Automatic Decision Method of Optimum Symptom Parameters and Frequency Bands for Intelligent Machinery Diagnosis: Application to Condition Diagnosis of Centrifugal Pump System

作者：Song Liuyang; Chen Peng^*; Wang Huaqing

来源：Advances in Mechanical Engineering, 2014, 6(0): 603408.

DOI：10.1155/2014/603408

A combination method of statistical filtering (SF) and the ant colony optimization (ACO) is proposed for automatic decision of optimum symptom parameters and frequency bands for machinery diagnosis. The noise of vibration signals is canceled by using SF. Similarity factor I-pq is defined to evaluate the filtering performance; the significance level alpha is optimized by genetic algorithms (GA). The optimum symptom parameters in different frequency bands, by which the states of rotating machinery can be sensitively distinguished, are automatically and sequentially selected by ACO based on the Mahalanobis distance between different machine states. Finally, the Mahalanobis distance is used to identify failure types based on the sequential diagnostic method. The new method proposed in this paper has been used to diagnose a centrifugal pump system for faults which often occur in the pump, such as impeller unbalance, shaft misalignment, and cavitation. The verification results of the condition diagnosis for a centrifugal pump show that the new method has good performance.

• 出版日期2014
• 单位北京化工大学