Machinery Failure Prediction Horizon Learning From Run-to-Failure Data
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Solution Overview
Problem
Existing methods for predicting machinery failures rely on manual selection of prediction horizons, which can be subjective and lead to inaccurate results, reducing the quality and reliability of failure prediction models.
Innovation Solution
A computer-implemented method that iteratively evaluates run-to-failure sequences of time series data to determine candidate cut-off values based on relative frequency distributions and distances between positive and negative sub-sequences, thereby automatically learning the prediction horizon for machinery failures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual selection of prediction horizon is used, then ease of operation is improved, but measurement precision deteriorates
Solution Approach 1:
The system performs self-service by automatically determining the prediction horizon through iterative evaluation of run-to-failure sequences. The algorithm independently analyzes condition monitoring data, calculates distances between frequency distributions, and selects optimal cut-off values without requiring manual intervention, thereby resolving the contradiction between ease of operation and measurement precision.
Solution Approach 2:
The system changes the parameter of prediction horizon from a manually selected fixed value to a dynamically determined value based on data analysis. By iteratively evaluating multiple potential cut-off points and selecting those corresponding to local peak points in distance calculations, the system optimizes the prediction horizon parameter to improve measurement precision while maintaining operational simplicity.
2Device complexity
If manual selection of prediction horizon is used, then device complexity is reduced, but reliability deteriorates
Solution Approach 1:
The system achieves self-service by implementing an automated algorithm that evaluates run-to-failure sequences and determines optimal prediction horizons independently. This self-determination process improves reliability by eliminating subjective manual selection errors while the modular algorithmic structure keeps device complexity manageable.
Solution Approach 2:
The system incorporates feedback mechanisms by iteratively evaluating potential cut-off points and using the results to refine the prediction horizon selection. The algorithm calculates distances between frequency distributions, identifies local peak points, and uses this feedback to select optimal cut-off values, thereby improving reliability through data-driven decision making.
3Measurement precision
If automated determination of prediction horizon is implemented, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system performs self-service by automatically determining the prediction horizon through iterative evaluation of run-to-failure sequences. The algorithm independently analyzes condition monitoring data, calculates distances between frequency distributions, and selects optimal cut-off values without requiring manual intervention, thereby resolving the contradiction between ease of operation and measurement precision.
Solution Approach 2:
The system changes the parameter of prediction horizon from a manually selected fixed value to a dynamically determined value based on data analysis. By iteratively evaluating multiple potential cut-off points and selecting those corresponding to local peak points in distance calculations, the system optimizes the prediction horizon parameter to improve measurement precision while maintaining operational simplicity.
Data Source
AI summary
The present disclosure relates to computer-implemented methods, software, and systems for predicting failure event occurrence for a machine asset. Run-to-failure sequences of time series data that include an occurrence of a failure event for the machine asset are received. One or more candidate cut-off values are determined based on iterative evaluation of a plurality of potential cut-off points. A candidate cut-off value is identified as substantially corresponding to a local peak point for calculated distances between relative frequency distributions of positive and negative sub-sequences. A failure prediction model is iteratively trained to iteratively extract sets of relevant features to determine a prediction horizon for an occurrence of the failure event for the machine asset. A candidate cut-off value associated with a model of highest quality from a set of failure prediction models determined during the iterations is selected to determine the prediction horizon for the machine asset.