Fault diagnosis of hydraulic piston pumps based on a two-step EMD method and fuzzy C-means clustering

Lu, Chuanqi; Wang, Shaoping; Zhang, Chao

doi:10.1177/0954406215602285

Cited by 34 publications

(25 citation statements)

References 30 publications

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“…Equation 4shows that the predictable parts are extracted at frequencies that are integer multiples of the cycle. As reported in Equations (4) and (6), the mathematical implementation of the SA requires a precise evaluation of the cycle length N and that both L and K are integers; furthermore, in Equation 4, N is an integer. When the samples come from an angular sampling, the conditions are all satisfied and N is a known integer, since it depends on the acquisition system.…”

Section: Analysis Proceduresmentioning

confidence: 99%

“…In the field of fluid power systems, a few parameters can easily be monitored such as fluid pressure, fluid temperature, and vibrations. An important signal is the delivery pressure that can be utilized for developing effective diagnostic algorithms [4][5][6][7]. The pump delivery pressure can be measured through low-price sensors, but only faults involving the system fluid dynamics can be detected.…”

Section: Introductionmentioning

confidence: 99%

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A Vibration Signal-Based Method for Fault Identification and Classification in Hydraulic Axial Piston Pumps

et al. 2019

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In recent years, the interest of industry towards condition-based maintenance, substituting traditional time-based maintenance, is growing. Indeed, condition-based maintenance can increase the system uptime with a consequent economic advantage. In this paper, a solution to detect the health state of a variable displacement axial-piston pump based on vibration signals is proposed. The pump was tested on the test bench in different operating points, both in healthy and faulty conditions, the latter obtained by assembling damaged components in the pump. The vibration signals were acquired and exploited to extract features for fault identification. After the extraction, the obtained features were reduced to decrease the computational effort and used to train different types of classifiers. The classification algorithm that presents the greater accuracy with reduced features was identified. The analysis has also showed that using the time sampling raw signal, a satisfying accuracy could be obtained, which will permit onboard implementation. Results have shown the capability of the algorithm to identify which fault occurred in the system (fault identification) for each working condition. In future works, the classification algorithm will be implemented onboard to validate its effectiveness for the online identification of the typical incipient faults in axial-piston pumps.

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Section: Analysis Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Vibration Signal-Based Method for Fault Identification and Classification in Hydraulic Axial Piston Pumps

et al. 2019

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“…Mou [52] discussed the application of an expert system in the fault diagnosis of a hydraulic system used in coking coal machinery. Lu [53] analyzed and diagnosed the vibration signals of a hydraulic pump by combining an EMD method with a fuzzy C-means clustering analysis. In addition, Tang [54] studied the hydraulic system of a concrete pump truck using an EMD, Hilbert envelope spectrum, and PSO-SVM.…”

Section: Fault Diagnosis Of Hydraulic System Based On Artificial Intementioning

confidence: 99%

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Dai

Tang

Huang

et al. 2019

Chin. J. Mech. Eng.

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Hydraulic systems have the characteristics of strong fault concealment, powerful nonlinear time-varying signals, and a complex vibration transmission mechanism; hence, diagnosis of these systems is a challenge. To provide accurate diagnosis results automatically, numerous studies have been carried out. Among them, signal-based methods are commonly used, which employ signal processing techniques based on the state signal used for extracting features, and further input the features into the classifier for fault recognition. However, their main deficiencies include the following: (1) The features are manually designed and thus may have a lack of objectivity. (2) For signal processing, feature extraction and pattern recognition are conducted using independent models, which cannot be jointly optimized globally. (3) The machine learning algorithms adopted by these methods have a shallow architecture, which limits their capacity to deeply mine the essential features of a fault. As a breakthrough in artificial intelligence, deep learning holds the potential to overcome such deficiencies. Based on deep learning, deep neural networks (DNNs) can automatically learn the complex nonlinear relations implied in a signal, can be globally optimized, and can obtain the high-level features of multi-dimensional data. In this paper, the main technology used in an intelligent fault diagnosis and the current research status of hydraulic system fault diagnosis are summarized and analyzed. The significant prospect of applying deep learning in the field of intelligent fault diagnosis is presented, and the main ideas, methods, and principles of several typical DNNs are described and summarized. The commonality between a fault diagnosis and other issues regarding typical pattern recognition are analyzed, and research ideas for applying DNNs for hydraulic fault diagnosis are proposed. Meanwhile, the research advantages and development trend of DNNs (both domestically and overseas) as applied to an intelligent fault diagnosis are reviewed. Furthermore, the fault characteristics of a complex hydraulic system are summarized and discussed, and the key problems and possible research ideas of applying DNNs to an intelligent hydraulic fault diagnosis are presented and comprehensively analyzed.

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“…Considering easy measurement and low cost, the pressure signal and vibration signal were usually observed for fault diagnosis of piston pump. On the one hand, pressure signal is one of the most important dynamic parameters which reveal the operating status of the piston pump [10][11][12][13]. Gao et al [10] found that the discharge pressure was an informative variable of pump operation status and carried sufficient information to support a sensitive and reliable pump fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A multi-fault diagnosis method for piston pump in construction machinery based on information fusion and PSO-SVM

Tang¹,

Wang²,

Wu³

2019

J. vibroeng.

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Piston pumps are key components in construction machinery, the failure of which may cause long delay of the construction work and even lead to serious accident. Because construction machines are exposed to poor working conditions, multiple faults of piston pumps are most likely to occur simultaneously. When multiple faults occur together, it is difficult to detect. A multi-fault diagnosis method for piston pump based on information fusion and PSO-SVM is proposed in this thesis. Information fusion is used as fault feature extraction and PSO-SVM is applied as the fault mode classifier. According to the method, vibration signal and pressure signal of piston pump in normal state, single fault state and multi-fault state are collected at first. Then the empirical mode decomposition (EMD) is used to decompose vibration signals into different frequency band and energy features are extracted. These energy features extracted from vibration signals and time-domain features extracted from pressure signal are information fused at the feature layer and constitute the eigenvectors. Finally, these eigenvectors are put into support vector machine (SVM) and the working conditions of piston pump were classified. Particle swarm optimization (PSO) is applied to optimize two parameters of SVM. The experimental results show that the recognition accuracy of the normal state, three single failure modes and multi-fault modes are 98.3 %, 97.6 % and 94 % respectively. These recognition accuracies are higher than which using vibration signal or pressure signal alone. So, the proposed method can not only identify the single fault, but also effectively identify the multi-fault of piston pump.

show abstract

Fault diagnosis of hydraulic piston pumps based on a two-step EMD method and fuzzy C-means clustering

Cited by 34 publications

References 30 publications

A Vibration Signal-Based Method for Fault Identification and Classification in Hydraulic Axial Piston Pumps

A Vibration Signal-Based Method for Fault Identification and Classification in Hydraulic Axial Piston Pumps

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

A multi-fault diagnosis method for piston pump in construction machinery based on information fusion and PSO-SVM

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