Radiotherapy Machine Log Analytics for Predictive Maintenance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional maintenance procedures for radiotherapy machines require specialized manual analysis of complex log files, leading to inefficiencies and prolonged downtime, which is costly and disrupts patient treatment.
Innovation Solution
A machine learning model is trained to analyze and categorize radiotherapy machine data, converting unstructured logs into machine-readable objects for predictive maintenance, and provides insights through customizable user interfaces and natural language processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of log files is used, then specialized expertise can diagnose issues, but it requires highly specialized maintenance workers and prolonged downtime
Solution Approach 1:
The patent replaces manual mechanical analysis of log files by specialized workers with an automated electronic system that uses machine learning models and natural language processing to diagnose machine issues. The system automatically parses, structures, and analyzes log data, substituting human expertise with computational intelligence to reduce downtime while maintaining diagnostic accuracy.
Solution Approach 2:
The system enables the radiotherapy machine to essentially diagnose its own issues by automatically generating and analyzing its own log files through an electronic system. The machine learning model processes the machine's operational data autonomously, allowing the system to self-monitor and self-diagnose without requiring external human intervention for basic troubleshooting.
2Measurement precision
If manual review of log files is used, then detailed analysis can be performed, but it is tedious and inconsistent
Solution Approach 1:
The patent replaces the tedious manual review process with an automated electronic system that consistently applies the same analysis algorithms to all log files. The machine learning model ensures uniform processing standards, eliminating the variability and inconsistency inherent in manual analysis while maintaining high levels of analytical detail.
Solution Approach 2:
The system incorporates feedback mechanisms where the machine learning model continuously learns from analyzed log data and improves its diagnostic accuracy over time. The system provides feedback to maintenance workers through natural language interfaces, explaining diagnostic results and recommended actions, thereby creating a continuous improvement loop that enhances consistency while reducing operational complexity.
3Measurement precision
If specialized maintenance workers are used, then accurate diagnosis can be achieved, but it requires in-depth knowledge of machine-generated logs
Solution Approach 1:
The patent substitutes the need for specialized human knowledge with an electronic system that automatically possesses and applies expertise through machine learning models. The system encodes specialized knowledge about log file interpretation and machine diagnostics into computational algorithms, eliminating the requirement for maintenance workers to have deep expertise in parsing and interpreting complex log data.
Solution Approach 2:
The system introduces a natural language processing intermediary that translates complex machine log data into human-understandable diagnostic conclusions. This intermediary layer bridges the gap between complex machine-generated data and simple maintenance worker queries, allowing non-experts to obtain accurate diagnoses without needing to understand the underlying complex log file structures.
4Loss of information
If the volume of information from radiotherapy machines is analyzed manually, then detailed insights can be obtained, but it is impracticable to analyze
Solution Approach 1:
The patent replaces manual information analysis with automated electronic processing capable of handling large volumes of machine-generated data at high speeds. The system uses machine learning models and natural language processing to rapidly parse, structure, and analyze log files, extracting meaningful insights from vast amounts of data that would be impossible to process manually while maintaining comprehensive information utilization.
Solution Approach 2:
The system segments the large volume of log information into manageable structured components using automated parsing and data structuring techniques. By dividing the complex log data into organized segments that can be processed individually and systematically, the system makes the analysis of large information volumes practicable while maintaining complete information utilization.
Data Source
AI summary
Disclosed herein are systems and methods for predicting performance attributes of radiotherapy machines. A processor generates a machine-readable object by executing an algorithm using an electronic log file comprising at least one operational attribute of a radiotherapy machine and a corresponding timestamp. The processor executes a machine learning model using the machine-readable object to predict a performance attribute of the radiotherapy machine. The processor provides the predicted performance attribute of the radiotherapy machine to an electronic device.