|Thesis Advisor||谭杰 ; 王学雷|
|Place of Conferral||北京|
|Keyword||多源数据融合 知识获取 生成式对抗网络 智能优化决策 锂离子电池单体电芯一致性分选|
Intelligent manufacturing, which is aimed at high-efficiency, green and safety of enterprises, and is characterized by intelligent optimization decision-making of manufacturing processes and intelligent autonomous control of machining equipment or processes, is the necessary way to realize the transformation and upgrade of Chinese manufacturing industry. As the core feature of intelligent manufacturing, intelligentization is reflected in many aspects of the life cycle activities in complex industrial process, such as optimization decision-making problems liking production planning and scheduling, operational index optimization, and abnormal diagnosis and analysis. In particular, the optimization decision-making problems are involved in a lot of factors in the life cycle activities of manufacturing processes, including a large number of operators, various production equipment and materials, complex technological rules, and uncertain production environment, thereby causing some difficulties in multiple conflict objectives, multiple conflict constraints and multi-scale dynamic optimization for solving them. At present, the optimization decision-making in complex industrial process are addressed by the knowledge workers with their experiences. However, because artificial decision has greater randomness and cannot capture in time the frequent or drastic changes in the life cycle activities of manufacturing processes, it is difficult to make timely and accurate decision-making response with experiential knowledge, resulting in poor product quality, high cost and high resource consumption and other issues.
Subject to the above problems, this paper focuses on the optimization decision-making problems involving the life cycle activities in complex industrial process and aims at realizing the efficient acquisition of multi-source data knowledge in industrial big data environment, semantic knowledge self-optimization and intelligent optimization decision-making of complex industrial process. The research on knowledge acquisition, knowledge learning and knowledge decision-making in a big data environment has been carried out. The detailed work and contributions of this paper have been summarized as follows:
(1) A knowledge acquisition approach based on fusing semantics and features of multi-source data is proposed, which mines effectively knowledge from massive data generated in the complex industrial processes. This paper defines a new form of knowledge representation and proposes a semantic extraction method based on the two-step time series clustering (SE-TTSC) and a feature extraction method based on the convolutional auto-encoder (FE-CAE). For multi-source data in complex industrial process, the paper extracts their semantics and features by SE-TTSC and FE-CAE, respectively. The features with semantics in different data sources are fused to obtain a fusion feature, combining its decision semantics to obtain a knowledge element. Therefore, a knowledge base consisting of many knowledge elements is built, which lays the foundation for the next research on the semantic knowledge base self-optimization.
(2) A semantic knowledge self-optimization approach based on the conditional generative adversarial networks is proposed, which predicts efficiently probability distribution of minority class knowledge elements, thereby generating the more diverse and accurate minority class knowledge elements. The proposed method addresses an unbalanced semantic knowledge problem caused by lack of high value knowledge mining from complex industrial process, thereby providing a reliable guarantee for establishing an intelligent optimization decision-making model based on semantic knowledge for complex industrial process.
(3) An intelligent optimization decision-making approach based on semantic knowledge for complex industrial process is proposed. This paper takes comprehensive indicators including product quality, efficiency, cost and energy consumption as different optimization goals to establish their global optimization description models and analyzes the optimization decision-making problems embedded in the life cycle activities of complex industrial process to establish their metric description models. For the lack of mapping function between optimization decision-making problems and the semantic knowledge mining from massive data generated by complex industrial process, the paper proposes the knowledge reasoning model based on the deep neural networks (DNNs) method for optimization decision-making problems, thereby realizing intelligent optimization decision-making for complex industrial process.
(4) Based on the integration of the above three key technologies, the effectiveness of the proposed approach is verified by the optimization decision-making problem for consistent lithium-ion cell screening in the new energy vehicle power lithium-ion battery PACK with multiple lithium-ion cells configured in series, parallel, and series–parallel. This paper proposes two application verification methods: the optimization decision-making method for consistent lithium-ion cell screening based on single dynamic characteristic curves of lithium-ion cells (ODM-CLCS-SDCC) and the optimization decision-making method for consistent lithium-ion cell screening based on multiple dynamic characteristic curves of lithium-ion cells (ODM-CLCS-MDCC). An optimization decision-making system for consistent lithium-ion cell screening based on the industrial big data platform was developed and applied to the actual production process. Application verification results show that the inconsistency rate of the screened cells using the ODM-CLCS-SDCC drops by an average of 91.08%, compared with the traditional screening method that is currently used in industrial processes; and compared with the ODM-CLCS-SDCC, the inconsistency rate of the screened cells using the ODM-CLCS-MDCC drops by an average of 93.74%. To summarize, the proposed approach can effectively screen inconsistent lithium-ion cells in electrochemical characteristics, thereby improving the consistency of lithium-ion battery PACKs and their durability, reliability and safety.
|刘承宝. 融合多源数据知识的复杂工业过程优化决策方法研究[D]. 北京. 中国科学院大学,2019.|
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