Integrating Predictive Maintenance in Adaptive Process Scheduling for a Safe and Efficient Industrial Process
| dc.contributor.author | Görür, Orhan Can | |
| dc.contributor.author | Yu, Xin | |
| dc.contributor.author | Sivrikaya, Fikret | |
| dc.date.accessioned | 2021-07-02T12:42:47Z | |
| dc.date.available | 2021-07-02T12:42:47Z | |
| dc.date.issued | 2021-05-29 | |
| dc.date.updated | 2021-06-12T03:29:20Z | |
| dc.description.abstract | Predictive maintenance (PM) algorithms are widely applied for detecting operational anomalies on industrial processes to schedule for a maintenance intervention before a possible breakdown; however, much less focus has been devoted to the use of such prognostics in process scheduling. The existing solutions mostly integrate preventive approaches to protect the machines, usually causing downtimes. The premise of this study is to develop a process scheduling mechanism that selects an acceptable operating condition for an industrial process to adapt to the predicted anomalies. As PM is largely a data-driven approach (hence, it relies on the setup), we first compare different PM approaches and identify a one-class support vector machine (OCSVM) as the best performing option for the anomaly detection on our setup. Then, we propose a novel pipeline to integrate maintenance predictions into a real-time, adaptive process scheduling mechanism. According to the abnormal readings, it schedules for the most suitable operation, i.e., optimizing for machine health and process efficiency, toward preventing breakdowns while maintaining its availability and operational state, thereby reducing downtimes. To demonstrate the pipeline on the action, we implement our approach on a small-scale conveyor belt, utilizing our Internet of Things (IoT) framework. The results show that our PM-based adaptive process control retains an efficient process under abnormal conditions with less or no downtime. We also conclude that a PM approach does not provide sufficient efficiency without its integration into an autonomous planning process. | en |
| dc.description.sponsorship | BMBF, 01IS16045, CHARIOT: A Scalable Holistic Middleware Approach for the Internet of Things | en |
| dc.description.sponsorship | DFG, 414044773, Open Access Publizieren 2021 - 2022 / Technische Universität Berlin | de |
| dc.identifier.eissn | 2076-3417 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/13351 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-12140 | |
| dc.language.iso | en | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject.ddc | 600 Technik, Technologie | de |
| dc.subject.other | predictive maintenance | en |
| dc.subject.other | predictive maintenance-based process scheduling | en |
| dc.subject.other | real-time anomaly detection | en |
| dc.title | Integrating Predictive Maintenance in Adaptive Process Scheduling for a Safe and Efficient Industrial Process | en |
| dc.type | Article | en |
| dc.type.version | publishedVersion | en |
| dcterms.bibliographicCitation.articlenumber | 5042 | en |
| dcterms.bibliographicCitation.doi | 10.3390/app11115042 | en |
| dcterms.bibliographicCitation.issue | 11 | en |
| dcterms.bibliographicCitation.journaltitle | Applied Sciences | en |
| dcterms.bibliographicCitation.originalpublishername | MDPI | en |
| dcterms.bibliographicCitation.originalpublisherplace | Basel | en |
| dcterms.bibliographicCitation.volume | 11 | en |
| tub.accessrights.dnb | free | en |
| tub.affiliation | Fak. 4 Elektrotechnik und Informatik::Inst. Wirtschaftsinformatik und Quantitative Methoden::FG Agententechnologien in betrieblichen Anwendungen und der Telekommunikation (AOT) | de |
| tub.affiliation.faculty | Fak. 4 Elektrotechnik und Informatik | de |
| tub.affiliation.group | FG Agententechnologien in betrieblichen Anwendungen und der Telekommunikation (AOT) | de |
| tub.affiliation.institute | Inst. Wirtschaftsinformatik und Quantitative Methoden | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |