Sensor Data Pattern Alignment for Failure Prediction Accuracy
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing pre-diagnostic technologies for equipment like regenerative thermal oxidizers face challenges in accurately predicting failures due to erroneous data, noise, missing data, and misalignment of historical and fresh data patterns, which affects the accuracy of failure prediction models.
Innovation Solution
A data processing system comprising a cyclic correlation establishing module, a data pattern establishing module, and a data pattern alignment module, which corrects sensor data using cyclic correlation and aligns historical and fresh data patterns based on processing steps and cyclic procedures, improving data accuracy and alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If historical baseline data and fresh data are combined to establish failure prediction models, then the predictive capability is improved, but data alignment accuracy deteriorates due to pattern misalignment between different periods
Solution Approach 1:
The system performs preliminary data alignment before combining historical and fresh data. The data alignment module compares data patterns from different periods and applies transformation rules to align them in advance, ensuring that feature parameters from different stages are properly synchronized before model training, thus preventing alignment issues from degrading prediction accuracy
Solution Approach 2:
The patent introduces an intermediary data alignment module that acts as a mediator between historical baseline data and fresh sensor data. This module analyzes pattern similarities and differences, applies alignment transformations, and produces aligned data that can be effectively combined for failure prediction without suffering from temporal pattern mismatches
2Quantity of substance
If sensor data is collected continuously for failure prediction, then data availability is improved, but data quality deteriorates due to erroneous information, noise, and missing data
Solution Approach 1:
The system extracts and removes erroneous information, noise, and missing data from the continuous sensor data stream. The data cleaning module identifies abnormal data points through statistical analysis and pattern recognition, extracts only the valid and reliable data portions, and eliminates corrupt or unreliable measurements before the data is used for prediction modeling
Solution Approach 2:
The patent implements a feedback mechanism where the system continuously monitors data quality metrics, identifies degradation patterns, and triggers corrective actions. The feedback loop analyzes collected data in real-time, detects quality issues, and adjusts data collection or preprocessing parameters to maintain data quality while preserving availability
3Measurement precision
If data processing operations are performed to correct and align data, then data quality is improved, but system complexity increases due to multiple processing modules
Solution Approach 1:
The patent merges multiple data processing operations into an integrated processing pipeline. The data cleaning, alignment, and transformation operations are combined into a unified sequence of modules that work together systematically, reducing the overall system complexity compared to separate independent processing systems while maintaining comprehensive data quality improvement
Data Source
AI summary
A data processing system, including a cyclic correlation establishing module, a data pattern establishing module, and a data pattern alignment module, is provided. The cyclic correlation establishing module receives a plurality of first sensor data, obtained from a first sensor operation performed on processing devices, and receives a table of processing steps and cyclic procedures. The cyclic correlation establishing module obtains a data correlation of the first sensor data according to the number of sample points in a data cycle of the first sensor data and the table to correct the first sensor data. The data pattern establishing module obtains a plurality of first data pattern features from the first sensor data. The data pattern alignment module aligns a plurality of second sensor data obtained from a second sensor operation performed on the processing devices with the first sensor data according to the first data pattern features.