An intrinsic timescale decomposition-based kernel extreme learning machine method to detect tool wear conditions in the milling process

Lei, Zhi; Zhou, Yuqing; Sun, Bin; Sun, Weifang

doi:10.1007/s00170-019-04689-9

Cited by 33 publications

(16 citation statements)

References 37 publications

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“…Step 2: Time domain, frequency domain, and time − frequency domain features are extracted from the original collected signal according to the definitions given in Table 1 [40,41], in which thee time − frequency domain features are the energy coefficients of the original collected signal by the ensemble empirical mode decomposition (EEMD) [42,43].…”

Section: Tool Wear Estimation Methods Based On the Gapso-enhanced Elmmentioning

confidence: 99%

A GAPSO-Enhanced Extreme Learning Machine Method for Tool Wear Estimation in Milling Processes Based on Vibration Signals

Lei

Zhu

Zhou

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

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Tools are the most vulnerable components in milling processes conducted using numerical control milling machines, and their wear condition directly influences work-product quality and operational safety. As such, tool wear estimation is an essential component of NC milling operations. This study addresses this issue by proposing an extreme learning machine (ELM) method enhanced by a hybrid genetic algorithm and particle swarm optimization (GAPSO) approach for conducting tool wear estimation based on workpiece vibration signals. Here, a few feature parameters in the time, frequency, and time-frequency (Ensemble empirical mode decomposition, EEMD) domains of the workpiece vibration signals are extracted as the input of the ELM model. Then, the initialized weights and thresholds of the ELM model are optimized based on the GAPSO approach with training dataset. Finally, tool wear is estimated using the optimized ELM model with testing dataset. The effectiveness of the proposed method is verified by its application to vibration signals collected from two milling tool wear experiments (an open-access benchmark dataset and a milling tool wear experiment) by comparison to the ELM, GA-ELM, and PSO-ELM methods. The results indicate that the estimation accuracy and optimization efficiency of the proposed method outperforms that of other three methods.

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Section: Tool Wear Estimation Methods Based On the Gapso-enhanced Elmmentioning

confidence: 99%

A GAPSO-Enhanced Extreme Learning Machine Method for Tool Wear Estimation in Milling Processes Based on Vibration Signals

Lei

Zhu

Zhou

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

Self Cite

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“…Yuan and Peng proposed smooth intrinsic time-scale decomposition for the fault diagnosis of rolling bearings [22]. Lei and Zhou et al used intrinsic time-scale decomposition to monitor tool wear during milling [23]. Ma and Zhan et al proposed complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN), which was applied to the feature extraction of rolling bearings [24].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Intrinsic Time-Scale Decomposition Method Based on Adaptive Lévy Noise and Its Application in Bearing Fault Diagnosis

Shi

et al. 2021

Symmetry

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When early failures in rolling bearings occur, we need to be able to extract weak fault characteristic frequencies under the influence of strong noise and then perform fault diagnosis. Therefore, a new method is proposed: complete ensemble intrinsic time-scale decomposition with adaptive Lévy noise (CEITDALN). This method solves the problem of the traditional complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN) method not being able to filter nonwhite noise in measured vibration signal noise. Therefore, in the method proposed in this paper, a noise model in the form of parameter-adjusted noise is used to replace traditional white noise. We used an optimization algorithm to adaptively adjust the model parameters, reducing the impact of nonwhite noise on the feature frequency extraction. The experimental results for the simulation and vibration signals of rolling bearings showed that the CEITDALN method could extract weak fault features more effectively than traditional methods.

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“…Time-frequency analysis based on the intrinsic time-scale decomposition can quantitatively describe the relationship between frequency and time, accurately analyzing time-varying signals [10]. On the basis of these advantages, scholars introduced this method from the medical field to the fault diagnosis of mechanical signals [11][12][13][14][15][16][17][18][19][20][21][22]. For example, Lin and Chang published a rolling-bearing fault diagnosis method based on an enhanced kurtosis spectrum and intrinsic time-scale decomposition [11].…”

Section: Introductionmentioning

confidence: 99%

“…Yuan and Peng proposed the use of smooth intrinsic time-scale decomposition for the fault diagnosis of rolling bearings [20]. Lei and Zhou et al used intrinsic time-scale decomposition to monitor tool wear during milling [21]. Ma et al proposed complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN) that was applied to the feature extraction of rolling bearings [22].…”

Section: Introductionmentioning

confidence: 99%

A New Method Based on Time-Varying Filtering Intrinsic Time-Scale Decomposition and General Refined Composite Multiscale Sample Entropy for Rolling-Bearing Feature Extraction

Han

et al. 2021

Entropy

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The early fault diagnosis of rolling bearings has always been a difficult problem due to the interference of strong noise. This paper proposes a new method of early fault diagnosis for rolling bearings with entropy participation. First, a new signal decomposition method is proposed in this paper: intrinsic time-scale decomposition based on time-varying filtering. It is introduced into the framework of complete ensemble intrinsic time-scale decomposition with adaptive noise (CEITDAN). Compared with traditional intrinsic time-scale decomposition, intrinsic time-scale decomposition based on time-varying filtering can improve frequency-separation performance. It has strong robustness in the presence of noise interference. However, decomposition parameters (the bandwidth threshold and B-spline order) have significant impacts on the decomposition results of this method, and they need to be artificially set. Aiming to address this problem, this paper proposes rolling-bearing fault diagnosis optimization based on an improved coyote optimization algorithm (COA). First, the minimal generalized refined composite multiscale sample entropy parameter was used as the objective function. Through the improved COA algorithm, optimal intrinsic time-scale decomposition parameters based on time-varying filtering that match the input signal are obtained. By analyzing generalized refined composite multiscale sample entropy (GRCMSE), whether the mode component is dominated by the fault signal is determined. The signal is reconstructed and decomposed again. Finally, the mode component with the highest energy in the central frequency band is selected for envelope spectrum variation for fault diagnosis. Lastly, simulated and experimental signals were used to verify the effectiveness of the proposed method.

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An intrinsic timescale decomposition-based kernel extreme learning machine method to detect tool wear conditions in the milling process

Cited by 33 publications

References 37 publications

A GAPSO-Enhanced Extreme Learning Machine Method for Tool Wear Estimation in Milling Processes Based on Vibration Signals

A GAPSO-Enhanced Extreme Learning Machine Method for Tool Wear Estimation in Milling Processes Based on Vibration Signals

An Enhanced Intrinsic Time-Scale Decomposition Method Based on Adaptive Lévy Noise and Its Application in Bearing Fault Diagnosis

A New Method Based on Time-Varying Filtering Intrinsic Time-Scale Decomposition and General Refined Composite Multiscale Sample Entropy for Rolling-Bearing Feature Extraction

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