Intelligent fault diagnosis can be related to applications of machine learning theories to machine fault diagnosis. Although there is a large number of successful examples, there is a gap in the optimization of the hyper-parameters of the machine learning model, which ultimately has a major impact on the performance of the model. Machine learning experts are required to configure a set of hyper-parameter values manually. This work presents a convolutional neural network based data-driven intelligent fault diagnosis technique for rotary machinery which uses model with optimized hyper-parameters and network structure. The proposed technique input raw three axes accelerometer signal as high definition 1-D data into deep learning layers with optimized hyper-parameters. Input is consisted of wide 12,800 × 1 × 3 vibration signal matrix. Model learning phase includes Bayesian optimization that optimizes hyper-parameters of the convolutional neural network. Finally, by using a Convolutional Neural Network (CNN) model with optimized hyper-parameters, classification in one of the 8 different machine states and 2 rotational speeds can be performed. This study accomplished the effective classification of different rotary machinery states in different rotational speeds using optimized convolutional artificial neural network for classification of raw three axis accelerometer signal input. Overall classification accuracy of 99.94% on evaluation set is obtained with the CNN model based on 19 layers. Additionally, more data are collected on the same machine with altered bearings to test the model for overfitting. Result of classification accuracy of 100% on second evaluation set has been achieved, proving the potential of using the proposed technique.

The goal of this article is to explore the link between lean and green management, reasons for their implementation, their effect throughout the whole life cycle, as well as the current state of use of lean tools, economic and environmental indicators in the context of Croatian manufacturing companies. A semi-structured interview was used in this research. As a result, the frequency of economic and environmental performance indicators and lean tools in Croatian companies has been defined, as well as the reasons for the implementation of lean management. Additionally, the understanding of the use of Life Cycle Assessment methods, environmental standards has been obtained and the integration of lean and green management in Croatian companies has been explored. Further on, results were compared to the similar study done in the UK. Finally, it can be concluded that the integration of lean and green management is not yet sufficiently present in manufacturing companies, although there are cases in which these two approaches are integrated, primarily in the process and food industry. It is for these reasons that in the integration of these two approaches lies great potential.

Accelerated technology developments caused by Industry 4.0 create problems in its implementation. One of the most important factors that hinder the transition of companies is ignorance and, therefore, the fear of new technologies present among employees. Learning factories have proven to be one of the best solutions for introducing employees to the technologies of Industry 4.0. Croatia is significantly behind in implementing the features of Industry 4.0, especially compared to more developed countries. To facilitate the transition of the Croatian industry to Industry 4.0, it is necessary to acquaint existing and future employees with its technologies through learning factories. There is currently only one learning factory in Croatia, which is too few. This paper presents the process of design and establishment of a learning factory at the Faculty of Mechanical Engineering and Naval Architecture in Zagreb, which facilitates research work and education of students and employees with Industry 4.0.

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