Predictive Maintenance (PdM) can only be implemented when the online knowledge of system condition is available, and this has become available with deployment of on-equipment sensors. To date, most studies on predicting the remaining useful lifetime of a system have been focusing on either single-component systems or systems with deterministic reliability structures. This assumption is not applicable on some realistic problems, where there exist uncertainties in reliability structures of complex systems. In this paper, a PdM scheme is developed by employing a Discrete Time Markov Chain (DTMC) for forecasting the health of monitored components and a Bayesian Network (BN) for modeling the multi-component system reliability. Therefore, probabilistic inferences on both the system and its components status can be made and PdM can be scheduled on both levels.
Auton Lab, Carnegie Mellon University（卡内基梅隆大学 Auton 实验室）
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 syst…
Predictive maintenance is an effective tool for reducing maintenance costs. Its effectiveness relies heavily on the ability to predict the future state of health of the system, and for this survival models have shown to be very useful. Due to the complex behavior of system degradation, data-driven methods are often preferred, and neural network-based methods have been shown to perform particularly very well. Many neural network-based methods have been proposed and successfully applied to many problems. However, most models rely on assumptions that often are quite restrictive and there is an in…
Recent years have seen an unprecedented growth in the use of sensor data to guide wind farm operations and maintenance. Emerging sensor-driven approaches typically focus on optimal maintenance procedures for single turbine systems, or model multiple turbines in wind farms as single component entities. In reality, turbines are composed of multiple components that dynamically interact throughout their lifetime. These interactions are central for realistic assessment and control of turbine failure risks. In this paper, an integrated framework that combines i) real-time degradation models used for…
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…
Integrating Prognostics, Maintenance, and Tail Assignment under Remaining Useful Life Uncertainty: A Stochastic Optimisation Approach for Airline Reliability
Delft University of Technology（代尔夫特理工大学）；Zurich University of Applied Sciences（苏黎世应用科学大学）；Swiss International Air Lines Ltd.（瑞士国际航空公司）
该研究针对航空公司运营中 RUL 不确定性下 TA 与 MS 单独优化的问题，提出联合整合 TA、MS 与 PdM 的随机优化框架，经真实数据验证可降低运营风险。
Real-Time Reconfiguration and Connectivity Maintenance for AUVs Network Under External Disturbances using Distributed Nonlinear Model Predictive Control
Within smart manufacturing, data driven techniques are commonly adopted for condition monitoring and fault diagnosis of rotating machinery. Classical approaches use supervised learning where a classifier is trained on labeled data to predict or classify different operational states of the machine. However, in most industrial applications, labeled data is limited in terms of its size and type. Hence, it cannot serve the training purpose. In this paper, this problem is tackled by addressing the classification task as a similarity measure to a reference sample rather than a supervised classificat…
Multilayer GNN for Predictive Maintenance and Clustering in Power Grids
organization= Industrial \& Manufacturing Engineering, North Dakota State University , addressline= NDSU Dept. 2485, PO Box 6050 , city= Fargo , postcode= 58108-6050 , state= ND , country= USA；organization= Department of Engineering Technology \& Construction Management, University of North Carolina at Charlotte , postcode= 28223 , state= NC , country= USA；organization= Department of Industrial \& Systems Engineering, North Carolina Agriculture \& Tech State University , city= Greensboro , postcode= 27411 , state= NC , country= USA；organization= Department of Industrial \& Management Systems Engineering, University of South Florida , addressline= 4202 East Fowler Avenue, ENG 030 , city= Tampa , postcode= 33620 , state= FL , country= USA
