Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions

Sayyad, S. S.; Kumar, Satish; Bongale, Arunkumar; Kamat, Pooja; Patil, Shruti; Kotecha, Ketan

doi:10.1109/access.2021.3101284

Cited by 51 publications

(19 citation statements)

References 182 publications

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“…Generally, tool wear includes two forms: flank wear(VB) and crater wear(KB) [43].Currently, most researchers focus on flank wear monitoring in studies on the prediction of tool RUL, as flank wear is an influential factor in the quality, reliability, and dimensional accuracy of workpiece machining [44]. Figure 5 shows an unworn insert and a worn-out cutting worn insert.…”

Section: Methodsmentioning

confidence: 99%

Remaining Useful Life Prediction of Milling Cutters Based on CNN-BiLSTM and Attention Mechanism

Nie

Zhang

et al. 2022

Symmetry

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Machining tools are a critical component in machine manufacturing, the life cycle of which is an asymmetrical process. Extracting and modeling the tool life variation features is very significant for accurately predicting the tool’s remaining useful life (RUL), and it is vital to ensure product reliability. In this study, based on convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM), a tool wear evolution and RUL prediction method by combining CNN-BiLSTM and attention mechanism is proposed. The powerful CNN is applied to directly process the sensor-monitored data and extract local feature information; the BiLSTM neural network is used to adaptively extract temporal features; the attention mechanism can selectively study the important degradation features and extract the tool wear status information. By evaluating the performance and generalization ability of the proposed method under different working conditions, two datasets are applied for experiments, and the proposed method outperforms the traditional method in terms of prediction accuracy.

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Section: Methodsmentioning

confidence: 99%

Remaining Useful Life Prediction of Milling Cutters Based on CNN-BiLSTM and Attention Mechanism

Nie

Zhang

et al. 2022

Symmetry

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“…In most studies, multiregression or supervised ML methods are used to develop RUL or tool wear prediction models that have been established successfully. In spite, the ML algorithms have scalability issues for big data (Sayyad et al. , 2021a, b).…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation for tool wear prediction based on Bi-directional, Encoder–Decoder and Hybrid Long Short-Term Memory models

Kumar

Kolekar

Kotecha

et al. 2022

IJQRM

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Purpose Excessive tool wear is responsible for damage or breakage of the tool, workpiece, or machining center. Thus, it is crucial to examine tool conditions during the machining process to improve its useful functional life and the surface quality of the final product. AI-based tool wear prediction techniques have proven to be effective in estimating the Remaining Useful Life (RUL) of the cutting tool. However, the model prediction needs improvement in terms of accuracy.Design/methodology/approachThis paper represents a methodology of fusing a feature selection technique along with state-of-the-art deep learning models. The authors have used NASA milling data sets along with vibration signals for tool wear prediction and performance analysis in 15 different fault scenarios. Multiple steps are used for the feature selection and ranking. Different Long Short-Term Memory (LSTM) approaches are used to improve the overall prediction accuracy of the model for tool wear prediction. LSTM models' performance is evaluated using R-square, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) parameters.FindingsThe R-square accuracy of the hybrid model is consistently high and has low MAE, MAPE and RMSE values. The average R-square score values for LSTM, Bidirection, Encoder–Decoder and Hybrid LSTM are 80.43, 84.74, 94.20 and 97.85%, respectively, and corresponding average MAPE values are 23.46, 22.200, 9.5739 and 6.2124%. The hybrid model shows high accuracy as compared to the remaining LSTM models.Originality/value The low variance, Spearman Correlation Coefficient and Random Forest Regression methods are used to select the most significant feature vectors for training the miscellaneous LSTM model versions and highlight the best approach. The selected features pass to different LSTM models like Bidirectional, Encoder–Decoder and Hybrid LSTM for tool wear prediction. The Hybrid LSTM approach shows a significant improvement in tool wear prediction.

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“…Guan and Huang [21] created a particle swarm optimization algorithm based on wavelet variation and a least squares support vector machine to avoid falling into local extremum problems. Sayyad and Kumar [22] introduced a survey to review service life prediction technologies of real-time health monitors of cutting tools from perspectives of modeling, systems, data sets and research trends. Capriglione and Carratu [23] proposed an FD method using a nonlinear autoregressive with Exogenous Inputs (NARX) neural network as a residual generator for online FD of travel sensors.…”

Section: Introductionmentioning

confidence: 99%

Fault Detection for High-Speed Trains Using CCA and Just-in-Time Learning

et al. 2022

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Online monitors of the running gears systems of high-speed trains play critical roles in ensuring operational safety and reliability. Status signals collected from high-speed train running gears are very complex regarding working environments, random noises and many other real-world constraints. This paper proposed fault detection (FD) models using canonical correlation analysis (CCA) and just-in-time learning (JITL) to process scalar signals of high-speed train gears, named as CCA-JITL. After data preprocessing and normalization, CCA transforms covariance matrices of high-dimension historical data into low-dimension subspaces and maximizes correlations between the most important latent dimensions. Then, JITL components formulate local FD models which utilize subsets of testing samples with larger Euclidean distances to training data. A case study introduced a novel system design of an online FD architecture and demonstrated that CCA-JITL FD models significantly outperformed traditional CCA models. The approach is applicable to other dimension reduction FD models such as PCA and PLS.

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Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions

Cited by 51 publications

References 182 publications

Remaining Useful Life Prediction of Milling Cutters Based on CNN-BiLSTM and Attention Mechanism

Remaining Useful Life Prediction of Milling Cutters Based on CNN-BiLSTM and Attention Mechanism

Performance evaluation for tool wear prediction based on Bi-directional, Encoder–Decoder and Hybrid Long Short-Term Memory models

Fault Detection for High-Speed Trains Using CCA and Just-in-Time Learning

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