[HTML][HTML] Physics-informed machine learning: a comprehensive review on applications in anomaly detection and condition monitoring
Condition monitoring plays a vital role in ensuring the reliability and optimal performance of
various engineering systems. Traditional methods for condition monitoring rely on physics …
various engineering systems. Traditional methods for condition monitoring rely on physics …
Advancements in condition monitoring and fault diagnosis of rotating machinery: A comprehensive review of image-based intelligent techniques for induction motors
Recently, condition monitoring (CM) and fault detection and diagnosis (FDD) techniques for
rotating machinery (RM) have witnessed substantial advancements in recent decades …
rotating machinery (RM) have witnessed substantial advancements in recent decades …
Intelligent fault diagnosis of worm gearbox based on adaptive CNN using amended gorilla troop optimization with quantum gate mutation strategy
The worm gearbox is a power transmission system that has various applications in
industries. Being vital element of machinery, it becomes necessary to develop a robust fault …
industries. Being vital element of machinery, it becomes necessary to develop a robust fault …
Advancing multimodal diagnostics: Integrating industrial textual data and domain knowledge with large language models
The rapid advancement and application of large language models (LLMs) in various
domains prompt an investigation into their potential in the field of prognostics and health …
domains prompt an investigation into their potential in the field of prognostics and health …
How do semiconductors, artificial intelligence, geopolitical risk, and their moderating effects shape renewable energy production in leading semiconductor …
Abstract Semiconductors, artificial intelligence (AI), and geopolitics may influence the future
of environmentally friendly energy. This research aims to offer a novel perspective within this …
of environmentally friendly energy. This research aims to offer a novel perspective within this …
[HTML][HTML] A Review of Digital Twinning for Rotating Machinery
This review focuses on the definitions, modalities, applications, and performance of various
aspects of digital twins (DTs) in the context of transmission and industrial machinery. In this …
aspects of digital twins (DTs) in the context of transmission and industrial machinery. In this …
FASER: Fault-affected signal energy ratio for fault diagnosis of gearboxes under repetitive operating conditions
Vibration signal analysis is crucial for gearbox fault diagnosis, yet its inherent stochastic
nature can challenge the identification of fault-induced changes amidst signal variability …
nature can challenge the identification of fault-induced changes amidst signal variability …
A new intelligent approach of surface roughness measurement in sustainable machining of AM-316L stainless steel with deep learning models
NS Ross, PM Mashinini, CS Shibi, MK Gupta… - Measurement, 2024 - Elsevier
Due to the manufacturing sector's digitalization and ability to combine quality measurement
and production data, machine learning and deep learning for quality assurance hold …
and production data, machine learning and deep learning for quality assurance hold …
Differgram: A convex optimization-based method for extracting optimal frequency band for fault diagnosis of rotating machinery
J Guo, Y Liu, R Yang, W Sun, J Xiang - Expert Systems with Applications, 2024 - Elsevier
The extraction of fault resonance bands from a full frequency band has always stood as a
classical and effective strategy for fault diagnosis in rotating machinery. Among the existing …
classical and effective strategy for fault diagnosis in rotating machinery. Among the existing …
A survey on fault diagnosis of rotating machinery based on machine learning
With the booming development of modern industrial technology, rotating machinery fault
diagnosis is of great significance to improve the safety, efficiency and sustainable …
diagnosis is of great significance to improve the safety, efficiency and sustainable …