Implementing self-* autonomic properties in self-coordinated manufacturing processes for the Industry 4.0 context

Sánchez, María Dolores Prieto; Expósito, Ernesto; Aguilar, José

doi:10.1016/j.compind.2020.103247

Cited by 28 publications

(24 citation statements)

References 59 publications

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“…To verify the feasibility of the proposed framework of knowledge acquisition, we accurately matched rule 1 with the breast cancer dataset, matching a total of twenty samples that were the same as the class b = 1. Similarly, rule 2 was hazily matched with the dataset, a total of two samples matching the class b = 2, i.e., "a2-a10" is equal to (7,8,7,6,4,3,8,8,4) or (7,8,8,7,3,10,7,2,3). By analyzing two rules and referring to relevant materials, it is found that rule 1 and rule 2 have important reference values for the auxiliary diagnosis of breast cancer.…”

Section: If Clump Thickness and Uniformity Of Cell Size And Uniformit...mentioning

confidence: 99%

“…In the context of Industry 4.0, an essential topic in the manufacturing area is to detect the fault diagnosis and recover the current status from potential faults as soon as possible for reducing maintenance costs and preventing unscheduled downtime [1]. Although various fault diagnosis approaches are proposed in recent years from different viewpoints and achieved fruitful results, which are knowledge-driven, data-driven, and value-driven methods [2][3][4]. Among them, the knowledge-driven approach, which depends on expert experience, is still widely applied in various areas because of the stronger interpretability [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Knowledge Extraction Framework Using Modified Adaptive Genetic Algorithm and BPNN

Ding

et al. 2022

IEEE Access

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Fault diagnosis based on the expert system (ES) is still a research topic of manufacturing in the Industry 4.0 because of the stronger interpretability. As the core component of the ES, fault diagnosis accuracy is positively correlated to the precise of the knowledge base. But it is difficult for users to understand the knowledge obtained from the original dataset utilizing the existing knowledge extraction method. Therefore, it is of great significance to extract easy-to-understand and exact rules from the NN framework. This paper proposes a hybrid extraction framework to perform the rule extraction for overcoming this drawback. First, an improved adaptive genetic algorithm (GA) using a logistic function, namely LAGA, is proposed to solve the traditional GA's insufficient prediction performance issue. Compared with the other three mainstream adaptive GAs, the experiment results of optimizing six selected test functions by these GA variants show that the LAGA algorithm's convergence accuracy and speed have been greatly improved, especially for high latitude functions. On this basis, a rule extraction method based on symbol rule and NN, namely the LAGA-BP framework, is discussed in this manuscript to classify the real-valued attributes. This framework obtains hidden knowledge (knowledge refinement process) by NN and further transforms the acquired hidden knowledge into more easy-to-understand rule knowledge (rule extraction process). The execution of the LAGA-BP framework could be separated into two phases. The first phase is to optimize a back propagation NN (BPNN) using the LAGA and refine prediction classification knowledge over the optimized BPNN. In the second phase, an attribute reduction algorithm using multi-layered NN (SD algorithm) based on two different superposed networks is used on this framework to reduce data-set attributes and then uses the K-means clustering algorithm to extract the ifthen rule from the simplified attributes. the Wisconsin breast cancer data-set is used as a case study to reveal the correctness and robustness of the proposed LAGA-BP method. Consulting relevant medical personnel and referencing relevant data shows that the rules extracted using this method help verify the diagnosis results, thus verifying the proposed framework's feasibility and practicality.

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Section: If Clump Thickness and Uniformity Of Cell Size And Uniformit...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Knowledge Extraction Framework Using Modified Adaptive Genetic Algorithm and BPNN

Ding

et al. 2022

IEEE Access

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“… Decision-making Tasks: They define the actions to be carried out in order to improve the process, considering the objectives defined for the autonomous cycle. ACODAT paradigm was initially proposed for smart classrooms [3,11] and later applied to different fields, such as Telecommunications [19] or Industry 4.0 [17,24,25].…”

Section: Figure 1 Autonomous Cycle Of Data Analysis Tasksmentioning

confidence: 99%

Autonomic Management of a Building’s Multi-HVAC System Start-Up

et al. 2021

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Most of the studies about control, automation, optimization and supervision of building HVAC systems are about the steady-state regime, i.e. when the equipment is already working on their setpoints. In this work, it is defined an approach for multi-HVAC systems to reach the setpoint at the startup, so that other steady statebased strategies can smoothly come into operation to control and supervise their operations. The proposed approach for the management of the transient regime for multi-HVAC systems is based on the "Autonomic Cycle of Data Analysis Tasks" concept. In this case, it is composed of two data analysis tasks: one for determining if the system is going towards the defined operational setpoint, and if it was not the case, another for reconfiguring the operational mode of the multi-HVAC system to redirect it. The proposal is proved in a real context that characterizes one multi-HVAC system and its operational setpoints. The performance obtained from the experiments in diverse situations is encouraging, since they show how this approach leads the multi-HVAC system to reach the setpoint optimizing contradictory objectives, such as to attain the desired comfort, or reduce the energy costs, among others.

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“…In particular, ACODAT uses the interaction of different successive tasks to extract the necessary knowledge to recommend improvements in a given process [8] . The use of ACODAT in different fields such as education, telecommunications and industry 4.0, have been reported [9] , [10] , [11] . For example, in the educational field, ACODAT has been used to determine learning styles in smart classrooms.…”

Section: Introductionmentioning

confidence: 99%

“…In Industry 4.0, ACODAT has been developed and implemented to improve the efficiency of production processes. For example, Sanchez et al [11] presented a framework that helps to solve the problems of integration and heterogeneity of the actors involved in manufacturing processes. The results show that ACODAT allowed to these actors (people, data, things and services) to interact for the creation of a self-configuration and self-optimization plan.…”

Section: Introductionmentioning

confidence: 99%

An autonomous cycle of data analysis tasks for the clinical management of dengue

Hoyos

Aguilar²,

Toro³

2022

Heliyon

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Implementing self-* autonomic properties in self-coordinated manufacturing processes for the Industry 4.0 context

Cited by 28 publications

References 59 publications

Hybrid Knowledge Extraction Framework Using Modified Adaptive Genetic Algorithm and BPNN

Hybrid Knowledge Extraction Framework Using Modified Adaptive Genetic Algorithm and BPNN

Autonomic Management of a Building’s Multi-HVAC System Start-Up

An autonomous cycle of data analysis tasks for the clinical management of dengue

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