The morphological undecimated wavelet decomposition – Discrete cosine transform composite spectrum fusion algorithm and its application on hydraulic pumps

Sun, Jizhou; Li, Hongru; Bao, Xu

doi:10.1016/j.measurement.2016.09.024

Cited by 26 publications

(15 citation statements)

References 23 publications

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“…Lu et al [12] created an EEMD paving and optimized support vector regression method to detect faults and estimate the fault sizes of a piston pump based upon discharge pressure signals. On the other hand, vibration signal also reflects the performance of the piston pumps and the faults of piston pumps are generally accompanied by changes in vibration signal [14][15][16][17][18]. Jiang et al [14] found the fault vibration signals of hydraulic pump were often modulated by the impact and the fault features were concealed when it became fault.…”

Section: Introductionmentioning

confidence: 99%

“…Aiming at the feature extraction from the signals, they proposed a fusion method based on local mean decomposition and improved adaptive multi-scale morphology analysis. In order to extract degradation feature, Sun et al [15] proposed a novel method based upon vibration signal using morphological un-decimated wavelet decomposition and discrete cosine transform. Based on vibration signal, Wang et al [16] created a method using deep belief networks to detect five classes of commonly occurred faults of in piston pumps.…”

Section: Introductionmentioning

confidence: 99%

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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.

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Section: Introductionmentioning

confidence: 99%

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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show abstract

“…Yuan et al applied the EMD method to the multifault diagnosis of bearing [9]. Unfortunately, these popular methods also have unsolved problems: one is the selection of thresholds and wavelet basis in wavelet transform, and another one is the mode fixing and end effect in the EMD method [10].…”

Section: Introductionmentioning

confidence: 99%

Degradation State Identification of Cracked Ultrasonic Motor by Means of Fault Feature Extraction Method

2019

Shock and Vibration

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The cracking of piezoelectric ceramics is the main reason of failure of an ultrasonic motor. Since the fault information is too weak to reflect the condition of piezoelectric ceramics especially in the early degradation stage, a fault feature extraction method based on multiscale morphological spectrum and permutation entropy is proposed. Firstly, a signal retaining the morphological feature under different scales is reconstructed with multiscale morphological spectrum components. Then, the permutation entropy of the reconstructed signal is taken as the fault feature of piezoelectric ceramics. Furthermore, a sensitivity factor is defined to optimize the embedded dimension and delay time of permutation entropy according to double sample Z value analysis. Finally, a matrix composed of the probability distributions, obtained from permutation entropy calculation, is applied for the degradation state identification by means of probability distribution divergence. The analysis of actual test data demonstrates that this method is feasible and effective.

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“…However, the strikes of the interaction are not always from only one direction [12]. Analysis based on a single vibration signal can hardly achieve the whole information needed for diagnosis and prognosis.…”

Section: Introductionmentioning

confidence: 99%

The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction

2018

SV-JME

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The morphological undecimated wavelet decomposition – Discrete cosine transform composite spectrum fusion algorithm and its application on hydraulic pumps

Cited by 26 publications

References 23 publications

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

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

Degradation State Identification of Cracked Ultrasonic Motor by Means of Fault Feature Extraction Method

The Application of LCS and Information Entropy as a Novel Fusion Algorithm for Degradation Feature Extraction

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