Failure Mode Analytics Using Topic Mapping for Asset Maintenance
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Solution Overview
Problem
Organizations face challenges in analyzing historical data from physical assets to identify failure modes effectively, as valuable information is often buried within millions of lines of text, making it difficult to determine the frequency of failures and identify equipment with higher failure occurrences.
Innovation Solution
A system for failure mode analytics that uses machine learning to extract topics from historical notification data, map them to predefined failure modes, and provide actionable insights, enabling users to validate and score models for proactive maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of historical notification data is performed, then detailed examination of each failure event is possible, but the analysis time and resources required increase significantly when dealing with millions of lines of text
Solution Approach 1:
The patent replaces manual mechanical analysis of notification data with automated machine learning systems. The system uses natural language processing and classification algorithms to automatically extract failure modes from millions of lines of text, transforming the manual information processing task into an automated computational process that maintains high accuracy while dramatically reducing analysis time
Solution Approach 2:
The patent introduces machine learning models as intermediaries between the raw notification data and the failure mode analysis results. These models serve as mediators that automatically process, classify, and extract meaningful failure mode information from unstructured text, eliminating the need for direct manual analysis while preserving measurement precision
2Loss of information
If comprehensive failure mode analysis is performed on all historical data, then complete understanding of failure patterns is achieved, but the computational resources and processing complexity increase
Solution Approach 1:
The patent segments the comprehensive failure mode analysis into multiple processing stages: data preprocessing, feature extraction, classification, and result aggregation. By dividing the analysis process into manageable segments, the system maintains complete failure pattern information while reducing the complexity of any single processing step through modular architecture
Solution Approach 2:
The patent applies partial action by focusing computational resources on extracting and analyzing the most relevant failure mode features from notification data. Rather than processing every detail equally, the system identifies and prioritizes key failure patterns, achieving comprehensive understanding with reduced computational complexity through selective analysis
Data Source
AI summary
Provided is a system and method for training and validating models in a machine learning pipeline for failure mode analytics. The machine learning pipeline may include an unsupervised training phase, a validation phase and a supervised training and scoring phase. In one example, the method may include receiving a request to create a machine learning model for failure mode detection associated with an asset, retrieving historical notification data of the asset, generating an unsupervised machine learning model via unsupervised learning on the historical notification data, wherein the unsupervised learning comprises identifying failure topics from text included in the historical notification data and mapping the identified failure topics to a plurality of predefined failure modes for the asset, and storing the generated unsupervised machine learning model via a storage device.