Application of Linear Prediction, Self-Adaptive Noise Cancellation, and Spectral Kurtosis in Identifying Natural Damage of Rolling Element Bearing in a Gearbox

Ruiz-Cárcel, Cristobal; Hernani-Ros, E.; Chandra, P Navin; Cao, Y.

doi:10.1007/978-3-319-06966-1_45

Cited by 8 publications

(4 citation statements)

References 11 publications

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“…The vibration signal extraction is observed to be stable and optimal with filter length of 2,000 and step size of 0.00001 for the number of iterations varying between 500 and 8,500. Ruiz-Carcel et al (2015) explained about the application of adaptive filter in signal noise reduction through adaptive noise canceling method for which a reference noise has been used as an input. The reference noise is iteratively adjusted and subtracted from the actual signal in order to recover the measured signal with high signal-to-noise ratio.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive prediction model for effective electrical machine maintenance

Ganga

Ramachandran

2019

JQME

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Purpose The purpose of this paper is to propose an optimal predictive model for the short-term forecast of real-time non-stationary machine variables by combining time series prediction with adaptive algorithms to minimize the error and to improve the prediction accuracy. Design/methodology/approach The proposed model is applied for prediction of speed and controller set point of three-phase induction motor operating on closed loop speed control with AC drive and PI controller. At Stage 1, the trend of the machine variables has been extracted and added to auto-regressive moving average (ARMA) time series prediction. ARMA prediction has been carried out using different combinations of AR and MA methods in order to make prediction with less Mean Squared Error (MSE). Findings The prediction error indicates the inadequacy of the model to estimate the data characteristics, which has been resolved at the subsequent stage by cascading an adaptive least mean square finite impulse response filter to the time series model. The adaptive filter receives the predicted output including training data and iteratively adjusts its coefficients for zero error convergence. Research limitations/implications The componentized data prediction based on time series and cascade adaptive filter algorithm decomposes the non-stationary data characteristics for predictive maintenance. Evaluation of the model with different combination of time series algorithms and parameter settings of adaptive filter has been carried out to illustrate the performance of the prediction model. This prediction accuracy is compared with existing linear adaptive filter prediction using MSE as comparison index. The wide margin in the MSE values substantiates the prediction efficiency of the proposed model for machine data. Originality/value This model predicts the dynamic machine data with component decomposition at high accuracy, which enables to interpret the system response under dynamic conditions efficiently.

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Section: Literature Reviewmentioning

confidence: 99%

Adaptive prediction model for effective electrical machine maintenance

Ganga

Ramachandran

2019

JQME

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“…DRS has been extensively discussed in [15,22]. Multiple articles have claimed successes on applying DRS for bearing and gear signal separation [17,25,26,27,28,29]. It is also worth noticing that, although DRS has an advantageous processing efficiency, its performance is susceptible to varying rotating speed due to the lack of adaptive process.…”

Section: Gear and Bearing Signal Separation Techniquesmentioning

confidence: 99%

Using frequency domain analysis techniques for diagnosis of planetary bearing defect in a CH-46E helicopter aft gearbox

Zhou

Duan

Wang

et al. 2018

Engineering Failure Analysis

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Condition monitoring for helicopters has always been one of the most critical technologies to guarantee the integrity of the rotorcrafts, enhance operational and personnel safety, and reduce the overall maintenance costs. Over the past decades, health and usage monitoring system (HUMS) has been developed and implemented in helicopters to monitor the health status for the main gearbox (MGB) and other key components of the transmission system, improving condition-based maintenance for helicopters. However, many studies have indicated that current HUMS has a limited sensitivity to MGB planetary bearing defects. To enhance HUMS' performance, this paper presents an approach based on frequency domain analysis techniques to diagnose planetary bearing defects using real helicopter data collected from a CH-46E helicopter aft MGB. Vibration data was processed using signal processing techniques including self-adaptive noise cancellation (SANC), discrete-random separation (DRS), cepstrum editing, kurtogram, envelope analysis and iterative envelope cancellation. Processing results conclude that frequency domain analysis techniques can provide distinct and intuitive indications of the seeded defects at both the inner race and the outer race of the faulty planetary bearing.

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“…They used intrinsic time-scale decomposition instead of STFT. Ruiz-Ca´rcel et al (2015) applied three diagnosis methods including linear prediction, self-adaptive noise cancellation, and SK to detect bearing defect at different levels of growth of a naturally seeded defect. These methods have the ability of fault detection in the presence of strong background noise.…”

Section: Introductionmentioning

confidence: 99%

“…Ruiz-Cárcel et al. (2015) applied three diagnosis methods including linear prediction, self-adaptive noise cancellation, and SK to detect bearing defect at different levels of growth of a naturally seeded defect.…”

Section: Introductionmentioning

confidence: 99%

Rolling element bearing diagnosis using spectral kurtosis based on optimized impulse response wavelet

Bastami

Bashari

2019

Journal of Vibration and Control

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Envelope analysis is widely used in fault diagnosis of rolling element bearings (REBs). In envelope analysis, it is necessary to select a frequency band which is related to the resonance of the bearing. Spectral kurtosis (SK) is known as a powerful method to find the resonance band in vibration of a defective REB. SK, calculated by short time Fourier transform, suffers from its dependency on the window length. In this article, a special wavelet transform is used to obtain a SK diagram. It is shown that choice of mother wavelet function has great influence on the resulting SK diagram. The proposed wavelet is based on the impulse response of a damped single degree of freedom system. An optimization algorithm is used to optimize the SK diagram for fault detection. The method is tested for both simulated and experimental vibration data.

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Application of Linear Prediction, Self-Adaptive Noise Cancellation, and Spectral Kurtosis in Identifying Natural Damage of Rolling Element Bearing in a Gearbox

Cited by 8 publications

References 11 publications

Adaptive prediction model for effective electrical machine maintenance

Adaptive prediction model for effective electrical machine maintenance

Using frequency domain analysis techniques for diagnosis of planetary bearing defect in a CH-46E helicopter aft gearbox

Rolling element bearing diagnosis using spectral kurtosis based on optimized impulse response wavelet

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