Challenges and opportunities of AI-enabled monitoring, diagnosis & prognosis: A review
Abstract Prognostics and Health Management (PHM), including monitoring, diagnosis,
prognosis, and health management, occupies an increasingly important position in reducing …
prognosis, and health management, occupies an increasingly important position in reducing …
A review on rolling bearing fault signal detection methods based on different sensors
G Wu, T Yan, G Yang, H Chai, C Cao - Sensors, 2022 - mdpi.com
As a precision mechanical component to reduce friction between components, the rolling
bearing is widely used in many fields because of its slight friction loss, strong bearing …
bearing is widely used in many fields because of its slight friction loss, strong bearing …
Fault diagnosis and severity analysis of rolling bearings using vibration image texture enhancement and multiclass support vector machines
Fault detection and diagnosis of its severity for machine health monitoring can be stated as a
nested classification problem. For a faulty bearing, each fault location whether belonging to …
nested classification problem. For a faulty bearing, each fault location whether belonging to …
Bearing fault diagnosis under variable working conditions based on domain adaptation using feature transfer learning
Bearings, as universal components, have been widely used in the important position of
rotating machinery. However, due to the distribution divergence between training data and …
rotating machinery. However, due to the distribution divergence between training data and …
A fault diagnosis method of rolling bearing based on improved recurrence plot and convolutional neural network
X Liu, L **a, J Shi, L Zhang, L Bai… - IEEE Sensors …, 2023 - ieeexplore.ieee.org
The recurrence plot (RP) method has been introduced into bearing fault diagnosis due to its
capability of effectively analyzing nonlinear and nonstationary waveform signals in dynamic …
capability of effectively analyzing nonlinear and nonstationary waveform signals in dynamic …
Enhancing bearing fault diagnosis using transfer learning and random forest classification: A comparative study on variable working conditions
Rotating machines require bearings to operate smoothly. However, wear, misalignment, and
poor lubrication can degrade bearings over time. Fault diagnosis models identify and …
poor lubrication can degrade bearings over time. Fault diagnosis models identify and …
A domain adaptation model for early gear pitting fault diagnosis based on deep transfer learning network
In recent years, research on gear pitting fault diagnosis has been conducted. Most of the
research has focused on feature extraction and feature selection process, and diagnostic …
research has focused on feature extraction and feature selection process, and diagnostic …
Fault diagnosis for industrial robots based on a combined approach of manifold learning, treelet transform and Naive Bayes
Y Wu, Z Fu, J Fei - Review of Scientific Instruments, 2020 - pubs.aip.org
This research introduces a novel fault diagnosis method for an industrial robot based on
manifold learning algorithms, Treelet Transform (TT) and Naive Bayes. The vibration signals …
manifold learning algorithms, Treelet Transform (TT) and Naive Bayes. The vibration signals …
Enhanced fault feature extraction and bearing fault diagnosis using shearlet transform and deep learning
Accurate bearing fault diagnosis is essential for ensuring the health and longevity of
mechanical systems. Traditional methods often struggle with the dynamic operating …
mechanical systems. Traditional methods often struggle with the dynamic operating …
A novel interpretable fault diagnosis method using multi-image feature extraction and attention fusion
Recent advances in visual intelligence, particularly in image recognition, have introduced a
novel research direction for fault diagnosis. However, existing fault diagnosis methods …
novel research direction for fault diagnosis. However, existing fault diagnosis methods …