Fault Detection Method Research Articles

MSIDetector: Detecting Multi-Scenario industrial defects using an adapted visual foundation model and dual thresholding discriminator

Industrial defect detection is crucial for enhancing product quality. The diverse nature of industrial scenarios has posed challenges for developing a unified defect detection model that can address multiple industrial scenarios. To meet this challenge, we started from a data perspective, developing three self-designed data acquisition apparatuses to capture defect samples from multiple industrial scenarios. Leveraging grounding DINO (GDINO), an open-set detection approach, we propose a unified defect detection method, MSIDetector. By integrating an industrial feature adapter and a context-aware dual-thresholding defect discriminator, we successfully incorporate prior industrial knowledge into the model. The performance of the MSIDetector was evaluated across four industrial scenarios, including a public defect dataset, achieving a total state-of-the-art mAP@50 of 76.2, surpassing the performance of FasterRCNN, YOLO-7X, and the visual foundation model GDINO, with quantitative increases of 13.93, 6.55, and 2.1, respectively. This report represents the first successful attempt to apply foundation models to defect detection.

Detection Method for Inter-Turn Short Circuit Faults in Dry-Type Transformers Based on an Improved YOLOv8 Infrared Image Slicing-Aided Hyper-Inference Algorithm

Detection Method for Inter-Turn Short Circuit Faults in Dry-Type Transformers Based on an Improved YOLOv8 Infrared Image Slicing-Aided Hyper-Inference Algorithm

Improved event-based fault detection filter for networked fuzzy systems under DoS attacks

This paper investigates an improved event-based fault detection method for networked fuzzy systems under denial-of-service (DoS) attacks. In order to solve the bandwidth occupation problem of communication network, a resilient event-triggered transmission strategy is developed. Additionally, a fault detection filter is designed to estimate the time of fault occurrence by using the residual signal. Under this framework, a novel Lyapunov functional related to attack parameters is established to analyze the exponential convergence of the error signals, and the filter gains and event-triggered parameters are obtained by solving linear matrix inequalities. The designed functional reduces the conservatism of the stability criteria significantly in contrast with the previous discontinuous Lyapunov functionals. Finally, a simulation example is provided to verify the effectiveness of the proposed method.

A fault detection and isolation method for independent metering control system based on adaptive robust observer

The independent metering control system (IMCS) demonstrates high control precision and energy efficiency for advanced electrohydraulic applications but tends to be faulty. Current model-based fault diagnosis methods face challenges in robustness and adaptability to high uncertainties and high nonlinearities of the IMCS. To enhance the safety and reliability of the IMCS, a novel fault detection and isolation method based on adaptive robust observer (ARO) is proposed. To reduce estimation errors, the designed ARO combines the advantages of the robust filter and parameter adaption, which can overcome the impact of uncertainties and nonlinearities. To detect the fault robustly and sensitively, three types of adaptive thresholds are designed according to the spool displacement residuals. To isolate the fault, a fault space is established, and spatial fault points are calculated. The verification experiments show that the proposed method can well estimate the spool displacement while accurately detecting and isolating faults of the IMCS.

Research on Rail Surface Defect Detection Based on Improved CenterNet

Rail surface defect detection is vital for railway safety. Traditional methods falter with varying defect sizes and complex backgrounds, while two-stage deep learning models, though accurate, lack real-time capabilities. To overcome these challenges, we propose an enhanced one-stage detection model based on CenterNet. We replace ResNet with ResNeXt and implement a multi-branch structure for better low-level feature extraction. Additionally, we integrate SKNet attention mechanism with the C2f structure from YOLOv8, improving the model’s focus on critical image regions and enhancing the detection of minor defects. We also introduce an elliptical Gaussian kernel for size regression loss, better representing the aspect ratio of rail defects. This approach enhances detection accuracy and speeds up training. Our model achieves a mean accuracy (mAP) of 0.952 on the rail defects dataset, outperforming other models with a 6.6% improvement over the original and a 35.5% increase in training speed. These results demonstrate the efficiency and reliability of our method for rail defect detection.

A Literature Review of Fault Detection and Diagnostic Methods in Three-Phase Voltage-Source Inverters

This review paper offers a comprehensive examination of the various types of faults that occur in inverters and the methods used for their identification. The introductory segment investigates the internal component failures of voltage-source inverters (VSIs), examining their failure rates and the consequent effects on the overall system performance. Subsequently, this paper classifies and clarifies the potential malfunctions in components and sensors, placing particular emphasis on their frequency of occurrence and the severity of their impact. The examination encompasses issues associated with transistors, including open circuits, short circuits, gate firing anomalies, as well as failures in capacitors, diodes, and sensors. Following this, the paper delivers a comparative assessment of fault diagnosis techniques pertinent to each type of component, appraised against specific criteria. The concluding section encapsulates the findings for each fault category, delineates the fault detection and diagnosis (FDD) methodologies, analyzes the outcomes, and provides recommendations for future scholarly investigation.

Improved YOLOv8 garment sewing defect detection method based on attention mechanism

For the garment sewing defect detection method, this paper proposes an improved YOLOv8-FPCA scheme based on the YOLOv8 algorithm, which improves the YOLOv8 target detection head to enhance the information extraction of small target defects, then introduces Focal Loss to optimize the loss function to guide the network to better handle target data sets with different difficulties and imbalances. Finally, the attention mechanism CA is added to the YOLOv8 network structure to achieve multi-scale feature fusion extraction, and the attention mechanisms CABM and SENet are added at the same locations for experimental comparison. The results show that after increasing the attention mechanisms of CBAM and SENet, the mAP@0.5 model increased by 1.7 % and 1.9 % respectively. The CA attention mechanism emphasizes the importance of location information, and the model has better accuracy and recall after adding the CA attention mechanism, with a 3.7 % increase in mAP@0.5, indicating that YOLOv8-CA has better performance in sewing defect detection.

A new structural reconfiguration for multilevel inverters with fault tolerance capability

Keeping electrical systems running is critical in industries especially in those that rely on static converters, for example in the case of inverters, continuous and safe of operation must be ensured even if one of the inverter’s static switch modules fails. This paper presents a method for fault detection in static converters. The study presents a 3-phase 3-level NPC (Neutral Point Clamped) inverter with IGBTs as static switches where open-circuit or closed-circuit faults are considered. In addition, leg failure and structural reconfiguration are also considered in this fault-tolerant inverter by adding a fourth 3-level leg.

Target localization and defect detection of distribution insulators based on ECA‐SqueezeNet and CVAE‐GAN

AbstractInsulators, as typical equipment for distribution networks, provide good electrical insulation between live conductors and earth. Timely and accurate detection is essential for insulator detection issues. However, as the complexity of neural networks increases, the detection efficiency is often lower. Therefore, this paper proposes a fast insulator positioning and defect detection method. Firstly, for insulator target localization, the SqueezeNet network is improved using ECA attention mechanism. In addition, to address the issue of low defect detection accuracy, a joint algorithm has been proposed. The integration of convolutional variational autoencoder (CVAE) and generative adversarial network (GAN) solve their own shortcomings due to different image focus angles. The target localization accuracy reaches 94.30%, and the defect detection accuracy reaches 89.60%. It solves the problems of difficulty in locating small targets in a large field of view and inaccurate detection due to a small number of abnormal samples. This method has been tried and tested in practical distribution network systems.

A two-stage defect detection method for unevenly illuminated self-adhesive printed materials

The process of printing defect detection usually suffers from challenges such as inaccurate defect extraction and localization, caused by uneven illumination and complex textures. Moreover, image difference-based defect detection methods often result in numerous small-scale pseudo defects. To address these challenges, this paper proposes a comprehensive defect detection approach that integrates brightness correction and a two-stage defect detection strategy for self-adhesive printed materials. Concretely, a joint bilateral filter coupled with brightness correction corrects uneven brightness properly, meanwhile smoothing the grid-like texture in complex printed material images. Then, in the first detection stage, an image difference method based on a bright–dark difference template group is designed to effectively locate printing defects despite slight brightness fluctuations. Afterward, a discriminative method based on feature similarity is employed to filter out small-scale pseudo-defects in the second detection stage. The experimental results show that the improved difference method achieves an average precision of 99.1% in defect localization on five different printing pattern samples. Furthermore, the second stage reduces the false detection rate to under 0.5% while maintaining the low missed rate.

A fault diagnosis method based on an improved diffusion model under limited sample conditions.

As a critical component in mechanical systems, the operational status of rolling bearings plays a pivotal role in ensuring the stability and safety of the entire system. However, in practical applications, the fault diagnosis of rolling bearings often encounters limitations due to the constraint of sample size, leading to suboptimal diagnostic accuracy. This article proposes a rolling bearing fault diagnosis method based on an improved denoising diffusion probability model (DDPM) to address this issue. The practical value of this research lies in its ability to address the limitation of small sample sizes in rolling bearing fault diagnosis. By leveraging DDPM to generate one-dimensional vibration data, the proposed method significantly enriches the datasets and consequently enhances the generalization capability of the diagnostic model. During the model training process, we innovatively introduce the feature differences between the original vibration data and the predicted vibration data generated based on prediction noise into the loss function, making the generated data more directional and targeted. In addition, this article adopts a one-dimensional convolutional neural network (1D-CNN) to construct a fault diagnosis model to more accurately extract and focus on key feature information related to faults. The experimental results show that this method can effectively improve the accuracy and reliability of rolling bearing fault diagnosis, providing new ideas and methods for fault detection and prevention in industrial applications. This advancement in diagnostic technology has the potential to significantly reduce the risk of system failures, enhance operational efficiency, and lower maintenance costs, thus contributing significantly to the safety and efficiency of mechanical systems.

Bearing fault detection using second-order moment spectrum and adaptive time-varying morphological filtering

The second-order moment spectrum is a method designed to simplify the complex shape of the spectrum, thus facilitating its interpretation for the identification and localization of defects based on peak frequency. Generally used as a final step in defect detection methods, this method offers the advantage of a more easily interpretable spectral shape. Compared to the shape of the spectrum of the vibration signal defined by the Fourier transform, which includes sidebands composed of peaks of large amplitude at different frequencies, the spectrum generated by the second-order moment spectrum method stands out for its simplicity. Starting from the mean and standard deviation of the vibration signal, the second-order moment can be defined as the power of the ratio between the standard deviation and the difference between the signal and the mean. Next, the Fourier transform is applied to express the second-order moment spectrum. The performance of the second-order moment spectrum is evaluated using the principle of comparison with the envelope spectrum obtained by the Hilbert transform. Vibration signals are analyzed using two methods: adaptive time-varying morphological filtering and second-order moment spectrum. After applying these methods to the signals from the database, we observe high-amplitude peaks at the frequencies corresponding to inner ring and ball defects. The second-order moment spectrum gives similar results to those obtained with the Hilbert transform envelope.

Data-Driven Fault Detection and Isolation for Multirotor System Using Koopman Operator

This paper presents a data-driven fault detection and isolation (FDI) for a multirotor system using Koopman operator and Luenberger observer. Koopman operator is an infinite-dimensional linear operator that can transform nonlinear dynamical systems into linear ones. Using this transformation, our aim is to apply the linear fault detection method to the nonlinear system. Initially, a Koopman operator-based linear model is derived to represent the multirotor system, considering factors like non-diagonal inertial tensor, center of gravity variations, aerodynamic effects, and actuator dynamics. Various candidate lifting functions are evaluated for prediction performance and compared using the root mean square error to identify the most suitable one. Subsequently, a Koopman operator-based Luenberger observer is proposed using the lifted linear model to generate residuals for identifying faulty actuators. Simulation and experimental results demonstrate the effectiveness of the proposed observer in detecting actuator faults such as bias and loss of effectiveness, without the need for an explicitly defined fault dataset.

High-Level Feature Fusion Deep Learning Model for Fault Detection in Handling Equipment in Dry Bulk Ports

The flexibility of handling equipment in dry bulk ports is poor, and frequent equipment fault induced by the high-load and high-power working conditions greatly impacts the overall port handling operations, making accurate fault detection play an important role in improving the efficiency and stability of dry bulk port operations. However, as we know, most fault detection methods for port handling equipment depend heavily on monitoring sensor data, which is not applicable in the dry bulk port due to high configuration and maintenance cost, as well as the high false alarm rate of monitoring sensors caused by strong background noise. To solve the problem, this study proposes a High-Level Feature Fusion Deep Learning Model, which uses different deep learning sub-models to extract features of structured and unstructured data. It fuses the extracted feature vectors to achieve fault detection in the handling equipment, establishing the mapping relationship between the fault (e.g., waiting for the pre-loading process, equipment fault, and others) and multi-source heterogeneous operation and maintenance data for the handling equipment, including reclaimers, belt conveyors, dumpers, and ship loaders. To verify the effectiveness of the proposed method, the actual data of a coal port in Northern China is employed as an example. The results show the deep learning model can achieve high prediction accuracy (over 86%) with high efficiency (0.5 s for each sample), which provides decision support for the fault detection in dry bulk port handling equipment.

An enhanced digital twin-driven fault detection and isolation method based on sensor series imaging mechanism for gas turbine engine

Condition based maintenance plays a crucial role in ensuring the safety and reliability of gas turbine engine. Specifically, fault detection and isolation (FDI) can improve efficiency and reduce costs during maintenance. The traditional FDI methods, which developed only using physical engine data, lack a comprehensive consideration of high accuracy and strong applicability across different engine individuals. In this paper, an enhanced digital twin-driven FDI method is proposed, which combines a digital engine, sensor series imaging mechanism and a convolutional neural network. The digital engine is constructed to mirror the operational status of the physical engine. The output residuals between the physical and digital engines are transformed into multi-channel images to train a convolutional neural network for FDI of the physical engine. The network is then fine-tuned using fault-free data from the target engine to further strengthen diagnostic performance across different engine individuals. The effectiveness of the digital engine is verified using actual ground test data, and the performance of the proposed method is demonstrated through comparative simulations. The network, trained on operational data from Engine A and fine-tuned with fault-free data from other engines, exhibits the highest diagnostic accuracy and fault isolation rate. Compared with the physical engine data-based method, the accuracy is increased from 68.25% to 97.95%, the fault isolation rate is improved from 85.91% to 99.26%, and the false alarm rate is decreased from 4.41% to 0.34%. The results show that the proposed method has better FDI capabilities across different engine individuals.

Fault detection method for transmission line components based on lightweight GMPPD-YOLO

Abstract This paper designs a lightweight high-precision transmission line component detection model, named grouped dense, monotonic self-regularized, and partial faster convolution, pruning, and distillation optimized—you only look once (GMPPD-YOLO), in transmission line inspection. It addresses the issue of low detection accuracy of target detection algorithms due to the complex background, large differences in target shape, location, texture, etc, as well as diversified and smaller defects in insulator and vibration hammer images taken by unmanned aerial vehicles from multiple angles. To enhance the model’s feature extraction capabilities in complex backgrounds and across different scales, the grouped dense C3 dense feature extraction module was designed, enabling the model to more effectively handle diverse defect forms. Simultaneously, the monotonic self-regularized pyramid pooling–fast (MSPPF) module is proposed to enhance the model’s capability to process multi-scale information. Additionally, the partial-faster C3 feature awareness module is designed to improve feature fusion performance, enhancing the model’s ability to perceive features at different scales. Finally, channel pruning was used to reduce redundant parameters, and knowledge distillation was employed to compensate for the accuracy loss caused by pruning. This approach further compressed the model size while ensuring its detection performance. The experimental results demonstrate that compared to the original YOLOv5s algorithm, the proposed GMPPD-YOLO algorithm achieves a reduction in parameters by 68.4%, a decrease in Giga floating-point operations per second by 58.2%, and a reduction in the model size by 66.4%, while achieving an increase in precision by 1%, mAP50 by 1.1%, and mAP95 by 0.4%. This confirms the significant potential of the GMPPD-YOLO algorithm for deployment in real-time drone-based power transmission line inspections.

Asymmetric Dual Redundant Inertial Sensors: A Method for Fault Detection and Diagnosis

Asymmetric Dual Redundant Inertial Sensors: A Method for Fault Detection and Diagnosis

Methods for Control and Detection of Fire-Hazardous Defects of Contacts and Contact Connections During Operation of Electrical Installations

Methods for Control and Detection of Fire-Hazardous Defects of Contacts and Contact Connections During Operation of Electrical Installations

Defect Recognition and Classification Using Ultrasonic Scattered Signals Based on Convolutional Neural Network

Abstract Carbon Fiber Reinforced Polymer (CFRP) is widely used in the aerospace, aircraft, and railway industries because of its remarkable advantages, such as its lightweight and high strength. Detecting and identifying defects in CFRP is crucial for ensuring its secure and efficient use. Ultrasonic non-destructive testing is one of the most practical and effective methods for defect detection and identification. The ultrasonic scattered signals of defects, which contain abundant defect feature information, are advantageous for defect recognition and classification. However, the anisotropy and inhomogeneity of CFRP, which lead to a low signal-to-noise ratio and poor image quality, pose challenges in flaw recognition. To address the issue, this paper proposes using Convolutional Neural Networks (CNN) methods for defect classification, which can adequately extract information from ultrasonic scattered signals directly. The scattering signals from various types of defects in CFRP, including the different sizes of holes and folds, were selected as inputs for CNN models. Different network models combining Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) with 1D-CNN were employed to classify and identify defect signals. To generate sufficient input sample signals, the data sets were further enlarged by incorporating noise into the original signals. The experimental results showed that using the 1D-LSTM-CNN and 1D-GRU-CNN network models can reduce computational resources while ensuring the accuracy of defect recognition.

Precision-Concentrated Battery Defect Detection Method in Real-World Electric Vehicles Crossing Different Temperatures and Vehicle States

Precision-Concentrated Battery Defect Detection Method in Real-World Electric Vehicles Crossing Different Temperatures and Vehicle States