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p>As the vibration signal characteristic s of hydraulic pump present non-stationary and the fault features is difficult to extract, a new feature extraction method was proposed .This approach combines wavelet packet analysis techniques, fuzzy entropy and LLTSA (liner local tangent space alignment) which is one of typical manifold learning methods t...
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Citations
... The frequently-used decomposition methods are shown in figure 3. These methods are mainly short time Fourier transform (STFT) [24], SVD [25], wavelet decomposition (WD) [26] and its variants, adaptive mode decomposition (such as empirical mode decomposition (EMD) [27] and its variants, variational mode decomposition (VMD)) [28], etc. [14,26] Adaptive mode decomposition EMD and its variants [27,29,30] VMD [6,28,31] The frequently-used signal decomposition methods is shown in table 1. ...
... The frequently-used decomposition methods are shown in figure 3. These methods are mainly short time Fourier transform (STFT) [24], SVD [25], wavelet decomposition (WD) [26] and its variants, adaptive mode decomposition (such as empirical mode decomposition (EMD) [27] and its variants, variational mode decomposition (VMD)) [28], etc. [14,26] Adaptive mode decomposition EMD and its variants [27,29,30] VMD [6,28,31] The frequently-used signal decomposition methods is shown in table 1. ...
Hydraulic component faults have the characteristics of nonlinear time-varying signal, strong concealment, and difficult feature extraction, etc. Timely and accurately fault diagnosis of hydraulic components is helpful to curb economic losses and accidents, so researches have carried out a lot of research on hydraulic components. Information fusion technology can combine multi-source data from multiple dimensions to mine fault data features, which effectively improves the accuracy and reliability of fault diagnosis results. However, there is currently a lack of a comprehensive and systematic review in this domain. Therefore, in this paper, the hydraulic components information fusion fault diagnosis technologies are summarized and analyzed, encompassing the main process information fusion fault diagnosis and the research status of information fusion fault diagnosis of hydraulic system. The methods and techniques involved in the fusion process, data source and fusion method of fault diagnosis of hydraulic components information fusion are elaborated and summarized. The problems of information fusion in fault diagnosis of hydraulic components are analyzed, the solutions are discussed, and the research ideas of improving information fusion fault diagnosis are put forward. Finally, digital twin (DT) technology is introduced, and the advantages and research status of intelligent fault diagnosis based on DT are summarized. On this basis, the intelligent fault diagnosis of hydraulic components based on information fusion is summarized, and the challenges and future research ideas of applying information fusion and DT to intelligent fault diagnosis of hydraulic components are put forward and analyzed comprehensively.
... Since the locally linear embedding algorithm cannot preprocess nonlinear signals, combining it with the wavelet transform and singular value decomposition enhanced its ability to extract significant features by decomposing and preprocessing nonstationary or noisy signals. Wang et al. [112] researched a fault diagnosis method that combines the wavelet packet transform, fuzzy entropy, and the linear local tangent space alignment algorithm. This method decomposed high-dimensional features into low-dimensional features with an improved classification performance. ...
As the modern industry rapidly advances toward digitalization, networking, and intelligence, intelligent fault diagnosis technology has become a necessary measure to ensure the safe and stable operation of mechanical equipment and effectively avoid major disaster accidents and huge economic losses caused by mechanical equipment failure. As the “power heart” of hydraulic transmission systems, hydraulic piston pumps (HPPs) occupy an important position in aerospace, navigation, national defense, industry, and many other high-tech fields due to their high-rated pressure, compact structure, high efficiency, convenient flow regulation, and other advantages. Faults in HPPs can create serious hazards. In this paper, the research on fault recognition technology for HPPs is reviewed. Firstly, the existing fault diagnosis methods are described, and the typical fault types and mechanisms of HPPs are introduced. Then, the current research achievements regarding fault diagnosis in HPPs are summarized based on three aspects: the traditional intelligent fault diagnosis method, the modern intelligent fault diagnosis method, and the combined intelligent fault diagnosis method. Finally, the future development trend of fault identification methods for HPPs is discussed and summarized. This work provides a reference for developing intelligent, efficient, and accurate fault recognition methods for HPPs. Moreover, this review will help to increase the safety, stability, and reliability of HPPs and promote the implementation of hydraulic transmission technology in the era of intelligent operation and maintenance.
... This method uses a combination of GA and grid search to optimize the parameters of SVM. Fei et al. [74] proposed a fault extraction method combining WPA, FE, and LLTSA, and then proposed a hydraulic pump fault diagnosis method combining SVM. Niu et al. [75] proposed a hybrid fault diagnosis method for hydraulic pumps that combines the RNS algorithm and SVM. ...
To implement Prognostics Health Management (PHM) for hydraulic pumps, it is very important to study the faults of hydraulic pumps to ensure the stability and reliability of the whole life cycle. The research on fault diagnosis has been very active, but there is a lack of systematic analysis and summary of the developed methods. To make up for this gap, this paper systematically summarizes the relevant methods from the two aspects of fault diagnosis and health management. In addition, in order to further facilitate researchers and practitioners, statistical and comparative analysis of the reviewed methods is carried out, and a future development direction is prospected.
... The selection of these two parameters will affect the accuracy but will increase the computation time. The measurement of multiscale entropy on the ECG signal can still be developed, for example, by using other entropy such as in the study of lung sound or other non-biological signal [30,31]. The use of various kinds of entropy is interesting to do in further research. ...
One of sleep-disordered breathing (SDB) form is sleep apnea, commonly known as snoring during sleep, based on various complex mechanisms and predisposing factors. Sleep apnea is also related to various medical problems. It impacts morbidity and mortality so that it becomes a burden on public health services. Its detection needs to be done correctly through electrocardiogram signals to detect sleep apnea more quickly and precisely. This study was conducted to detect sleep apnea based on electrocardiogram signals using multi-scale entropy analysis. Multi-scale entropy (MSE) is used in a finite length of time series for measuring the complexity of the signal. MSE can be applied to both physical and physiological data sets and. In this paper we used MSE to detect Sleep Apnea on electrocardiogram (ECG) signals. MSE was applied two classes of ECG data, normal ECG signals, and apnea ECG signals. In this paper, classification and verification were carried out using the Support Vector Machine (SVM) and N-fold cross-validation (N-fold CV). From the experimental results, the highest accuracy was 85.6% using 5-fold CV and MSE scale of 10. The result shows that the system model that can detect sleep using the multi-scale entropy method
... F. Zhou et al. used particle swarm optimization to decompose the original vibration signal, and used composite multi-scale dispersion entropy to extract fault information [7]. W. Fei et al. proposed a combination of wavelet packet analysis technology, fuzzy entropy and one of the typical manifold learning methods to extract the fault features [8]. W. Zhao et al. used a fully integrated empirical mode decomposition method to decompose the signal, and then combined with the STFT analysis method and time-frequency entropy calculation to extract fault features for subsequent diagnosis [9]. ...
As it is usually operating in bad working conditions and subjected to the severe interference from diverse paths, internal faults of the hydraulic valve are difficult to be detected using conventional hydraulic testing technology (such as relying on pressure sensors or flow sensors). Moreover, the information collected from a single sensor may not provide accurate diagnostic evidence or a complete description on faults of hydraulic valves, even though employing intelligent fault diagnosis methods. Therefore, a two-stage multi-sensor information fusion method is proposed, including the fault feature fusion and the decision-making information fusion. The aim is to realize the diagnosis on internal faults of hydraulic directional valves using the vibration signal analysis method instead of conventional hydraulic testing ones. The method is mainly divided into three steps. First, the noise reduction of the vibration information collected by multiple acceleration sensors is done using ensemble empirical mode decomposition (EEMD) and Teager-Kaiser energy operator (TEO), so that fault features are more obvious. Then, multi-class fault features including the severity and the location of the wear are extracted from the preprocessing signals to form the original feature set. Second, combined with feature ranking and subset selection based on euclidean distance (FRSSED) and maximum relevance minimum redundancy (mRMR) feature selection method, the statistical features extracted from multiple sensor signals are optimized to form the optimal feature subset. This is the first-level information fusion concerned with fault feature information fusion. In the third step, based on Dempster-Shafer (DS) evidence theory and convolutional neural network (CNN), decision-making information is fused (called second-level information fusion) to obtain the final diagnosis results. A hydraulic test bench is built to test different failure valves. Experimental results indicate that this method is effective in extracting the fault features from multi-sensor signals and detecting the fault states including severity and location of internal wear of the hydraulic valve.
