An Artificial Intelligence Approach for Improving Maintenance to Supervise Machine Failures and Support Their Repair

Abstract

Maintenance of production equipment has a key role in ensuring business continuity and productivity. Determining the implementation time and the appropriate selection of the scope of maintenance activities are necessary not only for the operation of industrial equipment but also for effective planning of the demand for own maintenance resources (spare parts, people, finances). A number of studies have been conducted in the last decade and many attempts have been made to use artificial intelligence (AI) techniques to model and manage maintenance. The aim of the article is to discuss the possibility of using AI methods and techniques to anticipate possible failures and respond to them in advance by carrying out maintenance activities in an appropriate and timely manner. The indirect aim of these studies is to achieve more effective management of maintenance activities. The main method applied is computational analysis and simulation based on the real industrial data set. The main results show that the effective use of preventive maintenance requires large amounts of reliable annotated sensor data and well-trained machine-learning algorithms. Scientific and technical development of the above-mentioned group of solutions should be implemented in such a way that they can be used by companies of equal size and with different production profiles. Even relatively simple solutions as presented in the article can be helpful here, offering high efficiency at low implementation costs.

Sixth-Generation (6G) Networks for Improved Machine-to-Machine (M2M) Communication in Industry 4.0

“…Currently, much research is being carried out on both the physical layer and higher layers of the International Organization for Standardization/Open Systems Interconnection (ISO/OSI) model, the results of which are intended to provide 6G networks with higher performance and the ability to implement new functions in M2M connections, thanks to global coverage, very low delays, high reliability, mass connectivity, improved intelligence and security, non-orthogonal multiple access, MIMO, energy harvesting, hybrid terrestrial and satellite relays, improved IIoT-based automation, millimeter wave band utilization, interference management, hybrid precoding, and statistical shaping beams [24]. Additional requirements for 6G networks are imposed by the sources of industrial data (including medical data, as in the case of the Internet of Medical Things-IoMT) and, on the other hand, by the methods of their use: mass processing, analysis, classification, inference, and prediction from data, including for the purposes of automation, computerization, and technical control as well as analysis of the life cycle of products and carbon footprint of processes [25][26][27].…”

Reviewing and Integrating AEC Practices into Industry 6.0: Strategies for Smart and Sustainable Future-Built Environments

“…Maintenance and manufacturing equipment reliability is crucial to solving these issues. Maintaining manufacturing equipment ensures firm continuity and productivity [75]. Maintenance length and scope must be determined to efficiently operate industrial equipment and plan spare parts, workforce, and cash.…”

The Role of Artificial Intelligence in Shaping the Future of Education: Opportunities and Challenges

“…These tasks involve computer vision, machine learning, natural language processing, recognizing patterns, solving problems, and making decisions (Chiu et al, 2023). At present, AI has become a booming technology whereas it brings numerous positive changes including enhanced productivity (Gao & Feng, 2023), improved healthcare (Davenport & Kalakota, 2019), and preventive maintenance in manufacturing (Rojek et al, 2023). AI opens up for new opportunities such as improvement of logistics and public transport (Jevinger et al, 2023).…”