Imbalanced Data Classification for Defective Product Prediction Based on Industrial Wireless Sensor Network

In the Industry 4.0 era, manufacturers can establish smart factories based on the industry wireless sensor network (IWSN). And data analysis plays a vital role to realize smart manufacturing. However, data collected in the real production through IWSN generally represent features as incomplete and imbalanced resulting in incorrect or biased analysis results. Therefore, a solution is proposed to resolve this problem, in which K Nearest Neighbor (KNN) algorithm is applied to do missing value imputation and Adaptive Synthetic Sampling algorithm is utilized to generate a balanced dataset. Furthermore, a 2-layer feedforward neural network (FNN) is designed as a classifier to predict defective products. The classification performance in testing using the resolution proposed is far superior to that of 2-layer FNN using the original dataset directly or employing the KNN algorithm for preprocessing first whose recall value is 94%, the precision value is 87.9%, and the F1-measure value is 90.8%. To sum up, the solution proposed can improve the classification performance dramatically, especially for the minority class, when encountering the incomplete and imbalanced data.