Rotating Component Health Monitoring for Fault Detection
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Solution Overview
Problem
Current health-based monitoring systems for devices with rotating components, such as aircraft generators, lack effective solutions for detecting faults in rotor windings, rectifier diodes, and exciter windings, and traditional time-based replacement methods are costly and inefficient, leading to potential catastrophic failures.
Innovation Solution
A prognostic and health monitoring system utilizing a plurality of sensors that generate sensor data, a controller to construct multivariate Gaussian distribution parameters using a central limit theorem, and an energy harvesting system to power the sensors, allowing for accurate fault detection and remote communication of device states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If time of usage replacement is used for rotating components, then safety is improved by preventing catastrophic failures, but cost increases due to expensive replacement parts and loss of revenue during downtime
Solution Approach 1:
The system performs preliminary health assessments of rotating components by collecting and analyzing sensor data (vibrations, temperature, acoustic emissions) to predict potential failures before they occur. This allows maintenance to be scheduled proactively, avoiding both premature replacement and catastrophic failure, thereby reducing costs while maintaining safety.
Solution Approach 2:
The system continuously monitors component health through sensors and provides feedback about the actual condition of rotating components. This feedback loop enables dynamic adjustment of maintenance schedules based on real-time component status, replacing parts only when necessary rather than following fixed time-based schedules, thus reducing waste and cost while ensuring safety.
2Loss of energy
If health-based monitoring is implemented, then cost is reduced by avoiding premature replacement, but measurement precision deteriorates due to sensor uncertainties, long term drifts and failures
Solution Approach 1:
The system combines multiple sensor types (vibration sensors, temperature sensors, acoustic emission sensors) to monitor component health from multiple perspectives. By fusing data from these diverse sensors, the system compensates for individual sensor limitations and uncertainties, achieving more reliable and precise health assessments than any single sensor could provide alone.
Solution Approach 2:
The system introduces signal processing algorithms and data fusion techniques as intermediaries between raw sensor data and health conclusions. These intermediaries filter out noise, correct for sensor drift, and integrate multiple data sources to produce accurate measurements despite individual sensor imperfections, thereby maintaining measurement precision while enabling cost-effective monitoring.
3Measurement precision
If sensors are installed for monitoring, then fault detection capability is improved, but device complexity increases due to additional components and power requirements
Solution Approach 1:
The system uses multi-functional sensors that can detect multiple types of faults simultaneously (e.g., vibration sensors that detect both bearing defects and imbalance, temperature sensors that monitor both overheating and lubrication issues). This reduces the total number of sensors needed compared to having dedicated sensors for each fault type, thereby improving fault detection capability while minimizing the increase in device complexity.
4Use of energy by moving object
If power is drawn through wires for prognostics, then sensor operation is enabled, but ease of operation deteriorates due to intrusive installation and expensive validation processes
Solution Approach 1:
The system replaces the mechanical/electrical connection method (wires) with a wireless power and data transmission solution. Sensors are powered wirelessly through electromagnetic energy transfer, eliminating the need for physical wire installation through rotating components. This substitution dramatically simplifies installation, avoids intrusive modifications to the generator system, and eliminates expensive validation and certification processes while maintaining sensor operation.
Data Source
Figure 1A~1C
Figure 1D~1E
Figure 1F
AI summary
A prognostic and health monitoring system for a device with a rotating component is provided. The system includes a plurality of sensors. Each sensor is configured to sense a parameter of the device. A controller is in communication output sensor signals. The controller, based on instructions stored in a memory, is configured to filter the output sensor signals based on operational speed data of the rotating component to obtain normalized sensor data, construct multivariate gaussian distribution parameters from the normalized sensor data using a central limit theorem, compare a model generated with a learning algorithm applied to previous constructed multivariate gaussian distribution parameters with the constructed multivariate gaussian distribution parameters, and determine a state of the device based at least in part on the comparison of model with the constructed multivariate gaussian distribution parameters. A communication system communicates the determined state of the device to a remote location.