Robot Operation Monitoring Using Autoencoder Pattern Groups
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods lack effective analysis and monitoring of robot operations to detect anomalies and classify temporal characteristics, such as environmental contacts, which hinders predictive maintenance and efficient operation modification.
Innovation Solution
A method utilizing artificial neural networks, specifically autoencoders with encoders and decoders, to identify pattern groups in robot data sets, allowing for anomaly detection and event classification, enabling predictive maintenance and operation modification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional robot monitoring methods are used, then the system is simple to implement, but the ability to detect anomalies and classify temporal characteristics is insufficient
Solution Approach 1:
The patent replaces traditional mechanical monitoring methods with artificial neural networks and autoencoder-based machine learning systems. The encoder maps robot operational data to temporal characteristic patterns, while the decoder reconstructs the data to identify anomalies, substituting conventional signal processing with intelligent algorithms that automatically learn temporal patterns.
Solution Approach 2:
The patent introduces temporal characteristic patterns as an intermediary representation between raw robot operational data and anomaly detection. The encoder transforms raw data into these intermediate patterns, which then serve as the basis for both reconstruction (by the decoder) and anomaly identification, enabling sophisticated analysis without direct complex processing of raw data.
2Reliability
If no pattern recognition is performed, then the system operates quickly, but predictive maintenance and operation modification cannot be performed
Solution Approach 1:
The patent performs preliminary action by training the autoencoder model offline on historical robot operational data before actual monitoring begins. During runtime, the pre-trained model quickly encodes new data into temporal patterns and compares them against learned patterns, enabling rapid anomaly detection without requiring complex real-time analysis computations.
Solution Approach 2:
The patent creates a virtual copy of normal robot operational patterns through the autoencoder's training process. The encoder learns to map normal operational data to temporal characteristic patterns, and the decoder reconstructs these patterns. During monitoring, deviations from these learned patterns indicate anomalies, allowing predictive maintenance without time-consuming manual analysis.
3Loss of information
If detailed temporal characteristic analysis is performed, then event classification improves, but computational resources increase
Solution Approach 1:
The patent extracts only the essential temporal characteristic patterns from detailed robot operational data through the encoder. Instead of processing all raw data in detail, the encoder identifies and extracts key temporal features that are then used for pattern matching and anomaly detection, retaining critical information while reducing computational energy requirements.
Solution Approach 2:
The patent segments the operational data analysis into two distinct functions: the encoder handles pattern extraction and transformation to temporal characteristics, while the decoder handles reconstruction and anomaly identification. This segmentation allows each component to specialize in specific computational tasks, improving information retention efficiency while managing computational energy consumption.
Data Source
AI summary
A method for analyzing an operation of a robot includes performing a training phase by obtaining a first dataset containing at least one temporal characteristic of at least one state parameter of a first robot and training an artificial neural network. The artificial neural network includes a first autoencoder having an encoder that maps the first dataset onto temporal characteristic patterns and the activation thereof, and a decoder that uses these temporal characteristic patterns to reconstruct the first dataset; and a second autoencoder having an encoder that maps the temporal characteristic patterns and the activation thereof onto pattern groups, and a decoder that uses these pattern groups to reconstruct the temporal characteristic patterns and the activation thereof. The method further includes performing a monitoring phase by obtaining a second dataset containing at least one temporal characteristic of the at least one state parameter of the first or of a second robot; and identifying at least one of the pattern groups of the trained second autoencoder within the second dataset.