The purpose of this work is to begin the development of a comprehensive time/frequency spectral analysis approach that can be applied to complex signals associated with real world systems, such as rotating machinery. Rotating machinery operating at nominally constant speed comprise a large class of important real world systems that have received relatively little attention in terms of stochastic characterizations of any greater sophistication than those associated with wide sense stationary processes. In this work, a periodictime/frequency characterization procedure is introduced in the context of vibration analysis associated with a diesel engine operating at nominally constant speed. This application highlights a number of difficulties, such as the need for accurate period estimation, accommodation of noninteger periods in relation to digital processing, and identification and separation of tonal components from the signature in order to arrive at a more parsimonious characterization. A theorem relating to the limiting influence of these difficulties is presented. These difficulties are addressed using advanced signal processing tools, such as a recently developed tone identification procedure and extended Kalman filtering, which to the authors' knowledge have not been considered to date in such a setting. Results include a simple correction algorithm for noninteger periods, excellent separation of tonal components whose frequencies are slowly varying, and subsequently a modest improvement in the spectral characterization of the remainder of the process. These results have some significance in relation to diesel engine vibration, since they unambiguously identify tonal vibration components, in addition to a random structure which appears to include random excitation of resonances. KeywordsDepartment of Statistics, algorithm, article, diesel engine, fourier transformation, machine, noise, power spectrum, priority journal, rotation, signal processing, spectroscopy, stochastic model, vibration Disciplines Aerospace Engineering | Statistics and Probability CommentsThe following article appeared in Journal of the Acoustical Society of America 98, 3285 (1995) The purpose of this work is to begin the development of a comprehensive time/frequency spectral analysis approach that can be applied to complex signals associated with real world systems, such as rotating machinery. Rotating machinery operating at nominally constant speed comprise a large class of important real world systems that have received relatively little attention in terms of stochastic characterizations of any greater sophistication than those associated with wide sense stationary processes. In this work, a periodic-time/frequency characterization procedure is introduced in the context of vibration analysis associated with a diesel engine operating at nominally constant speed. This application highlights a number of difficulties, such as the need for accurate period estimation, accommodation of noninteger periods in relation to digital process...
This paper is an introductory exposition of the application of the ratio of the 2pth order autoregressive (AR) power spectrum estimate to the p t h order AR power spectrum estimate to the problem of detecting sinusoids of unknown frequency in additive coloured noise with unknown power spectral density. The resulting threshold test is independent of a priori knowledge of both the sinusoid's parameters and the PSD of the additive noise. Simulations are presented for the Gaussian case, focussing primarily on the SNR thresholding behaviour of the scheme. These simulations illustrate the power of the technique when highly resonant background noise is present, such as is often the case in rotating machinery, for example.Significant threshold extension, and slightly superior subthreshold performance compared to the energy detector is observed in these cases. The paper also examines the application of the technique to the problem of identifying tones in diesel engine vibration data.
The Direct Digital Laser logging system is a fully self-contained logging unit designed to improve operating techniques and reduce rig time required for logging; this system increases data quality, eliminates human error and provides accurate digital recording of data while producing an instant hard copy output. The computer, programmed to handle the logging of each downhole tool combination, is also used to process this data at the well site and provide on-site interpretations to assist in well evaluation and decision-making.
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