Combustion machine learning: Principles, progress and prospects
Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …
Challenges and opportunities of deep learning models for machinery fault detection and diagnosis: A review
In the age of industry 4.0, deep learning has attracted increasing interest for various
research applications. In recent years, deep learning models have been extensively …
research applications. In recent years, deep learning models have been extensively …
[HTML][HTML] Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry
A Theissler, J Pérez-Velázquez, M Kettelgerdes… - Reliability engineering & …, 2021 - Elsevier
Recent developments in maintenance modelling fueled by data-based approaches such as
machine learning (ML), have enabled a broad range of applications. In the automotive …
machine learning (ML), have enabled a broad range of applications. In the automotive …
A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines
In traditional intelligent fault diagnosis methods of machines, plenty of actual effort is taken
for the manual design of fault features, which makes these methods less automatic. Among …
for the manual design of fault features, which makes these methods less automatic. Among …
Deep learning enabled fault diagnosis using time‐frequency image analysis of rolling element bearings
D Verstraete, A Ferrada, EL Droguett… - Shock and …, 2017 - Wiley Online Library
Traditional feature extraction and selection is a labor‐intensive process requiring expert
knowledge of the relevant features pertinent to the system. This knowledge is sometimes a …
knowledge of the relevant features pertinent to the system. This knowledge is sometimes a …
Gearbox fault diagnosis using a deep learning model with limited data sample
Massive volumes of data are needed for deep learning (DL) models to provide accurate
diagnosis results. Numerous studies of fault diagnosis systems have demonstrated the …
diagnosis results. Numerous studies of fault diagnosis systems have demonstrated the …
A comprehensive literature review of the applications of AI techniques through the lifecycle of industrial equipment
M Elahi, SO Afolaranmi, JL Martinez Lastra… - Discover Artificial …, 2023 - Springer
Driven by the ongoing migration towards Industry 4.0, the increasing adoption of artificial
intelligence (AI) has empowered smart manufacturing and digital transformation. AI …
intelligence (AI) has empowered smart manufacturing and digital transformation. AI …
Convolutional neural networks for automated damage recognition and damage type identification
C Modarres, N Astorga, EL Droguett… - Structural Control and …, 2018 - Wiley Online Library
Recurring expenses associated with preventative maintenance and inspection produce
operational inefficiencies and unnecessary spending. Human inspectors may submit …
operational inefficiencies and unnecessary spending. Human inspectors may submit …
Intelligent fault diagnosis for large-scale rotating machines using binarized deep neural networks and random forests
Recently, deep neural network (DNN) models work incredibly well, and edge computing has
achieved great success in real-world scenarios, such as fault diagnosis for large-scale …
achieved great success in real-world scenarios, such as fault diagnosis for large-scale …
DTCNNMI: A deep twin convolutional neural networks with multi-domain inputs for strongly noisy diesel engine misfire detection
Although machine learning-based intelligent detection methods have made many
achievements for diesel engine misfire diagnosis, they suffer from a certain degree of …
achievements for diesel engine misfire diagnosis, they suffer from a certain degree of …