Search
Search Results
-
An Integrated EMVO and ARBFN Algorithms for Output Power Forecasting and Fault Prediction in Solar PV Systems
Predicting the output power and detecting the faults on the solar photovoltaic (PV) systems are the challenging tasks in the modern decades. The...
-
Enhancing Software Reliability Forecasting Through a Hybrid ARIMA-ANN Model
This paper proposes a hybrid forecasting model combining auto-regressive integrated moving average (ARIMA) and artificial neural network (ANN)...
-
RFID library management software dependability through reliable fault-detection and fault correction procedures
Apart from the widespread acceptance of the digital library as a major research response among university lecturers and students, little is known...
-
Bootstrap aggregation with Christiano–Fitzgerald random walk filter for fault prediction in power systems
The ability to predict and preempt insulator failures holds the potential to enhance the reliability of electrical power grids. The increase in...
-
A novel optimized fault prediction in magnetic bearing using shaft vibration image database
The magnetic bearing is effectively employed in mechanical applications to run the device or particular applications. However, some faults have to be...
-
An unsupervised mechanical fault classification method under the condition of unknown number of fault types
This paper proposes a novel unsupervised classification method to solve the problem of mechanical fault diagnosis under the condition of unknown...
-
Earthquake forecasting in the Himalayan region using neural networks models
Earthquake forecasting using neural networks models is presented in the study. The problem of earthquake forecasting was modelled as a pattern...
-
Automatic Fault Detection of Photovoltaic Modules Using Recurrent Neural Network
AbstractEverywhere in the globe, the total capacity of photovoltaic (PV) panels is expanding at an exponential rate. Arc faults, open-circuit (OC)...
-
Sahand: A Software Fault-Prediction Method Using Autoencoder Neural Network and K-Means Algorithm
Software is playing a growing role in many safety-critical applications, and software systems dependability is a major concern. Predicting faulty...
-
Improved CEEMDAN-based aero-engine gas-path parameter forecasting using SCINet
Accurate gas-path parameter forecasting is very important for normal operations of aero-engines. In this study, the sample convolution and...
-
A new hybrid method for bearing fault diagnosis based on CEEMDAN and ACPSO-BP neural network
As an important part of rotating machinery, the failure of bearings will cause serious vibration and noise of mechanical equipment, which will affect...
-
Enhancing source domain availability through data and feature transfer learning for building power load forecasting
During the initial phases of operation following the construction or renovation of existing buildings, the availability of historical power usage...
-
Fault Identification Method for In-Core Self-Powered Neutron Detectors Combing Graph Convolutional Network and Stacking Ensemble Learning
Self-powered neutron detectors (SPNDs) play a critical role in monitoring the safety margins and overall health of reactors, directly affecting safe...
-
Big Data—Supply Chain Management Framework for Forecasting: Data Preprocessing and Machine Learning Techniques
This article systematically identifies and comparatively analyzes state-of-the-art supply chain (SC) forecasting strategies and technologies within a...
-
Fault detection method for flexible DC grid based on CEEMDAN multiscale entropy and GA-SVM
Compared with the traditional AC grid, the flexible DC grid has the advantages of low wire loss and large transmission capacity, but it is difficult...
-
An interpretable graph convolutional neural network based fault diagnosis method for building energy systems
Due to the fast-modeling speed and high accuracy, deep learning has attracted great interest in the field of fault diagnosis in building energy...
-
An improved empirical Fourier decomposition method and its application in fault diagnosis of rolling bearing
The vibration signals of faulty rolling bearings usually contain different components. The separation of the fault feature component from the bearing...
-
Investigation of Meta-heuristics Algorithms in ANN Streamflow Forecasting
The deterministic approach, which utilizes the gradient information in the search process, is prone to trap** at local minima, primarily due to the...
-
Stochastic software reliability growth modelling with fault introduction and change point
The rapid utilization of computer-based automated systems for human tasks has caused a significant shift in society. Today's society places a high...
-
A framework for now-casting and forecasting in augmented asset management
Asset Management of a complex technical system-of-systems needs cross-organizational operation and maintenance, asset data management and...
