𝐟𝐚𝐢𝐥𝐮𝐫𝐞𝐬 𝐢𝐧 𝐚 𝐡𝐲𝐝𝐫𝐚𝐮𝐥𝐢𝐜 𝐬𝐲𝐬𝐭𝐞𝐦 - ML model

🚀 Just wrapped up a machine learning project focused on predicting 𝐟𝐚𝐢𝐥𝐮𝐫𝐞𝐬 𝐢𝐧 𝐚 𝐡𝐲𝐝𝐫𝐚𝐮𝐥𝐢𝐜 𝐬𝐲𝐬𝐭𝐞𝐦 using real-world sensor data collected from industrial equipment!

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Over the course of this project, I tackled a complex, high-dimensional dataset derived from 17 industrial sensors, each capturing multiple metrics at a high sampling rate.

==> This resulted in over 400,000 raw cycle-based features per sample! posing significant challenges in data processing and model scalability.

🛠 So Sarah What did YOU do ?

I brought up the BIG GUNS 🔫

1. 𝐓𝐢𝐦𝐞-𝐒𝐞𝐫𝐢𝐞𝐬 𝐅𝐞𝐚𝐭𝐮𝐫𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 ✨ Extracted  𝘴𝘵𝘢𝘵𝘪𝘴𝘵𝘪𝘤𝘢𝘭 𝘢𝘯𝘥 𝘧𝘳𝘦𝘲𝘶𝘦𝘯𝘤𝘺-𝘥𝘰𝘮𝘢𝘪𝘯 𝘧𝘦𝘢𝘵𝘶𝘳𝘦𝘴 (mean, median, skewness, kurtosis, entropy, etc.) from each sensor signal to summarize patterns over time.

2. 𝐃𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐚𝐥𝐢𝐭𝐲 𝐑𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧 Applied #StandardScaler followed by #PCA to reduce the feature set from 400K+ to just 19 components, while preserving >95% variance.

3. 𝐓𝐚𝐫𝐠𝐞𝐭 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠: Focused on predicting internal pump leakage, a key indicator of system degradation. The target had 3 classes (normal, minor leakage, critical leakage).

4. 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡: Trained an XGBoost classifier, optimized using GridSearchCV, achieving an accuracy of over 90%, with an F1-score of 0.93+ and clear performance across all classes.

5. 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭: Built a Streamlit web app for real-time predictions, allowing users to enter new sensor readings and instantly receive a leakage status classification.

🧠 Key Learnings:

Tackled data imbalance using stratified sampling and robust evaluation metrics (macro and weighted F1-scores).

Learned to navigate highly correlated features, overfitting risks, and domain-specific engineering constraints.

Focused on explainability and deployment-readiness, ensuring the solution can be extended to other failure modes or future datasets.

✅ Project Impact:

This kind of predictive maintenance tool can help prevent costly machine breakdowns, reduce downtime, and ensure safety in heavy industrial settings. Early detection of internal pump leakage enables engineers to take preventive action before failure escalates.

🔧 Tools: #pandas, #numpy, #scikit-learn, #XGBoost, #Streamlit, #matplotlib, #seaborn

📍Key Skills:

- Feature extraction from time-series data

- PCA for dimensionality reduction

- XGBoost optimization

- Model deployment using Streamlit

- End-to-end ML pipelines

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the pdf is a sample of the important parts of the project, you can find the full notebook on Kaggle, GitHub and check other projects on my #portfolio (comment section)

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Let me know if you're working on similar use cases in predictive maintenance or industrial analytics—I'd love to connect!

#MachineLearning #PredictiveMaintenance #DataScience #FeatureEngineering #PCA #XGBoost #MLDeployment #Streamlit #SmartManufacturing #DataDriven