Dynamic Leaf Guide Fault Diagnosis with Deep Learning
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Solution Overview
Problem
Conventional predictive maintenance approaches for radiation therapy systems, particularly for dynamic leaf guides (DLGs) in linac systems, are complex, resource-intensive, and less adaptable due to reliance on physical models, requiring substantial domain knowledge and expertise, and often fail to accurately detect and diagnose faults.
Innovation Solution
A deep learning-based predictive maintenance model is employed to detect and diagnose faults in DLGs by training a neural network using machine data from normal and faulty components, enabling accurate classification of fault types and severity levels, and predicting remaining useful life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional predictive maintenance approaches based on physical models are used, then maintenance planning can be performed, but the system complexity and resource requirements increase significantly
Solution Approach 1:
The patent replaces complex physical models with data-driven machine learning models. Instead of relying on detailed mechanical and physical understanding of DLG components, the system uses trained algorithms that learn patterns from operational data, substituting mechanical modeling with computational learning.
Solution Approach 2:
The patent creates virtual copies of DLG components through digital twins that replicate operational behavior. These digital representations allow predictive maintenance analysis without requiring direct physical inspection or complex physical modeling of the actual components.
2Measurement precision
If physical models with many parameters are used for fault detection, then comprehensive analysis is possible, but the requirement for domain knowledge and expertise increases
Solution Approach 1:
The machine learning models perform self-learning from operational data without requiring expert intervention for parameter interpretation. The system automatically identifies fault patterns and provides diagnoses, making the process self-sufficient and reducing dependency on specialized domain knowledge.
Solution Approach 2:
The patent transforms complex physical parameters into simplified feature representations that the machine learning models can process efficiently. By changing the parameter space from detailed physical measurements to learned features, the system maintains detection accuracy while reducing operational complexity.
3Reliability
If conventional maintenance approaches are used, then regular inspection can be performed, but unnecessary maintenance costs and machine downtime increase
Solution Approach 1:
The system performs preliminary fault detection and diagnosis before actual failures occur. By identifying degradation patterns and predicting remaining useful life, the system enables scheduled maintenance at optimal times, preventing unexpected breakdowns and reducing unplanned downtime.
Solution Approach 2:
The patent implements continuous monitoring with feedback loops that track DLG component health in real-time. This feedback mechanism allows the system to adapt maintenance schedules based on actual component condition, performing maintenance only when necessary and avoiding unnecessary interventions.
Data Source
AI summary
Systems and methods for detecting and diagnosing faults in a radiotherapy system, such as a fault related to a dynamic leaf guide (DLG), are discussed. An exemplary predictive maintenance system includes a processor configured to receive machine data indicative of configuration and operation of a DLG in a target radiotherapy machine, apply a trained deep learning model to the received machine data, and detect and diagnose a DLG fault. The predictive maintenance system can train the deep learning model using data sequences constructed from the received machine data of the one more normal DLGs and the one or more faulty DLGs. Diagnosis of the DLG fault in the target radiotherapy machine includes classifying the DLG faults into different fault types or different fault severities.