Universiti Teknologi Malaysia Institutional Repository

Feature extraction in control chart patterns with missing data

Haghighati, R. and Hassan, A. (2019) Feature extraction in control chart patterns with missing data. In: 8th International Conference on Mechanical and Manufacturing Engineering, ICME 2018, 16 July 2018 through 17 July 2018, Johor Bharu, Johor, Malaysia.


Official URL: http://dx.doi.org/10.1088/1742-6596/1150/1/012013


Data preprocessing and feature extraction are critical steps in control chart pattern (CCP) recognition for reducing dimensionality and irrelevant information. To ensure good quality of input representation, it is important to handle missing values on control charts before feature extraction. Excluding missing values and imputing them with plausible values are two common missing data handling approaches in the literature. In this paper imputation capability of exponentially weighted moving average (EWMA) was investigated. Incomplete process data for three missingness mechanisms namely, missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR) were investigated using computer simulation. Missing data at severity levels i.e. 0, 5, 10, 15, 20, 25 and 50 % were evaluated. The investigation covers feature mean, standard deviation, skewness, kurtosis and quartiles extracted from imputed patterns. The imputation performance was measured by comparing the deviation between full patterns and patterns with missing values in term of mean square error (MSE). The results show that EWMA imputation was highly reliable to recover missing values as evident form low feature deviations, MSE values; 0.04 (random), 0.04 (trend-up), 0.3 (shift-up) and 0.5 (cycle) respectively. The results suggest that EWMA imputation technique is superior than the mean and median imputations.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:missing at randoms, missing not at random, missingness mechanism
Subjects:T Technology > TJ Mechanical engineering and machinery
Divisions:Mechanical Engineering
ID Code:89666
Deposited By: Yanti Mohd Shah
Deposited On:22 Feb 2021 14:09
Last Modified:22 Feb 2021 14:09

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