Using multi-scale entropy and principal component analysis to monitor gears degradation via the motor current signature analysis

Salim, Aouabdi; Taibi, Mahmoud; Bouras, Slimane; Boutasseta, Nadir

doi:10.1016/j.ymssp.2016.12.027

Cited by 45 publications

(24 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, based on the effectively denoising, the MSE method is used for calculating the MSE values of the reconstructed signal. To distinguish the fault types of bearings, the calculated sample entropy is input as a rolling bearing fault feature into an SVM model, which is more suitable for training small sample data [27]. Compared with the conventional entropy methods for rolling bearing fault diagnosis, the proposed method is more stable and suitable for practical engineering applications.…”

Section: Introductionmentioning

confidence: 99%

“…Hsieh et al [26] utilized the EMD and the MSE for high-speed spindle fault diagnosis. Their conclusion illustrates not only that MSE can accurately distinguish the fault types of high-speed spindles but also that the noise reduction performance of EMD still needs to be improved.Aouabdi et al [27] used MSE and principal component analysis (PCA) to analyze current signals to monitor and diagnose the degradation of the gear. Their conclusion illustrates that this method can detect gear tooth erosion better.…”

mentioning

confidence: 99%

“…Aouabdi et al [27] used MSE and principal component analysis (PCA) to analyze current signals to monitor and diagnose the degradation of the gear. Their conclusion illustrates that this method can detect gear tooth erosion better.…”

mentioning

confidence: 99%

See 2 more Smart Citations

A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

Niu

et al. 2020

Entropy

View full text Add to dashboard Cite

Feature extraction is one of the challenging problems in fault diagnosis, and it has a direct bearing on the accuracy of fault diagnosis. Therefore, in this paper, a new method based on ensemble empirical mode decomposition (EEMD), wavelet semi-soft threshold (WSST) signal reconstruction, and multi-scale entropy (MSE) is proposed. First, the EEMD method is applied to decompose the vibration signal into intrinsic mode functions (IMFs), and then, the high-frequency IMFs, which contain more noise information, are screened by the Pearson correlation coefficient. Then, the WSST method is applied for denoising the high-frequency part of the signal to reconstruct the signal. Secondly, the MSE method is applied for calculating the MSE values of the reconstructed signal, to construct an eigenvector with the complexity measure. Finally, the eigenvector is input to a support vector machine (SVM) to find the fault diagnosis results. The experimental results prove that the proposed method, with a better classification performance, can better solve the problem of the effective signal and noise mixed in high-frequency signals. Based on the proposed method, the fault types can be accurately identified with an average classification accuracy of 100%.Entropy 2020, 22, 290 2 of 28 functions (IMFs) by EMD [10]. Aiming at the shortcomings of the EMD, such as modal mixture and end effect, which affect the accuracy of signal decomposition [11], Wu et al. [12] improved the EMD method with auxiliary noise and proposed the ensemble empirical mode decomposition (EEMD) method.The EEMD method results in signal decomposition with anti-noise characteristics, reduces reconstruction errors, and improves the quality of IMFs by adding Gaussian white noise to the original signal. Therefore, the EEMD method is widely used in signal processing and fault diagnosis [13]. However, when the EEMD method is applied independently for decomposing signals and reducing noise, the information in the high-frequency components is also lost as some IMFs are discarded [14]. The WT method has a good performance on the suppression of random noise by having the properties of multi-scale, low entropy, and decorrelation [15]. Jumah et al. [16] proposed a method using wavelet transform and various thresholding techniques, which has a good effect on removing one-dimensional Gaussian white noise. However, there is no uniform standard for the operation of the wavelet threshold method, which has a greater impact on the final result. The wavelet hard threshold denoising method generates discontinuous points and loses some vital information [17]. The wavelet soft threshold denoising method causes distortion phenomena, such as edge blur effect [18].Although the wavelet threshold denoising method effectively removes noise from high-frequency signals, the effect is not ideal, and the useful signals still mix with noise signals. Nevertheless, the wavelet semi-soft threshold with the advantages of the hard and soft threshold can not only preserve the integrity of the signal but al...

show abstract

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

Niu

et al. 2020

Entropy

View full text Add to dashboard Cite

show abstract

“…To improve and optimize machining processes, a number of studies in the last decades proposed the use of on-line sensor systems for monitoring of tool conditions [17,18,19], machine tool state [20,21], chip formation [22,23], vibration control [24], chatter detection, surface integrity, process conditions, etc. [25,26,27].…”

Section: Introductionmentioning

confidence: 99%

“…The main procedures for signal features extraction which are typically used in sensor monitoring research can be classified as follows: time-domain methods (e.g., principal component analysis (PCA)) [21,22,23]), frequency domain methods (e.g., fast Fourier transform (FFT)) [20,30]) and time-frequency domain methods (e.g., wavelet transform (WT) [31,32,33]).…”

Section: Introductionmentioning

confidence: 99%

Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation

Segreto

Caggiano

Teti

2017

Sensors

View full text Add to dashboard Cite

Nickel-Titanium (Ni-Ti) alloys are very difficult-to-machine materials causing notable manufacturing problems due to their unique mechanical properties, including superelasticity, high ductility, and severe strain-hardening. In this framework, the aim of this paper is to assess the machinability of Ni-Ti alloys with reference to turning processes in order to realize a reliable and robust in-process identification of machinability conditions. An on-line sensor monitoring procedure based on the acquisition of vibration signals was implemented during the experimental turning tests. The detected vibration sensorial data were processed through an advanced signal processing method in time-frequency domain based on wavelet packet transform (WPT). The extracted sensorial features were used to construct WPT pattern feature vectors to send as input to suitably configured neural networks (NNs) for cognitive pattern recognition in order to evaluate the correlation between input sensorial information and output machinability conditions.

show abstract

Multiscale Entropy: Recent Advances

Liang

2017

Complexity and Nonlinearity in Cardiovascular Signals

View full text Add to dashboard Cite

Using multi-scale entropy and principal component analysis to monitor gears degradation via the motor current signature analysis

Cited by 45 publications

References 23 publications

A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

A Rolling Bearing Fault Diagnosis Method Based on EEMD-WSST Signal Reconstruction and Multi-Scale Entropy

Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation

Multiscale Entropy: Recent Advances

Contact Info

Product

Resources

About