One of the big problems of today’s manufacturing companies is the risks of the assembly line unexpected cessation. Although planned and well-performed maintenance will significantly reduce many of these risks, there are still anomalies that cannot be resolved within standard maintenance approaches. In our paper, we aim to solve the problem of accidental carrier bearings damage on an assembly conveyor. Sometimes the bearing of one of the carrier wheels is seized, causing the conveyor, and of course the whole assembly process, to halt. Applying standard approaches in this case does not bring any visible improvement. Therefore, it is necessary to propose and implement a unique approach that incorporates Industrial Internet of Things (IIoT) devices, neural networks, and sound analysis, for the purpose of predicting anomalies. This proposal uses the mentioned approaches in such a way that the gradual integration eliminates the disadvantages of individual approaches while highlighting and preserving the benefits of our solution. As a result, we have created and deployed a smart system that is able to detect and predict arising anomalies and achieve significant reduction in unexpected production cessation.

The contribution deals with measurement of acoustic absorption coefficient for different single or double-layer materials: cork, two layers of polyethylene, polyethylene and felt. The measurement was performed on an impedance tube of our own construction, using the two-microphone method transfer function (ISO 10534-2: 1998) and the PULSE measuring system. Values of the acoustic absorption coefficient for the frequencies from 100 Hz to 1600 Hz were determined experimentally. Subsequently, those values were processed graphically.

The friction processes as the most often physical phenomena cause many negative effects in production systems, and man try to minimize them. Coefficient of friction is one of the fundamental parameters which characterizes the process of friction between two parts, which perform mutual sliding of movement. Its measurement is realised using the experimental methods. In our experiments motion of samples is enforced motorically and various measurement sensors that monitor variables characterizing the process of friction are used. Experimentally obtained values of the coefficient of friction for the bushing aluminium - steel pairs are analysed in the paper. During the run up the value of the coefficient of friction sharply decreased from 0.8 to size 0.1 and then was stable till the end. Experimental tests were realised on the equipment Tribotestor M' 89, which was used for determination of the tribological parameters of bearing bushings. Following input variables were adjusted: the sliding speed, loading of rotanional sliding joint and duration of the test. The temperature in the contact surface of bushings and the roughness parameters (Ra, Rz) were also monitored before and after the tests.

This paper deals with the development of the equation of motion and practical implementation of low cost two-wheel self-balancing model of a Segway transporter. The experimental model of cart was designed and made under this study. Nonlinear equations of motion of real model and linearized model were derived. To develop the mathematical model, Matlab/Simulink was applied. The mechanical part was implemented into Simulink, and a DC motor was considered as a linear system. The real model was tested for its balance by implementation of a control algorithm consisting of a complementary filter and PID algorithm on an Arduino development board with peripheral devices. The fully functional self-balancing model was used as a demonstration in the teaching process of the Mechatronics courses.