High Resolution Order Tracking at Extreme Slew Rates, Using Kalman Tracking Filters

Vold, Håvard; Leuridan, Jan

doi:10.4271/931288

Cited by 119 publications

(56 citation statements)

References 6 publications

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“…The model of a reference vibration signal can be obtained through Equation (26). These processes are shown in Equation (27). The frequency of x n varies linearly with frequency difference f r x n = sin(2πn f r t) (27) where n = 5.3 denotes the FCF order of the bearings.…”

Section: Simulationmentioning

confidence: 99%

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Weak Fault Feature Extraction Scheme for Intershaft Bearings Based on Linear Prediction and Order Tracking in the Rotation Speed Difference Domain

Jiang¹,

HU²,

Feng³

et al. 2017

Applied Sciences

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Abstract:Because both the inner and outer rings rotate, the intershaft bearings used in gas turbines do not have fixed bearing housings. As a result, the vibration of intershaft bearings cannot be measured directly. Therefore, a vibration signal can only be collected through indirect measurement. First, it must be transferred to adjacent bearings through the shafting. Then, it should be transferred by the elastic supports and complex structure of the thin-walled strut. The vibration signal is severely weakened during transmission under the influences of the transfer path. In the meantime, in the vibration of other components, a huge amount of noise is produced by the air flow, and the variable speeds of the inner and outer rings of the intershaft bearings make it harder to analyze the signal. Hence, it is very difficult to extract the vibration fault features of intershaft bearings. To deal with the variable speed of dual rotors, as well as the weak signal, a fault feature extraction scheme for the weak fault signals of intershaft bearings is proposed in this paper. This scheme is based on linear prediction, spectral kurtosis, and order tracking in the rotation speed difference domain. First, a prewhitening process, based on linear prediction, is applied to the fault signal of the intershaft bearings to eliminate the stationary component. Thus, the remaining components, including the impulse signal of faulty bearings and nonstationary noise, can retain the features of the vibrational bearings, in addition to reducing the noise. Second, the optimal center frequency and bandwidth of the band-pass filter, applied to resonant demodulation, are selected by spectral kurtosis. Subsequently, the enveloped signal containing the features of the faults found in the intershaft bearings is obtained by resonance demodulation. The quasi-stationary signal in the angle domain is acquired by the even angle resampling of the nonstationary envelope signal, as a result of the variable speed. The final order spectrum is obtained through a Fourier transform. Fault diagnosis can be conducted for the intershaft bearings by comparing this spectrum with the feature order of the bearing fault. Experiments were conducted to verify the validity of the proposed scheme.

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Section: Simulationmentioning

confidence: 99%

“…These processes are shown in Equation (27). The frequency of x n varies linearly with frequency difference f r x n = sin(2πn f r t) (27) where n = 5.3 denotes the FCF order of the bearings. t j is the time when the value of x n reaches its maximum in every period,…”

Section: Simulationmentioning

confidence: 99%

Weak Fault Feature Extraction Scheme for Intershaft Bearings Based on Linear Prediction and Order Tracking in the Rotation Speed Difference Domain

Jiang¹,

HU²,

Feng³

et al. 2017

Applied Sciences

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“…In practice, industrial firms concerned by dynamic dimensioning of structures subjected to sine plus noise composite processes are led to extract the sinusoidal nature from the composite process by adapting Kalman filtering techniques [24]. Extraction of the periodic character of the process in the time domain by Kalman filtering is a way of characterising the random nature of the composite process correctly, by subtracting the extracted sinusoidal process from the composite process considered.…”

Section: Design Of Structures In Terms Of the Overall Stress Maximum mentioning

confidence: 99%

Statistics of extreme values of stochastic processes generated by rotating machines and associated probabilistic reliability model

Colin

2017

Mechanics & Industry

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-The random stress processes recorded on the mechanical structures of rotating machines have non-Gaussian structure. These processes are logically composed of a deterministic periodic process, centred on the harmonics of the rotor rotation frequency, on which is routinely superposed a Gaussian zero-mean random process. This article first presents the mathematical principles of extreme value models, adapted to their specific nature. The proposed probabilistic models are then compared with each other in order to examine their degree of similarity and conservatism. On the basis of Gumbel's theory, adopted as an asymptotic approach to extreme values, a statistical model of extreme values of sine plus noise composite random processes is proposed and discussed.

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“…Applying this technique to the time history of the measured signal (at constant sampling frequency) leads, from one side, to a positive signal down-sampling at low rotational speed and, from the other, may require excessive oversampling at higher speeds, which will have an impact in terms of computational effort. 6 Moreover, note that the spectrum obtained will have a finite order resolution. This represents a problem if there are harmonics that do not fall on spectral lines, since their energy would leak to the surrounding frequency bins.…”

Section: Introductionmentioning

confidence: 99%

An alternative method for order tracking using autopower spectrum

Guercioni

Galvagno

Vigliani

2015

Advances in Mechanical Engineering

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Order tracking is a method of analysis used by engineers in the diagnosis of rotating machinery. In many applications, order analysis of non-stationary signals is required. The direct extraction of the amplitude information from the shorttime Fourier transform may lead to inaccurate vibration-level estimation in the case of fast changes in the signal frequency content. This article discusses spectral smearing, which is the main reason of the problem, and its sensitivity to the characteristics of the signal (frequency and amplitude variations) and to the input parameters of discrete Fourier transform analysis (window size and type). Through the years, many different approaches to perform order analysis have been developed; this article introduces a novel method for order tracking based on the short-time Fourier transform, which applies a compensation of the smearing effect based on an invariant information contained in autopower spectrum. The limitations and capabilities of the proposed method with respect to other existing techniques are discussed: considering the accuracy of the results, low requirements of computational resources, and ease of implementation, this method proves a valid alternative to currently used techniques.

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High Resolution Order Tracking at Extreme Slew Rates, Using Kalman Tracking Filters

Cited by 119 publications

References 6 publications

Weak Fault Feature Extraction Scheme for Intershaft Bearings Based on Linear Prediction and Order Tracking in the Rotation Speed Difference Domain

Weak Fault Feature Extraction Scheme for Intershaft Bearings Based on Linear Prediction and Order Tracking in the Rotation Speed Difference Domain

Statistics of extreme values of stochastic processes generated by rotating machines and associated probabilistic reliability model

An alternative method for order tracking using autopower spectrum

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