Reliability, availability and OEE
Why a line of 98 percent machines is not a 98 percent line, and what the literature says about maintaining automated cells.
10 works. Metadata and abstracts from OpenAlex, harvested 2026-10-14. We have not summarized, graded or characterized this research ourselves, and the abstracts below are the authors' own.
Implementation of Total Productive Maintenance on Frame Welding Machine Maintenance Using the Overall Equipment Effectiveness (OEE) Method at PT Electronics Components Indonesia
PT. Electronics Components Indonesia manufactures capacitors and focuses on enhancing productivity and operational efficiency of the frame welding machines through effective maintenance. This study employs a quantitative method to analyze the Overall Equipment Effectiveness (OEE) values, including availability, performance efficiency, and rate of quality, as well as conducting a Six Big Losses analysis. The results indicate that the average availability reached 97.83%, with a significant decrease in August due to downtime. Performance efficiency remained consistently above 90%, although higher product output tended to reduce efficiency. The rate of quality was stable and high, reflecting improvements in production processes and quality control. The average OEE value reached 88%, exceeding the global standard of 85%. To further enhance the effectiveness of the frame welding machines...
The use of Digital Twin for predictive maintenance in manufacturing
This paper presents a methodology to calculate the Remaining Useful Life (RUL) of machinery equipment by utilising physics-based simulation models and Digital Twin concept, in order to enable predictive maintenance for manufacturing resources using Prognostics and health management (PHM) techniques. The resources and the properties of them are first modelled in a digital environment able to simulate the real machine’s behaviour. Data are gathered by machines’ controllers and external sensors to be used for the synchronous tuning of the digital models and their simulation. The outcome of the simulation is then used to assess the resource’s condition and to calculate RUL. In this way, the condition and the status of the machines can be monitored and predicted as a result from the simulation of physics-based models, without invasive techniques of common predictive maintenance solutions. A...
Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends
Predictive maintenance (PdM) is a policy applying data and analytics to predict when one of the components in a real system has been destroyed, and some anomalies appear so that maintenance can be performed before a breakdown takes place. Using cutting-edge technologies like data analytics and artificial intelligence (AI) enhances the performance and accuracy of predictive maintenance systems and increases their autonomy and adaptability in complex and dynamic working environments. This paper reviews the recent developments in AI-based PdM, focusing on key components, trustworthiness, and future trends. The state-of-the-art (SOTA) techniques, challenges, and opportunities associated with AI-based PdM are first analyzed. The integration of AI technologies into PdM in real-world applications, the human-robot interaction, the ethical issues emerging from using AI, and the testing and...
Overview of predictive maintenance based on digital twin technology
The upgrade and development of manufacturing industry makes predictive maintenance more and more important, but the traditional predictive maintenance can not meet the development needs in many cases. In recent years, predictive maintenance based on digital twin has become a research hotspot in the manufacturing industry field. Firstly, this paper introduces the general methods of digital twin technology and predictive maintenance technology, analyzes the gap between them, and points out the importance of using digital twin technology to realize predictive maintenance. Secondly, this paper introduces the predictive maintenance method based on digital twin (PdMDT), introduces its characteristics, and gives its differences from traditional predictive maintenance. Thirdly, this paper introduces the application of this method in intelligent manufacturing, power industry, construction...
Methodology for enabling Digital Twin using advanced physics-based modelling in predictive maintenance
This paper presents a methodology for advanced physics-based modeling aiming to enable the Digital Twin (DT) concept in predictive maintenance applications. The proposed methodology consists of two main pillars; Digital model creation and Digital Twin enabling. Following this procedure, which is analyzed in the paper, the user is able to define, create and utilize the digital model of a resource, as well as to enable the DT concept. A digital model of an industrial robot was created aiming to validate the proposed methodology which can be utilized potentially in predictive maintenance applications.
A new paradigm of cloud-based predictive maintenance for intelligent manufacturing
A deep learning-enhanced Digital Twin framework for improving safety and reliability in human-robot collaborative manufacturing
In Industry 5.0, Digital Twins bring in flexibility and efficiency for smart manufacturing. Recently, the success of artificial intelligence techniques such as deep learning has led to their adoption in manufacturing and especially in human-robot collaboration. Collaborative manufacturing tasks involving human operators and robots pose significant safety and reliability concerns. In response to these concerns, a deep learning-enhanced Digital Twin framework is introduced through which human operators and robots can be detected and their actions can be classified during the manufacturing process, enabling autonomous decision making by the robot control system. Developed using Unreal Engine 4, our Digital Twin framework complies with the Robotics Operating System specification, and supports synchronous control and communication between the Digital Twin and the physical system. In our...
Overall Environmental Equipment Effectiveness as a Metric of a Lean and Green Manufacturing System
This paper presents a new metric for describing the sustainability improvements achieved, relative to the company’s initial situation, after implementing a lean and green manufacturing system. The final value of this metric is identified as the Overall Environmental Equipment Effectiveness (OEEE), which is used to analyze the evolution between two identified states of the Overall Equipment Effectiveness (OEE) and the sustainability together, and references, globally and individually, the production steps. The OEE is a known measure of equipment utilization, which includes the availability, quality and performance of each production step, In addition to these factors, the OEEE incorporates the concept of sustainability based on the calculated environmental impact of the complete product life cycle. Action research based on the different manufacturing processes of a tube fabrication...
A survey on decision-making based on system reliability in the context of Industry 4.0
Data-driven and Knowledge-based predictive maintenance method for industrial robots for the production stability of intelligent manufacturing
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