A Condition Monitoring Method via Optimization-Based Adaptive Feature Extraction Strategy

In this article, a condition monitoring approach is proposed based on vibration signal, aiming at improving the adaptability of feature extraction and the accuracy of classification. First, the original vibration signal acquired under certain working condition is preprocessed by dividing it into multiple segments, followed by the signal decomposition. Then, the features of each decomposed signal are extracted based on the theory of diversity entropy (DE). Two parameters in the DE are optimized considering the fact that these parameters are crucial for the classification result. The optimization objective is to make the different segments of the signal collected in the same working condition have approximate feature characterization. By this means, the feature of the signal is captured adaptively and accurately using the optimized entropy value. Finally, the support vector machine is used to identify the extracted feature vectors to realize condition classification. The experiments on three representative platforms, including a crystal lifting-rotation system in our laboratory, are conducted to verify the effectiveness of the proposed method.