An approach to performance assessment and fault diagnosis for hydraulic pumps

Abstract

The hydraulic pump is the heart of the hydraulic system. Therefore, monitoring the condition of such a pump in real time is crucial to the reliability of the entire system. In this study, a method that assesses the performance of and diagnoses faults in hydraulic pumps is proposed. This method is based on wavelet packet transform (WPT) and a self-organizing mapping (SOM) neural network. First, WPT is used to decomposes the vibration signal into components. The energy of each component is then extracted and normalized to form feature vectors. Second, the SOM neural network, which is trained by normal data only, maps feature vectors into minimum quantization errors, which are then normalized into confidence values (CVs). Performance is assessed by tracking CV trends. Finally, SOM, which is trained by both normal and faulty samples, classifies faults into different groups when they occur. These groups represent the various fault modes of the hydraulic pump. In addition, Taguchi method is employed to reduce the number of redundant features and extract the principal components, thereby ensuring the effectiveness of the approach. A case study based on the vibration dataset of the rig of a test plunger pump is conducted to demonstrate the ability of the proposed method to assess the performance of a hydraulic pump and suitably diagnose faults