Classification and prediction of wear state based on unsupervised learning of online monitoring data of lubricating oil

Abstract

With the development of the times, the demand for high efficiency and reliability of machine performance in China’s industry has become higher than ever before, and the traditional equipment condition evaluation method has also encountered a huge challenge. As an important branch of machine learning, cluster analysis is widely used in fault diagnosis and other fields because of its advantages of no prior knowledge and massive data processing. This paper introduces the concept of principal component analysis and K-means method based on time series in the field of oil data analysis. Through the test data on the oil monitoring data of the steam turbine unit in the power plant, the condition evaluation and classification of the equipment are carried out, among which the classification effect of 6μm particles, dielectric constant and 4μm particles is very obvious, and the water content is relatively obvious, which can basically distinguish each state. At the same time, on the basis of feature extraction, use the special cyclic neural network (RNN) in deep learning-long and short-term memory network (LSTM) to build a model of data for data prediction, under the powerful ability of LSTM to process temporal data, they have unearthed more irregular and non-linear trends in a large amount of historical data, and achieved better prediction results than traditional methods (ARIMA). Through clustering and prediction of lubrication parameter data, it can realize early warning and abnormal diagnosis of lubrication status and reduce damage to machinery and equipment.