Industrial Asset Failure Forecasting via Time and Distance Metrics
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Solution Overview
Problem
Existing systems for forecasting industrial asset failures are unreliable due to their inability to accurately account for both the time to failure and the unit (distance) component of data trends, leading to improper prioritization of risk.
Innovation Solution
A system that normalizes failure risks into a single metric by concurrently analyzing time and distance to failure across different equipment types, enabling prioritization of failure risks based on a comprehensive evaluation of both factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional forecasting systems use only time to failure estimation, then the analysis is simpler, but the reliability of failure forecasting deteriorates
Solution Approach 1:
The patent introduces a second dimension to risk analysis by incorporating both time-to-failure (horizontal axis) and unit-distance-to-failure (vertical axis) components. This dimensional expansion allows the system to evaluate trends from multiple perspectives simultaneously, improving forecasting reliability without overwhelming complexity by providing a structured framework for multi-parameter analysis.
Solution Approach 2:
The risk analysis is segmented into two independent but complementary components: time-based assessment and unit-based assessment. Each component can be calculated and evaluated separately, then combined to produce an overall risk ranking. This segmentation reduces cognitive load and simplifies the evaluation process while maintaining comprehensive reliability assessment.
2Measurement precision
If the system analyzes both time and distance to failure, then the accuracy of risk prioritization improves, but the computational complexity increases
Solution Approach 1:
By adding the unit-distance-to-failure dimension to the traditional time-to-failure analysis, the system achieves more precise risk measurement. The vertical axis representation of distance to failure limit provides critical information about proximity to failure thresholds that time alone cannot capture, enhancing measurement precision through multi-dimensional data visualization and evaluation.
3Manufacturing precision
If operators manually monitor each trend, then the detail analysis is more thorough, but the time required for analysis increases
Solution Approach 1:
The system performs self-service by automatically calculating both time-to-failure and unit-distance-to-failure metrics, generating risk rankings, and identifying priority items without requiring manual operator intervention. This automation maintains thorough analysis precision while eliminating the time-consuming manual monitoring process, allowing the system to serve itself in the analysis workflow.
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computational system that processes trend data, calculates risk metrics, and generates prioritized recommendations. This substitution of human manual operations with automated algorithms maintains analytical precision while dramatically reducing the time investment required for comprehensive trend evaluation.
Data Source
AI summary
Forecasting industrial asset failures is described. A system determines a data start value associated with an industrial asset at a data start time. The system determines a data end value associated with the industrial asset at a data end time. The system estimates a failure time when a trend projected from the data start value through the data end value will reach a failure limit value. The system determines a distance to failure based on the failure limit value and the data end value. The system outputs a failure forecast, associated with the failure time and the distance to failure, for the industrial asset.