Predicting MRI Gradient Coil Amplifier Failure via Neural Network
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Solution Overview
Problem
Gradient coil amplifiers in MRI systems exhibit random failure behavior, making it impossible to predict failures based on wear-out information, resulting in unexpected downtime and significant maintenance costs due to surprise component failures.
Innovation Solution
A medical instrument and computer program that uses a trained neural network to predict the probability of gradient coil amplifier failure by constructing a measurement vector from environmental data from both the examination and technical rooms, allowing for advanced warning and reduced maintenance costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If gradient coil amplifiers are used in MRI systems, then the system can perform magnetic resonance imaging functions, but the amplifiers exhibit random failure behavior making failure prediction impossible
Solution Approach 1:
The system performs preliminary monitoring of environmental conditions (temperature, humidity, usage patterns) before actual failure occurs. By continuously collecting and analyzing this data through a neural network, the system predicts future failures in advance, enabling proactive maintenance before the amplifier actually fails.
Solution Approach 2:
The system establishes a feedback loop where environmental data and usage information are continuously fed into a neural network that processes this information and outputs failure probability predictions. This feedback mechanism transforms the random failure behavior into a predictable process by constantly updating the prediction based on current system state.
2Measurement precision
If traditional monitoring methods are used, then simple parameters can be tracked, but they cannot predict random failures of solid state components
Solution Approach 1:
The neural network serves as a universal predictor that processes multiple different types of input data (temperature, humidity, usage patterns, operational hours) through a single integrated system. This multi-functional approach allows the same computational model to handle various environmental and operational parameters without requiring separate monitoring systems for each parameter type.
Solution Approach 2:
The system transforms physical environmental parameters (temperature, humidity) and operational parameters (usage patterns, operational hours) into a predictive model that outputs failure probability. By changing the representation of these parameters through neural network processing, the system converts raw sensor data into meaningful failure predictions.
3Productivity
If reactive maintenance is performed after failure, then component replacement can be done, but significant downtime and costs occur
Solution Approach 1:
The system performs preliminary failure prediction by continuously analyzing environmental and operational data. When the neural network predicts a high probability of imminent failure, maintenance can be scheduled in advance, allowing for proactive replacement before actual failure occurs, thus avoiding unplanned downtime.
Solution Approach 2:
The monitoring and prediction system operates autonomously, continuously collecting data and generating failure predictions without requiring constant human intervention. This self-service capability enables the system to identify and flag potential failures automatically, allowing maintenance teams to respond efficiently when prompted by the system's predictions.
Data Source
AI summary
The invention provides for a medical instrument (100, 300, 500) comprising a processor. Execution of machine executable instructions (120, 122, 124, 350, 352, 354) cause the processor to repeatedly: construct (200, 416) a measurement vector (114, 700) comprising multiple data values (704) using a measurement database (116, 122), wherein the multiple data values comprise examination room data (332) descriptive of the environmental conditions of an examination room (322) of a magnetic resonance imaging system, wherein the multiple data values further comprise technical room data (330) descriptive of the environmental conditions of a technical room (326) of the magnetic resonance imaging system; and calculate (202, 418) a probability (706) of failure of a gradient coil amplifier (312) of the magnetic resonance imaging system a predetermined number of days in the future by inputting the measurement vector into a trained neural network program (124).