Monitoring of vibrating machinery using artificial neural networks

Conference · Tue Dec 31 04:00:00 EST 1991

OSTI ID:10106552

Alguindigue, I E; Loskiewicz-Buczak, A ^[1]; Uhrig, R E ^[1]

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1. Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering

The primary source of vibration in complex engineering systems is rotating machinery. Vibration signatures collected from these components render valuable information about the operational state of the system and may be used to perform diagnostics. For example, the low frequency domain contains information about unbalance, misalignment, instability in journal bearing and mechanical looseness; analysis of the medium frequency range can render information about faults in meshing gear teeth; while the high frequency domain will contain information about incipient faults in rolling-element bearings. Trend analysis may be performed by comparing the vibration spectrum for each machine with a reference spectrum and evaluating the vibration magnitude changes at different frequencies. This form of analysis for diagnostics is often performed by maintenance personnel monitoring and recording transducer signals and analyzing the signals to identify the operating condition of the machine. With the advent of portable fast Fourier transform (FFT) analyzers and ``laptop`` computers, it is possible to collect and analyze vibration data an site and detect incipient failures several weeks or months before repair is necessary. It is often possible to estimate the remaining life of certain systems once a fault has been detected. RMS velocity, acceleration, displacements, peak value, and crest factor readings can be collected from vibration sensors. To exploit all the information embedded in these signals, a robust and advanced analysis technique is required. Our goal is to design a diagnostic system using neural network technology, a system such as this would automate the interpretation of vibration data coming from plant-wide machinery and permit efficient on-line monitoring of these components.

• Research Organization: Tennessee Univ., Knoxville, TN (United States). Dept. of Nuclear Engineering
• Sponsoring Organization: USDOE, Washington, DC (United States)
• DOE Contract Number: FG07-88ER12824; AC05-84OR21400
• OSTI ID: 10106552
• Report Number(s): CONF-9109447--1; ON: DE93003566
• Country of Publication: United States
• Language: English

Related Subjects

42 ENGINEERING
420200
99 GENERAL AND MISCELLANEOUS
990200
ACCELERATION
FACILITIES, EQUIPMENT, AND TECHNIQUES
GEARS
MATHEMATICS AND COMPUTERS
MECHANICAL VIBRATIONS
MONITORING
NEURAL NETWORKS
VELOCITY