Machine Tool Anomaly Detection Using Synchronized Sensor Distance
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Solution Overview
Problem
Existing anomaly detection systems for machine tools require a large number of sensors and a significant amount of data for machine learning, making them cumbersome and inefficient.
Innovation Solution
An anomaly detection system that uses a single sensor, such as a strain sensor, to measure physical quantities in a machine tool, synchronizes the measurement data with reference data, calculates the distance between them, and compares it with a threshold to detect anomalies, eliminating the need for multiple sensors and extensive data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors and large data sets are used for machine learning-based anomaly detection, then detection accuracy is improved, but device complexity and data processing burden increase
Solution Approach 1:
The patent extracts only the essential vibration signal feature (envelope waveform) from the raw sensor data, eliminating the need for complex multi-sensor setups and large data sets. By focusing on the envelope waveform characteristic that directly reflects tool condition, the system achieves accurate anomaly detection with minimal sensing requirements
Solution Approach 2:
The patent replaces complex machine learning algorithms with a simpler signal processing approach based on envelope waveform analysis. This substitution of the detection methodology reduces computational complexity while maintaining detection accuracy, as the envelope waveform inherently contains the diagnostic information needed for anomaly detection
2Reliability
If machine learning methods are used for anomaly detection, then detection capability is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent extracts only the essential envelope waveform feature from vibration data, eliminating the need for processing large data sets through complex machine learning algorithms. This extraction of the critical diagnostic feature enables rapid processing while maintaining detection reliability
Solution Approach 2:
Instead of using complex machine learning to find patterns in large data sets, the patent inverts the approach by directly analyzing the envelope waveform structure that naturally contains anomaly information. This inversion simplifies the processing pipeline and reduces computational time while preserving detection capability
3Adaptability or versatility
If multiple types of measurement data are collected for comprehensive analysis, then detection coverage is improved, but system complexity and cost increase
Solution Approach 1:
The patent makes the single vibration sensor and envelope waveform analysis method universally applicable to various tool types and machining conditions. The envelope waveform characteristic is a fundamental vibration feature that appears across different tool conditions, allowing one simple system to provide comprehensive detection coverage without requiring multiple specialized sensors or complex configurations
Data Source
AI summary
An anomaly detection system includes: a sensor provided to a tool, the sensor being configured to measure a physical quantity, of the tool, that varies while a machine tool is machining a machining target object by means of the tool; and an anomaly detection device configured to detect an anomaly in the tool, based on measurement data obtained by the sensor. The anomaly detection device includes: a synchronization unit configured to achieve synchronization between target data being measurement data in time series obtained by the sensor, and reference data being a measurement result in time series of the physical quantity when the tool is in a normal state; a distance calculation unit configured to calculate a distance between the reference data and the target data; and an anomaly detection unit configured to detect an anomaly in the tool by comparing the distance with a threshold.