Use Case
Equipment Anomaly Detection from Vibration Sensor Data
Built an algorithm that uses vibration data from accelerometers to detect equipment-failure signs with high accuracy.
Overview
Automatically extracts statistical features from vibration data and achieves high-accuracy anomaly detection via supervised learning. We also established an analytical workflow that adapts to changes in equipment conditions.
Benefits
- Achieves 90–100% detection accuracy with supervised learning
- Standardized judgment through automatic extraction of statistical vibration features
- Analytical workflow adaptable to changes in equipment conditions
Target Industries
- Precision Instruments
- Manufacturing Equipment
- Equipment Maintenance
Challenges Before / Changes After
Before
- Analyzing vibration data required specialized expertise
- Anomaly definitions were vague, leading to low detection accuracy
- Retraining models after equipment adjustments was costly
After
- Achieves 90–100% detection accuracy with supervised learning
- Standardized judgment through automatic extraction of statistical vibration features
- Analytical workflow adaptable to changes in equipment conditions
Implementation
Vibration Data Feature Engineering & Preprocessing
Automatically extracts statistical features from triaxial vibration data and converts them into a form suitable for analysis.
Supervised and Unsupervised Anomaly Detection
Supervised learning delivers high accuracy; unsupervised methods cover previously unseen anomalies.
Adaptation to Changes in Equipment Conditions
Established mechanisms for efficient model retraining after equipment adjustments.
Input Formats
Accelerometer data (triaxial vibration, CSV)
Output Formats
Anomaly verdicts, accuracy reports, feature importance
Integrations
Equipment management systems, data collection platforms
Project Summary
Team & Timeline
- Data analysis → model construction → accuracy validation
- Equipment maintenance / quality management / a.s.ist engineers
- Built in integration with existing equipment management platform
Outcomes
- Achieved 90–100% anomaly detection accuracy
- Eliminated reliance on individuals through standardized feature engineering
- Reduced time to adapt to equipment condition changes
Contact Us
We propose end-to-end support — from PoC through operational design — for vibration-based anomaly detection.
- Vibration data feature engineering and preprocessing
- Anomaly detection model construction and accuracy validation
- Adaptation to changes in equipment conditions