800 publications from this institution
Monitoring time-between-events (TBE) data, where the goal is to track the time between consecutive events, has important applications across various fields. Many existing schemes for monitoring multivariate TBE data suffer from inherent delays, as they require waiting until all components of the observation vector are available before making inferences about the process state. In practice, however, these components are rarely recorded simultaneously. To address this issue, Zwetsloot et al. proposed a Shewhart chart for bivariate TBE data that updates the process status as individual observations arrive. However, like most Shewhart-type charts, their method evaluates the process based solely on the most recent observation and does not incorporate historical information. As a result, it is ineffective in detecting small to moderate changes. To overcome this limitation, we develop an adaptive CUSUM chart that updates with each incoming observation while also accumulating information over time. Simulation studies and real-data applications demonstrate that our method substantially outperforms the Shewhart chart of Zwetsloot et al., offering a robust and effective tool for real-time monitoring of bivariate TBE data.
Shear deformation often occurs during multistage fracturing of shale gas worldwide, increasing the operation cost of gas well completion and lowering the well's productivity; strike-slip fault slippage is thought to be a crucial underlying factor. This paper presents a detailed analysis of the influence of strike-slip fault slippage on casing inner diameter and establishes a technique for predicting casing inner diameter after fault slippage. Numerical simulation models were developed to simulate the process of fault slippage and calculate changes in the inner diameters of the casing, considering different engineering and geological conditions. The influential factors, including the fault strike angle, slip distance, radius-thickness ratio, and mechanical properties of the formation, were analyzed in engineering practice; the simulation results were shown to be consistent. Four methods, including adjusting the wellbore trajectory of shale gas horizontal wells, avoiding perforation at the formation interface, decreasing the fracturing parameters including the fracturing pressure, discharge capacity, volume of injecting fracturing fluid, and prediction of casing inner diameter after casing shear deformation, were proposed and verified through engineering application and theoretical calculation, respectively.
A novel damage detection approach using only two sensors to detect the damage in beam bridges subjected to a moving vehicle is proposed in this article. In this approach, a moving mass is considered representing a vehicle moving across the bridge, and structural vibration responses at two locations are measured from a pair of sensors. A moving window is defined with a certain length determined by the sampling frequency and the fundamental frequency of the measured responses. The windowed pair time series extracted from these two measured responses are used to calculate the cross-correlation, which is used to define the local damage index. A simply supported beam bridge subjected to a moving mass is simulated to demonstrate the effectiveness and accuracy of the proposed approach. Numerical results indicate that the proposed approach can accurately identify the single and multiple damages using both displacement and acceleration responses, even when the responses are smeared with a significant noise. This indicates a good robustness to the noise effect. Experimental verifications on a laboratory beam bridge model demonstrate that the proposed approach can successfully identify the damage location using different selections of sensor pairs. Both the numerical and experimental results demonstrate that the new damage index is a good candidate for structural damage detection with very limited measurement information.
Abstract An improved efficient uncertainty quantification (UQ) analysis framework is proposed by the combination of sparse polynomial chaos expansion (PCE) and universal Kriging (UK) metamodel to obtain the surrogate model (UK-PCE). Moreover, a challenging analytical test function and an engineering test are considered to investigate the response performance of UK-PCE method. The results show that the UK-PCE method reduces the computational cost by more than 70% in comparison to the typical PCE method. Then this method was applied to the UQ of the aerodynamic and heat transfer performance of GE-E3 rotor blade squealer tip. Additionally, a series of uncertainty quantities visualization methods based on the data mining method, parallel computing method, and Delaunay triangulation method is proposed to reveal more enlightening uncertainty phenomena in the actual operation. The results of UQ show that under the influence of uncertain inputs, the leakage flow rate and downstream entropy increase will be significantly increased. The statistical average of tip heat flux has increased by 8.56% relative to the design value, and the probability of it deviating from the design value by 10% is as high as 43.27%. In addition, the three-dimensional tip heat flux deviation distributions calculated by the proposed uncertainty quantities visualization method reveal a coupling of the hot corrosion and thermal fatigue of the squealer tip. It is also indicated that under the influence of the uncertain inputs, there is a marked increase in blade tip flux, and the blade tip flux deviation has been maintained at a high value, about 13.0%. The results of sensitivity analysis show that the largest contributor to the uncertainty of the blade tip aerodynamic performance is the tip clearance deviation and its variance index to the uncertainty of leakage flow rate and downstream entropy increase is as high as 88.21% and 62.63%. Therefore, the geometric accuracy of the tip clearance should be strictly ensured in the turbine blade assembly and marching process. The influence of the inlet total temperature deviation on the uncertainty of the heat transfer performance of the squealer tip must also be taken into account. So a satisfactory control system should be designed in the actual operation of the gas turbine to make sure that the fluctuation of inlet total temperature can be attenuated rapidly.
Micturition serves an essential physiological function that allows the body to eliminate metabolic wastes and maintain water-electrolyte balance. The urine spot assay (VSA), as a simple and economical assay, has been widely used in the study of micturition behavior in rodents. However, the traditional VSA method relies on manual judgment, introduces subjective errors, faces difficulty in obtaining appearance time of each urine spot, and struggles with quantitative analysis of overlapping spots. To address these challenges, we developed a deep learning-based approach for the automatic identification and segmentation of urine spots. Our system employs a target detection network to efficiently detect each urine spot and utilizes an instance segmentation network to achieve precise segmentation of overlapping urine spots. Compared with the traditional VSA method, our system achieves automated detection of urine spot area of micturition in rodents, greatly reducing subjective errors. It accurately determines the urination time of each spot and effectively quantifies the overlapping spots. This study enables high-throughput and precise urine spot detection, providing important technical support for the analysis of urination behavior and the study of the neural mechanism underlying urination.
The rib-to-diaphragm welded joints in the deck of an orthotropic steel bridge is most prone to fatigue cracking. A FE model was established and fracture mechanics was used to study the reinforcement effect of angle steel reinforcement methods on the penetrating crack at fatigue vulnerable details. Based on the finite element model corresponding to a full-foot segment fatigue test model, a penetrating fatigue model was established for the cracks in rib-to-diaphragm welded joints, and the reinforcement effect was evaluated on two reinforcement techniques:bolted angle steel and the bolted steel plate beside a longitudinal rib. The results indicate that:the fatigue cracks at the welded joint in the rib-to-diaphragm of a steel bridge deck expand into a certain length and will develop into penetrating cracks, the deformation of the crack surface under stress conditions is complicated, the fatigue crack propagation characteristics of the inner and outer side of longitudinal ribs are not the same, and as the crack propagation progresses, the cracking mode of the crack tip will be dominated by composite cracking. The existing research results show that:the bolted angle steel reinforcement method can well inhibit the longitudinal rib and diaphragm connection details of the vulnerable part with the fatigue crack type I cracking. Therefore, the short crack propagation can be well suppressed. However, the reinforcement effect on penetrating fatigue cracks extended in a composite type in this detail is not as good as the short crack that have not penetrated the web; the reinforcement method of the half U rib steel plates bolted to the outside of longitudinal ribs can effectively reduce the equivalent stress intensity factor of penetrating fatigue cracks, and stay below the crack propagation threshold after reinforcement. It shows that the reinforcement method has a good reinforcement effect on penetrating fatigue cracks.
The common reactive muffler has poor acoustic properties in high frequency, so aluminum foam is applied to the reactive muffler and the impendence compound muffler is designed. The acoustic performance of the muffler is analyzed; the interior sound field of the muffler is modeled and meshed in the ANSYS, then they are imported into SYSNOISE, and imposed reasonable boundary conditions to carry out the analysis of acoustic performance of the muffler. The results show that, compared to reactive muffler, the muffler with aluminum foam has a higher amount of noise reduction and a wider frequency band in middle and high frequency.
Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Jun Li, Ming Su; A Study on Turbine Unsteady Flow via PIV and Hotwire. AIP Conf. Proc. 5 June 2007; 914 (1): 580–586. https://doi.org/10.1063/1.2747484 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
We built a new system for object recognition by combining the joint transform structure with the wavelet transform. The system can perform preprocessing and correlation in a single step. It has many good features including high antinoise capability and sharp correlation peaks without sidelobes. To test the effectiveness of this system, experimental results on a plane model are also given.
The application of active linear absorber based on positive position feedback control strategy to suppress the high-amplitude response of a flexible beam subjected to a primary external excitation is developed and investigated. A mathematical nonlinear model that describes the single-mode dynamic behavior of the beam is considered. The perturbation method of multiple scales is employed to find the general nonlinear response of the system and four first-order differential equations governing the amplitudes and phases of the responses are derived. Then a stability analysis is conducted for the open- and closed-loop responses of the system and the performance of the control strategy is analyzed. A parametric investigation is carried out to investigate the effects of changing the damping ratio of the absorber and the value of the feedback gain as well as the effect of detuning the frequency of the absorber on the responses of the system. It is demonstrated that the positive position feedback control technique is effective in reducing the high-amplitude vibration of the model and the control scheme possesses a wide suppression bandwidth if the absorber's frequency is properly tuned. Finally, the numerical simulations are performed to validate the perturbation solutions.
Electromechanical impedance (EMI) based structural health monitoring is performed by measuring the variation in the impedance due to the structural local damage. The impedance signals are acquired from the piezoelectric patches that are bonded on the structural surface. The impedance variation, which is directly related to the mechanical properties of the structure, indicates the presence of local structural damage. Two traditional EMI-based damage detection methods are based on calculating the difference between the measured impedance signals in the frequency domain from the baseline and the current structures. In this paper, a new structural damage detection approach by analyzing the time domain impedance responses is proposed. The measured time domain responses from the piezoelectric transducers will be used for analysis. With the use of the Time Frequency Autoregressive Moving Average (TFARMA) model, a damage index based on Singular Value Decomposition (SVD) is defined to identify the existence of the structural local damage. Experimental studies on a space steel truss bridge model in the laboratory are conducted to verify the proposed approach. Four piezoelectric transducers are attached at different locations and excited by a sweep-frequency signal. The impedance responses at different locations are analyzed with TFARMA model to investigate the effectiveness and performance of the proposed approach. The results demonstrate that the proposed approach is very sensitive and robust in detecting the bolt damage in the gusset plates of steel truss bridges.
Numerous long-span suspension bridges have been built worldwide over the past few decades. To ensure the safety of such bridges and their users during the bridge service life, several bridges have been equipped with Structural Health Monitoring Systems (SHMSs), which measure dynamic bridge responses and various loading types on-site. Integrating SHMS and damage detection technology for condition assessment of these bridges has become a new development trend. Recent studies have proven that stress influence line (SIL)-based damage indices achieve excellent damage detection performance for a long suspension bridge. However, an accurate and prompt manner of identifying the SIL of a long suspension bridge is important to facilitate the development of the SIL for an effective damage index. Identifying the SIL from field measurement data under in-service conditions has several advantages over the traditional static loading test. This study proposes and develops a new SIL identification method by integrating the least squares solution and Weighted Moving Average (WMA) based on the measured train information and the corresponding train-induced stress time history. The efficacy of the proposed method is validated through its application to Tsing Ma Bridge (TMB). The good agreement between the identified and baseline SILs for a typical diagonal truss member verifies the effectiveness of the proposed method. Furthermore, robustness testing is performed by identifying SIL on the basis of information on different trains and train-induced stress responses and by identifying the SIL of different types of bridge components. Results indicate the feasibility of the application of the proposed approach to SIL identification for long-span bridges.
This paper presents an approach for structural damage quantification using a long short-term memory (LSTM) auto-encoder and impulse response functions (IRF). Among time domain responses-based methods for structural damage identification, using IRF is advantageous over the original time domain responses, since IRF consists of information of system properties and is loading effect independent. In this study, IRFs are extracted from the acceleration responses measured from different locations of structures under impact force excitations. The obtained IRFs are concatenated. Moving averaging with a suitable window size is performed to reduce random variations in the concatenated responses. Further, principal component analysis is performed for dimensionality reduction. These selected principal components are then fed to the LSTM auto-encoder for structural damage identification. A noise layer is added as an input layer to the LSTM auto-encoder to regularise the model. The proposed model consists of two phases: (1) reconstruction of the selected "principal components" to extract the features; and (2) damage identification of structural elements. Numerical studies are conducted to verify the accuracy of the proposed approach. The results demonstrate that the proposed approach can accurately identify and quantify structural damage for both single- and multiple-element damage cases with noisy measurements, as well as uncertainties in the stiffness parameters. Furthermore, the performance of the proposed approach is evaluated using the limited measurements from a few sensors.
