Reinforcement and deep reinforcement learning-based solutions for machine maintenance planning, scheduling policies, and optimization
O Ogunfowora, H Najjaran - Journal of Manufacturing Systems, 2023 - Elsevier
Abstract Systems and machines undergo various failure modes that result in machine health
degradation, so maintenance actions are required to restore them back to a state where they …
degradation, so maintenance actions are required to restore them back to a state where they …
Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions
Abstract Machine scheduling aims to optimally assign jobs to a single or a group of
machines while meeting manufacturing rules as well as job specifications. Optimizing the …
machines while meeting manufacturing rules as well as job specifications. Optimizing the …
Generative convolutional monitoring method for online flooding recognition in packed towers
Background Data-driven methods play an important role in monitoring the liquid flooding
process for ensuring the efficient and safe operation of packed towers. However, their online …
process for ensuring the efficient and safe operation of packed towers. However, their online …
Anomaly detection in automated fibre placement: Learning with data limitations
Introduction: Conventional defect detection systems in Automated Fibre Placement (AFP)
typically rely on end-to-end supervised learning, necessitating a substantial number of …
typically rely on end-to-end supervised learning, necessitating a substantial number of …
Imbalanced fault diagnosis using conditional wasserstein generative adversarial networks with switchable normalization
W Fu, Y Chen, H Li, X Chen, B Chen - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
Mechanical equipment usually runs under normal condition (NC), making it prohibitively
challenging to collect sufficient fault samples and the dataset is prone to imbalanced …
challenging to collect sufficient fault samples and the dataset is prone to imbalanced …
Intelligent condition monitoring of industrial plants: An overview of methodologies and uncertainty management strategies
Condition monitoring plays a significant role in the safety and reliability of modern industrial
systems. Artificial intelligence (AI) approaches are gaining attention from academia and …
systems. Artificial intelligence (AI) approaches are gaining attention from academia and …
An adaptive few-shot fault diagnosis method based on virtual samples generated by fault characteristics of rotating machines
P Wu, G Yu, Q Yu, P Wang, Y Han, B Ma - Engineering Applications of …, 2024 - Elsevier
The existing intelligent diagnostic methods based on the machine learning achieve good
diagnostic results under the condition of large amount of the failure data available. However …
diagnostic results under the condition of large amount of the failure data available. However …
Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review
Every novel technology adds hidden vulnerabilities ready to be exploited by a growing
number of cyber-attacks. Automated software testing can be a promising solution to quickly …
number of cyber-attacks. Automated software testing can be a promising solution to quickly …
Rotor fault diagnosis of centrifugal pumps in nuclear power plants based on CWGAN-GP-CNN for imbalanced dataset
D Cui, R Zhou, H Li, R Hua, Z Chen, H Liu… - Progress in Nuclear …, 2025 - Elsevier
As a crucial device in nuclear power plants, centrifugal pumps undertake the critical role of
cooling water circulation. Centrifugal pump rotor misalignment and unbalanced faults cause …
cooling water circulation. Centrifugal pump rotor misalignment and unbalanced faults cause …
A numerical simulation enhanced multi-task integrated learning network for fault detection in rotation vector reducers
H Wang, S Wang, R Yang, J Xiang - Mechanical Systems and Signal …, 2024 - Elsevier
Data-driven artificial intelligence (AI) models play an important role in mechanical fault
diagnosis. Generally, it is difficult to collect relative complete fault samples, which limits the …
diagnosis. Generally, it is difficult to collect relative complete fault samples, which limits the …