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Artificial Intelligence for Fault Detection and Diagnosis

Fault detection and diagnosis (FDD) is an important task in manufacturing and mechatronic systems for reducing costs and improving productivity. Traditionally, the states of machines and their faults are manually checked, a process which is time-consuming and expensive. Therefore, it is desirable to...

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Format: Online
Sprog:engelsk
Udgivet: MDPI - Multidisciplinary Digital Publishing Institute 2026
Fag:
DC-DC converters
prognostic analysis
multi-valued neuron neural network
support vector machine
Zeta converter
knowledge discovery in dataset
fault tree
causality analysis
aging system
machine learning
power systems
harmonic distortion
power quality
peak detection
fault diagnosis
frequency domain
low-load condition
rolling bearing
diagnosis
fault detection
fault type recognition
signal processing
quantum algorithms
quantum computation
combinatorial optimization
circuit fault diagnostics
steam turbine
t-distribution stochastic neighborhood embedding (t-SNE)
extreme gradient boosting (XGBoost)
clustering
operation and maintenance (O&M)
wind turbines (WTs)
predictive fault diagnostic
supervisory control and data acquisition (SCADA)
Time2Vec (T2V)
Long Short-Term Memory (LSTM)
T2V-LSTM
treatment effect
survival analysis
Nadaraya–Watson regression
Beran estimator
neural network
meta-learner
Online adgang:https://directory.doabooks.org/handle/20.500.12854/170623
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https://directory.doabooks.org/handle/20.500.12854/170623

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