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The prevention of emergencies at technical facilities is largely provided by the diagnostics of their functioning. One of the important problems is the diagnosis of the technical condition of the hydraulic Программные продукты и системы / Software & Systems 4 (33) 2020 634 unit. In the history of hydropower, examples are known where poor quality diagnostics led to serious accidents. To prevent such situations, vibration monitoring of the hydraulic unit is carried out, while the vibration data is sent to the data collection server and transmitted to the control rack, where load adjustments or complete unit shutdown occur. The need for prompt intervention is determined by many indicators that characterize the quality of functioning of the hydraulic unit. This paper explores the effectiveness of the use of neural network methods for vibration diagnostics of a hydraulic unit. The resulting sample is divided into three parts: training, control, and test. The training part is designed to build a neural network model the relationship between the indicators of the functioning of the unit and its states. The control sample is used for the training quality control and helps prevent network retraining. The quality of classification is evaluated by a test sample. When using cross-validation, the original sample is split into several blocks. To assess the diagnosis efficiency, three different quality criteria were used: average error on the test sample, AUC, and F-measure. The practical implementation was carried out using the MATLAB package. For a given set of input data, the best fit configuration was a neural network of three layers with 18 neurons in each layer. As a learning function, it uses the Levenberg-Marquardt algorithm with the backpropagation method of error. The percentage of the average error in recognizing the state of a hydraulic unit using a neural network is 4,85 %, AUC is 0,8833, and the F-measure is 0,8282. Analysis of the effectiveness of the obtained network configuration compared to the automatically built network using the Statistics and Machine Learning Toolbox library showed an increase in F-measure by 6,7 %.

Разработка нейронной сети для оценки исправности гидроагрегата по результатам вибромониторинга