Normalization of vibration signals generated under highly varying speed and load with application to signal separation

Urbanek, Jacek; Barszcz, Tomasz; Strączkiewicz, Marcin; Jabłoński, Adam

doi:10.1016/j.ymssp.2016.04.017

Cited by 37 publications

(22 citation statements)

References 44 publications

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“…Repeat the above steps for 50 times, that is, J = 50, 50 MAFS j vectors would be got, which would be combined into a matrix MAFS with the dimension of 50 3 1024. The vector of MMAFS k is obtained with equation (4). Without loss of generality, repeat the prior steps for K = 100 times, a matrix MMAFS with a size of 100 3 1024 is obtained.…”

Section: Application Of the Methods On Simulated Signalmentioning

confidence: 99%

“…However, in practice, it is much more complex, almost no machine can work under stationary condition. 4,6 Under varying operation conditions, the vibration signals collected from rolling bearing systems usually carry heavy background noise and the fault characteristic frequency is not only modulated as a series of harmonics but also is smeared on the frequency spectrum. 7,8 Therefore, existing techniques based on the assumption of working in stationary or approximate stationary condition such as FFT cannot work well in extracting the overwhelmed remarkable information for fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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A new fault feature for rolling bearing fault diagnosis under varying speed conditions

Ren

Zhu

et al. 2017

Advances in Mechanical Engineering

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Most fault detection methods based on the assumption of working in stationary or approximate stationary conditions are limited under varying operation conditions, for that the frequency aliasing phenomenon is inevitable in the spectrum. Therefore, in order to handle the problem of fault diagnosis under non-stationary conditions, researchers have proposed numerous methods and some achievements have been obtained. In this article, a new feature extraction method is proposed for fault diagnosis of rolling bearings under varying speed conditions. Based on the assumption that the energy will increase when balls cross over fault position, frequency values are divided by instantaneous speed and arranged in the descending order of corresponding amplitude to form a new fault feature array, that is, the ratio of frequency to instantaneous speed reconfiguration arrays. Thereafter, the Euclidean distance classifier is utilized for recognition. The efficacy of the proposed method is demonstrated by simulated and experimental data. Categorized results show that the new approach is capable of handling the bearing fault classification under varying speed conditions.

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Section: Application Of the Methods On Simulated Signalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new fault feature for rolling bearing fault diagnosis under varying speed conditions

Ren

Zhu

et al. 2017

Advances in Mechanical Engineering

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“…In the analysis of energy consumption data in order to avoid the different characteristics of the data due to the difference in the size, the number of different levels of the prediction error is large, the characteristics are not obvious, the common method is to normalize the data processing [8] . The formula is as follows:…”

Section: Energy Consumption Data Normalization Research On Energy Savmentioning

confidence: 99%

Research on Smart Home Energy Management Algorithm Based on Cloud Environment

Sun¹,

Ren²,

Ye³

et al. 2017

dtcse

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Abstract. Energy saving algorithm for smart home energy consumption budget optimization under the SVM model using a simple particle swarm optimization algorithm to find the optimal value caused by the slow speed gradient value based on PSO algorithm is proposed to optimize the use of machine learning algorithm GDPSO algorithm (Gradient Descent based on down Particle Swarm Optimization Algorithm). First of all, the establishment of energy structure and energy consumption model of optimal hyperplane selection of penalty factor and Gauss the appropriate parameters; secondly, parameter selection and optimization of energy consumption model for smart home energy-saving emission reduction requirements using GDPSO algorithm, and improves the efficiency of the optimal SVM parameters to improve the accuracy of solution. A simulation example shows the effectiveness of the algorithm.

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“…There is also an application of complex Bayesian inference [17] to detect the damage type for various variants of load and rotational speed [7]. Separation of components related to variable speed and load can be found in the literature [18].…”

Section: Introductionmentioning

confidence: 99%

Single-number statistical parameters in the assessment of the technical condition of machines operating under variable load

Pawlik¹

2019

Eksploatacja i Niezawodność – Maintenance and Reliability

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Normalization of vibration signals generated under highly varying speed and load with application to signal separation

Cited by 37 publications

References 44 publications

A new fault feature for rolling bearing fault diagnosis under varying speed conditions

A new fault feature for rolling bearing fault diagnosis under varying speed conditions

Research on Smart Home Energy Management Algorithm Based on Cloud Environment

Single-number statistical parameters in the assessment of the technical condition of machines operating under variable load

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