Acoustic Emission Sensors for Stator Vane Crack Detection
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Solution Overview
Problem
Conventional systems fail to detect small cracks in stator vanes of gas turbines, leading to potential safety hazards and significant monetary losses, as they can only detect cracks that cause noticeable vibrations, missing early-stage anomalies.
Innovation Solution
A system comprising sensing devices such as magnetostrictive, piezoelectric, or acoustic emission sensors on the outer surface of the turbine casing, which capture acoustic emission waves generated by stressed stator vanes, and a processing subsystem that analyzes these signals to predict crack occurrence, determine crack length, and estimate remaining useful life, using features like ring down count, amplitude, and frequency analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vibration sensors are used to monitor stator vane health, then large cracks causing noticeable vibrations can be detected, but small cracks that do not cause detectable vibrations cannot be detected
Solution Approach 1:
The patent replaces conventional vibration sensors (mechanical detection system) with acoustic emission sensors that detect high-frequency elastic waves generated by crack propagation. This substitution enables detection of small cracks before they cause noticeable vibrations, directly resolving the contradiction between detection precision and safety reliability
Solution Approach 2:
The patent introduces acoustic emission waves as an intermediary carrier to detect crack information. These waves are generated by the crack propagation process itself and can be detected by specialized sensors, allowing indirect observation of crack development without relying on vibration effects
2Reliability
If conventional vibration monitoring systems are used, then the system complexity remains low, but the ability to detect early-stage cracks is insufficient
Solution Approach 1:
The patent changes the detection parameter from low-frequency vibration (conventional method) to high-frequency acoustic emission waves (20-200 kHz range). This parameter change enables early crack detection while the signal processing system analyzes frequency content, amplitude, and arrival time to distinguish crack signals from noise
Solution Approach 2:
The patent adds a new detection dimension by placing sensors on the outer surface of the turbine casing to detect acoustic waves propagating through the casing wall from internal crack events. This external detection dimension complements traditional internal vibration monitoring and enables early crack detection without increasing internal system complexity
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
Enables real-time monitoring of stator vane health, detecting cracks and anomalies before they cause imbalance, allowing for proactive maintenance and preventing hazardous failures.
Implementation Method 1
sensing devices such as magnetostrictive, piezoelectric, or acoustic emission sensors on the outer surface of the turbine casing, which capture acoustic emission waves generated by stressed stator vanes
Implementation Method 2
sensing devices such as magnetostrictive, piezoelectric, or acoustic emission sensors
Implementation Method 3
sensing devices such as magnetostrictive, piezoelectric, or acoustic emission sensors
Data Source
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AI summary
A system (10,100) including a plurality of sensing devices (18,20) configured to generate acoustic emission (AE) signals (22,24,104) that are representative of acoustic emission waves propagating through a plurality of stator vanes (12) is presented. The system further includes a processing subsystem (26,114) that is in an operational communication with the plurality of sensing devices, and the processing subsystem is configured to generate a dynamic threshold based upon an initial threshold and the AE signals, determine whether a plurality of signals of interest exist in the AE signals based upon the dynamic threshold, extract the plurality of signals of interest from the AE signals based upon the dynamic threshold, determine one or more features corresponding to the plurality of signals of interest, and analyze the one or more features to monitor and validate the health of the plurality of stator vanes.