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Fault-attri-attention: a method for fault identification based on seismic attributes attention
The imaging principle of seismic images is different from natural images, which results in very limited resolution, complex reflection features and...
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Multi-fault diagnosis and fault degree identification in hydraulic systems based on fully convolutional networks and deep feature fusion
Normal and stable operations of hydraulic systems are of great importance to the safety and efficiency of industrial production processes. Accurate...
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Fault classification and identification through machine learning approaches for a solar PV – battery based water pum** system
The world progresses towards enabling renewable sources into the mainstream supply of energy and it is imperative to develop systems that can handle...
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Fault Classification and Its Identification in Overhead Transmission Lines Using Artificial Neural Networks
In modern power systems, fault classification and placement are critical for improving protection schemes, service reliability, and reducing line...
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Towards Geometry-Preserving Domain Adaptation for Fault Identification
In most industries, the working conditions of equipment vary significantly from one site to another, from one time of a year to another, and so on....
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Discover unknown fault categories through active query evidence model
Intelligent fault diagnosis plays an important role in machine health management. Fault data in real applications are usually imbalanced, which makes...
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Resnet-based deep learning multilayer fault detection model-based fault diagnosis
Fault detection has taken on critical relevance in today’s automated manufacturing processes. Defect tolerance, dependability, and safety are some of...
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Cognitive agent based fault tolerance in ubiquitous networks: a machine learning approach
Ubiquitous Networks play an essential role in accessing ubiquitous computing services at anytime, anywhere, and anyplace through computing nodes of...
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Multiscale dilated convolution and swin-transformer for small sample gearbox fault diagnosis
Mechanical equipment usually operates in noisy and variable load environments, which presents serious challenges for existing intelligent diagnostic...
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MTG_CD: Multi-scale learnable transformation graph for fault classification and diagnosis in microservices
The rapid advancement of microservice architecture in the cloud has led to the necessity of effectively detecting, classifying, and diagnosing run...
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Few-shot intelligent fault diagnosis based on an improved meta-relation network
AbstractIn recent decades, fault diagnosis methods based on machine learning and deep learning have achieved excellent results in fault diagnosis and...
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An empirical wavelet transform based fault detection and hybrid convolutional recurrent neural network for fault classification in distribution network integrated power system
The penetration of distributed renewable energy sources degrades the protection of microgrids, which leads to incorrect data flow in the energy...
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A deep learning-based protection scheme for fault detection and classification in wind integrated HVDC transmission system under dissimilar fault scenarios and uncertain conditions
The need of HVDC transmission is continuously escalating due to the far distances from power generation sites and load centers. HVDC transmission is...
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An empirical analysis of software fault proneness using factor analysis with regression
The fault prediction process becomes essential in the early stages of Software Development Life Cycle, so as to be able to generate various modules...
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Deep learning based insulator fault detection algorithm for power transmission lines
Aiming at the complex background of transmission lines at the present stage, which leads to the problem of low accuracy of insulator fault detection...
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A novel deep learning approach for intelligent bearing fault diagnosis under extremely small samples
Rotor bearing health is crucial for ensuring the operational stability of rotating equipment. Deep learning-based fault diagnosis methods have...
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A multi convolution pooling group fault diagnosis model with high generalization across data sets and large receptive field characteristics considering industrial environmental noise
Considering the noise impact in the bearing operating environment and the time-consuming and non-universal design of traditional diagnostic...
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A logistic software reliability model with Loglog fault detection rate
Research on software reliability modeling is essential for improving software quality, reducing costs, and ensuring customer satisfaction in the...
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The double-feature extraction method based on slope entropy and symbolic dynamic entropy for the fault diagnosis of rolling bearing
This paper explores the application of slope entropy in fault diagnosis. In order to improve the recognition rate of faults, double-feature...
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Photovoltaic system fault detection techniques: a review
Solar energy has received great interest in recent years, for electric power generation. Furthermore, photovoltaic (PV) systems have been widely...
