Failure Prediction Using Simulated Time-Series Baselines
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Solution Overview
Problem
Users face difficulty in identifying abnormalities in time series data related to apparatus control, making it challenging to predict failures effectively.
Innovation Solution
A failure prediction support device that synchronizes actual machine time series data with simulation time series data, detects differences, and notifies users when these differences satisfy predetermined abnormality conditions, facilitating easier identification of anomalies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users directly analyze time series data to predict failures, then failure prediction capability is maintained, but the ability to easily identify abnormalities deteriorates
Solution Approach 1:
The system creates a virtual copy of the apparatus through simulation, generating simulation time series data that mirrors actual machine behavior. This virtual model allows users to compare simulated normal operation with actual operation, making abnormalities easily identifiable without complex direct analysis of raw time series data
Solution Approach 2:
The simulation time series data acts as an intermediary between the raw actual machine data and the user's analysis. By comparing actual data against the simulated baseline, the system mediates the complexity of direct time series analysis, highlighting deviations automatically
2Measurement precision
If users manually analyze time series data for abnormalities, then detailed inspection is possible, but the time required for analysis increases
Solution Approach 1:
The system performs preliminary analysis by pre-generating simulation time series data that represents normal operation patterns. This preparatory work establishes a baseline for comparison, so when actual data is collected, abnormalities are immediately identifiable through direct comparison rather than requiring time-consuming manual analysis from scratch
Solution Approach 2:
By creating a virtual copy of normal operation through simulation, the system enables rapid comparison with actual data. This copying approach allows precise abnormality detection through automated comparison algorithms, eliminating the need for time-consuming manual inspection while maintaining high detection precision
Data Source
AI summary
Provided are a failure prediction support device, a failure prediction support method, and a failure prediction support program, by which a user can easily know an abnormality in time series data relating to an apparatus. The failure prediction support device includes: a difference detection part, acquiring actual machine time series data being time series data relating to control of an apparatus and simulation time series data being time series data relating to control of the simulated apparatus, and detecting a difference between the actual machine time series data and the simulation time series data; a determination part, determining whether or not the difference satisfies a condition predetermined in relation to an abnormality in the actual machine time series data; and a notification processing part, notifying that the difference has been detected when it is determined that the difference satisfies the condition.