Acoustic Emission Resonance Monitoring for Turbomachine Crack Detection
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Solution Overview
Problem
Conventional systems for monitoring the health of turbomachine components, such as stator vanes, are inadequate in detecting early stages of damage like small cracks that do not cause detectable vibrations, leading to potential device failure due to stress-corrosion cracking and operational fatigue.
Innovation Solution
A health monitoring system utilizing acoustic emissions (AE) signals and resonance frequency analysis to identify and track changes in the damage state of components, enabling early detection of cracks and predictive modeling of component wear and failure conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vibration sensors are used to monitor component health, then large cracks and anomalies can be detected, but small cracks that do not cause detectable vibration cannot be detected
Solution Approach 1:
The system utilizes acoustic emission waves generated by crack propagation and mechanical stress in the component. These waves are inherently linked to the mechanical behavior of the material and provide direct information about damage states, enabling detection of small cracks before they cause significant vibration.
Solution Approach 2:
The system monitors changes in resonance frequency as a key parameter that indicates damage state. As cracks develop in the component, the resonance frequency changes in a predictable manner. By tracking these parameter changes over time, the system can detect small cracks and monitor their progression, overcoming the limitation of vibration-based methods that only detect large anomalies.
2Reliability
If conventional vibration monitoring is used, then the system remains simple, but it cannot detect early stage damage
Solution Approach 1:
The acoustic emission sensor serves multiple functions: detecting crack initiation, monitoring crack propagation, and determining damage state through resonance frequency analysis. This multi-functionality improves reliability for early damage detection while avoiding the need for multiple separate sensing systems, thereby limiting the increase in device complexity.
Solution Approach 2:
The system replaces conventional vibration-based mechanical monitoring with acoustic emission wave detection and resonance frequency analysis. This substitution enables early detection of damage states through non-mechanical wave propagation measurements, improving reliability while maintaining relatively simple sensor and processing requirements.
3Measurement precision
If acoustic emission sensors are used to detect early cracks, then small cracks can be detected, but the system requires advanced signal processing to identify resonance frequencies
Solution Approach 1:
The component itself serves as the resonator, and its natural resonance frequency provides the reference for damage detection. The system leverages the inherent physical properties of the monitored component rather than requiring external calibration standards or complex reference systems, thereby reducing processing complexity while maintaining high measurement precision.
Solution Approach 2:
The system continuously monitors acoustic emission signals and compares them against expected resonance frequency patterns to identify damage states. This feedback mechanism enables automatic detection and classification of cracks based on resonance characteristics, improving measurement precision while using algorithmic approaches that manage signal processing complexity efficiently.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively monitors component health by analyzing AE signals to detect resonance frequency changes, allowing for predictive maintenance and preventing device failure by identifying crack growth and setting appropriate thresholds for inspection or decommissioning.
Implementation Method 1
A first aspect of this disclosure provides systems and methods for using a health monitoring system with acoustic emissions (AE) signals and the resonance frequency of the damage state of a component in a machine to monitor component health.
Implementation Method 2
The component has a damage state and the damage state has a resonance frequency. The resonance frequency of the damage state is identified during operation of the machine using received AE signals.
Data Source
AI summary
This disclosure provides systems and methods for using a health monitoring system with acoustic emissions (AE) signals and the resonance frequency of the damage state of a component in a machine to monitor component health. AE signals collected from sensors on an operating machine are analyzed to identify signal features or events that correspond to component resonance frequencies. The AE signal features proximate to the component resonance frequencies and how those features and the component resonance frequency changes over time enable the identification and monitoring of damage states, such as cracks in the stator vanes of a compressor, gas turbine, steam turbine, or generator.