cuong.nm

Hi, I'm

Cuong (Henry) Nguyen

AI Engineer · Vibration-Based Fault Diagnosis & Edge AI

My research focuses on applying AI to condition monitoring and fault diagnosis for industrial equipment, particularly rotating machinery. By combining signal analysis, frequency spectra, and the fault signatures of bearings, shafts, and gears, I aim to build intelligent diagnostic systems that are accurate and highly automated.

Cuong (Henry) Nguyen

Skills

Programming Languages

Python, C/C++, Matlab & Simulink

Web Development

HTML, CSS, JavaScript, TypeScript, React, Next.js, Tailwind, GSAP, next-intl

Software & Tools

Autocad, Solidworks, Inventor, Mathematica, VS Code, Office

Research Fields

Signal Processing for Fault Detection, Vibration Analysis, PHM, RUL Prediction, Explainable AI, Transfer Learning

Education

  1. Master of Mechanical Engineering

    National Central University • Taiwan • 2026 - present

  2. Bachelor of Engineering, Mechatronics

    Hanoi University of Science and Technology • Hanoi, Vietnam • 2021 - 2025

    Good Degree · English B2

Experience

  1. Researcher

    National Synchrotron Radiation Research Center (NSRRC) • from October 2026

  2. Teaching Assistant

    Precision Transmission Lab • from September 2026

  3. Backend Developer

    Aladin • in 2026

  4. PHM Researcher

    ITD Lab — Hanoi University of Science and Technology • 2025 - present

Research

Work

PT500 vibration test rig with sensors and data acquisition at ITD Lab

PT500 Vibration Test Rig

Hands-on vibration experiments on rotating machinery

July 2026

I have practical vibration experience with the PT500 machinery diagnostics rig at ITD Lab. I selected and installed the experimental setup, then carried out vibration measurements on the rig to collect data for fault diagnosis research.

My Role

I chose the rig configuration, assembled and mounted the components and sensors, and ran the vibration measurements.

Highlights

  • Selected and installed the experimental setup on the PT500 rig.
  • Mounted sensors and connected the data acquisition system.
  • Carried out vibration measurements and recorded data for analysis.
Teaching the machine-learning practice session at VICEM

Training at VICEM

Intelligent vibration monitoring & AI-based predictive maintenance for rotating equipment

June 8–12, 2026 · Hanoi

ITD Lab and the VICEM Institute of Cement Technology ran a 5-day intensive course (code VCN.VC.2026.13) for 18 engineers from 7 VICEM member units, going from vibration theory to AI methods for diagnostics and predictive maintenance.

My Role

Instructor

Highlights

  • Vibration monitoring foundations, maintenance strategies, the ISO 20816 standard, and an introduction to PHM and AI.
  • Gear and rolling-bearing fault diagnosis (GMF, BPFO/BPFI/BSF/FTF, envelope spectrum) and vertical roller mill (VRM) diagnosis.
  • Machine learning for bearing fault diagnosis (SVM, Random Forest, SHAP) and a RAG-based smart maintenance assistant.
  • Dynamic unbalance identification and field balancing practice.

Stack

  • Python
  • Google Colab
  • scikit-learn
  • SHAP
Learn more

ITD Lab Website

A bilingual home for ITD Lab

2026

I built this website to give ITD Lab a clear place to share its research, members, publications, and activities. It is available in Vietnamese and English, helping students and research partners quickly understand what the lab does and how to get in touch.

My Role

I handled the project from design and development to organizing the content and deploying the finished website.

Highlights

  • Publications are updated from BibTeX and automatically organized by year, making new papers quick to add.
  • News, events, member profiles, and admissions information each have a clear place and are easy to update.
  • The site works well on phones, supports Vietnamese and English, and is prepared for search engines.

Stack

  • Next.js 16
  • TypeScript
  • Tailwind v4
  • GSAP
  • next-intl
  • Vercel
Learn more

Aladata

Conversational Text-to-SQL (Vietnamese)

A Vietnamese-language interface for asking business-data questions without writing SQL. The system parses the question, generates the query, runs it on ClickHouse, and returns the result. Three modes: single questions, follow-ups, and switching context between topics.

My Role

Backend Engineer. I own the Memory subsystem (Redis + Postgres + Graphiti/Neo4j) and the FastAPI service that wires the pipeline together.

Highlights

  • Built and own the full Memory stack: Redis for short-term context, Postgres for long-term storage, Graphiti/Neo4j for the knowledge graph.

Stack

  • Python
  • FastAPI
  • LangGraph
  • Redis
  • PostgreSQL
  • Neo4j
  • Qdrant
  • ClickHouse
  • Docker
  • LangFuse

Personal Projects

  • Real-Time Fault Diagnosis on Edge

    Edge AIVibration

    Softmax Regression and Random Forest deployed on edge hardware for standalone real-time fault diagnosis from raw vibration signals.

    View on GitHub
  • Random Forest & Logistic Regression

    Signal Processing

    Vibration-based fault diagnosis pipeline with time–frequency analysis, advanced signal preprocessing and optimized classical models.

    View on GitHub
  • CNN Transfer Learning

    CNNTransfer Learning

    CNN-based transfer learning to adapt fault diagnosis models across operating conditions with limited labeled target-domain data.

    View on GitHub
  • Transformer-Based Fault Diagnosis

    Transformer

    Conv-Transformer and Vision Transformer for time-series and time-frequency data, achieving accurate diagnosis of complex mechanical faults.

    View on GitHub
  • Softmax Regression from Scratch

    PythonC++

    Softmax-based neural network implemented from scratch (no ML/DL libraries) and deployed in C++ for low-level, embedded AI execution.

    View on GitHub