Machine Learning Model for Maintenance Log Interpretation
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Solution Overview
Problem
Existing asset-health-management systems face challenges in accurately predicting the remaining useful life (RUL) of equipment due to scarcity of training data in certain domains, as conventional NLP techniques struggle to interpret maintenance logs with jargon, abbreviations, and incomplete sentences, and require recalibration after maintenance actions that alter operational signatures.
Innovation Solution
A machine learning model is trained to differentiate between generic and domain-specific terms in maintenance logs, with weights learned in one domain generalized to another, allowing the model to detect trigger events and recalibrate RUL predictions across domains with scarce data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional NLP techniques are used to process maintenance logs, then the system can handle basic text processing, but it fails to accurately interpret maintenance logs containing jargon, abbreviations, and incomplete sentences
Solution Approach 1:
The patent applies domain adaptation techniques to make the NLP model universally applicable across multiple domains (e.g., electronics, mechanical, chemical). The model is trained on source domain data and then adapted to target domains with scarce data, enabling it to handle domain-specific jargon, abbreviations, and terminology while maintaining accurate interpretation across different industrial contexts
Solution Approach 2:
The patent uses transfer learning where knowledge and patterns learned from source domain maintenance logs are copied and transferred to target domains. The pre-trained model's weights and linguistic patterns are copied and fine-tuned on target domain data, allowing the system to accurately interpret maintenance logs in new domains without requiring extensive retraining from scratch
2Reliability
If training data is abundant in one domain, then the machine learning model can be trained effectively, but the model cannot be applied to domains with scarce training data
Solution Approach 1:
The patent implements domain adaptation to create a universally applicable RUL prediction model. The model is trained on abundant data from source domains and then adapted to target domains with scarce data through fine-tuning and transfer learning, maintaining reliable predictions across multiple industrial domains including electronics, mechanical systems, and chemical processes
Solution Approach 2:
The patent uses a pre-trained language model as an intermediary that bridges source and target domains. This intermediary model learns general linguistic patterns and domain-specific terminology from source domains, then serves as a foundation for target domain applications, enabling effective RUL prediction even when target domain training data is limited
3Reliability
If maintenance actions are performed to repair or replace faulty components, then equipment reliability is improved, but the operational signature changes requiring algorithm reset or recalibration
Solution Approach 1:
The patent implements a self-adapting RUL prediction system that automatically detects maintenance events from maintenance log entries and performs self-recalibration. The system uses the trained machine learning model to identify maintenance actions, automatically adjusts the operational signature parameters, and continues predictions without requiring manual intervention or system reset, thereby eliminating recalibration time loss
Data Source
AI summary
One embodiment provides a system for processing natural-language entries. The system obtains a plurality of historical natural-language entries associated with a first domain and pre-processes the historical natural-language entries to obtain a set of generic terms and a set of domain-specific terms. The system trains a machine learning model in the first domain using the plurality of historical natural-language entries associated with the first domain. The training comprises learning weight values of one or more generic terms, a weight value of a respective generic term indicating likelihood that the generic term is related to a trigger event. The system generalizes the machine learning model trained in the first domain, thereby allowing the model to be applied to a second domain. The system processes a natural-language entry associated with the second domain using the generalized machine learning model to determine whether the natural-language entry associated with the second domain corresponds to the trigger event.