Malfunction Analysis Apparatus for Mechanical Systems
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Solution Overview
Problem
Existing malfunction analysis techniques fail to accurately distinguish between direct and indirect causes of malfunctions in mechanical systems, leading to insufficient extraction of attention attributes.
Innovation Solution
A malfunction analysis apparatus and method that calculates a malfunction-contribution degree and specifies malfunctioning elements and their causes using a relative relationship between data indicators, allowing for precise identification and output of attention attributes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional malfunction analysis techniques are used to extract attention attributes from sensor and log information, then various attributes can be provided to users, but the accuracy of distinguishing direct causes from indirect causes of malfunctions is insufficient
Solution Approach 1:
The patent segments the malfunction analysis process into distinct functional modules: a malfunction-contribution degree calculator that quantifies the contribution of each malfunctioning element, a malfunctioning elements specifier that identifies occurred malfunctions, a cause indicator specifier that distinguishes direct causes from indirect causes, and an outputter that presents the results. This segmentation enables precise differentiation between direct and indirect malfunction causes by assigning specific analytical functions to each module.
2Reliability
If manual analysis of large amounts of mechanical system data is performed, then thorough investigation is possible, but the analysis process becomes time-consuming and difficult
Solution Approach 1:
The patent replaces manual mechanical analysis with an automated computational system that processes sensor information and log information through algorithmic calculations. The malfunction-contribution degree calculator automatically computes contribution degrees for multiple malfunctioning elements simultaneously, and the cause indicator specifier automatically distinguishes direct from indirect causes, eliminating the need for time-consuming manual data examination while maintaining thorough investigative capability.
3Productivity
If principal component analysis or similar techniques are used to calculate characteristic attributes from sensor information, then attention attributes can be obtained, but the distinction between direct and indirect malfunction causes is not achieved
Solution Approach 1:
The patent introduces a malfunction-contribution degree as an intermediary quantitative measure that bridges sensor information and malfunction cause identification. This intermediary metric allows the system to efficiently process multiple sensor inputs while maintaining high precision in cause identification, as the contribution degree serves as a mediating variable that captures the relationship between sensor data and malfunction causes without losing discriminative information.
Data Source
AI summary
A malfunction analysis apparatus (100) is provided with a malfunction-analysis processor (107), an attribute-extraction processor (108), and an outputter (105). The malfunction-analysis processor (107) obtains a malfunction-contribution degree, which indicates a degree that individual malfunctions (to be called malfunctioning elements, hereafter) contribute to the malfunctioning of the object being analyzed, on the basis of the relative relationship between the data to be analyzed that has, as elements thereof, values generated on the basis of a plurality of indicator values of the object being analyzed, and representative values for the plurality of indicators corresponding to each of the plurality of malfunctions. Then, the malfunctioning elements being generated is specified, on the basis of the obtained malfunction-contribution degree. The attribute-extraction processor (108) specifies, when a malfunctioning is taking place that is a combination of the malfunctioning elements, the indicators that are estimated as the cause of the specified malfunctioning elements, on the basis of the representative values of the plurality of indicators, and the values of each of the elements of the data to be analyzed that was stored. The outputter (105) outputs the specified malfunctioning elements and/or the indicators.