Use Case
Pattern Search and Anomaly Detection on Vehicle Sensor Data
Built an analytics platform that rapidly searches large volumes of vehicle driving data for similar phenomena and surfaces abnormal behavior early.
Overview
We developed algorithms that rapidly find similar phenomena across multi-sensor time-series data. This enabled automatic detection of complex patterns and established a systematic foundation for leveraging historical data.
Benefits
- Rapid similarity search across multi-sensor time-series data
- Automatic detection of complex composite patterns
- Systematic foundation for leveraging historical data
Target Industries
- Automotive
- Transportation Equipment
Challenges Before / Changes After
Before
- Manual search for specific patterns in large driving datasets took enormous time
- Composite anomaly judgments across multiple sensors were difficult
- Use of past cases depended on individuals
After
- Rapid similarity search across multi-sensor time-series data
- Automatic detection of complex composite patterns
- Systematic foundation for leveraging historical data
Implementation
Multi-Dimensional Time-Series Similarity Search
Rapidly finds similar patterns across multi-dimensional data such as RPM, acceleration, and temperature.
Composite Sensor Pattern Anomaly Detection
Automatically detects anomalies from composite behavioral patterns across multiple sensors.
Search & Visualization Interface
Provides UIs to visualize search results and compare against historical data.
Input Formats
Vehicle sensor data (RPM, acceleration, temperature, etc.; CSV / JSON)
Output Formats
Similarity scores, list of anomaly patterns, visualization reports
Integrations
Vehicle data management systems, analytics workstations
Project Summary
Team & Timeline
- Algorithm design → validation → tool implementation
- Vehicle development / data analysis team / a.s.ist engineers
- Built in integration with existing vehicle data platform
Outcomes
- Drastic reduction in pattern search time
- Improved automatic detection rate for anomaly patterns
- Better analysis efficiency by leveraging historical data
Contact Us
We propose end-to-end support — from algorithm development through operational design — for sensor data analytics.
- Development of time-series similarity search algorithms
- Composite sensor pattern anomaly detection
- Building search and visualization tools