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Sensor data anomaly detection and correction for improving the life prediction of cutting tools in the slot milling process

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The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Effective cutting tool life prediction is significant for ensuring processing quality and improving production efficiency. Data-driven prediction methods have been widely used. However, traditional methods assume that there are high-quality sensor data, whereas, in practice, factors such as poor installation of sensors and environmental interference often lead to poor-quality data, leading to unreliable analysis results and incorrect decisions. Thus, in this paper, a sensor data anomaly detection and correction method is proposed. It mainly includes four parts: data preprocessing, abnormal data detection, correction of detected abnormal data, and tool life prediction and evaluation. First, the raw condition monitoring data are preprocessed for feature extraction and health index (HI) construction. Second, the HIs of historical training samples are clustered based on the dynamic time warping (DTW) algorithm, and the abnormal data are detected based on error calculation with a preset error threshold. Third, the detected abnormal data are optimized via similarity matching using k-nearest neighbors with dynamic time warping (KNN-DTW). Finally, the optimized data are used for tool life prediction and evaluation. The proposed method has been tested on real data acquired from a turbine factory. The comparison results show that the prediction effect can be significantly improved after adopting the proposed method, which verifies the necessity of sensor data anomaly detection and correction.

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Abbreviations

HI:

Health index

DTW:

Dynamic time warping

KNN:

K-nearest neighbors

KNN-DTW:

K-nearest neighbors with dynamic time warping

DL:

Deep learning

SVR:

Support vector regression

LS-SVR:

Least squares support vector regression

MAE:

Mean absolute error

RMSE:

Root mean square error

AE:

Acoustic emission

A:

Amplitude

E:

Energy

ASL:

Average signal level

RMS:

Root mean square

SS:

Signal strength

AbE:

Absolute energy

PCA:

Principal component analysis

LS-SVM:

Least squares support vector machine

HISBM:

Health index similarity-based method

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Funding

This project was supported by the National Key Research and Development Program of China (grant number 2018YFB1700502).

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Authors and Affiliations

  1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

    Yingchao Liu, Jian Zhang, Xiaofeng Hu & Shixu Sun

Authors
  1. Yingchao Liu
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  2. Jian Zhang
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  3. Xiaofeng Hu
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  4. Shixu Sun
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Contributions

Yingchao Liu designed the model and the computational framework, analyzed the data, and wrote the manuscript. Xiaofeng Hu helped supervise the project and contributed to the final manuscript. Jian Zhang and Shixu Sun contributed to data analysis.

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Correspondence to Xiaofeng Hu.

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This study was conducted by the corresponding author under the guidance of professor Xiaofeng Hu at Shanghai Jiao Tong University. The involved researchers have been listed in the article, and all authors have no objection.

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The authors confirm that the work has not been published before and does not consider other places. Its publication has been approved by all coauthors, and the authors agree to publish the article in this journal.

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Liu, Y., Zhang, J., Hu, X. et al. Sensor data anomaly detection and correction for improving the life prediction of cutting tools in the slot milling process. Int J Adv Manuf Technol 119, 463–475 (2022). https://doi.org/10.1007/s00170-021-08275-w

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  • DOI: https://doi.org/10.1007/s00170-021-08275-w

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