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.

The development of Industry 4.0 and Predictive Maintenance Systems allows for effective utilization of equipment by incorporating ML methods for identifying tool condition. However, including large number of Condition Indicators for machinery monitoring increases computational complexity, hence the response of the system elongates. Therefore, it is important to check the utility of indicators and reduce them. In this paper, we investigate different feature selection methods: AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), Random Forest, Lasso Regression (L1 Regularization) for NASA Gearbox Fault Detection Dataset, PHM 2009. We processed the raw data and calculated CI from time domain, frequency domain and envelope. An SVM Classifier model was trained on full collection of indicator and reduced, then performances were compared. The obtained results highlight the advantage of feature selection, proving that effective PdM systems can be based on diminished number of Health Indicators.

The article presents an intelligent energy guardian for a polygeneration device. The proposed solution aims to optimize energy usage and minimize wastage by incorporating smart control algorithms that continuously monitor and adjust the energy flow between different subsystems of the device. The energy guardian utilizes machine learning techniques to learn the device’s energy usage patterns and adapt to changing conditions, such as varying energy demands and supply constraints. The article outlines the design and implementation of the energy guardian, and presents experimental results that demonstrate its effectiveness in improving energy efficiency and reducing operational costs. Overall, the intelligent energy guardian offers a promising solution for enhancing the performance of polygeneration devices and promoting sustainable energy use.

Industry 4.0 and the rise of predictive maintenance have led to increased interest in developing efficient and accurate methods for detecting tool wear in manufacturing processes. In this work, we investigate the use of machine learning techniques, specifically Long Short- Term Memory (LSTM) networks, for the detection of tool wear using NASA Milling Data Set. We first preprocess the raw milling data and extract relevant features using signal processing techniques. Then, we train and evaluate the performance of the LSTM network for detecting tool wear in the milling process. Our results demonstrate the effectiveness of the LSTM network for detecting tool wear, achieving high accuracy and recall scores. Additionally, we compare our results to those obtained using traditional statistical process control methods and highlight the advantages of using machine learning techniques for tool wear detection in the context of Industry 4.0 and predictive maintenance. Our work provides a practical example of how machine learning techniques can be applied to manufacturing processes to improve efficiency and reduce downtime.