This article concerns church monuments, which are part of the cultural heritage of Dukla. This is a town with medieval origins, located within the Podkarpackie Voivodeship, in the Krosno district. In the past it was an important centre of commerce on the Hungarian Route. Merchant traditions in Dukla are mainly associated with trade and storage of wine, which was kept in the cellars under the main market square in Dukla. Because of the rank, which the town used to have in the past, it can boast numerous monuments including objects associated with the administrative, commercial and economic functions of the town, as well as — or perhaps primarily — sacred objects. Among the latter, one has to mention firstly the parish church of St Mary Magdalene, the church and monastery complex of the Bernardine Order, and the synagogue of the Jewish community, that once used to live in Dukla. Those objects, their cultural value and issues related to their protection constitute the subject of this study.

Last decades, rolling bearing faults assessment and their evolution with time have been receiving much interest due to their crucial role as part of the Conditional Based Maintenance (CBM) of rotating machinery. This paper investigates bearing faults diagnosis based on classification approach using Gaussian Mixture Model (GMM) and the Mel Frequency Cepstral Coefficients (MFCC) features. Throughout, only one criterion is defined for the evaluation of the performance during all the cycle of the classification process. This is the Average Classification Rate (ACR) obtained from the confusion matrix. In every test performed, the generated features vectors are considered along to discriminate between four fault conditions as normal bearings, bearings with inner and outer race faults and ball faults. Many configurations were tested in order to determinate the optimal values of input parameters, as the frame analysis length, the order of model, and others. The experimental application of the proposed method was based on vibration signals taken from the bearing datacenter website of Case Western Reserve University (CWRU). Results show that proposed method can reliably classify different fault conditions and have a highest classification performance under some conditions.