This paper reviews current literature in the field of predictive maintenance from the system point of view. We differentiate the existing capabilities of condition estimation and failure risk forecasting as currently applied to simple components, from the capabilities needed to solve the same tasks for complex assets. System-level analysis faces more complex latent degradation states, it has to comprehensively account for active maintenance programs at each component level and consider coupling between different maintenance actions, while reflecting increased monetary and safety costs for system failures. As a result, methods that are effective for forecasting risk and informing maintenance decisions regarding individual components do not readily scale to provide reliable sub-system or system level insights. A novel holistic modeling approach is needed to incorporate available structural and physical knowledge and naturally handle the complexities of actively fielded and maintained assets.
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…
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…
INESC TEC（葡萄牙国家电气与计算机工程系统研究所）；University Portucalense（波尔图卡伦斯大学）；University of Porto（波尔图大学）
The paper describes the MetroPT data set, an outcome of a eXplainable Predictive Maintenance (XPM) project with an urban metro public transportation service in Porto, Portugal. The data was collected in 2022 that aimed to evaluate machine learning methods for online anomaly detection and failure prediction. By capturing several analogic sensor signals (pressure, temperature, current consumption), digital signals (control signals, discrete signals), and GPS information (latitude, longitude, and speed), we provide a dataset that can be easily used to evaluate online machine learning methods. Thi…
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…
Nanyang Technological University（南洋理工大学）；Singapore University of Technology and Design（新加坡科技设计大学）
Failure of mission-critical equipment interrupts production and results in monetary loss. The risk of unplanned equipment downtime can be minimized through Predictive Maintenance of revenue generating assets to ensure optimal performance and safe operation of equipment. However, the increased sensorization of the equipment generates a data deluge, and existing machine-learning based predictive model alone becomes inadequate for timely equipment condition predictions. In this paper, a model-free Deep Reinforcement Learning algorithm is proposed for predictive equipment maintenance from an equip…
Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Northeast Electric Power University（东北电力大学）；Changchun Institute of Technology（长春工程学院）；Southern University of Science and Technology（南方科技大学）
In recent years, with the development of wind energy, the number and scale of wind farms are developing rapidly. Since offshore wind farm has the advantages of stable wind speed, clean, renewable, non-polluting and no occupation of cultivated land, which has gradually become a new trend of wind power industry all over the world. The operation and maintenance mode of offshore wind power is developing in the direction of digitization and intelligence. It is of great significance to carry out the research on the monitoring, operation and maintenance of offshore wind farm, which will be of benefit…
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…
DETECTA 2.0: Research into non-intrusive methodologies supported by Industry 4.0 enabling technologies for predictive and cyber-secure maintenance in SMEs
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）
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…
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）
Solar energy forecasting has seen tremendous growth in the last decade using historical time series collected from a weather station, such as weather variables wind speed and direction, solar radiance, and temperature. It helps in the overall management of solar power plants. However, the solar power plant regularly requires preventive and corrective maintenance activities that further impact energy production. This paper presents a novel work for forecasting solar power energy production based on maintenance activities, problems observed at a power plant, and weather data. The results accompl…
Software Competence Center Hagenberg（哈根贝格软件能力中心）；Technological University of Dublin（都柏林理工大学）
An open research question in deep reinforcement learning is how to focus the policy learning of key decisions within a sparse domain. This paper emphasizes combining the advantages of inputoutput hidden Markov models and reinforcement learning towards interpretable maintenance decisions. We propose a novel hierarchical-modeling methodology that, at a high level, detects and interprets the root cause of a failure as well as the health degradation of the turbofan engine, while, at a low level, it provides the optimal replacement policy. It outperforms the baseline performance of deep reinforceme…
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）
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）
University of Bologna（博洛尼亚大学）；INFN Bologna（意大利国家核物理研究所博洛尼亚分部）；Federal University of Lavras（拉夫拉斯联邦大学）
Detection of anomalous behaviors in data centers is crucial to predictive maintenance and data safety. With data centers, we mean any computer network that allows users to transmit and exchange data and information. In particular, we focus on the Tier-1 data center of the Italian Institute for Nuclear Physics (INFN), which supports the high-energy physics experiments at the Large Hadron Collider (LHC) in Geneva. The center provides resources and services needed for data processing, storage, analysis, and distribution. Log records in the data center is a stochastic and non-stationary phenomenon…
An Explainable Machine Learning Framework for Railway Predictive Maintenance using Data Streams from the Metro Operator of Portugal
Information Technologies Group, atlanTTic, University of Vigo, Spain（信息科技组，atlanTTic，维戈大学，西班牙）；REMIT, Universidade Portucalense, Portugal（REMIT，葡萄牙普拉亚恩斯大学）；Faculty of Economics, University of Porto, Portugal（经济学院，波尔图大学，葡萄牙）；INESC TEC, Porto, Portugal（INESC TEC，波尔图，葡萄牙）；ISEP, Porto, Portugal（ISEP，波尔图，葡萄牙）
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 …
A Neuro-Symbolic Explainer for Rare Events: A Case Study on Predictive Maintenance
Faculty of Economics, University of Porto（波尔图大学经济学院）；INESC TEC；Faculty of Sciences, University of Porto（波尔图大学理学院）；ICMC - University of S. Paulo（圣保罗大学数学与计算机科学研究所）
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）
On the Tradeoffs of On-Device Generative Models in Federated Predictive Maintenance Systems
Consiglio Nazionale delle Ricerche, IEIIT institute（意大利国家研究委员会IEIIT研究所）；Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano（米兰理工学院电子、信息与生物工程系）；Department of Energy, Politecnico di Milano（米兰理工学院能源学院）；Mines Paris - PSL, CRC, Sophia Antipolis, France（法国巴黎 Mines - PSL CRC，索菲亚安蒂波利斯）
University of Engineering and Technology, Peshawar（白沙瓦工程技术大学）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）
Since the depletion of fossil fuels, the world has started to rely heavily on renewable sources of energy. With every passing year, our dependency on the renewable sources of energy is increasing exponentially. As a result, complex and hybrid generation systems are being designed and developed to meet the energy demands and ensure energy security in a country. The continual improvement in the technology and an effort towards the provision of uninterrupted power to the end-users is strongly dependent on an effective and fault resilient Operation and Maintenance (O&M) system. Ingenious algorithm…
Wind energy has emerged as a highly promising source of renewable energy in recent times. However, wind turbines regularly suffer from operational inconsistencies, leading to significant costs and challenges in operations and maintenance (O&M). Condition-based monitoring (CBM) and performance assessment/analysis of turbines are vital aspects for ensuring efficient O&M planning and cost minimisation. Data-driven decision making techniques have witnessed rapid evolution in the wind industry for such O&M tasks during the last decade, from applying signal processing methods in early 2010 to artifi…
