Predictive Maintenance Dashboard for Small IoT Deployments

Core Functionality

• Real‑time Data Ingestion: Connects to MQTT or HTTP endpoints, stores streams in a lightweight TimeSeries DB (InfluxDB).
• Automated Feature Engineering: Uses Pandas and tsfresh to extract statistical and frequency domain features on the fly.
• Anomaly & Failure Prediction: Implements Isolation Forest and Prophet models via scikit‑learn and fbprophet; outputs risk scores and maintenance windows.
• Interactive Dashboard: Built with Streamlit or Dash, showing live charts, alerts, and model explanations (SHAP).
• Export & API: Allows CSV export of predictions and a REST endpoint for integration into existing Ops tools.

Problem It Solves

Many tech enthusiasts and small businesses deploy IoT sensors but lack the expertise to interpret data or predict failures. Traditional enterprise solutions are expensive and over‑engineered, leaving hobbyists without affordable predictive insights, leading to unexpected downtimes and higher maintenance costs.

Technical Requirements

• Python 3.11
• Data storage: InfluxDB or TimescaleDB
• Data processing: Pandas, NumPy, tsfresh
• Modeling: scikit‑learn, fbprophet, SHAP
• Dashboard: Streamlit (or Dash)
• Deployment: Docker + Kubernetes or Heroku for quick prototyping

Monetization Strategy

1. Freemium: Basic dashboard and anomaly detection free; advanced predictive analytics & export features behind a monthly subscription.
2. Hardware Bundle: Partner with low‑cost sensor vendors to offer bundled licenses.
3. Enterprise Add‑on: Offer custom model training for larger deployments via a one‑time fee.

Implementation Approach

1. Prototype Data Layer: Set up InfluxDB, write MQTT ingestion script.
2. Feature Pipeline: Build Pandas pipeline; integrate tsfresh.
3. Model Training Module: Train Isolation Forest and Prophet on historical data; save models with joblib.
4. Dashboard Skeleton: Create Streamlit app with live charts.
5. Alert System: Use FastAPI to expose prediction endpoint; push alerts via email/SMS.
6. Testing & CI: Unit tests for ingestion, feature extraction, and model inference; use GitHub Actions.
7. Containerization: Dockerfile for all services; deploy to a cloud VM or Kubernetes cluster.

Potential Challenges

• Data Volume Management: High-frequency sensor streams can overwhelm storage – solution: downsample and aggregate data in InfluxDB retention policies.
• Model Drift: Models may degrade over time – implement periodic retraining scheduler and monitor performance metrics.

Future Expansion

• Integrate edge computing with ONNX models for offline inference on Raspberry Pi.
• Add NLP sentiment analysis of maintenance logs to improve failure context.
• Expand to multi‑tenant SaaS offering with role‑based access control.
• Incorporate reinforcement learning to optimize maintenance schedules dynamically.