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In this paper, the operating states of a wheel loader were studied for diagnostics purposes using a real time simulation model of an articulated-frame-steered wheel loader. Test drives were carried out to obtain measurement data, which were then analyzed. The measured time series data were analyzed to find the sequences of operating states using two different data sets, namely the variables of hydrostatic transmission and working hydraulics. A time series is defined as a collection of observations made sequentially in time. In our proposed method, the time series data were first segmented to find operating states. One or more segments build up an operating state. A state is defined as a combination of the patterns of the selected variables. The segments were then clustered and classified. The operating states were further analyzed using the quantization error method to detect anomalies.

The goal of this paper is to study a proactive condition monitoring system for fluid power systems where the Self-Organizing Maps (SOM) with unsupervised learning is used to classify and interpret high-dimensional data measurements. If all the damages are not assumed to be known before diagnostics, an ordinary neural network with supervised learning for their detection can not be used. Operation of the proactive condition monitoring system is tested in a test system where two fault types are used. The test system is run in normal and two different fault situations. Measurement results are used for training and testing the SOM. In this paper these measurement results and also the quality of state recognition are shown.