Prognostic Maintenance System Using Automated Machine Learning
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Solution Overview
Problem
Existing prognostic maintenance systems rely heavily on human intervention, require vast amounts of truth data for learning, and are limited by their ability to process free-form or natural language data, which hinders their efficiency and accuracy in making optimal maintenance decisions.
Innovation Solution
The system employs automated machine learning to capture historical maintenance information, correct and predict maintenance activities, and generate work orders with high accuracy, without requiring human intervention. It processes both free-form and fixed record format descriptions, deriving maintenance operational logic directly from historical records.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated machine learning is used to process maintenance data, then productivity and accuracy of maintenance decisions improve, but the system complexity increases
Solution Approach 1:
The system divides maintenance data processing into distinct modules: data collection from multiple sources, free-form text processing, structured data processing, machine learning model training, and work order generation. Each module handles specific tasks independently, improving productivity while managing complexity through functional segmentation.
Solution Approach 2:
The patent introduces intermediary components such as natural language processing layers that translate free-form maintenance records into structured data, and machine learning models that act as intermediaries between historical data and maintenance decisions. These intermediaries bridge the gap between unstructured data and actionable insights without requiring direct human intervention.
2Adaptability or versatility
If the system processes free-form and natural language data, then adaptability improves, but measurement precision and data processing accuracy deteriorate
Solution Approach 1:
The system replaces manual data entry and structured form-filling with automated natural language processing. Machine learning models automatically extract and structure information from free-form text, maintaining adaptability to various data formats while improving accuracy through automated pattern recognition and validation algorithms.
Solution Approach 2:
The patent transforms free-form text data into structured parameters through natural language processing. The system changes the state of data from unstructured text to structured numerical and categorical parameters that can be processed by machine learning models, thereby maintaining adaptability while achieving measurement precision.
3Manufacturing precision
If vast amounts of truth data are collected for learning, then manufacturing precision of maintenance predictions improves, but loss of time and data collection effort increase
Solution Approach 1:
The system performs preliminary processing of maintenance data during routine operations, continuously collecting and preprocessing data in the background. Historical maintenance records are pre-processed and stored in structured formats, so when prediction is needed, the system can quickly retrieve and analyze pre-prepared data without time-consuming collection efforts.
Solution Approach 2:
The patent implements continuous data collection and model training processes that operate in the background during normal system operations. The machine learning models are continuously refined using incoming maintenance data, ensuring prediction accuracy improves over time without requiring dedicated data collection periods that would cause operational interruptions.
4Reliability
If human intervention is required for maintenance decisions, then reliability of decisions improves, but extent of automation deteriorates
Solution Approach 1:
The system performs self-service by automatically generating maintenance work orders and recommendations without human intervention. The machine learning models autonomously analyze maintenance data, predict failures, and generate actionable work orders, achieving both high automation and reliability through validated algorithms and continuous learning from historical data.
Solution Approach 2:
The patent incorporates feedback mechanisms where maintenance outcomes are fed back into the system to continuously improve prediction accuracy. The machine learning models learn from actual maintenance results and adjust their predictions accordingly, ensuring reliability improves while maintaining full automation. The feedback loop validates and refines automated decisions without requiring human intervention.
Data Source
AI summary
A system and method accepts data from direct fault and parameters measurements and historical data in formatted and unformatted form organized by device or system class, specific unit and unit subsystems, and by operation to learn how to predict or correct specified maintenance or other operations. Disclosed examples use known data and learned information historical data to correct or elaborate service requests based on specified and likely (based on learned information) needed maintenance operations. The system and method are applicable to any device requiring prognostic maintenance or other service based on service orders and direct measurement of operating parameters.