Early bearing fault analysis using high frequency enveloping techniques

High frequency acceleration enveloping is one of many tools that vibration analysts have at their disposal for the diagnosis of bearing faults in rotating machinery. This technique is believed to facilitate very early detection of potential failures by detecting low amplitude repetitive impacts in frequency ranges above conventional condition monitoring. One traditional enveloping method uses a mathematical operation known as the Hilbert transform along with other signal processing procedures such as band-pass filtering and full-wave rectification. For comparison, another method uses a proprietary algorithm included in National Instruments’ LabVIEWTM add-on package: Sound and Measurement Suite. Enveloping’s inherent problem with noise introduction is also addressed herein. A controlled, three-stage fault was induced and diagnosed utilizing both acceleration enveloping methods and traditional fast Fourier transformation (FFT) described herein. A performance assessment of the enveloping process with respect to FFT as well as the performance between individual enveloping methods is presented. In summary, several high frequency acceleration enveloping methods exist that can be effective tools in detection of bearing faults earlier than FFT alone

Experimental set-up for investigation of fault diagnosis of a centrifugal pump

Centrifugal pumps are complex machines which can experience different types of fault. Condition monitoring can be used in centrifugal pump fault detection through vibration analysis for mechanical and hydraulic forces. Vibration analysis methods have the potential to be combined with artificial intelligence systems where an automatic diagnostic method can be approached. An automatic fault diagnosis approach could be a good option to minimize human error and to provide a precise machine fault classification. This work aims to introduce an approach to centrifugal pump fault diagnosis based on artificial intelligence and genetic algorithm systems. An overview of the future works, research methodology and proposed experimental setup is presented and discussed. The expected results and outcomes based on the experimental work are illustrated

Framework for a space shuttle main engine health monitoring system

A framework developed for a health management system (HMS) which is directed at improving the safety of operation of the Space Shuttle Main Engine (SSME) is summarized. An emphasis was placed on near term technology through requirements to use existing SSME instrumentation and to demonstrate the HMS during SSME ground tests within five years. The HMS framework was developed through an analysis of SSME failure modes, fault detection algorithms, sensor technologies, and hardware architectures. A key feature of the HMS framework design is that a clear path from the ground test system to a flight HMS was maintained. Fault detection techniques based on time series, nonlinear regression, and clustering algorithms were developed and demonstrated on data from SSME ground test failures. The fault detection algorithms exhibited 100 percent detection of faults, had an extremely low false alarm rate, and were robust to sensor loss. These algorithms were incorporated into a hierarchical decision making strategy for overall assessment of SSME health. A preliminary design for a hardware architecture capable of supporting real time operation of the HMS functions was developed. Utilizing modular, commercial off-the-shelf components produced a reliable low cost design with the flexibility to incorporate advances in algorithm and sensor technology as they become available