This paper considers a distributed decision-making approach for manufacturing task assignment and condition-based machine health maintenance. Our approach considers information sharing between the task assignment and health management decision-making agents. We propose the design of the decision-making agents based on Markov decision processes. The key advantage of using a Markov decision process-based approach is the incorporation of uncertainty involved in the decision-making process. The paper provides detailed mathematical models along with the associated practical execution strategy. In order to demonstrate the effectiveness and practical applicability of our proposed approach, we have included a detailed numerical case study that is based on open source milling machine tool degradation data. Our case study indicates that the proposed approach offers flexibility in terms of the selection of cost parameters and it allows for offline computation and analysis of the decision-making policy. These features create and opportunity for the future work on learning of the cost parameters associated with our proposed model using artificial intelligence.
Connected vehicle fleets are deployed worldwide in several industrial IoT scenarios. With the gradual increase of machines being controlled and managed through networked smart devices, the predictive maintenance potential grows rapidly. Predictive maintenance has the potential of optimizing uptime as well as performance such that time and labor associated with inspections and preventive maintenance are reduced. In order to understand the trends of vehicle faults with respect to important vehicle attributes viz mileage, age, vehicle type etc this problem is addressed through hierarchical modifi…
A Systematic Review of Digital Twin-Driven Predictive Maintenance in Industrial Engineering: Taxonomy, Architectural Elements, and Future Research Directions
Intelligent Distributed Computing and Systems (INDUCE) Lab, Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al-Ain, United Arab Emirates, Emirates Center for Mobility Research, United Arab Emirates University, Al-Ain, United Arab Emirates（智能分布式计算与系统实验室，计算机科学与软件工程系，信息科技学院，阿联酋大学，阿恩，阿联酋，移动性研究中心，阿联酋大学，阿恩，阿联酋）；Department of Architectural Engineering, College of Engineering, United Arab Emirates University, Al-Ain, United Arab Emirates（建筑工程系，工程学院，阿联酋大学，阿恩，阿联酋）；Department of Information Systems and Security, College of Information Technology, United Arab Emirates University, Al-Ain, United Arab Emirates（信息系统与安全系，信息科技学院，阿联酋大学，阿恩，阿联酋）；Department of Computer and Network Engineering, United Arab Emirates University, Al-Ain, United Arab Emirates（计算机与网络工程系，阿联酋大学，阿恩，阿联酋）
A Recommendation System-Based Framework for Enhancing Human-Machine Collaboration in Industrial Timetabling Rescheduling: Application in Preventive Maintenance
IndustryAssetEQA: A Neurosymbolic Operational Intelligence System for Embodied Question Answering in Industrial Asset Maintenance
Artificial Intelligence Institute, University of South Carolina（南卡罗来纳大学人工智能研究所）；University of South Carolina（南卡罗来纳大学）；IBM Yorktown（IBM约克镇分公司）；Indian AI Research Organization（印度人工智能研究组织）
Rapid progress in natural language processing has led to its utilization in a variety of industrial and enterprise settings, including in its use for information extraction, specifically named entity recognition and relation extraction, from documents such as engineering manuals and field maintenance reports. While named entity recognition is a well-studied problem, existing state-of-the-art approaches require large labelled datasets which are hard to acquire for sensitive data such as maintenance records. Further, industrial domain experts tend to distrust results from black box machine learn…
University of Navarra - Tecnun（纳瓦拉大学特克南工程学院）；NEM Solutions（NEM解决方案公司）
A failure detection system is the first step towards predictive maintenance strategies. A popular data-driven method to detect incipient failures and anomalies is the training of normal behaviour models by applying a machine learning technique like feed-forward neural networks (FFNN) or extreme learning machines (ELM). However, the performance of any of these modelling techniques can be deteriorated by the unexpected rise of non-stationarities in the dynamic environment in which industrial assets operate. This unpredictable statistical change in the measured variable is known as concept drift.…
Predictive maintenance is used in industrial applications to increase machine availability and optimize cost related to unplanned maintenance. In most cases, predictive maintenance applications use output from sensors, recording physical phenomenons such as temperature or vibration which can be directly linked to the degradation process of the machine. However, in some applications, outputs from sensors are not available, and event logs generated by the machine are used instead. We first study the approaches used in the literature to solve predictive maintenance problems and present a new publ…
OmniFuser: Adaptive Multimodal Fusion for Service-Oriented Predictive Maintenance
School of Software Technology, Zhejiang University（浙江大学软件学院）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）；Faculty of Computing, Harbin Institute of Technology（哈尔滨工业大学计算机学院）；Hangzhou School of Automation, Zhejiang Normal University（浙江师范大学杭州自动化学院）；Distributed Systems Group at the TU Wien and with ICREA at the UPF, Barcelona（维也纳大学分布式系统组和巴塞罗那大学ICREA）
This paper highlights the trends in the field of predictive maintenance with the use of machine learning. With the continuous development of the Fourth Industrial Revolution, through IoT, the technologies that use artificial intelligence are evolving. As a result, industries have been using these technologies to optimize their production. Through scientific research conducted for this paper, conclusions were drawn about the trends in Predictive Maintenance applications with the use of machine learning bridging Artificial Intelligence and IoT. These trends are related to the types of industries…
Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed) University（共生国际（被视为）大学共生技术学院（SIT））；Machine Intelligence Research Labs（机器智能研究实验室）
Industry 4.0 is an era of smart manufacturing. Manufacturing is impossible without the use of machinery. Majority of these machines comprise rotating components and are called rotating machines. The engineers' top priority is to maintain these critical machines to reduce the unplanned shutdown and increase the useful life of machinery. Predictive maintenance (PDM) is the current trend of smart maintenance. The challenging task in PDM is to diagnose the type of fault. With Artificial Intelligence (AI) advancement, data-driven approach for predictive maintenance is taking a new flight towards sm…
ExxonMobil Research and Engineering Company（埃克森美孚研究与工程公司）
As Artificial Intelligent (AI) technology advances and increasingly large amounts of data become readily available via various Industrial Internet of Things (IIoT) projects, we evaluate the state of the art of predictive maintenance approaches and propose our innovative framework to improve the current practice. The paper first reviews the evolution of reliability modelling technology in the past 90 years and discusses major technologies developed in industry and academia. We then introduce the next generation maintenance framework - Intelligent Maintenance, and discuss its key components. Thi…
Halmstad University（哈尔姆斯塔德大学）；RISE Research Institutes of Sweden（瑞典RISE研究院）；INESC TEC（葡萄牙系统与计算机工程研究所（TEC））；NOS Comunicações S.A.（NOS通信股份有限公司）；University of Porto（波尔图大学）；Jagiellonian University（雅盖隆大学）
Artificial Intelligence Approaches for Predictive Maintenance in the Steel Industry: A Survey
AGH University of Science and Technology（AGH科技大学）；ArcelorMittal Poland（安赛乐米塔尔波兰公司）；INESC TEC（INESC TEC机构）；University of Porto（波尔图大学）；RISE Research Institutes of Sweden（瑞典RISE研究院）；Halmstad University（哈尔姆斯塔德大学）；Jagiellonian University（雅盖隆大学）
Predictive Maintenance for Ultrafiltration Membranes Using Explainable Similarity-Based Prognostics
Jheronimus Academy of Data Science, Eindhoven University of Technology, Eindhoven, The Netherlands（杰赫罗尼穆斯数据科学学院，埃因霍温理工大学，埃因霍温，荷兰）；School of Industrial Engineering, Eindhoven University of Technology（工业工程学院，埃因霍温理工大学）
A Hybrid Vision-Language Architecture for Automated Defect Reasoning and Report Generation in Industrial Inspection
School of Computing, Telkom University（Telkom大学计算机学院）；Faculty of Engineering and Technology, School of Computing and Artificial Intelligence（工程与技术学院，计算与人工智能学院）
Institute for Human and Machine Cognition (IHMC)（人机认知研究所（IHMC））；ABB Future Labs（ABB未来实验室）
Industrial robots play an increasingly important role in a growing number of fields. For example, robotics is used to increase productivity while reducing costs in various aspects of manufacturing. Since robots are often set up in production lines, the breakdown of a single robot has a negative impact on the entire process, in the worst case bringing the whole line to a halt until the issue is resolved, leading to substantial financial losses due to the unforeseen downtime. Therefore, predictive maintenance systems based on the internal signals of robots have gained attention as an essential c…
Liverpool John Moores University（利物浦约翰摩尔斯大学）；Central Group（中央集团）
Rotary machine breakdown detection systems are outdated and dependent upon routine testing to discover faults. This is costly and often reactive in nature. Real-time monitoring offers a solution for detecting faults without the need for manual observation. However, manual interpretation for threshold anomaly detection is often subjective and varies between industrial experts. This approach is ridged and prone to a large number of false positives. To address this issue, we propose a Machine Learning (ML) approach to model normal working operation and detect anomalies. The approach extracts key …
