Analysis of selected vibroacoustic signals recorded on EMU vehicle running on chosen routes under supervised operating conditions

The subject matter of the paper is an analysis of the results of acceleration signals measurement obtained from the prototype of Rail Vehicle and Rail Track Monitoring System. The prototype of the system measures acceleration signals on some elements of electric multiple unit, such as bogie frames, wheels and railway vehicles body. The results of measurement were converted to values of statistical parameters, e.g.: an amplitude, a root mean square, a kurtosis coefficient, an interquartile range. The values of diagnostic parameters, when compared with the permissible values, allow to monitor the typical dynamic behaviors of rail vehicle and track condition and to determine a condition of a rail vehicle construction. The main purpose of the analysis described hereby was to determine the usefulness of various diagnostic parameters and to identify the direction of further research connected to condition monitoring and diagnosing of rail vehicles and track

Application of Hybrid Agents to Smart Energy Management of a Prosumer Node

We outline a solution to the problem of intelligent control of energy consumption of a smart building system by a prosumer planning agent that acts on the base of the knowledge of the system state and of a prediction of future states. Predictions are obtained by using a synthetic model of the system as obtained with a machine learning approach. We present case studies simulations implementing different instantiations of agents that control an air conditioner according to temperature set points dynamically chosen by the user. The agents are able of energy saving while trying to keep indoor temperature within a given comfort interval

Analysis of selected acceleration signals measurements obtained during supervised service conditions – study of hitherto approach

The subject matter of the paper is an analysis of chosen results of acceleration signals measurements obtained from the prototype of the Rail Vehicle and Rail Track Monitoring System. This prototype of the monitoring system measures acceleration signals on designated elements of the electric multiple unit (EMU). These elements comprise components such as: bogie frames, wheels and bodies of railway vehicles. The analysis was prepared on the basis of rail vehicle journeys on sample sections of the Polish National Railways (PKP Polskie Linie Kolejowe S.A.) network. The products of measurements were converted to values of specific diagnostic parameters (statistical parameters), e.g. an amplitude (zero-peak), a root mean square, a kurtosis coefficient, an interquartile range. Comparing the values of diagnostic parameters with their permissible values allows the monitoring of distinctive dynamic behaviors of rail vehicles and track condition, as well as the temperature of the bearings of rail vehicle wheelsets. It also allows the determining of the condition of rail vehicle structure. The permissible values of certain diagnostics parameters could not be obtained from the literature. Therefore, this paper in part presents a way of obtaining these permissible values. The main intention of the analysis described here is to determine the usability of various diagnostic parameters and to identify the course of further research related to condition monitoring and diagnostics of rail vehicles and tracks

Acoustic signal processing to diagnose transiting electric trains

This paper deals with a method based on vibroacoustic techniques to diagnose electric trains and other important railway components. The proposed method can detect the following main subsystems: pantographs, electrical contact lines, rails, bogies, wheels, etc. Signal-processing techniques are thoroughly applied to acoustic vibrations that are directly acquired on the field when the train is transiting. Signals are picked up, processed, and stored by acquisition systems installed along the railway on structures jointed to the soil. The processed data can be sent to a remote dedicated diagnostic center to get continuous monitoring and supervision of the railway system. The received diagnostic information can be conveniently used to improve safety, to schedule a correct maintenance, and to reduce costs. The results of the experimental tests conducted to validate the method during an extensive measurement program are reported and commented