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  • Dr. Muhammad Farooq Siddique
  • Assistant Professor / Assistant Professor
  • farooqsiddique.mech@uetmardan.edu.pk
  • Department of
    Mechanical Engineering
  • Name
    Dr Muhammad Farooq Siddique

  • Personal

    Email: farooqsiddique.mech@uetmardan.edu.pk

    Department: Mechanical Engineering

    PEC No: MECH/36725

  • Experience
    1. 05, 2026 ~ Present, Assistant Professor, Mechanical Engineering, University of Engineering and Technology (UET), Mardan, KPK, Pakistan.
    2. 02, 2026 ~ 05, 2026, Postdoctoral Researcher, Robotics and Mechanical Engineering Department, Gwangju Institute of Science and Technology (GIST), South Korea.
    3. 09, 2019 ~ 08, 2022, Assistant Works Manager, Technology Division, Aircraft Manufacturing Factory, Pakistan Aeronautical Complex, Attock, Kamra, Pakistan.
    4. 03, 2018 ~ 11, 2018, Mechanical Design Engineer, 109 AED, 3D Design laboratory, Pakistan Airforce, Badaber Base, Peshawar

  • Qualification
    • PhD.  Artificial Intelligence and Computer Engineering (Ulsan Industrial and Artificial Lab) 09,2022 ~ 02, 2026, University of Ulsan (UOU), Republic of Korea.
    • M. Sc. Mechanical (TSE) Engineering 09, 2018 ~ 12, 2020, University of Engineering and Technology (UET), Peshawar, KPK, Pakistan.
    • B. Sc. Mechanical Engineering 09, 2013 ~ 09, 2017, National University of Science and Technology (NUST), Islamabad, Pakistan. 
    • F.Sc. Pre Engineering, 07, 2011 ~ 07, 2013, Islamia College University (ICUP), Peshawar, KPK, Pakistan.

  • Memberships

    Pakistan Engineering Council

  • Brief Statement of Research Interest

    Fault Diagnosis using Artificial Intelligence, Rotating Machinery, Signal Processing, Prognostics.

  • Publications

    Google Scholar

    (https://scholar.google.com/citations?user=OsFI8ZAAAAAJ&hl=en)

     

    First Author

    1. Pipeline leak diagnosis based on leak-augmented scalograms and deep learning, Engineering Applications of Computational Fluid Mechanics 17 (1), 2225577, Q1, 2023, IF 5.40.
    2. A Hybrid Deep Learning Approach: Integrating Short-Time Fourier Transform and Continuous Wavelet Transform for Improved Pipeline Leak Detection, Sensors 23 (19), 8079, Q1, 2023, IF 3.50.
    3. Advanced Fault Diagnosis in Milling Cutting Tools Using Vision Transformers with Semi-Supervised Learning and Uncertainty Quantification, Scientific Reports 15 (1), 42460, Q1, IF 3.90.
    4. A Hybrid Deep Learning Framework for Fault Diagnosis in Milling machines, Sensors 25 (18), 5866, Q1, 2025, IF 3.50.
    5. Pipeline Leak Detection: A Comprehensive Deep Learning Model Using CWT Image Analysis and an Optimized DBN-GA-LSSVM Framework, Sensors 24 (12), 4009, Q1, 2024, IF 3.50.
    6. A Hybrid Deep Learning Approach for Bearing Fault Diagnosis Using Continuous Wavelet Transform and Attention-Enhanced Spatiotemporal Feature Extraction, Sensors 25 (9), 2712 Q1, 2024, IF 3.50.
    7. A Deep Learning Approach for Fault Diagnosis in Centrifugal Pumps through Wavelet Coherent Analysis and S-Transform Scalograms with CNN-KAN, Computers, Materials & Continua 84 (2), 3577–360,3 Q2, 2025, IF 1.70.
    8. Advanced Bearing-Fault Diagnosis and Classification Using Mel-Scalograms and FOX-Optimized ANN, Sensors 24 (22), 7303, Q1, 2024, IF 3.50.
    9. A Multistage Transfer Learning Framework for Fault Diagnosis in Milling Cutting Tools under Varying Operating Conditions, Scientific Reports, Q1, 2026, IF 3.90.

    Manuscripts Under Review / In Progress

    1. Advanced Bearing Fault Diagnosis Based on Physics Guided Vision Transformer and Band Aware Attention, Results in Engineering, IF=7.9 (Under Review-Round 02).

    International Conference Publications

    1. Pipeline Leak Detection: Leveraging Acoustic Emission Signal Processing and Machine Learning, IHCI, Netherlands, 2025.
    2. Comprehensive Pipeline Leak Detection Using Induced-Leak Enhanced Scalogram Analysis and Deep Learning, IEEE HPCC, Australia, 2023.

    Accepted / In Progress

    1. Advanced Fault Diagnosis in Milling Machines Using CQ-NSGT and Deep Learning, (FICTA, UK London, 2025).
    2. Bearing Fault Diagnosis: Class-Conditional Deep Domain Adaptation for Generalization Across Machines, (ICRAI, NUST, Pakistan, 2026)

    Co-author

    1. A new dual-input CNN for multimodal fault classification using acoustic emission and vibration signals, Engineering Failure Analysis, 109787, Q1, 2025, IF 5.70.
    2. Burst-Informed Acoustic Emission Framework for Explainable Failure Diagnosis in Milling Machines, Engineering Failure Analysis, 110373, Q1, IF=5.70.
    3. Multi-sensor observer-based residual learning with Auto-Permutation Feature Importance for fault diagnosis of multistage centrifugal pumps under variable pressures, Scientific Reports volume 15, Article number: 45735 (2025).
    4. Centrifugal Pump Fault Diagnosis Based on a Novel SobelEdge Scalogram and CNN, Sensors 23 (11), 5255, Q1, 2023, IF 3.50.
    5. Advanced Fault Diagnosis in Milling Machines Using Acoustic Emission and Transfer Learning, IEEE Access, Q2, 2025, IF 3.60.
    6. An Intelligent Framework for Fault Diagnosis of Centrifugal Pump Leveraging Wavelet Coherence Analysis and Deep Learning, Sensors 23 (21), 8850, Q1, 2024, IF 3.50.
    7. Pipeline Leak Detection System for a Smart City: Leveraging Acoustic Emission Sensing and Sequential Deep Learning, Smart Cities 7 (4), 2318-2338, Q1, 2025, IF 5.50.
    8. Milling Machine Fault Diagnosis Using Acoustic Emission and Hybrid Deep Learning with Feature Optimization, Applied Sciences 14 (22), 10404, Q2, 2024, IF 2.50.
    9. Spatio-Temporal Feature Extraction for Pipeline Leak Detection in Smart Cities Using Acoustic Emission Signals: A One-Dimensional Hybrid CNN–LSTM Approach, Q1, 2024, IF 3.50.
    10. Acoustic Emission-Based Pipeline Leak Detection and Size Identification Using a Customized One-Dimensional DenseNet, Sensors 25 (4), 1112, Q1, 2025, IF 3.50.
    11. Hybrid Deep Learning Model for Fault Diagnosis in Centrifugal Pumps: A Comparative Study of VGG16, ResNet50, and Wavelet Coherence Analysis, Machines 12 (12), 905, Q2, 2024, IF 2.50.
    12. Advanced Fault Diagnosis in Rotary Machines Using Optimized Transfer Learning, IEEE Access, Volume 14, Pages 45104 – 45117, 2026, IF=3.60.
    13. Early-warning industrial fault detection based on physics-guided residual learning and calibrated CRNNs, Scientific Reports, Q1, (2026).
    14. Smart Predictive Maintenance: A TCN-Based System for Early Fault Detection in Industrial Machinery, Machines, Q2, 2026, Volume 14, Issue 2, Page 164, If=2.50.

    Manuscripts Under Review / In Progress

    1. Layer-wise Domain Discrepancy Guided Transfer Learning with ScaloNet for Fault Diagnosis in Rotating Machines, EAAI, IF=8.0 (Under Review, Round 01).
    2. A Distribution-Level Statistical Framework for Reliable Pipeline Leak Detection Using Multi-Domain Signal Analysis, Scientific Reports, Q1, (Under Review, Round 02).

    International Conference/Book Chapters

    1. A Hybrid Classification Framework of Centrifugal Pumps Using Wavelet Coherence Visuals and Principal Component Analysis, IEEE HPCC, Australia, 2023.
    2. Centrifugal Pump Fault Detection with Hybrid Feature Pool and Deep Learning, IEEE IBCAST, Pakistan, 2023.
    3. Centrifugal Pump Health Condition Identification Based on Novel Multi-filter Processed Scalograms and CNN, IHCI, South Korea, 2024.
    4. A Framework for Centrifugal Pump Diagnosis Using Health Sensitivity Ratio Based Feature Selection and KNN, ACPR, South Korea, 2023.
    5. Local and Global Feature Extraction Using Convolutional Autoencoders and CNNs for Diagnosing Milling Machine Faults, Information System Design: AI and ML Applications, 37, (ISDIA 2025).

    Accepted / In Progress

    1. An Interpretable Lightweight CNN Framework for Fault Diagnosis in Centrifugal Pumps Using Time-Frequency Scalograms (FICTA, UK London, 2025).
    2. Stockwell Transform and CNN-Based Pipeline Leak Detection Using Sobel-Filtered Acoustic Emission Signals, (ICCIIOT, Pakistan 2024).