Unsupervised Trend Extraction for Prognostics and Condition Assessment

Maintenance is becoming more expensive nowadays due to the increased complexity in design and function of industrial systems. Continuous health monitoring is thus of high importance to increase the availability of industrial systems and consequently reduce the costs. This paper presents an algorithm for unsupervised trends extraction from multidimensional sensory data so as to use such trend in machinery health monitoring and maintenance needs. The proposed method does not assume any prior knowledge about the nature and type of the input signals. It is based on extracting successive multi-dimensional features from machinery sensory signals. Then, unsupervised feature selection on the features domain is applied without making any assumptions concerning the source of the signals and the number of the extracted features. Finally, empirical mode decomposition algorithm (EMD) is applied on the projected features with the purpose of following the evolution of data in a compact representation over time. The algorithm is demonstrated on accelerated degradation dataset of bearings acquired from PRONOSTIA experimental platform and a second dataset acquired form NASA repository where it is shown to be able to extract interesting signal trends.