Time-Series Quality Determination With Pretrained DTW Models
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Solution Overview
Problem
Current systems lack an efficient method for determining the quality of a target's state using time-series measurement data, particularly in industrial facilities, where accurate and real-time monitoring is crucial for predicting future states and preventing defects.
Innovation Solution
A measurement data recording apparatus and generation apparatus system that acquires, records, and transmits time-series measurement data to generate a determination model, which is then used to assess the quality of the target's state, incorporating dynamic time warping (DTW) processing and learning algorithms for accurate health index calculation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a determination model is generated using time-series measurement data to predict future states, then the accuracy of state quality determination is improved, but the complexity of the system increases due to model generation and validation requirements
Solution Approach 1:
The system is divided into two distinct functional modules: a generation apparatus that creates determination models using time-series measurement data, and a measurement data recording apparatus that executes these models for real-time state quality determination. This segmentation allows complex model generation to be separated from simple model execution, improving both accuracy and managing system complexity.
Solution Approach 2:
The determination model is generated in advance using historical time-series measurement data before being deployed for real-time prediction. This preliminary action allows the system to learn from past data and establish prediction criteria beforehand, enabling accurate real-time state quality determination without complex real-time computation.
2Reliability
If encryption and validity check processing are implemented to prevent model tampering, then the reliability of the determination model is improved, but the processing time and computational load increase
Solution Approach 1:
Encryption and validity check information are embedded into the determination model during the generation phase, before the model is deployed for execution. This preliminary action ensures model integrity without requiring additional validation processing during real-time operation, thus maintaining both reliability and processing efficiency.
3Difficulty of detecting and measuring
If cluster analysis is performed on time-series measurement data to identify defect patterns, then the ability to detect and classify defects is improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
Cluster analysis is performed in advance on historical time-series measurement data to identify defect patterns and create classification criteria. These pre-computed clusters are then used by the determination model to quickly classify new defects without requiring complex real-time analysis, thus improving defect detection capability while minimizing processing complexity during operation.
Data Source
AI summary
Provided is a measurement data recording apparatus including: an acquisition portion configured to acquire time-series measurement data obtained by measuring a state of a target; a recording portion configured to record the acquired measurement data; a measurement data transmission portion configured to transmit the measurement data recorded in the recording portion to a generation apparatus configured to generate a determination model for determining a quality of the state of the target; a model reception portion configured to receive the determination model generated by the generation apparatus; and a determination portion configured to determine the quality of the state of the target from newly acquired measurement data using the received determination model.