Mechanical Component Failure Prediction Using Time-Series Damage Models
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Solution Overview
Problem
Existing failure probability evaluation methods for mechanical systems are limited by varying operating statuses and lack of consideration for individual load factors, leading to inaccurate predictions of failure rates and remaining life.
Innovation Solution
A failure probability evaluation system that utilizes a failure history database, time-series operation database, and statistical processing to identify a failure rate function, incorporating time-series physical quantity data to minimize life variation and automatically generate a damage model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If simple evaluation of failure rate per unit time is used, then the evaluation process is simple, but the accuracy of estimating number of failures and remaining life is limited
Solution Approach 1:
The patent transforms the evaluation from simple time-based failure rate to a comprehensive model incorporating multiple parameters: operational status variables, load factors, environmental conditions, and component-specific parameters. This parameter expansion enables accurate prediction of failure probability while accounting for varying operating conditions across different mechanical systems.
Solution Approach 2:
The patent introduces a damage accumulation model as an intermediary between operational data and failure prediction. This model integrates sensor data, historical failure records, and load factors to compute cumulative damage, which then feeds into the failure probability calculation, bridging the gap between simple monitoring and accurate prediction.
2Reliability
If statistical analysis based on failure records is used, then failure prediction can be made, but individual operating condition variations are not considered
Solution Approach 1:
The patent segments the failure prediction approach into component-level analysis, where each mechanical component is evaluated individually based on its specific operational data, load factors, and failure mechanisms. This segmentation allows the system to capture individual operating condition variations while maintaining overall system reliability assessment.
Solution Approach 2:
The patent implements a dynamic evaluation framework where failure probability is continuously updated based on real-time sensor data, changing operational status, and accumulated damage. This dynamic approach adapts to individual operating conditions rather than relying on static historical averages, enabling versatile prediction across diverse operating scenarios.
3Measurement precision
If cumulative load amount is calculated from sensor data, then accurate prediction of number of failures and remaining life is possible, but the system complexity increases
Solution Approach 1:
The patent implements self-service through automated data collection from integrated sensors, automatic calculation of cumulative load using predefined models, and autonomous updating of failure probability assessments. This automation reduces the need for manual intervention and complex system management, making the sophisticated prediction capability more practical despite the increased analytical complexity.
Solution Approach 2:
The patent replaces complex manual analysis and mechanical assessment methods with computational models and algorithms. Digital signal processing, statistical analysis, and damage accumulation calculations are performed automatically through software, substituting manual mechanical evaluation processes with efficient computational approaches that handle complexity while maintaining accuracy.
Data Source
AI summary
The remaining life of a component of a mechanical system is predicted with high accuracy. A failure probability evaluation system evaluates a failure of a component of each mechanical system for a mechanical system group including a plurality of mechanical systems, and includes a failure history database for accumulating past failure history data about the mechanical system. A time-series operation database stores time-series physical quantity data representing an operating state of the mechanical system. A failure rate function identification unit calculates a failure rate function of a mechanical system by statistical processing based on the failure history data, and a damage model generation/update unit having a function of generating an explanatory variable expression of a failure rate function which minimizes a variation in life is defined by the failure rate function using the time-series physical quantity data.