Machine Degradation Estimation Using Drive-Parameter Clustering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for estimating mechanical degradation in machines with multiple components are challenging due to limited sensor data and complexity, particularly in applications like robotic grasping where direct measurement of degradation is difficult.
Innovation Solution
A method using cluster analysis based on drive parameters measured during the movement of a machine's movable component, employing algorithms like K-means to determine a degradation value independently of direct component measurements, allowing for reliable estimation of mechanical degradation in complex systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model-based approaches are used to reconstruct degradation dynamics, then physical degradation can be modeled, but applicability is limited due to multiple and non-linear degradation processes
Solution Approach 1:
The patent replaces model-based physical degradation reconstruction with a data-driven cluster analysis approach. Instead of using complex physical models to represent crack growth, fatigue or wear dynamics, the invention uses unsupervised machine learning (cluster analysis) on sensor data to identify degradation patterns, thereby achieving broader applicability across different degradation processes while maintaining reliability
Solution Approach 2:
The patent changes the approach from modeling physical parameters directly to analyzing patterns in operational parameter data through cluster analysis. By transforming the problem from physical model reconstruction to data pattern recognition, the method achieves versatility across multiple degradation types while preserving estimation reliability through systematic cluster-based degradation value determination
2Loss of information
If data-driven methods are used for degradation estimation, then historical system data can be utilized, but direct measurement focus limits application to single components
Solution Approach 1:
The patent merges the degradation estimation of multiple components into a unified cluster analysis framework. By combining sensor data from multiple components and analyzing them together through cluster analysis, the method estimates overall system degradation rather than treating each component separately, thereby handling complex multi-component systems effectively
Solution Approach 2:
The patent creates a universal degradation estimation method that can handle multiple component types and degradation processes simultaneously. The cluster analysis approach is not limited to specific components but can be applied universally across different machine components and degradation scenarios, making the method multi-functional and adaptable to complex systems
3Measurement precision
If direct measurement of component degradation is performed, then accurate degradation status can be obtained, but sensor data is limited in complex machines with many small components
Solution Approach 1:
The patent introduces cluster analysis as an intermediary between limited sensor data and degradation assessment. Instead of directly measuring degradation of each small component (which would require extensive sensors), the cluster analysis acts as a mediator that processes available sensor data from drive units and other sources to infer degradation patterns, thereby achieving accurate assessment with limited data
Solution Approach 2:
The patent creates a virtual representation of degradation through cluster analysis results. By analyzing patterns in operational data and creating clusters that represent different degradation states, the method produces a virtual model of component health that accurately reflects actual degradation without requiring direct physical measurement of each component
4Extent of automation
If cluster analysis is performed on drive parameters, then automated degradation estimation is achieved, but direct measurement capability is reduced
Solution Approach 1:
The patent enables the system to perform self-diagnosis through automated cluster analysis of its own operational data. The drive units and sensors automatically generate data, and the cluster analysis algorithm automatically processes this data to estimate degradation without requiring external intervention or direct measurement equipment, achieving full automation while maintaining assessment accuracy
Data Source
AI summary
A method for estimating a mechanical degradation of a machine comprises using a drive unit of the machine to move a movable component of the machine during an evaluation period. A drive parameter is measured during the evaluation period to set up a set of input data. A cluster analysis is performed by a processor on the input data and a degradation value for the machine is determined by the processor depending on a result of the cluster analysis.