49 citations · 84 across the 9 of their papers we have counts for
9 papers
A Hybrid Intelligent Framework for Uncertainty-Aware Condition Monitoring of Industrial Systems
Maryam Ahang, Todd Charter, Masoud Jalayer +1
Hybrid approaches that combine data-driven learning with physics-based insight have shown promise for improving the reliability of industrial condition monitoring. This work develo…
Multi-Channel Swin Transformer Framework for Bearing Remaining Useful Life Prediction
Ali Mohajerzarrinkelk, Maryam Ahang, Mehran Zoravar +2
Precise estimation of the Remaining Useful Life (RUL) of rolling bearings is an important consideration to avoid unexpected failures, reduce downtime, and promote safety and effici…
Feature-Weighted MMD-CORAL for Domain Adaptation in Power Transformer Fault Diagnosis
Hootan Mahmoodiyan, Maryam Ahang, Mostafa Abbasi +1
Ensuring the reliable operation of power transformers is critical to grid stability. Dissolved Gas Analysis (DGA) is widely used for fault diagnosis, but traditional methods rely o…
An Innovative Next Activity Prediction Using Process Entropy and Dynamic Attribute-Wise-Transformer in Predictive Business Process Monitoring
Hadi Zare, Mostafa Abbasi, Maryam Ahang +1
Next activity prediction in predictive business process monitoring is crucial for operational efficiency and informed decision-making. While machine learning and Artificial Intelli…
An Innovative Data-Driven and Adaptive Reinforcement Learning Approach for Context-Aware Prescriptive Process Monitoring
Mostafa Abbasi, Maziyar Khadivi, Maryam Ahang +3
The application of artificial intelligence and machine learning in business process management has advanced significantly, however, the full potential of these technologies remains…
Condition Monitoring with Incomplete Data: An Integrated Variational Autoencoder and Distance Metric Framework
Maryam Ahang, Mostafa Abbasi, Todd Charter +1
Condition monitoring of industrial systems is crucial for ensuring safety and maintenance planning, yet notable challenges arise in real-world settings due to the limited or non-ex…
Intelligent Condition Monitoring of Industrial Plants: An Overview of Methodologies and Uncertainty Management Strategies
Maryam Ahang, Todd Charter, Mostafa Abbasi +3
Condition monitoring is essential for ensuring the safety, reliability, and efficiency of modern industrial systems. With the increasing complexity of industrial processes, artific…
Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions
Maziyar Khadivi, Todd Charter, Marjan Yaghoubi +4
Machine scheduling aims to optimize job assignments to machines while adhering to manufacturing rules and job specifications. This optimization leads to reduced operational costs,…
Synthesizing Rolling Bearing Fault Samples in New Conditions: A framework based on a modified CGAN
Maryam Ahang, Masoud Jalayer, Ardeshir Shojaeinasab +3
Bearings are one of the vital components of rotating machines that are prone to unexpected faults. Therefore, bearing fault diagnosis and condition monitoring is essential for redu…