Machine Learning Failure Mode Discovery for Mechanical Components
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Solution Overview
Problem
In industrial settings, identifying the specific failure modes of mechanical components and machines is challenging due to their increasing complexity, hindering scheduled maintenance, cost reduction, and improving manufacturing processes.
Innovation Solution
An automated framework using a machine learning model, such as a word2vec model, to analyze unstructured and structured maintenance records, vectorize textual data, and apply clustering algorithms to identify and label failure modes, facilitating the detection of failure modes in mechanical components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machines and mechanical components increase in complexity, then functionality and performance are improved, but identifying failure modes becomes more difficult
Solution Approach 1:
The patent replaces manual failure mode identification methods with an automated machine learning system. The system uses word embedding models (such as word2vec) to automatically analyze maintenance records and identify failure modes, substituting human expertise and manual analysis with computational algorithms that can process complex data patterns efficiently.
Solution Approach 2:
The system enables self-service by automatically analyzing maintenance records and identifying failure modes without requiring expert intervention. The machine learning model autonomously processes unstructured text data, extracts relevant features, and generates failure mode classifications, allowing the organization to leverage its own maintenance data for continuous improvement.
2Measurement precision
If manual analysis of maintenance records is used, then failure modes can be identified, but it is time-consuming and inefficient
Solution Approach 1:
The patent replaces manual analysis with automated machine learning processing. The system uses natural language processing techniques to rapidly analyze maintenance records, extracting failure mode information that would take human analysts significant time to identify manually, while maintaining or improving accuracy through consistent application of analysis criteria.
Solution Approach 2:
The system performs preliminary action by pre-processing and vectorizing maintenance records into structured formats suitable for analysis. The word embedding model pre-computes representations of maintenance terminology, enabling rapid querying and analysis when failure modes need to be identified, thus reducing analysis time for actual failure investigations.
3Productivity
If automated machine learning methods are used, then failure mode identification efficiency is improved, but system complexity increases
Solution Approach 1:
The patent applies a universal word embedding model (such as word2vec) that can be used across different types of maintenance records and failure scenarios. This single model serves multiple functions: it processes unstructured text, extracts features, and generates classifications, reducing the need for multiple specialized systems and simplifying the overall architecture despite the automated processing capabilities.
Data Source
AI summary
The failure modes of mechanical components may be determined based on text analysis. For example, a word embedding may be determined based on a plurality of text documents that include a plurality of maintenance records characterizing failure of mechanical components. A vector representation for a particular maintenance record may then be determined based on the word embedding. Based on the vector representation, the particular maintenance record may then be identified as belonging to a particular failure mode out of a set of possible failure modes.