Machine Condition Classification Using Deterioration Feature Extraction and Anomaly Determination

Abstract

Condition classification has been widely used for assessing equipment status for machine condition monitoring and diagnostics. An engine was fitted with one temperature and two pressure sensors to study the machine conditions in prognostics with an added abnormal state, in addition to the conventional normal and failure states. This work enables a better classification capability in order to predict deterioration in the engine. Information related to three deterioration processes was collected, and preprocessed using singular point elimination, deviation value acquisition, and data normalization. Wavelet transforms were used to extract deterioration features with different mother wavelets. The mother wavelets were selected using tests to optimize the wavelet selection. The deterioration was related to the amount of anomaly, with the abnormal states defined to distinguish the functional from the failure states. A Learning Vector Quantization (LVQ) neural network was used to classify the machine conditions, including normal, abnormal, and failure states. The results showed that the deterioration features defined using the Daubechies wavelet (db8) most strongly correlated with the original signal, so that the classification accuracy based on the deterioration features was greatly improved. The LVQ classification system had good accuracy for machine condition classification, and was adaptable to various engine conditions.