Abstract
With the rapid expansion of Industrial Internet of Things, cloud computing and artificial intelligence, many intelligent information services have been developed in smart factories. One of the most important applications is helping factory managers predict the quality of assembled products. Traditional prediction methods of assembly quality mainly focus on building classification or regression models with high accuracy. However, less attention is paid to high-dimensional and imbalanced data, which is a special but common scenario at real-life assembly quality prediction. In this paper, we first use random forest to reduce dimension and analyze critical-to-quality characteristics. Then, a SMOTE-Adaboost method with jointly optimized hyperparameters is proposed for imbalanced data classification in assembly quality prediction. In addition, edge computing is introduced to improve the efficiency and flexibility of quality prediction. Finally, the practicality and effectiveness of the proposed method are verified by a case study of wheel bearing assembly line, and the experimental results show that the proposed method is superior to other classification methods in assembly quality prediction.
Highlights
With the popularization and application of the industrial internet of things (IoT), artificial intelligence (AI) and cloud computing (CC), various devices in factory are connected to industrial IoT through wired and wireless sensor networks
2) CLASSIFICATION RESULTS WITH OTHER MACHINE LEARNING METHODS In order to verify the effectiveness of the proposed predictive model, we take five performance indicators (ACC, Area Under Curve (AUC), recall, specificity, and G-mean) commonly used in classification algorithms as the evaluation indicators of quality predictive models
Aiming at the problem of high-dimensional and imbalanced data in assembly process of assembly products, this paper proposes a prediction method of assembly quality based on edge intelligent service
Summary
With the popularization and application of the industrial internet of things (IoT), artificial intelligence (AI) and cloud computing (CC), various devices in factory are connected to industrial IoT through wired and wireless sensor networks. The predictive model in edge device is regarded as an aided prediction tool for factory managers in this time These labeled-data are transmitted to the cloud center layer when the network is free. The existing quality predictive models in the cloud center layer will be updated with these time-insensitive data by some incremental learning methods In this way, it helps to solve the problem caused by dynamic data [41], [42]. We calculate the importance of each quality characteristics according to formula (3), and use step 2 to sort the quality characteristics in the assembly process This technique provides an analysis and decision-making method for factory managers in quality problems, and helps factory managers to focus on monitoring and optimizing CTQs. IV. A QUALITY PREDICTION METHOD BASED ON SMOTE-ADABOOST WITH JOINTLY OPTIMIZED HYPERPARAMETERS In order to predict the assembly quality under the condition of data imbalance, this section builds a predictive model for the historical data in the assembly line, including two steps: the establishment of the predictive model and the optimization of hyperparameters
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