The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Aiming at the problem of multi-fault identification method of axial piston pump, a new method combining correlation dimension and support vector machine was proposed. Firstly, the envelope demodulation was performed to several typical fault vibration signals of pump by using wavelet packet decomposition and Hilbert transform method. Then, the useful signals corresponding to feature band were obtained...
The fault diagnosis of hydraulic pump is always a challenging issue in the field of machinery fault diagnosis. Not only is manual feature extraction time-consuming and laborious, but also the diagnostic result is easily affected by subjective experience. The stacked autoencoders (SAE) which has powerful learning and representation ability is applied in hydraulic pump fault diagnosis, and it is directly...
We discuss appropriate feature models for the automatic diagnosis of faults in two different application scenarios in a comparative study. The first test case is the Case Western Reserve University Bearing Data, the second is a submersible pump used in offshore oil exploration. Additionally we provide a visual comparison of the discriminative capabilities of the employed feature models using Principal...
We apply computational intelligence methods to the domain of fault diagnosis of rotating machinery, specifically submersible motor pumps used in offshore oil exploration. We propose distinct feature models to assemble a global feature pool from which the most discriminative information is filtered by feature selection. Statistically robust performance estimation for representative classifier models...
This paper presents the results achieved by fault classifier ensembles based on supervised learning for diagnosing faults on oil rigs motor pumps. The main goal is to apply two feature-based ensemble construction methods to a real-world problem. Recent studies have shown that the use of ensembles of classifiers that are accurate and at the same time have diversifying results can improve the final...
This paper presents the results achieved by fault classifier ensembles based on a model-free supervised learning approach for diagnosing faults on oil rigs motor pumps. The main goal is to compare two feature-based ensemble construction methods, and present a third variation from one of them. The use of ensembles instead of single classifier systems has been widely applied in classification problems...
Various fault types and difficult diagnosis restricted the improvement of economic benefit and system efficiency of electrical submersible progressing cavity pump (ESPCP) production system. A novel method for status recognition of electrical parameters in fault diagnosis of ESPCP based on biomimetic pattern recognition (BPR) is presented. Application results show the proposed BPR classifier produces...
Various fault types and difficult diagnosis restricted the improvement of economic benefit and system efficiency of Electrical submersible progressing cavity pump(ESPCP) production system. A novel method for status recognition of electrical parameters in fault diagnosis of ESPCP by using support vector machine (SVM) based on small samples of statistical learning theory is presented. Application results...
An intelligent fault diagnosis method based on principal component analysis (PCA) and least squares support vector machines (LS-SVM) is proposed. The characteristic parameter set is obtained by wavelet packet transform (WPT). And PCA is used to extract the principal features associated with the diagnosing object. Then, the training data set which is reduced from the original parameters are used as...
We present a collection of pattern recognition techniques applied to fault detection and diagnosis of motor pumps. Vibrational patterns are the basis for describing the condition of the process. We rely on the data-driven approach to the learning of the fault classes, i.e. supervised learning in pattern recognition. Our work is motivated by the diversity of the studied defects, the availability of...
We report about fault diagnosis experiments to improve the maintenance quality of motor pumps installed on oil rigs. We rely on the data-driven approach to the learning of the fault classes, i.e. supervised learning in pattern recognition. Features are extracted from the vibration signals to detect and diagnose misalignment and mechanical looseness problems. We show the results of automatic pattern...
Due to inherent delivery fluctuation of piston pump, its measurable signals are full of structure coupling and noise besides failure feature that make the system illegible and fault diagnosis difficult. This paper presents a contract support vector machine to extract the effective information from data and eliminate the redundant attribute among different data. Then utilize the support vector machine...
The experiment research shows that as the crank bearing gradually wears the relative energy contents of low-frequency bands slightly reduce and these of high-frequency bands greatly increase. The relative energy distribution of frequency bands of vibration signal provides the quantitative evidence to the identification of the crank bearing wear fault. So the feature extraction of wavelet packet energy...
In the traditional fault diagnosis technology, classical life and reliability tests require sufficient sample size when diagnose the faults and forecast the future states. However, there is even less sample size for machinery products, especially for major equipment. The support vector machine based on statistical learning theory can solve this problem. In this paper, a forecast model for reactor...
Considering the issues that the relationship between the fault of screw oil pump existent and fault information is a complicated and nonlinear system, and it is very difficult to found the process model to describe it. The support vector machine (SVM) has the ability of strong nonlinear function approach and the ability of strong generalization and also has the feature of global optimization. In this...
Statistical learning theory is introduced to fault detection of oil pump. Considering the issues that the relationship between the fault of oil pump existent and fault information is a complicated and nonlinear system, and it is very difficult to found the process model to describe it. The support vector machine (SVM) has the ability of strong nonlinear function approach and the ability of strong...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.