The authors have developed a comprehensive, high frequency (1–100 kHz) vibration monitoring system for incipient fault detection of critical rotating components within engines, drive trains, and generators. The high frequency system collects and analyzes vibration data to estimate the current condition of rotary components; detects and isolates anomalous behavior to a particular bearing, gear, shaft or coupling; and assesses the severity of the fault in the isolated faulty component. The system uses either single/multiple accelerometers, mounted on externally accessible locations, or non-contact vibration monitoring sensors to collect data. While there are published instances of vibration monitoring algorithms for bearing or gear fault detection, there are no comprehensive techniques that provide incipient fault detection and isolation in complex machinery with multiple rotary and drive train components. The author’s techniques provide an algorithm-driven system that fulfills this need. The concept at the core of high frequency vibration monitoring for incipient fault detection is the ability of high frequency regions of the signal to transmit information related to component failures during the fault inception stage. Unlike high frequency regions, the lower frequency regions of vibration data have a high machinery noise floor that often masks the incipient fault signature. The low frequency signal reacts to the fault only when fault levels are high enough for the signal to rise over the machinery noise floor. The developed vibration monitoring system therefore utilizes high frequency vibration data to provide a quantitative assessment of the current health of each component. The system sequentially ascertains sensor validity, extracts multiple statistical, time, and frequency domain features from broadband data, fuses these features, and acts upon this information to isolate faults in a particular gear, bearing, or shaft. The techniques are based on concepts like mechanical transmissibility of structures and sensors, statistical signal processing, demodulation, time synchronous averaging, artificial intelligence, failure modes, and faulty vs. healthy vibration behavior for rotating components. The system exploits common aspects of vibration monitoring algorithms, as applicable to all of the monitored components, to reduce algorithm complexity and computational cost. To isolate anomalous behavior to a particular gear, bearing, shaft, or coupling, the system uses design information and knowledge of the degradation process in these components. This system can function with Commercial Off-The-Shelf (COTS) data acquisition and processing systems or can be adapted to aircraft on-board hardware. The authors have successfully tested this system on a wide variety of test stands and aircraft engine test cells through seeded fault and fault progression tests, as described herein. Verification and Validation (V&V) of the algorithms is also addressed.

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