11/29/2023 0 Comments Random forest prediction![]() ![]() The assets are maintained on an as-needed basis. In the reactive maintenance strategy, assets are deliberately allowed to operate until failures actually occur. The most basic approach to maintenance is reactive, also known as run-to-failure maintenance planning. In order to increase manufacturing productivity while reducing maintenance costs, it is crucial to develop and implement an intelligent maintenance strategy that allows manufacturers to determine the condition of in-service systems in order to predict when maintenance should be performed.Ĭonventional maintenance strategies include reactive, preventive, and proactive maintenance. The degradation and failures of engineering systems or components will often incur higher costs and lower productivity due to unexpected machine downtime. Some of the typical failure modes include excessive load, overheating, deflection, fracture, fatigue, corrosion, and wear. For example, almost all engineering systems (e.g., aerospace systems, nuclear power plants, and machine tools) are subject to mechanical failures resulting from deterioration with usage and age or abnormal operating conditions. As opposed to traditional factories, a smart factory utilizes interoperable information and communications technologies (ICT), intelligent automation systems, and sensor networks to monitor machinery conditions, diagnose the root cause of failures, and predict the remaining useful life (RUL) of mechanical systems or components. Smart manufacturing aims to integrate big data, advanced analytics, high-performance computing, and Industrial Internet of Things (IIoT) into traditional manufacturing systems and processes to create highly customizable products with higher quality at lower costs. Experimental results have shown that RFs can generate more accurate predictions than FFBP ANNs with a single hidden layer and SVR. Specifically, the performance of FFBP ANNs, SVR, and RFs are compared using an experimental data collected from 315 milling tests. The objective of this research is to introduce a random forests (RFs)-based prognostic method for tool wear prediction as well as compare the performance of RFs with feed-forward back propagation (FFBP) ANNs and SVR. With the rapid advancement in artificial intelligence, various machine learning algorithms have been developed and widely applied in many engineering fields. While previous research has demonstrated the effectiveness of data-driven methods, most of these prognostic methods are based on classical machine learning techniques, such as artificial neural networks (ANNs) and support vector regression (SVR). To complement model-based prognostics, data-driven methods have been increasingly applied to machinery prognostics and maintenance management, transforming legacy manufacturing systems into smart manufacturing systems with artificial intelligence. However, prior knowledge of system behavior is not always available, especially for complex manufacturing systems and processes. Classical model-based or physics-based prognostics often require an in-depth physical understanding of the system of interest to develop closed-form mathematical models. Manufacturers have faced an increasing need for the development of predictive models that predict mechanical failures and the remaining useful life (RUL) of manufacturing systems or components. Journal of Verification, Validation and Uncertainty Quantification.Journal of Thermal Science and Engineering Applications.Journal of Offshore Mechanics and Arctic Engineering.Journal of Nuclear Engineering and Radiation Science.Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems.Journal of Nanotechnology in Engineering and Medicine.Journal of Micro and Nano-Manufacturing.Journal of Manufacturing Science and Engineering.Journal of Engineering Materials and Technology.Journal of Engineering for Sustainable Buildings and Cities.Journal of Engineering for Gas Turbines and Power.Journal of Engineering and Science in Medical Diagnostics and Therapy.Journal of Electrochemical Energy Conversion and Storage.Journal of Dynamic Systems, Measurement, and Control.Journal of Computing and Information Science in Engineering.Journal of Computational and Nonlinear Dynamics.Journal of Autonomous Vehicles and Systems.ASME Letters in Dynamic Systems and Control.ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering.Mechanical Engineering Magazine Select Articles. ![]()
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