Monitoring of a machining process using kernel principal component analysis and kernel density estimation

Lee, Wo Jae; Mendis, Gamini P.; Triebe, Matthew J.; Sutherland, John W.

doi:10.1007/s10845-019-01504-w

Cited by 75 publications

(35 citation statements)

References 29 publications

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“…Their work was shown to outperform both support-vector machine and neural network-based approaches on data sampled at 20 kHz. Lee et al [25] used KDE to monitor tool wear using by using T2-statistics and Q-statistics with control charts. They used NASA's milling dataset [26] for which data were acquired at a minimum of 100 kHz.…”

Section: Methodsmentioning

confidence: 99%

Condition Monitoring of Manufacturing Processes under Low Sampling Rate

Bernard

Achiche

Girard³

et al. 2021

JMMP

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Manufacturing processes can be monitored for anomalies and failures just like machines, in condition monitoring and prognostic and health management. This research takes inspiration from condition monitoring and prognostic and health management techniques to develop a method for part production process monitoring. The contribution brought by this paper is an automated technique for process monitoring that works with low sampling rates of 1/3Hz, a limitation that comes from using data provided by an industrial partner and acquired from industrial manufacturing processes. The technique uses kernel density estimation functions on machine tools spindle load historical time signals for distribution estimation. It then uses this estimation to monitor the manufacturing processes for anomalies in real time. A modified version was tested by our industrial partner on a titanium part manufacturing line.

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Section: Methodsmentioning

confidence: 99%

Condition Monitoring of Manufacturing Processes under Low Sampling Rate

Bernard

Achiche

Girard³

et al. 2021

JMMP

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“…PCA employs linear permutation for conserving unique information to maximum extent [ 45 ]. Thus, it converts multi-response optimization to single response optimization without compromising original information [ 46 ]. It begins by setting a structure of linear combinations arrays of multi-responses.…”

Section: Optimization Methodologymentioning

confidence: 99%

An Integrated Approach of GRA Coupled with Principal Component Analysis for Multi-Optimization of Shielded Metal Arc Welding (SMAW) Process

et al. 2020

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Welding distortion is a critical issue as it leads to severe deterioration of structural integrity of welded work piece and dimensional precision. This study aims at studying the effects of shielded metal arc welding (SMAW) parameters on the evolution of mechanical properties, including tensile strength, impact toughness, and hardness, along with angular distortion on a welded joint from SA 516 grade 70. Such parameters are analyzed and optimized by employing the Taguchi method and Grey relational analysis. SA 516 grade 70 is commercially used for fabrication of storage tanks, boilers and pressure vessels. SMAW is investigated with three levels of root gap, groove angle, electrode diameter, and pre-heat temperature, which were varied on a butt joint in flat (1 G) position to determine their effects on response variables at room temperature. Nine experiments were designed using a Taguchi L9 orthogonal array, welded according to American Society of Mechanical Engineers (ASME) section IX, and samples were prepared and tested as per ASTM A 370. The Taguchi method and Grey relational analysis were employed to observe the most significant parameters and optimal levels that synergically yield improved responses. Results are validated by conducting confirmatory experiments that show good agreement with optimum results.

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“…The two-step KPCA preserves both the global and local data structure information. Lee et al [18] proposed a tool wear/tool condition monitoring system by using KPCA and kernel density estimation. Though PCA has shown accurate applications for fault detection, it cannot produce good results for fault classification easily as the correlation between fault types is not considered in PCA transform.…”

Section: The State Of the Artmentioning

confidence: 99%

Fault Classification of Nonlinear Small Sample Data through Feature Sub-Space Neighbor Vote

Yan

2020

Electronics

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The fault classification of a small sample of high dimension is challenging, especially for a nonlinear and non-Gaussian manufacturing process. In this paper, a similarity-based feature selection and sub-space neighbor vote method is proposed to solve this problem. To capture the dynamics, nonlinearity, and non-Gaussianity in the irregular time series data, high order spectral features, and fractal dimension features are extracted, selected, and stacked in a regular matrix. To address the problem of a small sample, all labeled fault data are used for similarity decisions for a specific fault type. The distances between the new data and all fault types are calculated in their feature subspaces. The new data are classified to the nearest fault type by majority probability voting of the distances. Meanwhile, the selected features, from respective measured variables, indicate the cause of the fault. The proposed method is evaluated on a publicly available benchmark of a real semiconductor etching dataset. It is demonstrated that by using the high order spectral features and fractal dimensionality features, the proposed method can achieve more than 84% fault recognition accuracy. The resulting feature subspace can be used to match any new fault data to the fingerprint feature subspace of each fault type, and hence can pinpoint the root cause of a fault in a manufacturing process.

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Monitoring of a machining process using kernel principal component analysis and kernel density estimation

Cited by 75 publications

References 29 publications

Condition Monitoring of Manufacturing Processes under Low Sampling Rate

Condition Monitoring of Manufacturing Processes under Low Sampling Rate

An Integrated Approach of GRA Coupled with Principal Component Analysis for Multi-Optimization of Shielded Metal Arc Welding (SMAW) Process

Fault Classification of Nonlinear Small Sample Data through Feature Sub-Space Neighbor Vote

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