Intelligent diagnosis method for a centrifugal pump using features of vibration signals

Abstract

In the field of machinery diagnosis, the utilization of vibration signals is effective in the detection of fault, because the signals carry dynamic information about the machine state. However, knowledge of a distinguishing fault is ambiguous because definite relationships between symptoms and fault types cannot be easily identified. This paper presents an intelligent diagnosis method for a centrifugal pump system using features of vibration signals at an early stage. The diagnosis algorithm is derived using wavelet transform, rough sets and a partially linearized neural network (PNN). ReverseBior wavelet function is used to extract fault features from measured vibration signals and to capture hidden fault information across optimum frequency regions. As the input parameters for the neural network, the nondimensional symptom parameters that can reflect the characteristics of a signal are defined in the amplitude domain. The diagnosis knowledge for the training of the PNN can be acquired by using the rough sets. We also propose a diagnosis method based on the PNN, one which can deal with the ambiguity problem of condition diagnosis, and distinguish fault types on the basis of the possibility distributions of symptom parameters automatically. The decision method of optimum frequency region for extracting feature signals is also discussed using real plant data. Practical examples of diagnosis for a centrifugal pump system are shown in order to verify the efficiency of the method.

Intelligent Methods for Condition Diagnosis of Plant Machinery

“…A large set of SPs has been defined in the pattern recognition field (Mitoma et al,2008) (Wang et al, 2008a) (Wang et al, 2007b). In the present work, nondimensional symptom parameters (NSPs) in time domain are considered, and are calculated with the reconstructed time signals of each level in each state to be diagnosed, respectively (Wang et al, 2008a).…”

“…To solve this ambiguous problem, the fuzzy neural network is realized with the developed back propagation NN called as "the partially-linearized neural network (PNN)". The detailed principle of the PNN had been described in (Wang et al, 2008a).…”

“…In addition, due to the complexity of plant machinery conditions, and the number of fault states to be identified is enormous, it is very hard to find one or several symptom parameters that can identify all of those faults perfectly, simultaneously. Particularly, it is difficult to judge the relationship between fault states and the symptom parameters by a theoretical approach (Pusey, 2000) (Mitoma et al, 2008) (Wang et al, 2008a). For the above reasons, in order to process the uncertain relationship between symptom parameters and machinery conditions, and improve the efficiency and accuracy of fault diagnosis at an early stage, the authors reviewed their recent researches on intelligent diagnosis methods for rotating machinery based on artificial intelligence methods and feature extraction of vibration signals.…”

Cavitation detection in centrifugal pumps using pressure time-domain features

“…Wang and Chen presented an intelligent diagnosis method based on features of the vibration signal. In this method, a wavelet was employed to extract features from vibration signals, and the rough set was utilized to provide diagnosis knowledge for a neural network [8]. Sakthivel et al utilized a decision tree to extract statistical features from vibration measurement and classify these features simultaneously.…”

Intelligent diagnosis methods for plant machinery

“…Many studies have been carried out to investigate the use of neural networks for the automatic diagnosis of machinery, and most of them have been proposed to deal with discrimination of fault types collectively. However, the conventional neural network cannot reflect the possibility of ambiguous diagnosis problems, and will never converge when the first layer symptom parameters have the same values in different states [7][8][9][10][11][12][13][14].…”