Asset Degradation Monitoring Using Command-Feedback Delay Signals
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Solution Overview
Problem
Existing methods for detecting and notifying asset degradation are inaccurate and fail to predict maintenance needs effectively, leading to potential operational failures and increased maintenance costs due to continuous or delayed maintenance.
Innovation Solution
A computer-implemented method using command-feedback difference values and delay values to determine asset degradation, comparing these values to thresholds and generating degradation indicators, which can include trend data and predicted maintenance times, utilizing machine learning models for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous maintenance is performed to prevent asset degradation, then asset reliability is improved, but resource expenditure and maintenance costs increase
Solution Approach 1:
The system performs preliminary detection of asset degradation by analyzing command-feedback differences and delay values before complete failure occurs. This allows maintenance to be scheduled proactively based on actual degradation trends rather than performing continuous maintenance, optimizing resource expenditure while maintaining reliability.
Solution Approach 2:
The system implements a feedback mechanism that continuously monitors the difference between commanded values and actual asset responses, as well as response delays. This feedback loop enables dynamic adjustment of maintenance scheduling based on real-time degradation indicators, preventing both over-maintenance and under-maintenance.
2Loss of energy
If maintenance is delayed to reduce costs, then resource expenditure decreases, but asset reliability and service quality deteriorate
Solution Approach 1:
The asset monitoring system operates autonomously, automatically detecting degradation through command-feedback analysis and generating maintenance notifications without continuous human intervention. This self-service capability enables cost-effective delayed maintenance while maintaining service quality through automated surveillance.
Solution Approach 2:
The system performs preliminary detection of degradation trends using command-feedback difference values and delay values, allowing maintenance to be scheduled at optimal times before failure occurs. This preliminary action enables delayed maintenance without compromising service quality.
3Device complexity
If conventional degradation detection methods are used, then implementation simplicity is maintained, but detection accuracy and maintenance prediction capability are insufficient
Solution Approach 1:
The system introduces intermediary metrics (command-feedback difference values and delay values) that mediate between simple data collection and complex degradation analysis. These intermediaries enable accurate degradation detection through straightforward comparisons against thresholds, maintaining implementation simplicity while improving measurement precision.
Data Source
AI summary
Embodiments of the disclosure provide for determination and/or notifying of when an asset requires maintenance. Such embodiments enable outputting notifications in circumstances where tracked data for operations of an asset indicate or are predicted to violate particular thresholds. Some embodiments receive a feedback data set for an asset, identify a command data set for the asset, determine a delay value based at least in part on the feedback data set and the command data set, determine a command-feedback difference value based at least in part on the feedback data set and the command data set, and output a degradation indicator based at least in part on the delay value and the command-feedback difference value.