Order tracking for discrete-random separation in variable speed conditions

Borghesani, Pietro; Pennacchi, Paolo; Randall, R.B.; Ricci, R.

doi:10.1016/j.ymssp.2012.01.015

Cited by 72 publications

(26 citation statements)

References 5 publications

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“…Therefore, some signal preprocessing procedures are required before the application of more advanced diagnostic approaches. Secondly, there is always a long transmission from a defect to a transducer in applications [26,27]. Taking Figure 1 as an example, the gear vibration will successively transmit through the gear disk, the shaft, the bearing, the gearbox casing, and finally arrives at the vibration transducer.…”

Section: Vibration Model For Gearbox In the R/c Processmentioning

confidence: 99%

Feature Mining and Health Assessment for Gearboxes Using Run-Up/Coast-Down Signals

Zhao

Lin

Miao

et al. 2016

Sensors

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Vibration signals measured in the run-up/coast-down (R/C) processes usually carry rich information about the health status of machinery. However, a major challenge in R/C signals analysis lies in how to exploit more diagnostic information, and how this information could be properly integrated to achieve a more reliable maintenance decision. Aiming at this problem, a framework of R/C signals analysis is presented for the health assessment of gearbox. In the proposed methodology, we first investigate the data preprocessing and feature selection issues for R/C signals. Based on that, a sparsity-guided feature enhancement scheme is then proposed to extract the weak phase jitter associated with gear defect. In order for an effective feature mining and integration under R/C, a generalized phase demodulation technique is further established to reveal the evolution of modulation feature with operating speed and rotation angle. The experimental results indicate that the proposed methodology could not only detect the presence of gear damage, but also offer a novel insight into the dynamic behavior of gearbox.

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Section: Vibration Model For Gearbox In the R/c Processmentioning

confidence: 99%

Feature Mining and Health Assessment for Gearboxes Using Run-Up/Coast-Down Signals

Zhao

Lin

Miao

et al. 2016

Sensors

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“…(2) Decompose signal by EMD algorithm and obtain IMFs. Compute Kurtosis value of IMFs based on (11) and select an appropriate IMF.…”

Section: Feature Extraction Of Faulty Bearing Based On Idmm and Emdmentioning

confidence: 99%

“…However, because of gear noise interference, it is difficult to directly extract the bearing rotational frequency from the mixed vibration signal. Traditional methods to eliminate the interference of the gear noise mainly include Time Synchronous Average (TSA) [7], Linear Prediction [8], Adaptive Noise Cancellation (ANC) [9], Self-Adaptive Noise Cancellation (SANC) [10], Discrete/Random Separation (DRS) [11], and Edited Cepstrum [12]. These algorithms require rotational speed to keep stable except the ANC algorithm.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of Faulty Rolling Element Bearing under Variable Rotational Speed and Gear Interferences Conditions

Zhao

Chen

2015

Shock and Vibration

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In the field of rolling element bearing fault diagnosis, variable rotational speed and gear noise are main obstacles. Even though some effective algorithms have been proposed to solve the problems, their process is complicated and they may not work well without auxiliary equipment. So we proposed a method of faulty bearing feature extraction based on Instantaneous Dominant Meshing Multiply (IDMM) and Empirical Mode Decomposition (EMD). The new method mainly consists of three parts. Firstly, IDMM is extracted from time-frequency representation of original signal by peak searching algorithm, which can be used to substitute the bearing rotational frequency. Secondly, resampled signal is obtained by an IDMM-based resampling algorithm; then it is decomposed into a number of Intrinsic Mode Functions (IMFs) based on the EMD algorithm. Calculate kurtosis values of IMFs and an appropriate IMF with biggest kurtosis value is selected. Thirdly, the selected IMF is analyzed with envelope demodulation method which can describe the fault type of bearing. The effectiveness of the proposed method has been demonstrated by both simulated and experimental mixed signals which contain bearing and gear vibration signal.

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“…If the latter is not true, the response at a given time (and corresponding speed) has been excited a short time previously when the speed was different. This is discussed by Borghesani et al [6]. The other extra limitation given by using the response signal is that it is often amplitude modulated as well as frequency modulated, and thus the total bandwidth of each order is larger, further restricting the possible range of deviation which could be achieved in each segment [3].…”

Section: Typical Proceduresmentioning

confidence: 99%

Order Tracking Under Run-Up and Run-Down Conditions

Coats

Randall

2015

Proceedings of the 9th IFToMM International Conference on Rotor Dynamics

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In recent years there has been considerable interest in the analysis of machine vibration signals in the presence of speed variations, sometimes over wide ranges. Computed order tracking based on phase demodulation to obtain a map of phase (rotation angle) versus time is limited to a maximum speed range of 2:1. This paper shows how analysis over a much greater speed range, for example a run-up or run-down, can be achieved by dividing the signal into overlapping segments, in each of which the speed range is less than 2:1. In the order domain, the highest frequency to be retained is proportional to the speed, and so at lower speeds the required sampling frequency can be reduced accordingly, as long as higher frequency components are first removed to avoid aliasing. The paper explains how this can be done in the most efficient way.

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Order tracking for discrete-random separation in variable speed conditions

Cited by 72 publications

References 5 publications

Feature Mining and Health Assessment for Gearboxes Using Run-Up/Coast-Down Signals

Feature Mining and Health Assessment for Gearboxes Using Run-Up/Coast-Down Signals

Feature Extraction of Faulty Rolling Element Bearing under Variable Rotational Speed and Gear Interferences Conditions

Order Tracking Under Run-Up and Run-Down Conditions

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