Feature Extraction and Self-Organizing Maps in Condition Monitoring of Hydraulic Systems

Abstract

This thesis deals with condition monitoring of hydraulic components and systems. In the thesis the state of the hydraulic components and system are studied using data analysis methods to analyze measurement data. The main goal is to apply data analysis methods to measured raw data so that different fault situations, and their locations and levels, can be detected and identified. Data analysis methods are used in feature extraction of measurement data and classification of the system state using these extracted features. In feature extraction three different methods, descriptive statistics, principal component analysis (PCA) and wavelet analysis, are applied to extract relevant and discriminating information from the measurement data and to reduce the data dimensionality. The extracted features which describe the condition of a single component or even the whole hydraulic system in a specified situation are classified using Self-Organizing Maps (SOMs). In summary, the thesis brings an understanding of which variables should be monitored and how those measured variables should be processed so that the state of the hydraulic system or components can be ascertained.

The applicability of SOMs for analysis of operation of hydraulic components and systems is discussed and first proved with three different test systems, and finally with a work machine, which is a forklift (reach stacker) in this study. Different feature extraction methods are also compared, as well as different measurement variables. The study is based mainly on measurements from these systems and two different hydraulic components: the cylinder and valve. Both oil and water hydraulic components are studied.

A simulation model of the lifting movement of the forklift was developed to test the sensitivity of the data analysis methods used in this research. By means of this model the effects of changes in the loading and operating conditions on the performance of the data analysis methods are studied. The results of the study show that the state of the hydraulic system and its components can be effectively followed using the studied data analysis methods. Using descriptive statistics or wavelet coefficients of pressure transients as features in the classification made by the SOM it is possible to detect and identify several fault situations and their levels during normal operation of the system.