9 citations · 22 across the 21 of their papers we have counts for
22 papers
XMDCA-TL: An Explainable Multi-Domain Channel Attention Transfer Learning Framework for Fault Diagnosis in Industrial Gas Turbines
Amir Jahangard Takaloo, Mahdi Aliyari Shoorehdeli, Ehsan Mohammadi
In this study, a multi-task, interpretable transfer learning framework, XMDCA-TL, is proposed for fault diagnosis in industrial gas turbines. In the proposed method, the vibration…
Temporal Cross-Modal Knowledge-Distillation-Based Transfer-Learning for Gas Turbine Vibration Fault Detection
Ali Bagheri Nejad, Mahdi Aliyari-Shoorehdeli, Abolfazl Hasanzadeh
Preventing machine failure is inherently superior to reactive remediation, particularly for critical assets like gas turbines, where early fault detection (FD) is a cornerstone of…
PINN vs LSTM: A Comparative Study for Steam Temperature Control in Heat Recovery Steam Generators
Mojtaba Fanoodi, Farzaneh Abdollahi, Mahdi Aliyari Shoorehdeli
This paper introduces a direct comparative study of Physics-Informed Neural Networks (PINNs) and Long Short-Term Memory (LSTM) networks for adaptive steam temperature control in He…
Fault-Tolerant Control of Steam Temperature in HRSG Superheater under Actuator Fault Using a Sliding Mode Observer and PINN
Mojtaba Fanoodi, Farzaneh Abdollahi, Mahdi Aliyari Shoorehdeli
This paper presents a novel fault-tolerant control framework for steam temperature regulation in Heat Recovery Steam Generators (HRSGs) subject to actuator faults. Addressing the c…
Fault-Tolerant Temperature Control of HRSG Superheaters: Stability Analysis Under Valve Leakage Using Physics-Informed Neural Networks
Mojtaba Fanoodi, Farzaneh Abdollahi, Mahdi Aliyari Shoorehdeli +1
Faults and operational disturbances in Heat Recovery Steam Generators (HRSGs), such as valve leakage, present significant challenges, disrupting steam temperature regulation and po…
Multilayer Perceptron Neural Network Model: A Novel Approach for LFP Contrast Sensitivity Tuning
Sahar Maleki, Reza Lashgari, Mahdi Aliyari Shoorehdeli +1
Local field potentials (LFPs) have been demonstrated to be an important measurement to study the activity of a local population of neurons. The response tunings of LFPs have been m…
Predictive Modeling of Rat Brain Local Field Potentials using Single-Variable and Multivariable Approaches
AmirAli Kalbasi, Shole Jamali, Mahdi Aliyari Shoorehdeli +1
Accurate prediction of neural dynamics in the brain's reward circuitry is crucial for elucidating how natural and pharmacological rewards influence neural activity and connectivity…
Applications of wavelet transform in classification of local field potential recorded from the rat brain in conditioned place preference paradigm
AmirAli Kalbasi, Mahdi Aliyari Shoorehdeli, Shole Jamali +1
This study investigates the multi-label classification of Local Field Potential (LFP) data from the hippocampus (HIP) and nucleus accumbens (NAc) in the rat brain, focusing on rewa…
Learning a Factorized Orthogonal Latent Space using Encoder-only Architecture for Fault Detection; An Alarm management perspective
Vahid MohammadZadeh Eivaghi, Mahdi Aliyari Shoorehdeli
False and nuisance alarms in industrial fault detection systems are often triggered by uncertainty, causing normal process variable fluctuations to be erroneously identified as fau…
Dynamic Importance Learning using Fisher Information Matrix (FIM) for Nonlinear Dynamic Mapping
Vahid MohammadZadeh Eivaghi, Mahdi Aliyari Shoorehdeli
Understanding output variance is critical in modeling nonlinear dynamic systems, as it reflects the system's sensitivity to input variations and feature interactions. This work pre…
A Global Data-Driven Model for The Hippocampus and Nucleus Accumbens of Rat From The Local Field Potential Recordings (LFP)
Maedeh Sadeghi, Mahdi Aliyari Shoorehdeli, Shole jamali +1
In brain neural networks, Local Field Potential (LFP) signals represent the dynamic flow of information. Analyzing LFP clinical data plays a critical role in improving our understa…
Exploiting the capacity of deep networks only at training stage for nonlinear black-box system identification
Vahid MohammadZadeh Eivaghi, Mahdi Aliyari Shooredeli
To benefit from the modeling capacity of deep models in system identification, without worrying about inference time, this study presents a novel training strategy that uses deep m…
Contrastive Multi-Modal Representation Learning for Spark Plug Fault Diagnosis
Ardavan Modarres, Vahid Mohammad-Zadeh Eivaghi, Mahdi Aliyari Shoorehdeli +1
Due to the incapability of one sensory measurement to provide enough information for condition monitoring of some complex engineered industrial mechanisms and also for overcoming t…
Adaptive output feedback tracking control for bilinear systems
Amir Reza Zare, Mahdi Aliyari shoorehdeli, Mehdi Tavan +1
This paper deals with the trajectory tracking control problem for a class of bilinear systems with unmeasurable states and unknown parameters. Firstly, a full-information controlle…
Imbalanced Classification In Faulty Turbine Data: New Proximal Policy Optimization
Mohammad Hossein Modirrousta, Mahdi Aliyari Shoorehdeli, Mostafa Yari +1
There is growing importance to detecting faults and implementing the best methods in industrial and real-world systems. We are searching for the most trustworthy and practical data…
Analysis of Anomalous Behavior in Network Systems Using Deep Reinforcement Learning with CNN Architecture
Mohammad Hossein Modirrousta, Parisa Forghani Arani, Mahdi Aliyari Shoorehdeli
In order to gain access to networks, different types of intrusion attacks have been designed, and the attackers are working on improving them. Computer networks have become increas…
Time-Domain Classification of the Brain Reward System: Analysis of Natural and Drug Reward Driven Local Field Potential Signals in Hippocampus and Nucleus Accumbens
AmirAli Kalbasi, Shole Jamali, Mahdi Aliyari Shoorehdeli +2
Addiction is a major public health concern characterized by compulsive reward-seeking behavior. The excitatory glutamatergic signals from the hippocampus (HIP) to the Nucleus accum…
Adaptive Model Learning of Neural Networks with UUB Stability for Robot Dynamic Estimation
Pedram Agand, Mahdi Aliyari Shoorehdeli
Since batch algorithms suffer from lack of proficiency in confronting model mismatches and disturbances, this contribution proposes an adaptive scheme based on continuous Lyapunov…
Detection of Anomalies and Faults in Industrial IoT Systems by Data Mining: Study of CHRIST Osmotron Water Purification System
Mohammad Sadegh Sadeghi Garmaroodi, Faezeh Farivar, Mohammad Sayad Haghighi +2
Industry 4.0 will make manufacturing processes smarter but this smartness requires more environmental awareness, which in case of Industrial Internet of Things, is realized by the…
A Novel Method For Designing Transferable Soft Sensors And Its Application
Hossein Shahabadi Farahani, Alireza Fatehi, Alireza Nadali +1
In this paper, a new approach is proposed for designing transferable soft sensors. Soft sensing is one of the significant applications of data-driven methods in the condition monit…
Between-Domain Instance Transition Via the Process of Gibbs Sampling in RBM
Hossein Shahabadi Farahani, Alireza Fatehi, Mahdi Aliyari Shoorehdeli
In this paper, we present a new idea for Transfer Learning (TL) based on Gibbs Sampling. Gibbs sampling is an algorithm in which instances are likely to transfer to a new state wit…
Improving the Izhikevich Model Based on Rat Basolateral Amygdala and Hippocampus Neurons, and Recognizing Their Possible Firing Patterns
Sahar Hojjatinia, Mahdi Aliyari Shoorehdeli, Zahra Fatahi +2
Introduction- Identifying the potential firing patterns following different brain regions under normal and abnormal conditions increases our understanding of events at the level of…