Construction Machine Maintenance Prediction Using Wear and Load Data
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Solution Overview
Problem
Current maintenance scheduling for construction machines is inaccurate due to individual variations in usage environment and load, leading to premature failures or extended service life, resulting in inefficient maintenance timing and productivity losses.
Innovation Solution
A system that predicts individual service life by combining wear state and cumulative load parameters, using detection data from equipped machines and historical data from others, to set prioritized maintenance schedules and adjust operating conditions for optimal component availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If maintenance timing is determined only by cumulative operating time period, then maintenance scheduling is simple, but prediction accuracy deteriorates due to individual discrepancies in wear state
Solution Approach 1:
The patent applies parameter changes by transitioning from a single parameter (cumulative operating time) to multiple parameters (operating time, cumulative load amount, and wear state indicators). This allows the system to adapt to individual discrepancies in wear state while maintaining a manageable scheduling framework through weighted composite indices.
Solution Approach 2:
The patent creates a composite service life prediction model that combines multiple parameters (operating time, load amount, wear state) into a unified prediction framework. This composite approach integrates different data sources and parameter types to achieve more accurate predictions than any single parameter could provide alone.
2Reliability
If maintenance timing is determined by cumulative load amount, then individual wear differences are considered, but prediction accuracy deteriorates due to environmental and task variations
Solution Approach 1:
The patent enhances the cumulative load amount parameter by introducing wear state indicators and operating time data as additional parameters. This multi-parameter approach compensates for environmental and task variations that affect individual machines, providing a more robust prediction model that considers multiple aspects of machine degradation.
3Measurement precision
If service life prediction accuracy is low, then maintenance timing cannot be set appropriately, but increasing prediction complexity increases system complexity
Solution Approach 1:
The patent balances prediction accuracy and system complexity by selecting a specific set of parameters (operating time, cumulative load amount, wear state) with appropriate weighting. This parameter selection strategy achieves improved accuracy without requiring excessively complex measurement and data processing systems.
Solution Approach 2:
The patent replaces complex mechanical monitoring systems with information-based approaches, using data processing and computational models to predict service life. This substitution achieves high prediction accuracy through software-based analysis rather than requiring complex hardware modifications.
4Measurement precision
If multiple prediction methods are used to improve accuracy, then maintenance timing is more accurate, but the number of parameters and data requirements increases
Solution Approach 1:
The patent merges multiple prediction methods into a unified composite index model. By combining operating time, cumulative load amount, and wear state indicators into a single integrated framework with weighted parameters, the system achieves accurate maintenance timing predictions while avoiding the data redundancy and complexity of completely separate prediction systems.
Data Source
AI summary
To predict the service life of a construction machine more accurately, and to make it possible to draw up an appropriate overhaul implementation plan at an early stage.A first service life prediction unit 311 predicts the service lives of main components such as an engine and the like, based upon their actual wear states. And a second service life prediction unit 312 predicts the service lives of the same components, based upon their cumulative load amounts. An order setting unit 320 selects the ones of these two predicted service lives which are the shorter, and sets a priority order for overhaul in order of shortness of predicted service life. An overhaul schedule table generation unit 330 creates a schedule table D1 based upon this priority order. The contents of this schedule table D1 are decided upon by a judgment unit 340, and, if necessary, are corrected by a correction unit 341. And a plan creation unit 350 creates an overhaul implementation plan document D2 and the like, based upon the corrected schedule table D1a. The result of the overhaul task and the present states of components are tested, and these test results are reflected by the service life prediction units 311 and 312.