Search

The paper has brought in the Hjorth Complexity parameter and combined it with Intrinsic time decomposition (ITD) algorithm as characteristic parameter index in order to implement accurate identification of multiple faults of rolling bearings. Firstly, concerning about the much uncertainty in manual setting of decomposition layer number in ITD, overdoing of automatic decomposition and the fact that a larger correlation coefficient of signal relates with the greater correlation of signals before and after decomposition and vice versa, the paper has carried out self-adaptive determination of the number of ITD decomposition layers. Secondly, regarding the insensitivity of Hjorth Complexity parameter to noise and the fact that with larger Complexity parameter, signals are simpler and it becomes more available to dig out characteristic information of fault from signals. With Complexity parameter as the index of characteristic parameter, option of optimal Proper rotation component (PRC) is made after ITD. Finally, through the comparison with other methods and the analysis of multiple faults of bearings, it indicates that correlation coefficient can self-adaptively determine the number of ITD decomposition layers and prevent from overdoing and underdoing of decomposition. The Hjorth Complexity parameter can be treated as index parameter to implement optimal PRC option, based on which multiple fault characteristics of bearings can be effectively extracted and the type precisely determined.

To solve the difficulty in extracting the characteristics of rotor–stator rubbing faults, the paper has proposed the combined method of Complexity parameter, the harmonic fusion vector bispectrum (HFVB), and intrinsic time-scale decomposition (ITD). First, to fully embody the characteristic information of fault, the HFVB is used to blend the information of signals collected from sensors installed in different positions. Second, taking in mind that the ITD algorithm can embody the effective separation of nonstationary and nonlinear signals, the ITD algorithm makes the separation of blended signals. Third, regarding the important influence of signal complexity on fault characteristic extraction, the complexity parameter of Hjorth parameters can provide very great embodiment of signal complexity. Complexity parameter of Hjorth parameters is introduced as a characteristic parameter index to make a option of proper rotation component (PRC) which can show the characteristics of rubbing fault better. Fourth, signals are reconstructed based on chosen signal components. Meanwhile, to reduce the influence of noise, reconstructed signals are denoised accordingly. Finally, implement the characteristic extraction and fault identification of rubbing faults according to the square demodulation spectrum (SDF) of denoised signals. The result indicates that the harmonic fusion vector bispectrum method can embody the effective blending of fault information; the complexity parameter in Hjorth parameter can serve as the index parameter for option of sensitive characteristic components of rubbing faults. Based on the proposed method, in the square demodulation spectrum of reconstructed signals, it can effectively and precisely provide the characteristics of rotor–stator rubbing fault and successfully identify a fault type.8714542030

Nowadays, with the explosive growth of sensor-based devices connected to Internet of Thing (IoT), massive amount of data are generated every day with potential tremendous value. We argue that the value of those data can be extracted through monetize data platform in IoT-Edge-Cloud ecosystems for many parts of the business. In such monetize data platform, the data can be computed and transformed into services in IoT-Edge-Cloud ecosystems and provide Data-As-A-Service (DAAS) for applications. The key to implement such a monetize data platform is to evenly distribute DAAS computing tasks to network devices to maximize the benefits of the system. So, in this paper, we study the Task Type-based Computation Offloading algorithm (TTCO) to implement such platform. We use the "IoT-Edge-Cloud" three-layer multi-hop model, which is closer to the complex scene in monetize data platform. We divide tasks into data-intensive tasks and CPU-intensive tasks, and then combine the cost model of computation offloading with task type to make data-intensive tasks prefer local computing and CPU-intensive tasks prefer offload computing, thereby reducing the monetize data platform transmission volume and improving the overall quality of computation offloading. We then use a hierarchical game model combined with fictitious play to solve the Nash Equilibrium (NE) of the system and obtain the mixed strategies of the devices. Finally, we propose a TTL-constrained flood strategy transmission mechanism to make the algorithm apply to practice. The experimental results prove that our algorithm has a large performance gain in various scenarios, which can be severed as a monetize data platform for IoT-Edge-Cloud ecosystems.

To effectively identify the rotor–stator rubbing fault, the paper has brought forward a method combining principal component analysis (PCA), intrinsic time-scale decomposition (ITD), and information entropy (IE). Firstly, in considering that the characteristic information of faults extracted from the information collected by single sensor is not complete or comprehensive, the approach blends the vibration signals collected from 4 different positions at the same moment based on PCA algorithm; secondly, regarding that ITD algorithm can effectively avoid the problems of poor adaptivity and end effect, blended signals are broken down based on ITD algorithm; thirdly, calculate the IE of self-correlation function of each PRC based on the fact that the smaller IE is, the less confusion system has and the easier it is to extract fault characteristics, and treat the self-correlation function of PRC related with the minimum IE as optimal component to represent fault characteristics; fourthly, characteristic extraction of rotor–stator rubbing fault and identification are done on the basis of the frequency spectrum of optimal component. To prove the availability of method, vibration signals are subjected to validation and analysis, which are collected from different rotation speeds, casing thicknesses, rubbing positions, and types. The result indicates that the proposed PCA–ITD–IE can equally and effectively extract the characteristics of rotor–stator rubbing faults of aero-engine involved in various conditions.

To accurately locate rotor–stator rubbing faults in aero-engine, a method combining intrinsic time scale decomposition (ITD), Hjorth parameter and cepstrum analysis has been proposed. First, the method works by decomposing vibration signals from casing into proper rotation components (PRCs) based on ITD; second, calculates the autocorrelation function of each rotation component and the complexity parameters of autocorrelation functions; third, chooses PRCs for signal reconstruction according to complexity from Hjorth parameters and reconstructs the signals based on chosen PRCs; at last, considering that with different positions of rubbing, transfer paths of signals collected by the sensors from the same position are different, cepstrum analysis has been made according to reconstructed signals, and the transfer paths characteristics from cepstrum are taken as feature vectors and inputted into support vector machine for identifying the positions of rotor-stator rubbing faults. The results indicate proposed ITD-Hjorth-Cepstrum method can work well in the identification rate of training and test samples as for the identification rate for an unknown sample.

In view of the difficulty to effectively extract compound faults of rolling bearing from aero-engine and precisely identify their types, the paper has proposed a method integrating signal separation algorithm and information fusion. Firstly, the method decomposes the vibration acceleration signals collected by sensors from different positions at the same moment based on intrinsic time scale decomposition algorithm. Secondly, cross correlation analysis is given to the proper rotation component (PRC) of the same layer, which are obtained after decomposition and correspond to the sensors from different positions and cross-correlation function is introduced to embody information fusion. Thirdly, signals are reconstructed according to cross-correlation function of each PRC. Finally, based on the frequency spectrum of reconstructed signal, extract the characteristics of rolling bearing and identify the type of faults under different sensor combinations and multiple compound fault types. The result shows, the proposed method can effectively extract the characteristics of compound faults of bearing and precisely identify the type of faults under different sensor combinations and multiple compound fault types of rolling bearing.

To effectively identify the rotor–stator rubbing positions in aero-engine, the paper has proposed the combination of intrinsic time-scale decomposition (ITD) and classification algorithm. Regarding that with larger noise component in proper rotation component (PRC) signals after ITD, it will be more difficult to extract the characteristic information of rubbing faults, the PRC correspondings to the largest noise was eliminated. Meanwhile, signals were reconstructed based on residual proper rotation components, and positions of rubbing faults were identified according to the reconstructed signal. As rubbing extent and other factors cannot be completely the same in each rubbing, energy of reconstructed signal has been normalized to reduce the difference. Normalized energy indexes were inputted into classification algorithm as feature vectors to identify the positions of rubbing faults. To identify the superiority of approach, a comparison has been made between the proposed approach and the method of directly extracting normalized energy indexes of acceleration signals. The result of comparison shows that the two methods both work well in the identification rate of training and test samples; as for the identification rate for an unknown sample, the proposed method is superior to the other, with identification rate increasing by 17% and 9.4%.

To effectively extract the information of compound faults of inter-shaft bearing of an aero-engine based on casing vibration signals, the paper has introduced the concept of weighted Katz fractal dimension and proposed the method combining information fusion, wavelet transform (WT), singular value decomposition (SVD), and Katz fractal dimension, the cross-correlation function (CCF-WT-SVD-Katz algorithm). The method includes homologous information fusion achieved by the CCF of horizontal and vertical vibration signals of the rotor from the same section; signal separation and denoising of blended signals through WT and SVD; reinforcement of fault characteristics of signals according to weighted Katz fractal dimension; and extraction of characteristic frequencies of compound faults of inter-shaft bearing by frequency spectrum of weighted and reconstructed signals. The result indicates that the proposed CCF-WT-SVD-Katz algorithm is capable of effectively extracting compound fault characteristics of inter-shaft bearing and precisely identifying a fault type based on whole casing vibration signals and will be of very good application value in engineering.

Rolling bearing and Squeeze Film Damper (SFD) are used in rotor support structures, and most researches on the nonlinear rotor-bearing system are focused on the simple rotor-bearing systems. This work emphasizes the comparative analysis of the influence of SFD on the nonlinear dynamic behavior of the dual-rotor system supported by rolling bearings. Firstly, a reduced dynamic model is established by combining the Finite Element (FE) method and the free-interface method of component mode synthesis. The proposed model is verified by comparing the natural characteristics obtained from an FE model with those from the experiment. Then, the steady-state vibration responses of the system with or without SFD are solved by the numerical integration method. The influences of the ball bearing clearance, unbalance, centralizing spring stiffness and oil film clearance of SFD on the nonlinear steady-state vibration responses of the dual-rotor system are analyzed. Results show that SFD can effectively suppress the amplitude jump of the dual rotor system sustaining two rotors unbalance excitations. As the ball bearing clearance or unbalance increases, the amplitude jump phenomenon becomes more obvious, the resonance hysteresis phenomenon strengthens or weakens, the resonant peaks shift to the left or the right, respectively. SFD with unreasonable parameters will aggravate the system vibration, the smaller the oil film clearance, the better the damping performance of the SFD, the larger the centralizing spring stiffness is, the larger resonance amplitudes are.

The autocorrelation function is combined with wavelet transform and cyclostationary theory (WT-AF-CT) in place of threshold denoising, and meanwhile the mean power ratio (MPR) is calculated by the proposed method. Furthermore, extracted characteristic as well as calculated MPR is used to identify compound faults of rolling bearings in aero-engine based on casing vibration acceleration signal-including the ones of common rolling bearing (inner race rotates and outer race is constant) and intershaft bearing (co-rotates with outer and inner race). A comparative analysis was carried out between conventional researches (cyclostationary theory (CT) or wavelet transform combined with threshold value denoising (WT-TD)) and proposed WT-AF-CT method. Additionally, the effect of sensors installation direction for feature separation and extraction of compound faults is considered. The results indicate that the proposed WT-AF-CT method can separate and extract characteristics of compound faults exactly and identify fault types of bearings precisely, no matter sensors are installed horizontally or vertically, while CT or WT cannot.

In considering that variational modal decomposition (VMD) can fulfill the self-adaptive separation of complex signals, the paper has applied VMD to the identification of rotor–stator rubbing fault and position in aero-engine. The paper has proposed a method combing autocorrelation function (AF), variational modal decomposition (VMD) and k-nearest neighbor (KNN) classification algorithm. To reduce noise and reinforce the characteristics of faults, the method firstly calculates the autocorrelation function of vibration acceleration signals from casing. Secondly, the autocorrelation function is decomposed by VMD algorithm to obtain the intrinsic modal functions of different frequency bands. Thirdly, calculates the normalized energy of intrinsic modal components and carry out cluster analysis on the normalized energy set by k-means clustering. Finally, the normalized energy obtained are inputted into KNN classifier as characteristic vectors to identify rotor–stator rubbing fault and rubbing positions. The result indicates that the proposed AF-VMD-KNN method has the recognition rate of rubbing faults as high as over 94% in terms of different dates, rotation speeds and rubbing severity with training and testing samples randomly divided.

In view of the cyclostationary characteristics of vibration signals from aero-engine, the combination of cyclic autocorrelation function and intrinsic timescale decomposition (ITD) has been proposed. According to the proposed method, vibration signals are decomposed by ITD algorithm to obtain the autocorrelation function of proper rotation components (PRC), based on which characteristic extraction and identification of compound faults of rolling bearings is made possible. To validate the effectiveness of method, an analysis has been given to the vibration signals of rolling bearings collected by sensors of different positions in different compound fault modes. As shown by results, the method combining ITD and cyclostationary theory can precisely and effectively extract the characteristic frequency relative to the type of faults and identify the compound faults.

As it is difficult to extract the combined faults from rolling bearings of aero-engine in strong noise, a fault diagnosis method based on wavelet transform (WT), principal component analysis (PCA) and self-correlation noise reduction is proposed to solve this problem. The proposed method is then compared with the target matrix composed of maximum component of kurtosis, the largest and the second largest kurtosis value. The result of comparative analysis reveals that the 2D target matrix proposed in this paper works better to extract the characteristic frequency of combined faults of rolling bearings in terms of accuracy and effectiveness.

In order to solve the difficulty to correctly identify a compound failure in intermediate bearing of aircraft engine, the paper has brought forward the method of strengthening fault feature information of intermediate bearing with weighted multiscale Lempel–Ziv complexity parameter. The method works by combining multiscale Lempel–Ziv complexity parameter (M-LZC) with wavelet transform (WT) and singular value decomposition (SVD) and implements the feature extraction and identification of compound failure in intermediate bearing. Firstly, failure information from different frequency bands of casing vibration signals is separated and signals are denoised based on WT and SVD difference spectrum. Secondly, concerning that the larger Lempel–Ziv complexity (LZC) of signal is, the more abundant failure information in signal will be; meanwhile, so as to avoid “excessive coarse graining” caused by traditional binarization method, M-LZC values of component signals after denoising are used to describe the failure feature information included in component signals. Thirdly, to further strengthen the failure feature information of intermediate bearing, larger weight value will be endowed to the component signals containing more abundant failure feature information. Failure information is boosted by M-LZC weight coefficient of component signal. Finally, component signals of an enhanced feature are reconstructed and the spectrum of reconstructed signal is used to make precise identification of compound failure type of intermediate bearing in aircraft engine.

Intrinsic time-scale decomposition and graph signal processing are combined to effectively identify a rotor–stator rubbing fault. The vibration signal is decomposed into mutually independent rotational components, and then, the Laplacian energy index is obtained by the graph signal of the autocorrelation function of rotational components, and the signal is reconstructed by an autocorrelation function of each proper rotation (PR) component relative to smaller Laplacian energy index (less complexity). Finally, characteristics are extracted from rotor–stator rubbing faults in an aeroengine according to square demodulation spectrum of a reconstructed signal. To validate the effectiveness of the algorithm, a comparative analysis is made among traditional intrinsic time-scale decomposition algorithm, combination of intrinsic time-scale decomposition and autocorrelation function, and the proposed intrinsic time-scale decomposition–graph signal processing algorithm. Comparative result shows that the proposed intrinsic time-scale decomposition–graph signal processing algorithm is more precise and effective than the traditional intrinsic time-scale decomposition and intrinsic time-scale decomposition and autocorrelation function algorithms in extracting characteristic frequency and frequency multiplication of rotor–stator rubbing faults and can greatly reduce the number of noise components irrelevant to faults.

A method of combining autocorrelation function with cyclostationary theory and Hilbert envelope analysis is proposed and applied to extract characteristic frequency of rolling bearing. Meanwhile, mean power ratio is calculated and used to identify the fault types of aero-engine rolling bearing based on single-channel casing vibration signal. To verify the effectiveness of proposed method, a comparing analysis is carried out between traditional studies and proposed new method. Furthermore, the influences on the extraction of characteristics and calculation of mean power ratio are taken into account, including the ones of sensor installation position, fault types, type of experiment rigs, failure mode and rotational speed of rolling bearing. The result shows that the proposed method can diagnose running conditions and identify fault types of rolling bearing accurately and effectively just by single-channel casing vibration signal.

In an engineering practice, the faults of rolling bearing are mostly represented as being compound and hard to diagnose. For that, intrinsic time-scale decomposition (ITD) algorithm was combined with Auto-correlation Function (AF) to extract the characteristics of compound faults of rolling bearing in aviation engine. Firstly, ITD algorithm was used to decompose acceleration signal into multiple rotational and residual trend component; secondly, rotational components were reconstructed to figure out their AF; finally, characteristic frequency of rolling bearing under compound faults mode was extracted by Hilbert spectrum envelope. To validate the effectiveness of the method, a comparative study on sensor installation positions and vibration acceleration signal of different compound faults has been carried out. The result of study shows that the proposed ITD-AF method is capable to extract compound fault characteristics of rolling bearing in an effective and precise manner and the installation positions of sensors, rotation speed and fault type shows insensitivity to extraction.

Considering the difficulty in the diagnosis of compound faults in rolling bearings, the paper combines Intrinsic Time-scale Decomposition (ITD) and Singular Value Decomposition (SVD) for extracting the characteristics of compound faults from rolling bearings. Rotational components obtained from ITD decomposition are denoised according to Singular Value Decomposition algorithm; signal is reconstructed by denoised rotational components; at last, characteristics of compound faults of rolling bearings are extracted by Hilbert spectrum envelope of reconstructed signal. In validation, the paper has made a comparative study on the proposed ITD-SVD method and conventional one based on ITD algorithm and PCA method, and the result shows that ITD-SVD method works better on noise control and thereby provides more precise extraction of characteristic frequency of compound faults from rolling bearings of aero-engine.

To effectively extract the characteristics of weak imbalance fault of aircraft hydraulic pump, autocorrelation function (AF) is combined with wavelet transform (AFWT) instead of threshold denoising. Meanwhile, power ratio (PR) was obtained by extracted characteristic frequency and applied to the identification of weak imbalance fault. A contrastive analysis was conducted among different signals, including acceleration and displacement signal. The results indicate that displacement signal, rather than acceleration signal, can effectively identify a weak imbalance fault.

Bearing is the most vulnerable key part in rotating machine and bears important influence on the safety of equipment. Weakness and complexity are the two features of fault characteristic information carried by signals in the early stage of fault. For that, a fault is difficult to be recognized correctly. To identify a compound failure of bearing, the paper has brought forward a new self-adaptive option method for component signals that are sensitive to failure feature information of bearing. The sensitivity of kurtosis to bearing failure is exploited and the influence of signal complexity on the extraction of failure feature information is taken seriously, the paper has proposed the self-adaptive option method for component signals that are sensitive to failure feature by combined kurtosis with Complexity parameter included in Hjorth parameters. Furthermore, as the mid-value represents the general level of signal and is not affected by larger or smaller data, with the mid-values of kurtosis and Complexity parameter as the boundary, the paper has chosen the component signals which can more comprehensively show the failure features of bearing. Additionally, by principal component analysis (PCA), component signals selected are blended and reconstructed. Finally, by the Hilbert envelope spectrum of signals reconstructed, failure types of bearing are identified. To verify the effectiveness of presented method, the presented method is compared with conventional method on the basis of the exactly consistent data. The result indicates that the proposed method is superior to the traditional one in extracting fault information and identifying the multiple failure types of bearing.

Inter-shaft bearing is an important component of dual-rotor aeroengine and it is difficult to extract fault characteristics and identify a fault type. Therefore, the paper has proposed a method (CCF–Complexity–VMD–SVD) combining information fusion, Complexity parameter of Hjorth parameters, variational mode decomposition (VMD), and singular value decomposition (SVD). Firstly, to fully embody fault characteristic information, the method has established the cross-correlation function (CCF) of homologous acceleration information from the casing of one of the aeroengines (acceleration signals collected from the same section at the same moment in orthogonal directions) to blend homologous information, reinforce fault characteristic components, and lessen the influence of noise. Secondly, considering the great influence of signal complexity on the extraction of fault characteristics and the Complexity parameter of Hjorth parameters can express the complexity of signals better, the Complexity parameter of Hjorth parameters is placed into the fault characteristic extraction of inter-shaft bearing. The Complexity parameter is applied to optimize the number of layers and central frequency of VMD. Thirdly, signals are denoised through SVD to further lessen the influence of noise on characteristic extraction. Finally, frequency spectrum (FS) of signal denoised is used to make characteristic extraction and fault identification of compound faults of inter-shaft bearing. The result of comparative analysis with other methods has further illustrated the proposed method CCF–Complexity–VMD–SVD that can actualize the effective characteristic extraction of compound faults and precise identification in the early stage of fault based on vibration signals from casing of aeroengine. The performance of engineering appliance of the proposed method has been further verified through fault analysis of disassembly for the aeroengine.

The combined effect of polystyrene (PS) particles and triphenyltin chloride (TPTCl) to the green algae Chlorella pyrenoidosa was studied. The 96 h IC50 of TPTCl to the green algae C. pyrenoidosa was 30.64 μg/L. The toxicity of PS particles to C. pyrenoidosa was size-dependent, with the 96 h IC50 at 9.10 mg/L for 0.55 μm PS but no toxicity observed for 5.0 μm PS. The exposure to 0.55 μm PS led to damage on structure of algal cells, which could in turn cause inhibition on photosynthesis and population growth of the green algae. TPTCl concentrations in test medium were lowered by 15–19% at presence of 0.55 μm PS particles, indicating a reduced bioavailability of TPTCl. In spite of this reduced bioavailability, the presence of PS increased the toxicity of TPTCl, which might be attributed to facilitated uptake of TPTCl by the green algae after the damage of cell structure. The overall results of the present study provided important information on the effect of PS on the bioavailability and toxicity of TPTCl to phytoplankton species.

A method of combining auto-correlation and Hilbert envelope analysis is proposed and used to identify weakness imbalance fault of aero-hydraulic pump, the central part of hydraulic system of aircraft. Firstly, the integral and polynomial least square fitting is applied to convert acceleration signal to velocity one; secondly, the Hilbert envelope spectrum of auto-correlation function of velocity signal is obtained and used to identify the weakness imbalance fault of aero-hydraulic pump; finally, the energy ratio of velocity signal is calculated according to Hilbert envelope spectrum for identifying imbalance fault of aero-hydraulic pump by means of easier and more visual method. Meanwhile, the comparing analysis is carried out between traditional research method and proposed new one. The result shows that the weakness imbalance fault of aero-hydraulic pump can be identified and diagnosed effectively and correctly according to the velocity signal whether Hilbert envelope spectrum or calculation energy ratio while direct acceleration signal cannot.

© 1995-2012 IEEE. Endoscopic integrated photoacoustic and ultrasound imaging has the potential for early detection of cancer in the gastrointestinal tract. Currently, a slow imaging speed is one of the limitations for clinical translation. Here, we developed a high-speed integrated endoscopic PA and US imaging system, which is able to perform PA and US imaging simultaneously up to 50 frames per second. Using this system, the architectural morphology and vasculature of the rectum wall were visualized from a Sprague Dawley rat in vivo.