Lifetime Prediction Using Event Acquisition Rate Correction
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Solution Overview
Problem
The Kaplan-Meier method for predicting product lifetime is unreliable when events such as repairs are inaccurately recorded, particularly in work machines where users may perform self-repairs with imitation parts, leading to incorrect lifetime predictions.
Innovation Solution
A lifetime prediction system that includes a server device with operation and history information databases, calculating an event data acquisition rate based on accurately recorded events to correct lifetime predictions using the Kaplan-Meier method.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the Kaplan-Meier method is used to predict product lifetime based on event records, then lifetime prediction can be performed, but the prediction reliability deteriorates when event records are inaccurate
Solution Approach 1:
The patent introduces an intermediary correction mechanism that mediates between the inaccurate event records and the Kaplan-Meier prediction method. A correction coefficient is calculated based on the ratio of accurately recorded events to total events, and this coefficient serves as a mediator to adjust the prediction results, thereby compensating for the information loss in event records while maintaining the usability of the Kaplan-Meier method.
2Ease of repair
If self-repair with imitation parts is allowed, then ease of repair improves, but measurement precision of event data deteriorates
Solution Approach 1:
The patent converts the harmful effect of inaccurate event records (caused by self-repair with imitation parts) into a beneficial correction mechanism. By calculating the correction coefficient from the available data and applying it to adjust the prediction results, the system transforms the previously harmful information loss into an opportunity for targeted correction, thereby maintaining prediction reliability despite the ease of self-repair.
3Device complexity
If event records are not accurately maintained, then device complexity reduces, but prediction accuracy deteriorates
Solution Approach 1:
The patent applies parameter changes by introducing a correction coefficient that transforms the prediction model from using raw event records to using adjusted event records. This parameter transformation allows the system to maintain prediction accuracy without requiring complex record-keeping systems, as the correction coefficient compensates for the simplicity of the recording mechanism.
Data Source
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AI summary
An objective is to provide a lifetime prediction system that allows a highly reliable lifetime prediction even when record of events having occurred in products are inaccurate. A lifetime prediction system 1 includes a server device 5 that predicts a lifetime of a product as a machine such as a work machine 2 or a machine part. The server device 5 includes an operation information database 52 that stores operation information for each of a plurality of products, a history information database 53 that stores history information indicating a history of event data as a record of an event having occurred in each of the plurality of products and an arithmetic processing device 54 that predicts the lifetime of the product. The arithmetic processing device 54 calculates an event data acquisition rate indicating a rate of product having the event data in which occurrence of the event is accurately recorded to the plurality of products, based on the operation information and history information, and predicts a corrected lifetime of the product based on the calculated event data acquisition rate.