Test Plan Support Points From Real Machine Usage Profiles
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Solution Overview
Problem
Existing test cycles for measuring machine characteristics, such as fuel consumption, often use unrealistic speed profiles and fail to consider multidimensional factors like speed and motor temperature, lacking methods for quickly adapting to real usage scenarios.
Innovation Solution
A method to determine test points for a test cycle based on metrologically established operating values of fielded machines, aggregating and classifying these values to minimize deviation in relative frequencies, using a Markov chain process and segmentation to create realistic, adaptable test cycles that reflect actual usage profiles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standardized test cycles (e.g., NEDC) are used for measuring fuel consumption, then measurement standardization is achieved, but the speed profiles become unrealistic and do not reflect actual usage
Solution Approach 1:
The patent creates test cycles by copying and analyzing actual usage data from fielded machines. Operating values from real-world usage are aggregated and used to generate test cycles that replicate actual usage patterns, thereby achieving both measurement accuracy and realism simultaneously
Solution Approach 2:
The patent performs preliminary aggregation and analysis of operating values from fielded machines before creating the test cycle. This preliminary action of collecting and processing real usage data enables the subsequent test cycle to accurately reflect actual usage patterns while maintaining standardization
2Adaptability or versatility
If test cycles measure multiple machine parameters simultaneously, then multidimensional characteristics are captured, but the test cycle complexity increases
Solution Approach 1:
The patent segments the test cycle into multiple independent dimensions, each corresponding to a different machine parameter. This segmentation allows each parameter to be measured and optimized independently while maintaining overall test cycle coherence, thereby capturing multidimensional characteristics without excessive complexity
Solution Approach 2:
The patent creates a universal test cycle framework that can simultaneously measure multiple machine parameters through a single integrated test cycle. This multi-functional approach allows the same test cycle structure to capture various characteristics (fuel consumption, emissions, performance) without requiring separate test cycles for each parameter
3Adaptability or versatility
If test cycles are designed to reflect real usage profiles, then measurement realism is improved, but the measurement time increases significantly
Solution Approach 1:
The patent applies partial action by selecting and weighting the most significant operating conditions and parameters based on their actual occurrence frequency in real usage. Instead of measuring all possible conditions equally, the test cycle focuses on the most representative scenarios, thereby achieving realism while reducing measurement time
Solution Approach 2:
The patent transforms the test cycle parameters based on statistical analysis of real usage data. By changing the parameters (speed profiles, load conditions, duration) to match actual usage distributions, the test cycle achieves realism while the optimization process ensures the measurement time remains acceptable
4Adaptability or versatility
If operating values from fielded machines are collected and analyzed, then realistic test cycles are generated, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent extracts only the essential and most relevant features from the raw operating values of fielded machines. By taking out and focusing on key parameters (speed, load, duration) and their statistical distributions, the system generates realistic test cycles without requiring complex processing of all available data
Solution Approach 2:
The patent creates simplified representations (copies) of the complex real-world operating patterns. Instead of directly using raw field data, statistical models and aggregation algorithms generate condensed test cycle definitions that capture the essence of real usage with reduced computational complexity
Data Source
AI summary
The invention relates to a method (1) for determining supporting points of a test plan (9) for measuring pre-defined test variables of a test machine on the basis of previously measured operating values (3) of operating variables of at least one field machine during the normal use thereof. In an aggregation step (2), the detected operating values (3) are allocated to categories (4) with regard to at least one selected operating variable, according to a predefined classification rule. Default variables are selected in a default step (5) before or after the aggregation step (2). The default variables form at least one subset of the operating variables. The operating category frequency (7) for each category (4) is determined in a determination step (6) following the aggregation step (2). In a subsequent determination step (8), the supporting points of the test plan (9) are determined on the basis of the operating category frequency (7). The supporting points are determined in the determination step (8) such that a deviation of a relative test category frequency, on the basis of the test plan (9), of determined default values of the default variables, associated with categories (4) according to the classification rules, from a relative operating category frequency of the operating values (3) classed according to the classification rules, of the operating variables corresponding to the default variables, is minimised according to a predefined optimisation criterion.