An intelligent and multi-channel deep feature enhanced framework for predicting difficult-to-measure CTQ. (July 2022)

• Record Type: Journal Article
• Authors: Wang, Xueqing; Yin, Xianhui; He, Zhen; Liu, Zixian; Gao, Yuan
• Is Part Of: Computers & industrial engineering. Volume 169(2022)
• Publication Date: 2022-07
• Subjects: CTQ prediction -- Difficult-to-measure CTQ -- Integrated computational intelligence model -- Multi-channel deep feature extraction; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2022.108300
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A machine learning framework is proposed to predict difficult-to-measure quality indicators. Cascade deep feature learning is used to obtain more abstract features. Multi-channel features are concatenated to enhance the information abundance. An inverse label propagation strategy alleviates the unmatched problem caused by data imbalance. Abstract: Some CTQs (Critical-To-Quality) are difficult to measure online due to economic or technical limitations, hindering the timely decision-making to promote the production operation. Meanwhile, existing CTQ prediction methods may have inferior performance when considering the specific characteristic of data under the new industrial revolution context. Fortunately, various operating data have been increasingly collected by widely deployed sensors, enabling the online measurement of numerous operating parameters and partial CTQs. In addition, deep exploration of these data can be achieved with the assistance of emerging data analytic techniques. Hence, an integrated framework is proposed to predict difficult-to-measure CTQs using computational intelligence models, in which the multi-channel deep features could be extracted sequentially to enhance the prediction performance. Specifically, a feature extractor is first constructed to obtain deep time-dependent features using Bidirectional Gated Recurrent Unit. These extracted features describe the dependency between the easy-to-measure CTQ and operating parameters. Then, these features and original input features are concatenated to enrich the available information for predicting difficult-to-measure CTQs. To reduce the risk of high-dimensionality and remove redundant information introduced by the concatenated operation, Kernel Principal Component Analysis is further employed to obtain abstract features. Next, an inverse label propagation strategy is proposed to find matched feature-CTQ pairs to alleviate the unmatched problem caused by data imbalance. Afterwards, Random Forest, which has the dual advantages of ensemble learning and intelligence algorithm, is used to build the feature-CTQ function and achieve CTQ prediction. Finally, the effectiveness and superiority of the proposed model are verified by forecasting tensile strength characteristic for a commutator welding process.