Predictive Maintenance Sequence Model for Medical Imaging Root Cause Analysis
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Solution Overview
Problem
Predictive maintenance models for medical imaging devices fail to provide specific service actions needed to address component failures, leading to unoptimized maintenance and increased downtime, as they only predict failures without identifying root causes or recommending corrective actions.
Innovation Solution
A system that integrates a predictive model with a sequence model, such as a Hidden Markov Model, to analyze historical data and generate alerts with recommended service actions by identifying the most probable root cause and corresponding service action for a failing component, using a table with features, root causes, and service actions, allowing for automatic identification and recommendation of necessary maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a predictive failure model is used to predict component failures, then failure prediction capability is improved, but the ability to identify root causes and recommend service actions deteriorates
Solution Approach 1:
The patent combines a predictive failure model with a sequence model (such as Hidden Markov Model) in an integrated system. The predictive model identifies potential component failures, while the sequence model analyzes historical data to determine root causes and recommend service actions. This merging allows the system to maintain failure prediction capability while simultaneously providing root cause analysis and actionable recommendations, resolving the contradiction between prediction reliability and information completeness.
Solution Approach 2:
The system performs preliminary analysis by training the sequence model on historical service data before actual failures occur. The model learns patterns from past cases where failures were diagnosed and serviced, enabling it to automatically determine root causes and service actions for future predictive alerts without requiring manual analysis, thus preserving information that would otherwise be lost.
2Measurement precision
If multiple possible root causes are considered for a component failure, then diagnostic accuracy is improved, but the complexity of determining the correct service action increases
Solution Approach 1:
The sequence model is trained to automatically analyze historical service data and independently determine the most likely root cause and recommended service action for each predictive alert. The model processes the complexity of multiple possible causes internally through its trained patterns, outputting a clear diagnostic conclusion without requiring manual intervention to navigate the complexity, thus maintaining diagnostic accuracy while reducing operational complexity.
Solution Approach 2:
The system uses feedback from historical service data to continuously improve the sequence model's ability to accurately determine root causes and service actions. By training on past cases where the correct service actions were known, the model learns to distinguish between multiple possible causes and provides increasingly accurate recommendations, reducing the complexity of decision-making for service engineers.
3Adaptability or versatility
If service engineers manually diagnose and determine service actions, then flexibility in problem-solving is improved, but the time spent on diagnosis increases
Solution Approach 1:
The sequence model performs preliminary diagnostic work by analyzing historical data and determining the most likely root cause and service action before the service engineer arrives at the site. This preliminary action provides a structured starting point that reduces the time engineers need to spend on initial diagnosis while preserving their flexibility to adapt to actual conditions found during service, as the model's recommendations are based on patterns from historical cases rather than rigid procedures.
Data Source
AI summary
A non-transitory computer readable medium (107, 127) stores: a predictive model (130) configured to generate an alert (132) predicting a failure of a component of a medical imaging device (120) by applying patterns to values of a set of features; a table (136) having records corresponding to the patterns of the predictive model; and instructions readable and executable by at least one electronic processor (101, 113) to (i) train: a sequence model (134) to receive values of the set of features for a current case and to output a most probable root cause and at least one service action for the current case, and (ii) determine a root cause and at least one recommended service action for the alert generated by the predictive model by applying the trained sequence model to the values of the set of features for the medical imaging device.