Detecting operation regimes using unsupervised clustering with infected group labelling to improve machine diagnostics and prognostics

Estimating the stress level of components while operation modes are varying is a key issue for many prognostic models in condition monitoring. The identification of operation profiles during production is therefore important. Clustering condition monitoring data with regard to operation regimes will provide more detailed information about the variation of stress levels during production. The distribution of the operation regimes can then support prognostics by revealing the cause-and-effect relationship between the operation regimes and the wear level of components. In this study unsupervised clustering technique was used for detecting operation regimes for an underground LHD (load-haul-dump machine) by using features extracted from vibration signals measured on the front axle and the speed of the Cardan axle. The clusters were also infected with a small portion of the data to obtain the corresponding labels for each cluster. Promising results were obtained where each sought-for operation regime was detected in a sensible manner using vibration RMS values together with speed.