Fault prognosis for batch production based on percentile measure and gamma process: Application to semiconductor manufacturing

Nguyen, Thi-Bich-Lien; Djeziri, Mohand; Ananou, Bouchra; Ouladsine, Mustapha; Pinaton, Jacques

doi:10.1016/j.jprocont.2016.10.003

Cited by 34 publications

(19 citation statements)

References 30 publications

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“…The former combines artificial intelligence and statistical approaches, such as artificial neural networks (ANN), principal component analysis (PCA), fuzzy logic, Kalman filters and so on, to set up a discriminant model to extract fault [38,39]. For example, Nguyen et al [40] proposed percentile measurements and a gamma process model to estimate the remaining useful life (RUL) in batch manufacturing processes. Samir et al [41] proposed a dynamic model to generate fault indicators to identify a cluster for each operation state.…”

Section: Introductionmentioning

confidence: 99%

Self-Adaptive Fault Feature Extraction of Rolling Bearings Based on Enhancing Mode Characteristic of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

Zhan

et al. 2019

Symmetry

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Originally, a rolling bearing, as a key part in rotating machinery, is a cyclic symmetric structure. When a fault occurs, it disrupts the symmetry and influences the normal operation of the rolling bearing. To accurately identify faults of rolling bearing, a novel method is proposed, which is based enhancing the mode characteristics of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). It includes two parts: the first is the enhancing decomposition of CEEMDAN algorithm, and the second is the identified method of intrinsic information mode (IIM) of vibration signal. For the first part, the new mode functions (CIMFs) are obtained by combing the adjacent intrinsic mode functions (IMFs) and performing the corresponding Fast Fourier Transform (FFT) to strengthen difference feature among IMFs. Then, probability density function (PDF) is used to estimate FFT of each CIMF to obtain overall information of frequency component. Finally, the final intrinsic mode functions (FIMFs) are obtained by proposing identified method of adjacent PDF based on geometrical similarity (modified Hausdorff distance (MHD)). FIMFs indicate the minimum amount of mode information with physical meanings and avoid interference of spurious mode in original CEEMDAN decomposing. Subsequently, comprehensive evaluate index (Kurtosis and de-trended fluctuation analysis (DFA)) is proposed to identify IIM in FIMFs. Experiment results indicate that the proposed method demonstrates superior performance and can accurately extract characteristic frequencies of rolling bearing.

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Section: Introductionmentioning

confidence: 99%

Self-Adaptive Fault Feature Extraction of Rolling Bearings Based on Enhancing Mode Characteristic of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

Zhan

et al. 2019

Symmetry

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show abstract

“…Besides, a model for multistage processes from binary data was proposed by Shang et al [2] by using the hierarchical likelihood approach to illustrate the cumulative effects. Nguyen et al [3] suggested a three-stage prognosis for batch manufacturing composed of the extraction of the health index, the variation of representative values using the percentile measure, and the modeling of profiles as gamma processes.…”

Section: Introductionmentioning

confidence: 99%

Spatial Monitoring of Wafer Map Defect Data Based on 2D Wavelet Spectrum Analysis

Lim

Bae

2019

Applied Sciences

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Since machine vision systems (MVS) lead to a wide usage of monitoring systems for industrial applications, the research on the statistical process control (SPC) of image data has been promoted as an automated method for early detection and prevention of unusual conditions in manufacturing processes. In this paper, we propose a non-parametric SPC approach based on the 2D wavelet spectrum (WS-SPC) to extract the feature that contains the spatial and directional information of each subspace in an image. Using the 2D discrete wavelet transform and spectrum analysis, the representative statistic, the Hurst index, is calculated, and a single matrix space that consists of estimated statistics is reconstructed into a spatial control area for SPC. When a control limit is determined by the density of statistics, real-time monitoring based on WS-SPC is available for time releasing images. In the application, an analysis of wafer bin maps (WBMs) is conducted at a semiconductor company in Korea in order to evaluate the performance of the suggested approach. The results show that the proposed method is effective in terms of its fast computation speed and spectral monitoring.

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“…This research focused on proposing an efficient data-driven fault diagnostic method to monitor the equipment condition, and consequently to detect and classify the faults. In [19], Nguyen et al proposed a data-driven prognostic method for BMP organized in three steps. The emphasis is on the use of the percentile measure to process the raw health index.…”

Section: Introductionmentioning

confidence: 99%

KECA Similarity-Based Monitoring and Diagnosis of Faults in Multi-Phase Batch Processes

Meng

et al. 2019

Entropy

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Multiple phases with phase to phase transitions are important characteristics of many batch processes. The linear characteristics between phases are taken into consideration in the traditional algorithms while nonlinearities are neglected, which can lead to inaccuracy and inefficiency in monitoring. The focus of this paper is nonlinear multi-phase batch processes. A similarity metric is defined based on kernel entropy component analysis (KECA). A KECA similarity-based method is proposed for phase division and fault monitoring. First, nonlinear characteristics can be extracted in feature space via performing KECA on each preprocessed time-slice data matrix. Then phase division is achieved with the similarity variation of the extracted feature information. Then, a series of KECA models and slide-KECA models are established for steady and transitions phases respectively, which can reflect the diversity of transitional characteristics objectively and preferably deal with the stage-transition monitoring problem in multistage batch processes. Next, in order to overcome the problem that the traditional contribution plot cannot be applied to the kernel mapping space, a nonlinear contribution plot diagnosis algorithm is proposed, which is easier, more intuitive and implementable compared with the traditional one. Finally, simulations are performed on penicillin fermentation and industrial application. Specifically, the proposed method detects the abnormal agitation power and the abnormal substrate supply at 47 h and 86 h, respectively. Compared with traditional methods, it has better real-time performance and higher efficiency. Results demonstrate the ability of the proposed method to detect faults accurately and effectively in practice.

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Fault prognosis for batch production based on percentile measure and gamma process: Application to semiconductor manufacturing

Cited by 34 publications

References 30 publications

Self-Adaptive Fault Feature Extraction of Rolling Bearings Based on Enhancing Mode Characteristic of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

Self-Adaptive Fault Feature Extraction of Rolling Bearings Based on Enhancing Mode Characteristic of Complete Ensemble Empirical Mode Decomposition with Adaptive Noise

Spatial Monitoring of Wafer Map Defect Data Based on 2D Wavelet Spectrum Analysis

KECA Similarity-Based Monitoring and Diagnosis of Faults in Multi-Phase Batch Processes

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