Search
Search Results
-
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...
-
Fault diagnosis of HVAC system with imbalanced data using multi-scale convolution composite neural network
Accurate fault diagnosis of heating, ventilation, and air conditioning (HVAC) systems is of significant importance for maintaining normal operation,...
-
A novel HB-SC-MCCNN model for intelligent fault diagnosis of rolling bearing
The incompleteness and lack of bearing fault data have become important problems in bearing fault diagnosis. This paper presents an intelligent fault...
-
Fault Identification in Distributed Generation System Using Shallow ANN Model
A distributed power system holds distinct advantages over its traditional centralized counterpart. However, when considering protection aspects, the...
-
Machinery fault diagnostic method based on numerical simulation driving partial transfer learning
Artificial intelligence (AI), which has recently gained popularity, is being extensively employed in modern fault diagnostic research to preserve the...
-
Multi-fault Diagnosis of Rotating Machine Under Uncertain Speed Conditions
BackgroundMulti-faults in rotating machines are critical and create an unfavourable working environment. Research on multi-faults is still in the...
-
Domain Knowledge Regularised Fault Detection
Unsupervised data-driven methods are attractive options for fault detection in rotating machinery since they do not require any failure data during...
-
Soft Fault Diagnosis of Analog Circuits Based on Classification of GAF_RP Images With ResNet
Analog circuit fault diagnosis is widely used to ensure normal operation and fault location electronic equipment. In this study, a new method for...
-
Research on deep learning rolling bearing fault diagnosis driven by high-fidelity digital twins
Effective fault data is the basis for intelligent fault diagnosis. In actual engineering applications, it is often impossible to obtain sufficiently...
-
Proportional periodic sampling for cross-load bearing fault diagnosis
Bearing vibration data under various loads shows different distributions, which leads to the poor performance of existing deep learning methods in...
-
Deep optimal feature extraction and selection-based motor fault diagnosis using vibration
The rolling elements of the induction motor are highly susceptible to faults. The detection and diagnosis of rolling element faults are accurate and...
-
Systematic Review on Fault Diagnosis on Rolling-Element Bearing
PurposeTo maintain machinery operations smoothly, Rolling-Element Bearings (REBs) are utilized so that the entire equipment’s safety is ensured....
-
Series arc fault detection based on multi-domain depth feature association
In low voltage distribution systems, series arc fault current is small and hidden, and traditional circuit protection devices cannot effectively...
-
Multi-tasking atrous convolutional neural network for machinery fault identification
As fault identification algorithms for rotating machinery based on deep learning are develo** rapidly, convolutional neural networks (CNNs) have...
-
Concurrent fault localization using ANN
The software is becoming more capable of providing better solutions to our day-to-day activities. In order to increase performance, concurrent...
-
Distributed Fault Estimation and Fixed-Time Fault-Tolerant Formation Control for Multi-UAVs subject to Sensor Faults
This paper develops a fault estimation-based fixed-time fault-tolerant formation control strategy for multiple unmanned aerial vehicles under the...
-
Fault diagnosis of rolling bearing under limited samples using joint learning network based on local-global feature perception
Deep learning is widely used in the field of rolling bearing fault diagnosis because of its excellent advantages in data analysis. However, in...
-
Fault diagnosis based on feature enhancement multiscale network under nonstationary conditions
Convolution neural network (CNN) is widely used in rotating machinery fault diagnosis. However, in real industries, the rotating machinery often...
-
Fault diagnosis based on feature enhancement and spatial adjacent region dropout strategy
Ensuring safe machine operation in industrial environments requires accurate bearing fault diagnosis. However, maintaining consistent data...
-
Fault estimation for nonlinear uncertain systems utilizing neural network-based robust iterative learning scheme
In this paper, a novel neural network-based robust iterative learning fault estimation scheme is proposed to address the problem of fault modeling...
