Multi-Sensor Anomaly Cause Detection for Adaptive Robot Motion
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Solution Overview
Problem
Industrial robots struggle to flexibly respond to product changes, leading to increased production of defective products and requiring manual adjustments, as they cannot accurately detect and analyze anomalies in real-time.
Innovation Solution
A determination device that uses sensor data from multiple sensors to identify anomalies by converting data into structural graph form and generating classifiers to determine the cause of anomalies, allowing for autonomous decision-making and immediate action by the robot.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If industrial robots are introduced to automate production, then productivity and labor efficiency are improved, but the robots cannot flexibly respond to product changes and generate defective products
Solution Approach 1:
The patent implements a feedback mechanism where sensor data from the robot's operations is continuously collected and analyzed by a determination device. The system detects anomalies in real-time, identifies their causes, and provides feedback to adjust robot operations, enabling flexible adaptation to product changes while maintaining automated productivity.
Solution Approach 2:
The determination device enables the robot system to autonomously detect and analyze anomalies without continuous human intervention. By automatically generating cause identification results and adjusting operations, the system serves itself, reducing the need for manual adjustments while maintaining adaptability to product variations.
2Adaptability or versatility
If workers manually adjust robots to respond to product changes, then adaptability is improved, but productivity decreases due to continuous manual intervention
Solution Approach 1:
The determination device enables autonomous anomaly detection and cause identification, allowing the robot system to self-adjust to product changes without continuous worker intervention. This maintains high adaptability while preserving productivity by eliminating manual adjustment cycles.
Solution Approach 2:
The patent replaces manual mechanical adjustment with an automated determination device that uses sensor data analysis and machine learning algorithms to identify anomaly causes and guide robot adjustments, substituting human labor with intelligent automation.
3Measurement precision
If multiple sensors are used to detect anomalies, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The determination device extracts and focuses on the most relevant sensor data related to specific anomaly causes. By selectively processing only the critical information from multiple sensors rather than all sensor data, the system maintains high detection precision while reducing processing complexity.
Solution Approach 2:
The patent segments the anomaly detection process into distinct stages: data collection from multiple sensors, anomaly detection based on threshold values, and cause identification using structural data. This segmentation allows each component to focus on specific tasks, maintaining precision while managing overall system complexity.
4Productivity
If anomaly detection and analysis are performed rapidly, then productivity is improved by preventing defective products, but measurement precision may be compromised
Solution Approach 1:
The determination device performs preliminary anomaly detection using threshold values from sensor data before full cause analysis is completed. This allows rapid identification of potential issues to prevent defective products, while more detailed precision analysis follows for confirmed anomalies.
Solution Approach 2:
The patent transforms sensor data into structural data with graph structures that represent causal relationships. This dimensional transformation enables parallel processing of multiple anomaly indicators, achieving both rapid detection and precise cause identification simultaneously by analyzing data from multiple dimensions.
Data Source
AI summary
A determination device includes: a memory; and a processor coupled to the memory and configured to: obtain sensor data on motion of a device from a plurality of sensors, extract, from the sensor data, data related to an anomaly based on a threshold value used in detecting the anomaly with use of the sensor data, convert the data related to the anomaly into structural data having a graph structure focusing on an analogous relationship between or among the plurality of sensors, and generate a classifier that identifies a cause of the anomaly with use of the structural data.