International Scientific Journals
of Scientific Technical Union of Mechanical Engineering "Industry 4.0"

This paper proposes a novel architecture for pdm-tools, a dedicated library designed to streamline the development process of predictive maintenance (PdM) algorithms within the Industry 4.0 paradigm. pdm-tools tackles the time-consuming nature of PdM algorithm development by encompassing five key components within a unified framework. pdm-tools tackles the time challenge by offering a unified workflow: generate synthetic vibration data (gearboxes, bearings, shafts) for rapid prototyping, eliminating initial real-world data collection. Data preparation and feature extraction ensure readiness through scaling, filtering, and extracting diverse condition indicators, empowering robust algorithms. Finally, user-defined model development and evaluation allow training, optimization, and selection of optimal PdM algorithms (machine learning/deep learning). By integrating these functionalities within a single architecture, pdm-tools empowers rapid development and evaluation of PdM algorithms, ultimately leading to a more efficient and effective implementation of predictive maintenance strategies within the Industry 4.0 landscape.

Artificial intelligence become more widespread in all manufacturing subjects. In manufacturing artificial intelligence deals with such tasks as quality control, robot navigation, computer vision, processes controlling, etc. The area of maintenance in machining is a great prospect for implementing artificial intelligence tools for analysis, prediction of monitored parameters, optimization, and improvement of the quality of the maintenance process. In particular, the article refers to predictive maintenance as a modern trend in mechanical engineering. In this article, a quick review of using methods of artificial intelligence and predictive analytics in maintenance and one p ractical implementation case of NAR network for time-series prediction was provided.

This article describes the actual trends and applications in industry where artificial intelligence models are deployed. This paper provides a more detailed description of the principles and methods of deploying models in the field of quality evaluation in industry and also in the areas of predictive maintenance and data analytics in the manufacturing process. Computer vision is increasingly coming to the fore due to its wide range of applications – object detection, categorisation of objects, reading QR codes and others. The area of predictive maintenance is important in terms of reducing downtime and saving costs for machine components. Models designed for data analytics, in turn, help to optimize the parameters of the production process so that the desired parameter is maximized or its optimal value is achieved.

Digitalization of production lines is the most important issue in the world in recent years. One of the most important issues of this digitalization for today’s manufacturing enterprises is the need to update maintenance practices and maintenance work processes in production lines with technological developments. The fact that sawing machines are in the first part of the production lines shows that it is of critical importance. In this study, it is aimed to determine the necessary principles for digitizing sawing machines and integrating the predictive maintenance system into the machine. As a result of the evaluation, the necessity of real-time data collection, data analysis and artificial intelligence algorithms for predictive maintenance requirements has been determined.

Wet wipe machines are machines for high capacity production at high speeds. Even the short downtimes of the machines in the production area lead to huge losses for manufacturers. However, in these machines; unexpected breakdowns such as belt breakage, bearing damage, sensor motor failures, pneumatic air leaks are frequently encountered. In order to prevent unwanted stoppages, a sensor, actuator and plc based application has been implemented to prevent failure parameters and to detect faultlessly by monitoring the operation parameters. As a result of the studies carried out, our wet wipe machines that we produce have been adapted to the industry 4.0 concept.

The Industrial Predictive Analytics for Industry 4.0 is a system that predict and prevent machine failures and breakdown by analyzing time-series data (temperature, pressure, vibration etc.) received from sensors embedded in machines and equipment. The system can analyze machine parameters to identify patterns and predict breakdowns before they happen. The core of the proposed system is based on Artificial Neural Network approach (both Deep and Shallow Neural Networks). Artificial Intelligence and Artificial Neural Networks allow analyses the huge amounts of data collected from the manufacturing process and predict what will go wrong, and when. The proposed system works in the paradigm of Industry 4.0 and provides the abilities in the area of predictive maintenance. The Industrial Predictive Analytics for Industry 4.0 also contains a decision-making system and support system that significantly increases the level of maintenance.

In this article the author describes particular maintenance systems used in the past, some of which are used also at present. The basic maintenance systems include maintenance after use, preventive maintenance with predetermined intervals, and conditioned-based preventive maintenance – predictive maintenance. The current trend in the field of vehicle maintenance tend to continous monitoring of their actual status. By the help of a vehicle monitoring in use, it is possible based on current operating parameters to determinate the technical condition of the vehicle parts. Ideally to prevent the failure or damage of groups of vehicle. Tracking of vehicles in use can be effected through the telemetry. Telemetry is a technology that allows remote measurement and reporting of information.

In this article the author describes particular maintenance systems used in the past, some of which are used also at present. The basic maintenance systems include maintenance after use, preventive maintenance with predetermined intervals, and conditioned-based preventive maintenance – predictive maintenance. The current trend in the field of vehicle maintenance tend to continous monitoring of their actual status. By the help of a vehicle monitoring in use, it is possible based on current operating parameters to determinate the technical condition of the vehicle parts. Ideally to prevent the failure or damage of groups of vehicle. Tracking of vehicles in use can be effected through the telemetry. Telemetry is a technology that allows remote measurement and reporting of information.