index.md

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title	IEEE IES Industrial AI Lab
header	overlay_color overlay_filter actions #00629B 0.75 label url Explore Projects #projects label url GitHub Organization https://github.com/IEEE-IES-Industrial-AI-Lab
excerpt	Open-source, research-grade frameworks for applying AI to industrial systems — predictive maintenance, time-series modeling, power electronics diagnostics, and smart manufacturing.
intro	excerpt **4 research repos**  ·  **20+ SOTA models**  ·  **15+ benchmark datasets**  ·  **20+ tutorial notebooks**
feature_row	title excerpt url btn_label btn_class Industrial Predictive Maintenance AI pipelines for **Remaining Useful Life (RUL) prediction** and fault detection on NASA CMAPSS, CWRU, IMS, and Paderborn bearing datasets. Includes LSTM, Transformer, TCN, and LSTM Autoencoder with a unified `fit / predict / evaluate` API and ONNX edge-deployment support. https://github.com/IEEE-IES-Industrial-AI-Lab/Industrial-Predictive-Maintenance View Repo → btn--primary title excerpt url btn_label btn_class Industrial Time-Series AI SOTA benchmark suite for multivariate industrial sensor stream analysis — **forecasting and anomaly detection**. Implements PatchTST (ICLR 2023), DLinear (AAAI 2023), TCN, Transformer, and LSTM Autoencoder on ETT, SWaT, PSM, and SMAP datasets. https://github.com/IEEE-IES-Industrial-AI-Lab/Industrial-Time-Series-AI View Repo → btn--primary
feature_row2	title excerpt url btn_label btn_class AI Power Electronics Diagnostics Deep learning pipelines for **fault detection in inverters and motor drives** from voltage, current, and harmonic signals. Covers FFT, STFT spectrograms, wavelet analysis, and 5 model architectures (1D CNN, Spectrogram CNN, Transformer, BiLSTM, Autoencoder). https://github.com/IEEE-IES-Industrial-AI-Lab/AI-Power-Electronics-Diagnostics View Repo → btn--primary title excerpt url btn_label btn_class Smart Manufacturing AI AI pipelines for **vision-based defect detection** (MVTec AD, NEU Steel), robot joint anomaly detection, RL-based production scheduling with PPO, and discrete-event digital twin simulation with MQTT/OPC-UA sync. https://github.com/IEEE-IES-Industrial-AI-Lab/Smart-Manufacturing-AI View Repo → btn--primary

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Research Projects

Four independent frameworks covering the core domains of industrial AI

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Benchmark Datasets

Real industrial datasets used across the four frameworks

Dataset	Domain	Task	Used In
NASA CMAPSS	Turbofan engine	RUL prediction	Predictive Maintenance
CWRU Bearing	Rolling bearing	Fault classification	Predictive Maintenance
IMS Bearing	Rolling bearing	RUL / anomaly	Predictive Maintenance
Paderborn Bearing	Rolling bearing	Fault classification	Predictive Maintenance
ETT (h1/h2/m1/m2)	Power grid	Forecasting	Time-Series AI
PSM / SMAP / MSL	Server / NASA telemetry	Anomaly detection	Time-Series AI
SWaT / WADI	Water treatment / distribution	Both	Time-Series AI
Kaggle Motor Temp	PMSM motor drive	Temp / anomaly	Power Electronics
MVTec AD	Industrial surfaces	Defect detection	Smart Manufacturing
NEU Surface Defect	Hot-rolled steel	Defect classification	Smart Manufacturing

Full dataset details →{: .btn .btn--primary}

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Key Results

Reproducible benchmark results — run any benchmark with a single command

Framework	Dataset	Model	Metric	Result
Predictive Maintenance	CMAPSS FD001	Transformer	RMSE ↓	12.89
Predictive Maintenance	CMAPSS FD001	Transformer	NASA Score ↓	198.7
Time-Series AI	SWaT (synthetic)	LSTM Autoencoder	ROC-AUC ↑	0.9999
Time-Series AI	SWaT (synthetic)	LSTM Autoencoder	F1-PA ↑	1.000
Power Electronics	Inverter (9 classes)	1D CNN	Accuracy ↑	~99%
Smart Manufacturing	MVTec AD — bottle	ViT-B/16	AUROC ↑	0.982

Full benchmark tables →{: .btn .btn--primary}

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About the Lab

The IEEE IES Industrial AI Lab develops open-source, research-grade AI frameworks for the IEEE Industrial Electronics Society community. Our goal is to provide reproducible baselines and benchmarks that bridge the gap between academic research and industrial deployment.

Each framework is designed as a mini research platform — not just code, but reproducible experiments, standardized evaluation protocols, and tutorial notebooks that make the work accessible to both engineers and researchers.

GitHub Organization ↗{: .btn .btn--primary} IEEE IES ↗{: .btn .btn--inverse}