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Cluster analysis is widely used in the machine diagnostic and monitoring field. This article discusses the issue of recognizing the states of rotary-support systems with fluid-friction bearings. An experiment was carried out to investigate the effect of tightening the bolts that connect rotor-support unit body to the frame; to investigate the effect of tightening the bolts that connect electric motor to the frame; to investigate the rotor imbalance, as well as a combination of these factors. Cluster analysis based on the K-means method was applied. The readings of the eddy- current transducer were used as an input data for training. Analysis of the results revealed two groups of defects. During testing, the accuracy of group identification was 100%.

The article considers general approaches and modern monitoring systems for rotary machines of electric generating equipment. The main characteristics of monitoring and diagnostics systems of Russian and foreign manufacturers are presented. Modern trends in the construction of intelligent systems for analyzing the performance of turbo generators and predicting possible failures in order to minimize the cost of repairs and forced shutdown of equipment are outlined. The concept of adaptive-predictive use of rotary machines, the difference from existing systems is the presence of adaptive module that allows to react to unwanted changes in real time and increase the predicted residual resource or eliminate the predicted probability of initially refusal.

The laser speckle contrast imaging allows the determination of the flow motion in a sequence of images. The aim of this study is to combine the speckle contrast imaging and machine learning methods to recognition of physiological fluids flow rate. Data on the flow of intralipid with average flow rate of 0-2 mm/s in a glass capillary were obtained using a developed experimental setup. These data were used to train a feed-forward artificial neural network. The accuracy of random image recognition was quite low due to pulsations and the uneven flow set by the pump. To increase the recognition accuracy, various methods for calculating speckle contrast were used. The best result was obtained when calculating the mean spatial speckle contrast. The application of the mean spatial speckle contrast imaging together with the proposed artificial neural network allowed to increase the fluid flow rate recognition accuracy from about 65 % to 89 % and make it possible to exclude an expert from the data processing.

RUL (remaining useful life) estimation is one of the main functions of the predictive analytics systems for rotary machines. Data-driven models based on large amounts of multisensory measurements data are usually utilized for this purpose. The use of adjustable bearings, on the one hand, improves a machine’s performance. On the other hand, it requires considering the additional variability in the bearing parameters in order to obtain adequate RUL estimates. The present study proposes a hybrid approach to such prediction models involving the joint use of physics-based models of adjustable bearings and data-driven models for fast on-line prediction of their parameters. The approach provides a rather simple way of considering the variability of the properties caused by the control systems. It has been tested on highly loaded locomotive traction motor axle bearings for consideration and prediction of their wear and RUL. The proposed adjustable design of the bearings includes temperature control, resulting in an increase in their expected service life. The initial study of the system was implemented with a physics-based model using Archard’s law and Reynolds equation and considering load and thermal factors for wear rate calculation. The dataset generated by this model is used to train an ANN for high-speed on-line bearing RUL and wear prediction. The results show good qualitative and quantitative agreement with the statistics of operation of traction motor axle bearings. A number of recommendations for further improving the quality of predicting the parameters of active bearings are also made as a summary of the work.