroluminescence testing and machine learning, to augment fault detection precision and optimize photovoltaic system efficacy.

Multiphoton and Harmonic Imaging of Microarchitected Materials.

Microadditive manufacturing has revolutionized the production of complex, nano- to microscale components across various fields. This work investigates two-photon (2P) and three-photon (3P) fluorescence imaging, as well as third-harmonic generation (THG) microscopy, to examine periodic microarchitected lattice structures fabricated using multiphoton lithography (MPL). By immersing the structures in refractive index matching fluids, we demonstrate high-fidelity 3D reconstructions of both fluorescent structures using 2P and 3P microscopy as well as low-fluorescence structures using THG microscopy. These results show that multiphoton fluorescence (MPF) imaging offers reduced signal decay with respect to depth compared to single-photon techniques in the examined structures. We further demonstrate the ability to nondestructively identify intentional internal modifications of the structure that are not immediately visible with scanning electron microscope (SEM) images and compression-induced fractures, highlighting the potential of these techniques for quality control and defect detection in microadditively manufactured components.

Surface defect detection of steel strip at low resolution based on SAC-YOLOv5

Abstract Surface defects are common occurrences in the production process of strip steel. The development of automatic and efficient intelligent detection algorithms for strip steel is crucial for enhancing product quality and operational safety. While deep learning-based defect detection techniques have achieved satisfactory accuracy when applied to high-resolution images, obtaining a sufficient number of high-resolution images in practical engineering scenarios is challenging. The degradation of image quality often results in a significant decline in the performance of existing detection techniques. To address these challenges, this paper proposes SAC-you only look once (YOLO)v5-based surface defect detection model specifically designed for low-resolution strip steel images. SAC, SIoU based K-Means++, asymmetric convolutional neural networks (ACNNs) and composite down-sampling feature fusion block (CDFFB). Firstly, we introduce an anchor boxes clustering algorithm called Shape-IoU based K-Means++ (SIoU based K-Means++) to enhance the efficiency of anchor boxes regression. Secondly, we construct an ACNNs for multi-level feature extraction. Utilizing reparameterization techniques, we reduce the inference resources required by the model. Additionally, we propose a CDFFB which enhances key texture information and improves the model’s nonlinear fitting ability. Experimental analysis using NEU-DET surface defect data demonstrates the superiority of the proposed model in processing low-resolution strip steel surface defect images. In comparison to YOLOv8, YOLOv7, YOLOX, YOLOv3SPP, CenterNet and Faster RCNN, SAC-YOLOv5 outperforms all other models in terms of both speed and accuracy.

A novel method for fault detection and diagnosis in DFIG coupled grid connected wind energy systems using spiking deep residual network

This paper presents a novel technique for the optimal detection of faults in doubly fed induction generators (DFIGs), which are widely used in wind turbines. The proposed method leverages a spiking deep residual network (SDRN), a type of spiking neural network (SNN), to accurately detect and classify the operational conditions of the DFIG, distinguishing between healthy and faulty states. The primary goal of the proposed approach is to minimize errors in both fault detection and classification, improving the reliability of the protection system. The present study analyzes the effects of faults on key electrical parameters, including stator phase current, the d-component of stator current, and reactive power. These effects are quantitatively compared using a sensitivity factor, with fault indices assessed under varying fault severities, rotor speeds, and power reference levels. To evaluate the performance of the SDRN-based fault detection method, the results are benchmarked against existing fault detection techniques such as singular spectrum analysis (SSA), artificial neural networks (ANNs), and bee colony optimization (BCO). The proposed method is implemented in MATLAB/Simulink, and its performance is quantitatively evaluated. The results demonstrate that the SDRN approach significantly outperforms the existing techniques in terms of fault detection accuracy, with a reduction in both classification errors and detection errors. The proposed spiking deep residual network (SDRN) achieves an accuracy of 96.2% in fault detection for doubly fed induction generators (DFIGs), outperforming traditional methods like artificial neural networks (ANNs) and singular spectrum analysis (SSA) by 5.5% and 7.2%, respectively, in terms of classification accuracy. The error rate is reduced by 3% compared to existing approaches, demonstrating the effectiveness and robustness of SDRN in fault classification. The results highlight the advantages of using spiking neural networks over traditional methods, particularly in minimizing errors and improving fault classification performance.

Integrating Artificial Intelligence for Enhanced Fault Detection in Power Transmission Systems: A Smart Grid Approach

Modern electrical systems rely heavily on sensors and relays for fault detection in three-phase transmission lines and distribution transformers. However, these devices often need more time complexity and are prone to false alarms (erroneous signals). The study was guided by the PRISMA guidelines and methodology. The study's objective was to compare the levels of accuracy of fault detection presented by different models studied between 2023 and 2024. The general objective was further divided into sub-objectives, which included determining types of faults in power transmission systems, examining fault detection machine-learning models in power transmission systems, and assessing levels of accuracy of fault detection techniques in power transmission systems. The study used a qualitative research design, which entailed a systematic literature review that involved identifying scholarly articles from respectable international journal websites that aligned with examining how the deep-learning model enhanced fault detection capabilities in a three-phase transmission line simulated dataset. The inclusion criteria were that the study used journals published between 2023 and 2024. The study also included journals that tested Artificial Intelligence systems' accuracy in fault detection in three-phase transmission lines and distribution transformers. The study excluded sources that were older than 2023. The sources of information were journals written by professionals and scholars in the field of Artificial intelligence and engineering. The sourced journals used simulated and real databases to test fault detection accuracy in three-phase transmission lines and distribution transformers. The source of data for review was obtained from the internet via Google search with various credible academic journal websites being searched by the researcher. A total of 12 sources were searched. To assess the risk of bias in the included studies, the researcher used the Robvis methods that visualized risk-of-bias during the inclusion criteria for sources used in the systematic review. The techniques used to present the findings were narratives, graphs, and tables. The method used to synthesize results was comparative data analysis. The study included two studies, the sample size from the three possible journals the researcher identified. The characteristics of the studies were that they both informed the study's general and specific sub-objectives. The two studies also used the same methods to arrive at their findings, which made them ideal for comparative analysis. Finally, both studies compared older systems to new systems regarding Artificial Intelligence accuracy in fault detection. The study's findings were as follows: The study ranked the Novel glass box-based model as the best artificial intelligence machine-learning approach for accurately detecting electric power transmission lines and systems faults. The Novel glass box-based approach's accuracy was 99%, followed by the Convolution Neural Network model's accuracy of 98%, the Gated Recurrent Unit model's accuracy of 92%, the Random Forest model's accuracy of 90%, the Logistic Regression model's accuracy of 74%, and the Support Vector Classifier model's accuracy of 63%. The review was adequate to conclude that the Novel glass-box-based proposed approach was the most advanced and suitable model for detecting faults in electric power transmission lines. The study had one limitation: it reported the results based on two sources, which made the exclusion criteria a point of bias. The survey results implied that builders of new Artificial intelligence systems for fault detection in electric line transmitters should aim to produce 99% accurate systems to meet the recommendations and fill the gap that this study has reported. This study was funded by the researcher using family contributions. The study's author is Nijat Taghizad (ORCID ID: 0009-0005-0505-8767).

Feature analysis and ensemble-based fault detection techniques for nonlinear systems

AbstractMachine learning approaches play a crucial role in nonlinear system modeling across diverse domains, finding applications in system monitoring, anomaly/fault detection, control, and various other areas. With technological advancements, today such systems might include hundreds or thousands of sensors that generate large amounts of multivariate data streams. This inevitably results in increased model complexity. In response, feature selection techniques are widely employed as a means to reduce complexity, avoid the curse of high dimensionality, decrease training and inference times, and eliminate redundant features. This paper introduces a sensitivity-inspired feature analysis technique for regression tasks. Leveraging the energy distance on the model prediction errors, this approach performs both feature ranking and selection. Additionally, this paper introduces an ensemble-based unsupervised fault detection methodology that incorporates homogeneous units, specifically long short-term memory (LSTM) predictors and cumulative sum-based detectors. The proposed predictors utilize a variant of the teacher forcing (TF) algorithm during both the training and inference phases. Additionally, predictors are used to model the normal behavior of the system, whereas detectors are used to identify deviations from normality. The detector decisions are aggregated using a majority voting scheme. The validity of the proposed approach is illustrated on the two representative datasets, where numerous experiments are performed for feature selection and fault detection evaluation. Experimental assessment reveals promising results, even compared to well-established techniques. Nevertheless, the results also demonstrate the need to perform additional experiments with datasets originating from both simulators and real systems. Further possible refinements of the detection ensemble include the addition of heterogeneous units and other decision fusion techniques.

Non-Invasive Techniques for Monitoring and Fault Detection in Internal Combustion Engines: A Systematic Review

This article provides a detailed analysis of non-invasive techniques for the prediction and diagnosis of faults in internal combustion engines, focusing on the application of the Proknow-C and Methodi Ordinatio systematic review methods. Initially, the relevance of these techniques in promoting energy sustainability and mitigating greenhouse gas emissions is discussed, aligning with the Sustainable Development Goals (SDGs) of Agenda 2030 and the Paris Agreement. The systematic review conducted in the subsequent sections offers a comprehensive mapping of the state of the art, highlighting the effectiveness of combining these methods in categorizing and systematizing relevant scientific literature. The results reveal significant advancements in the use of artificial intelligence (AI) and digital signal processors (DSP) to improve fault diagnosis, in addition to highlighting the crucial role of non-invasive techniques such as the digital twin in minimizing interference in monitored systems. Finally, concluding remarks point towards future research directions, emphasizing the need to develop the integration of AI algorithms with digital twins for internal combustion engines and identify gaps for further improvements in fault diagnosis and prediction techniques.

Fault diagnosis of inter‐turn short circuits in PMSM based on deep regulated neural network

AbstractPermanent Magnet Synchronous Machine (PMSM) is widely utilised in numerous industrial applications due to its precise control capabilities. However, these motors frequently encounter operational faults, potentially leading to severe safety and performance issues. Consequently, effective health monitoring techniques for early fault detection are essential to maintain optimal performance and extend the lifespan of these systems. This study presents a qualification‐based methodology for diagnosing faults in three‐phase PMSMs through vibration–current data fusion analysis. The stator faults, specifically inter‐turn short circuits (ITSC) induced via bypassing resistances, were investigated using experimental data from a custom‐built test rig. The collected current and vibration signals were transformed into statistical features. Various operating scenarios were diagnosed utilising a deep regulated neural network (RegNet), an improved convolutional neural network based on an enhanced residual architecture. The proposed approach was assessed through various metrics including training efficiency, precision, recall, f1‐score, and accuracy, and compared against several neural network methods. The findings reveal that the proposed RegNet model achieves perfect accuracy, attaining 100%. This research highlights the efficacy of data fusion analysis and deep learning in fault diagnosis, facilitating proactive maintenance strategies and improving the reliability of PMSMs in diverse industrial applications and renewable energy systems.

STRIVE: Empowering a Low Power Tensor Processing Unit with Fault Detection and Error Resilience

Rapid growth in Deep Neural Network (DNN) workloads has increased the energy footprint of the Artificial Intelligence (AI) computing realm. For optimum energy efficiency, we propose operating a DNN hardware in the Low-Power Computing (LPC) region. However, operating at LPC causes increased delay sensitivity to Process Variation (PV). Delay faults are an intriguing consequence of PV. In this paper, we demonstrate the vulnerability of DNNs to delay variations, substantially lowering the prediction accuracy. To overcome delay faults, we present STRIVE—a post-fabrication fault detection and reactive error reduction technique. We also introduce a time-borrow correction technique to ensure error-free DNN computation.

Comparative Analysis of Machine Learning Techniques for Fault Detection in Solar Panel Systems

Comparative Analysis of Machine Learning Techniques for Fault Detection in Solar Panel Systems

Enhanced Fault Detection in Satellite Attitude Control Systems Using LSTM-Based Deep Learning and Redundant Reaction Wheels

Reliable fault detection in satellite attitude control systems stands as a critical aspect of ensuring the safety and success of space missions. Central to these systems, reaction wheels (RWs), despite being the most frequently used actuators, present a vulnerability given their susceptibility to faults—a factor with the potential to precipitate catastrophic failures such as total satellite loss. In light of this, we introduce a fault detection methodology grounded in deep learning techniques specifically designed for satellite attitude control systems. Our proposed method utilizes a Long Short-Term Memory (LSTM) model adept at learning temporal patterns inherent to both healthy and faulty system behaviors. Incorporated into our model is a torque allocation algorithm designed to circumvent specific velocities known to induce torque disturbances, a factor known to influence LSTM performance adversely. To bolster the robustness of our fault detection technique, we also incorporated denoising autoencoders within the LSTM framework, thereby enabling the model to identify temporal patterns in healthy and faulty system behavior, even amidst the noise. The method was evaluated using cross-validation on simulated satellite data comprising 1000 time series samples and across different fault scenarios, such as stiction and resonance at varying intensities (90%, 50%, and 30%). The results confirm achieving performance metrics such as Mean Squared Error for accurate fault identification. This research underscores a stride in the evolution of fault detection and control strategies for satellite attitude control systems, holding promise to boost the reliability and efficiency of future space missions.

A Composite Pulse Excitation Technique for Air-Coupled Ultrasonic Detection of Defects in Wood.

To overcome the problems of the low signal-to-noise ratio and poor performance of wood ultrasonic images caused by ring-down vibrations during the ultrasonic quality detection of wood, a composite pulse excitation technique using a wood air-coupled ultrasonic detection system is proposed. Through a mathematical analysis of the output of the ultrasonic transducer, the conditions necessary for implementing composite pulse excitation were analyzed and established, and its feasibility was verified through COMSOL simulations. Firstly, wood samples with knot and pit defects were used as experimental samples. We refined the parameters for the composite pulse excitation technique by conducting A-scan measurements on both defective and non-defective areas of the samples. Moreover, two stepper motors were employed to control the path for C-scan imaging to detect wood defects. The experiment results showed that the composite pulse excitation technique significantly enhanced the precision of nondestructive ultrasonic testing for wood defects compared to the traditional single-pulse excitation method. This technique successfully achieved precise detection and location of pit defects, with a detection accuracy rate of 90% for knot defects.

Graph-based feature engineering for enhanced machine learning in rolling element bearing fault diagnosis

Abstract Fault diagnosis in rolling element bearings is critical for ensuring machinery reliability. This study improves machine learning techniques for predictive fault detection using the benchmark CWRU bearing dataset. Vibration signal data is preprocessed via balancing and graph-based feature engineering is performed to enable effective model training. Diverse classifiers including Random Forests, Support Vector Machines and Neural Networks are systematically evaluated through 10-fold cross-validation. Most of the models demonstrate exceptional performance, with top accuracies and AUC scores of 1.00. The research highlights the potential of hidden features that consider the implicit relations between the entities to improve predictive maintenance through data-driven bearing fault diagnosis.

Going deeper on magneto-optical Faraday effect analysis to detect fatigue crack with high-spatial resolution for non-destructive inspection

In this study, we introduce a high-resolution technique for defect detection that uses the magneto–optical (MO) Faraday effect. Our system combines a portable polarized microscope and an MO sensor for the high spatial observation of a magnetic domain structure. We accurately localized and characterized the fatigue defects through integrated image pre-processing and cross-power spectral density analysis. This approach enhances our analysis of the magnetic domain structure, increasing the spatial capabilities of the microscope to detect fatigue defects. We conducted experiments on fatigue cracks with defect depth angles of 0°, 30°, and 60°, analyzing their relationship through signal amplitude and the tendency of the signal shift with increasing defect angle. Our findings were validated using a tunnel magnetoresistance sensor. Future research will focus on the optimization of feature extraction complexity, considering the limited computational power available for portable non-destructive testing devices.

An innovative MRAS-based technique for online detection of short-circuit faults in three-phase induction motor windings

An innovative MRAS-based technique for online detection of short-circuit faults in three-phase induction motor windings

Unsupervised Fault Detection in a Controlled Conical Tank

Current trends in the Industrial Internet of Things (IIoT) have increased the sensorization of systems, thus increasing data availability to apply data-driven fault detection and diagnosis techniques to monitor these systems. In this work, we show the capabilities of an information-driven method for detecting and quantifying faults in a subsystem common among a broad range of industries: the conical tank. Our main experiment consists of using a simple black-box model (multi-layer perceptron -- MLP) to capture the dynamics of a PID-controlled conical tank built in Simulink and then induce pump failures of different severities; the derived data-driven indicators that we developed increase with the severity of the fault validating its usefulness in this controlled setting. A complementary experiment is carried out to enrich our analysis; this consists of simulating an open-loop discrete-time version of the conical tank to explore a range of fault severity and analyze the distribution of the indicators across this range. All our results show the applicability of the data-driven fault monitoring method in conical tanks subjected to either open- or closed-loop operation.