Fault diagnosis of rotating machinery plays a significant role in the industrial production and engineering field. Owing to the drawbacks of traditional fault diagnosis methods, such as heavily ...dependence on human knowledge and professional experience, intelligent fault diagnosis based on deep learning (DL) has aroused the interest of researchers. DL achieves the desirable automatic feature learning and fault classification. Therefore, in this review, DL and DL-based intelligent fault diagnosis techniques are overviewed. DL-based fault diagnosis approaches for rotating machinery are summarized and discussed, primarily including bearing, gear/gearbox and pumps. Finally, with respect to modern intelligent fault diagnosis, the existing challenges and possible future research orientations are prospected and analyzed.
Rotating machinery is of vital importance in the field of engineering, including aviation and navigation. Its failure will lead to severe loss to personnel safety and the stability of the equipment ...system. It is a long way to investigate the relevant fault diagnosis method, especially the intelligent fault diagnosis method on the basis of deep learning. In consideration of the limitations of traditional fault diagnosis approaches based on shallow layer network structure, the methods based on deep neural network (DNN) are worthy of thorough exploration. As a common DNN with special structure, deep convolutional neural network is of great concern in intelligent fault diagnosis due to its advantages in processing nonlinear problems. This review will play an emphasis on convolutional neural network (CNN). The basic structure and principle are introduced. The applications of CNN-based fault diagnosis method in rotating machinery are summarized and analyzed. Furthermore, the diagnosis performance and potential mechanism from different CNN methods are discussed. In the end, this review is highlighted on the challenges and the potential key points in research on novel intelligent fault diagnosis strategies. The corresponding analysis and discussion will provide some references and lay the foundation for the investigation in related fields.
Rotating machinery plays a critical role in many significant fields. However, the unpredictable machinery faults may lead to the severe damage and losses. Hence, it is of great value to explore the ...precise approaches for fault diagnosis. With the development of the intelligent fault diagnosis methods based on deep learning, convolutional neural network (CNN) has aroused the attention of researchers in machinery fault diagnosis. In the light of the reduction of difficulty in feature learning and the improvement of final diagnosis accuracy, data preprocessing is necessary and crucial in CNN-based fault diagnosis methods. This review focuses on CNN-based fault diagnosis approaches in rotating machinery. Firstly, data preprocessing methods are overviewed. Then, we emphatically analyze and discuss several main techniques applied in CNN-based intelligent diagnosis, principally including the fast Fourier transform, wavelet transform, data augmentation, S-transform, and cyclic spectral analysis. Finally, the potential challenges and research objects are prospected on data preprocessing in intelligent fault diagnosis of rotary machinery.
A hydraulic axial piston pump is the essential component of a hydraulic transmission system and plays a key role in modern industry. Considering varying working conditions and the implicity of ...frequent faults, it is difficult to accurately monitor the machinery faults in the actual operating process by using current fault diagnosis methods. Hence, it is urgent and significant to investigate effective and precise fault diagnosis approaches for pumps. Owing to the advantages of intelligent fault diagnosis methods in big data processing, methods based on deep learning have accomplished admirable performance for fault diagnosis of rotating machinery. The prevailing convolutional neural network (CNN) displays desirable automatic learning ability. Therefore, an integrated intelligent fault diagnosis method is proposed based on CNN and continuous wavelet transform (CWT), combining the feature extraction and classification. Firstly, CWT is used to convert the raw vibration signals into time-frequency representations and achieve the extraction of image features. Secondly, a new framework of deep CNN is established via designing the convolutional layers and sub-sampling layers. The learning process and results are visualized by t-distributed stochastic neighbor embedding (t-SNE). The results of the experiment present a higher classification accuracy compared with other models. It is demonstrated that the proposed approach is effective and stable for fault diagnosis of a hydraulic axial piston pump.
As a critical part of a hydraulic transmission system, a hydraulic axial piston pump plays an indispensable role in many significant industrial fields. Owing to the practical undesirable working ...environment and hidden faults, it is challenging to precisely and effectively detect and diagnose the varying fault in the engineering. Deep learning-based technology presents special strengths in processing mechanical big data. It can simultaneously complete the feature extraction and classification, and achieve the automatic information learning. The popular convolutional neural network (CNN) is exploited for its potent ability of image processing. In this paper, a novel combined intelligent method is developed for fault diagnosis towards a hydraulic axial piston pump. First, the conversion of signals to images is conducted via continuous wavelet transform; the effective feature is preliminarily extracted from the transformed time-frequency images. Second, a novel deep CNN model is constructed to achieve the fault classification. To disclose the potential learning in the disparate layers of the CNN model, the visualization of reduced features is performed by employing
-distributed stochastic neighbor embedding. The effectiveness and stability of the proposed model are validated through the experiments. With the proposed method, different fault types can be precisely identified and high classification accuracy is achieved in a hydraulic axial piston pump.
Hydraulic piston pump is the heart of hydraulic transmission system. On account of the limitations of traditional fault diagnosis in the dependence on expert experience knowledge and the extraction ...of fault features, it is of great meaning to explore the intelligent diagnosis methods of hydraulic piston pump. Motivated by deep learning theory, a novel intelligent fault diagnosis method for hydraulic piston pump is proposed via combining wavelet analysis with improved convolutional neural network (CNN). Compared with the classic AlexNet, the proposed method decreases the number of parameters and computational complexity by means of modifying the structure of network. The constructed model fully integrates the ability of wavelet analysis in feature extraction and the ability of CNN in deep learning. The proposed method is employed to extract the fault features from the measured vibration signals of the piston pump and realize the fault classification. The fault data are mainly from five different health states: central spring failure, sliding slipper wear, swash plate wear, loose slipper, and normal state, respectively. The results show that the proposed method can extract the characteristics of the vibration signals of the piston pump in multiple states, and effectively realize intelligent fault recognition. To further demonstrate the recognition property of the proposed model, different CNN models are used for comparisons, involving standard LeNet-5, improved 2D LeNet-5, and standard AlexNet. Compared with the models for contrastive analysis, the proposed method has the highest recognition accuracy, and the proposed model is more robust.
Hydraulic automatic gauge control (AGC) system is the core control system to ensure the thickness accuracy of cold-rolled plate, and the reliability of its load roll system is the key to guarantee ...the rolling process with high precision, high speed, continuity and stability. However, the working mechanism of hydraulic AGC system is complex. And it possesses some features such as high nonlinearity, time variability and strong coupling. The vertical vibration easily happens in its working process. Moreover, the stability of system is seriously affected. Nevertheless, the incentive is difficult to determine. In this paper, the theory and method of nonlinear dynamics were introduced to establish the load vertical vibration (LVV) model of HAGC system under nonlinear action. The model was solved with multi-scale method, and the amplitude-frequency characteristic equation of system was obtained. Moreover, the research object of this article also focused on the nonlinear elastic force, nonlinear damping force and nonlinear excitation force, and deeply explored the effect of nonlinear parameters change on the amplitude-frequency characteristics of LVV system. The mechanism and incentive for LVV of hydraulic AGC system were further revealed. The research results lay a theoretical foundation for the vibration inducement and suppression of hydraulic AGC system.
Background
To establish and validate a radiomics-based model for staging liver fibrosis at contrast-enhanced CT images.
Materials and methods
This retrospective study developed two radiomics-based ...models (R-score: radiomics signature; R-fibrosis: integrate radiomic and serum variables) in a training cohort of 332 patients (median age, 59 years; interquartile range, 51–67 years; 256 men) with biopsy-proven liver fibrosis who underwent contrast-enhanced CT between January 2017 and December 2020. Radiomic features were extracted from non-contrast, arterial and portal phase CT images and selected using the least absolute shrinkage and selection operator (LASSO) logistic regression to differentiate stage F3–F4 from stage F0–F2. Optimal cutoffs to diagnose significant fibrosis (stage F2–F4), advanced fibrosis (stage F3–F4) and cirrhosis (stage F4) were determined by receiver operating characteristic curve analysis. Diagnostic performance was evaluated by area under the curve, Obuchowski index, calibrations and decision curve analysis. An internal validation was conducted in 111 randomly assigned patients (median age, 58 years; interquartile range, 49–66 years; 89 men).
Results
In the validation cohort, R-score and R-fibrosis (Obuchowski index, 0.843 and 0.846, respectively) significantly outperformed aspartate transaminase-to-platelet ratio (APRI) (Obuchowski index, 0.651;
p
< .001) and fibrosis-4 index (FIB-4) (Obuchowski index, 0.676;
p
< .001) for staging liver fibrosis. Using the cutoffs, R-fibrosis and R-score had a sensitivity range of 70–87%, specificity range of 71–97%, and accuracy range of 82–86% in diagnosing significant fibrosis, advanced fibrosis and cirrhosis.
Conclusion
Radiomic analysis of contrast-enhanced CT images can reach great diagnostic performance of liver fibrosis.
Efficient delivery of brain-targeted drugs is highly important for successful therapy in Parkinson's disease (PD). This study was designed to formulate borneol and lactoferrin co-modified ...nanoparticles (Lf-BNPs) encapsulated dopamine as a novel drug delivery system to achieve maximum therapeutic efficacy and reduce side effects for PD. Dopamine Lf-BNPs were prepared using the double emulsion solvent evaporation method and evaluated for physicochemical and pharmaceutical properties. In vitro cytotoxicity studies indicated that treatment with dopamine Lf-BNPs has relatively low cytotoxicity in SH-SY5Y and 16HBE cells. Qualitative and quantitative cellular uptake experiments indicated that Lf modification of NPs increased cellular uptake of SH-SY5Y cells and 16HBE cells, and borneol modification can promote the cellular uptake of 16HBE. In vivo pharmacokinetic studies indicated that AUC
0-12 h
in the rat brain for dopamine Lf-BNPs was significantly higher (p < .05) than that of dopamine nanoparticles. Intranasal administration of dopamine Lf-BNPs effectively alleviated the 6-hydroxydopamine-induced striatum lesion in rats as indicated by the contralateral rotation behavior test and results for striatal monoamine neurotransmitter content detection. Taken together, intranasal administration of dopamine Lf-BNPs may be an effective drug delivery system for Parkinson's disease.
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