Machine Learning Work Order Analytics for Failure Prediction
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Solution Overview
Problem
Existing work order management systems lack the ability to provide higher-level insights into maintenance operations, are susceptible to errors in submission and closure processes, and fail to identify abnormal maintenance behaviors that can lead to asset failures and increased downtime.
Innovation Solution
A work order analysis system that applies statistical and machine learning analytics to cluster and analyze work orders, identifying anomalies and assigning risk scores based on deviations from normal maintenance procedures, and providing enterprise-specific recommendations to optimize maintenance processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional work order management systems are used, then the systems are simple to operate, but they lack the ability to provide higher-level insights and identify abnormal maintenance behaviors
Solution Approach 1:
The patent introduces an intermediary analytics engine that sits between the traditional work order management system and the users. This intermediary component processes work order data, performs statistical analysis, applies machine learning models, and generates insights without replacing the entire system. The intermediary approach allows advanced analytics capabilities to be added while maintaining the simplicity of the underlying work order management infrastructure.
Solution Approach 2:
The patent segments the analytics functionality into distinct modular components including data preprocessing modules, statistical analysis modules, machine learning model modules, and reporting modules. Each module handles a specific aspect of the analytics process, allowing the system to provide comprehensive insights while maintaining manageable complexity through modular architecture.
2Reliability
If manual work order processing is used, then the system is easy to implement, but it is susceptible to errors in submission and closure processes
Solution Approach 1:
The patent implements feedback mechanisms where the analytics engine continuously monitors work order data, compares actual maintenance outcomes with predicted outcomes, and uses this feedback to refine machine learning models and improve anomaly detection accuracy over time. This automated feedback loop reduces human error in submission and closure processes while maintaining systematic complexity through algorithmic rather than manual processes.
3Measurement precision
If comprehensive statistical analysis is applied to all work orders, then anomaly detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by performing comprehensive statistical analysis and machine learning inference only on work orders that are identified as potential anomalies through initial filtering. The system uses a two-stage approach: first a quick filter identifies suspicious work orders, then comprehensive analysis is applied only to those cases. This reduces overall processing time while maintaining high anomaly detection precision for the work orders that require detailed analysis.
Data Source
AI summary
An industrial work order analysis system applies statistical and machine learning analytics to both open and closed work orders to identify problems and abnormalities that could impact manufacturing and maintenance operations. The analysis system applies algorithms to learn normal maintenance behaviors or characteristics for different types of maintenance tasks and to flag abnormal maintenance behaviors that deviate significantly from normal maintenance procedures. Based on this analysis, embodiments of the work order analysis system can identify unnecessarily costly maintenance procedures or practices, as well as predict asset failures and offer enterprise-specific recommendations intended to reduce machine downtime and optimize the maintenance process.