Health Management Device Real-Time Sensor Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current maintenance practices for complex machines rely on time-based schedules, which can lead to unnecessary maintenance and inadequate timely diagnosis of issues, particularly in critical applications like military or aviation, where periodic data analysis may fail to detect emerging problems in a timely manner.
Innovation Solution
A real-time health management device (HMD) that generates diagnostic and prognostic analysis results based on real-time sensor information and updates analytic models over time, allowing for continuous improvement of analysis accuracy without disrupting operations, using both diagnostic and prognostic models and incorporating a Gaussian mixture model for fault prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If time-based maintenance schedules are used, then maintenance can be performed systematically, but maintenance may be performed prior to or subsequent to actual need, leading to unnecessary costs and downtime
Solution Approach 1:
The system continuously collects sensor data from the machine and feeds it back to the health management device, which updates the analytic models with new information. This closed-loop feedback mechanism enables the system to adapt maintenance scheduling based on actual machine condition rather than fixed time intervals, resolving the contradiction between systematic maintenance and unnecessary downtime.
Solution Approach 2:
The analytic models automatically update themselves using sensor information and fault data without requiring manual intervention. The system self-adjusts maintenance predictions based on accumulated data, enabling maintenance to be performed exactly when needed rather than following rigid schedules, thus eliminating unnecessary downtime while maintaining systematic approach.
2Reliability
If periodic data analysis is used, then some diagnostic capability is provided, but problems may not be diagnosed in a timely manner, especially in critical applications
Solution Approach 1:
The system performs continuous real-time analysis of sensor data rather than periodic sampling. The health management device continuously updates diagnostic and prognostic results as new sensor information becomes available, ensuring problems are detected immediately when they arise rather than waiting for the next analysis interval, thus eliminating diagnosis delay in critical applications.
Solution Approach 2:
The continuous feedback loop between sensor data collection and real-time analysis enables immediate detection and diagnosis of problems. As soon as sensor data indicates an anomaly, the analytic models process it and generate diagnostic results without delay, ensuring timely response to critical issues while maintaining continuous monitoring capability.
3Measurement precision
If analytic models are updated continuously with new sensor information, then diagnostic and prognostic accuracy improves over time, but system complexity increases
Solution Approach 1:
The analytic models automatically update themselves using sensor information and fault data without requiring manual intervention or complex external management. The system self-adjusts by incorporating new data points and learning from actual machine behavior, improving accuracy over time while maintaining manageable complexity through automated processes rather than manual model management.
Solution Approach 2:
The feedback mechanism continuously incorporates sensor data and fault information into the analytic models, allowing them to adapt and improve accuracy automatically. The models learn from actual machine performance and failure patterns, progressively refining their predictions while the feedback loop manages the complexity by systematically processing data rather than requiring complex manual updates.
4Reliability
If real-time sensor information is continuously analyzed, then timely diagnostic results are generated, but more data processing and computational resources are required
Solution Approach 1:
The system extracts only the most relevant features and parameters from the continuous sensor data stream for analysis, rather than processing all raw data in full detail. The health management device identifies and focuses on critical indicators that predict failures, reducing computational load and energy consumption while maintaining real-time diagnostic capability by analyzing only the most informative data elements.
Solution Approach 2:
The system transforms raw sensor data into meaningful parameters and features that capture essential machine health information in a compressed form. By changing the parameter representation from raw sensor readings to derived health indicators, the system reduces the computational complexity and energy required for real-time analysis while preserving the diagnostic information needed for timely detection.
Data Source
AI summary
Mechanisms for generating an analysis result about a machine are provided. A device generates a first health management (HM) analysis result regarding a machine based on real-time first sensor information received during a first period of time and on a first version HM analytic model. The device provides, to an off-board device, a plurality of sensor information comprising the real-time first sensor information and that is generated during the first period of time. The device receives a second version HM analytic model that is based at least in part on the plurality of sensor information and fault information that identifies actual faults that have occurred on the machine. The device generates a second HM analysis result regarding the machine based on real-time second sensor information received during a second period of time and on the second version HM analytic model.