CUSUM and Page-Hinkley statistical drift detection on sensor data streams
Scanned 9/11/2026
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---
name: monitor-drift-sensors
description: CUSUM and Page-Hinkley statistical drift detection on sensor data streams
triggers:
- "drift detection"
- "sensor drift"
- "monitor sensors"
allowed-tools:
- Bash
provides:
- monitor-drift-sensors
composes:
- create-figure
- task-monitor
- agentic-evals
disciplines:
- observability-operations
- data-engineering
---
# monitor-drift-sensors
Statistical drift detection on sensor data streams using CUSUM (Cumulative Sum) and Page-Hinkley algorithms.
## Usage
```bash
# Analyze a JSONL file of sensor readings for drift
./run.sh analyze <data.jsonl>
# Analyze with built-in test fixtures (no file needed)
./run.sh analyze --dry-run
# Watch a live sensor stream (requires running daemons)
./run.sh watch --sensor vibration --threshold 3.0
# Show algorithm parameters and last detection results
./run.sh status
```
## Input Format
Each JSONL line:
```json
{"timestamp": "2026-02-19T10:00:00", "sensor": "vibration", "value": 50.3}
```
## Output
Table of detected drift points:
```
SAMPLE_IDX | TIMESTAMP | CUSUM_VALUE | METHOD | SEVERITY
17 | 2026-02-19T10:00:16 | 12.45 | CUSUM | HIGH
18 | 2026-02-19T10:00:17 | 8.32 | PAGE_HINKLEY | MEDIUM
```
## Algorithms
- **CUSUM**: Tracks cumulative sum of deviations from running mean. Detects sustained shifts.
- **Page-Hinkley**: Monitors difference between observed values and their running mean. Sensitive to gradual drift.
Both run simultaneously; drift is flagged when either detector triggers.
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