Applied intelligence (Dordrecht, Netherlands)最新文献

{"title":"Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review.","authors":"Marta Fernandes, Juan Manuel Corchado, Goreti Marreiros","doi":"10.1007/s10489-022-03344-3","DOIUrl":"10.1007/s10489-022-03344-3","url":null,"abstract":"<p><p>When put into practice in the real world, predictive maintenance presents a set of challenges for fault detection and prognosis that are often overlooked in studies validated with data from controlled experiments, or numeric simulations. For this reason, this study aims to review the recent advancements in mechanical fault diagnosis and fault prognosis in the manufacturing industry using machine learning methods. For this systematic review, we searched Web of Science, ACM Digital Library, Science Direct, Wiley Online Library, and IEEE Xplore between January 2015 and October 2021. Full-length studies that employed machine learning algorithms to perform mechanical fault detection or fault prognosis in manufacturing equipment and presented empirical results obtained from industrial case-studies were included, except for studies not written in English or published in sources other than peer-reviewed journals with JCR Impact Factor, conference proceedings and book chapters/sections. Of 4549 records, 44 primary studies were selected. In 37 of those studies, fault diagnosis and prognosis were performed using artificial neural networks (n = 12), decision tree methods (n = 11), hybrid models (n = 8), or latent variable models (n = 6), with one of the studies employing two different types of techniques independently. The remaining studies employed a variety of machine learning techniques, ranging from rule-based models to partition-based algorithms, and only two studies approached the problem using online learning methods. The main advantages of these algorithms include high performance, the ability to uncover complex nonlinear relationships and computational efficiency, while the most important limitation is the reduction in model performance in the presence of concept drift. This review shows that, although the number of studies performed in the manufacturing industry has been increasing in recent years, additional research is necessary to address the challenges presented by real-world scenarios.</p>","PeriodicalId":72260,"journal":{"name":"Applied intelligence (Dordrecht, Netherlands)","volume":"25 1","pages":"14246-14280"},"PeriodicalIF":3.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8894092/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88939510","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-source fast transfer learning algorithm based on support vector machine.","authors":"Peng Gao, Weifei Wu, Jingmei Li","doi":"10.1007/s10489-021-02194-9","DOIUrl":"10.1007/s10489-021-02194-9","url":null,"abstract":"<p><p>Knowledge in the source domain can be used in transfer learning to help train and classification tasks within the target domain with fewer available data sets. Therefore, given the situation where the target domain contains only a small number of available unlabeled data sets and multi-source domains contain a large number of labeled data sets, a new Multi-source Fast Transfer Learning algorithm based on support vector machine(MultiFTLSVM) is proposed in this paper. Given the idea of multi-source transfer learning, more source domain knowledge is taken to train the target domain learning task to improve classification effect. At the same time, the representative data set of the source domain is taken to speed up the algorithm training process to improve the efficiency of the algorithm. Experimental results on several real data sets show the effectiveness of MultiFTLSVM, and it also has certain advantages compared with the benchmark algorithm.</p>","PeriodicalId":72260,"journal":{"name":"Applied intelligence (Dordrecht, Netherlands)","volume":"51 11","pages":"8451-8465"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8023540/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39612337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An imperialist competition algorithm using a global search strategy for physical examination scheduling.","authors":"Hui Yu,&nbsp;Jun-Qing Li,&nbsp;Lijing Zhang,&nbsp;Peng Duan","doi":"10.1007/s10489-020-01975-y","DOIUrl":"https://doi.org/10.1007/s10489-020-01975-y","url":null,"abstract":"<p><p>The outbreak of the novel coronavirus clearly highlights the importance of the need of effective physical examination scheduling. As treatment times for patients are uncertain, this remains a strongly NP-hard problem. Therefore, we introduce a complex flexible job shop scheduling model. In the process of physical examination for suspected patients, the physical examiner is considered a job, and the physical examination item and equipment correspond to an operation and a machine, respectively. We incorporate the processing time of the patient during the physical examination, the transportation time between equipment, and the setup time of the patient. A unique scheduling algorithm, called imperialist competition algorithm with global search strategy (ICA_GS) is developed for solving the physical examination scheduling problem. A local search strategy is embedded into ICA_GS for enhancing the searching behaviors, and a global search strategy is investigated to prevent falling into local optimality. Finally, the proposed algorithm is tested by simulating the execution of the physical examination scheduling processes, which verify that the proposed algorithm can better solve the physical examination scheduling problem.</p>","PeriodicalId":72260,"journal":{"name":"Applied intelligence (Dordrecht, Netherlands)","volume":"51 6","pages":"3936-3951"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10489-020-01975-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39879782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decision making under measure-based granular uncertainty with intuitionistic fuzzy sets.","authors":"Yige Xue, Yong Deng","doi":"10.1007/s10489-021-02216-6","DOIUrl":"10.1007/s10489-021-02216-6","url":null,"abstract":"<p><p>Yager has proposed the decision making under measure-based granular uncertainty, which can make decision with the aid of Choquet integral, measure and representative payoffs. The decision making under measure-based granular uncertainty is an effective tool to deal with uncertain issues. The intuitionistic fuzzy environment is the more real environment. Since the decision making under measure-based granular uncertainty is not based on intuitionistic fuzzy environment, it cannot effectively solve the decision issues in the intuitionistic fuzzy environment. Then, when the issues of decision making are under intuitionistic fuzzy environment, what is the decision making under measure-based granular uncertainty with intuitionistic fuzzy sets is still an open issue. To deal with this kind of issues, this paper proposes the decision making under measure-based granular uncertainty with intuitionistic fuzzy sets. The decision making under measure-based granular uncertainty with intuitionistic fuzzy sets can effectively solve the decision making issues in the intuitionistic fuzzy environment, in other words, it can extend the decision making under measure-based granular uncertainty to the intuitionistic fuzzy environment. Numerical examples are applied to verify the validity of the decision making under measure-based granular uncertainty with intuitionistic fuzzy sets. The experimental results demonstrate that the decision making under measure-based granular uncertainty with intuitionistic fuzzy sets can represent the objects successfully and make decision effectively. In addition, a practical application of applied intelligence is used to compare the performance between the proposed model and the decision making under measure-based granular uncertainty. The experimental results show that the proposed model can solve some decision problems that the decision making under measure-based granular uncertainty cannot solve.</p>","PeriodicalId":72260,"journal":{"name":"Applied intelligence (Dordrecht, Netherlands)","volume":"51 8","pages":"6224-6233"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7862861/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39720281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}