Study and Design of Diaphragm Pump Vibration Detection Fault Diagnosis System Based on FFT

Abstract: This study has proposed a fault diagnosis system based on vibration detection. The system mainly includes four modules: signal acquisition module, signal processing module, state identification module, fault diagnosis and alarm module. The system uses CMSS 2200 acceleration sensor to collect vibration signals, processing spectrum with FFT (Fast Fourier Transform) which is used effectively in current industry and finally achieve fault diagnosis and prediction for diaphragm pump. Through collection and analysis of the history signal data, set threshold value in the fault diagnosis system. According to the characteristics of different types, set the corresponding effective threshold value. The simulation results show that, the spectrum after FFT transformation processing, can really and effectively reflect equipment operating condition of the diaphragm. This system is not only simple and stable, but also can predict pump failure effectively, so that it reduces equipment downtime, plan maintenance time and unplanned maintenance time

Stochastic approximations for reduced complexity signal processing algorithms in MIMO wireless communications

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Dynamic heterogeneous graph embedding via heterogeneous Hawkes process

Agency for Science, Technology and Researc

Output-feedback control design for NCSs subject to quantization and dropout

In this paper, the output-feedback control problem is considered for networked systems involving in signal quantization and data packet dropout. The states of the controlled system are unavailable and the output signals are quantized before being communicated. An estimation method is introduced to cope with the effect of random packet loss that is modelled as a Bernoulli process. The quantized measurement signals are dealt with by utilizing the sector bound method, in which the quantization error is treated as sector-bounded uncertainty. The output-feedback controller is designed which guarantees the closed-loop system is exponentially mean-square stable. The simulation example is given to illustrate the proposed method

Dynamic prediction of hospital admission with medical claim data

Abstract Background Congestive heart failure is one of the most common reasons those aged 65 and over are hospitalized in the United States, which has caused a considerable economic burden. The precise prediction of hospitalization caused by congestive heart failure in the near future could prevent possible hospitalization, optimize the medical resources, and better meet the healthcare needs of patients. Methods To fully utilize the monthly-updated claim feed data released by The Centers for Medicare and Medicaid Services (CMS), we present a dynamic random survival forest model adapted for periodically updated data to predict the risk of adverse events. We apply our model to dynamically predict the risk of hospital admission among patients with congestive heart failure identified using the Accountable Care Organization Operational System Claim and Claim Line Feed data from Feb 2014 to Sep 2015. We benchmark the proposed model with two commonly used models in medical application literature: the cox proportional model and logistic regression model with L-1 norm penalty. Results Results show that our model has high Area-Under-the-ROC-Curve across time points and C-statistics. In addition to the high performance, it provides measures of variable importance and individual-level instant risk. Conclusion We present an efficient model adapted for periodically updated data such as the monthly updated claim feed data released by CMS to predict the risk of hospitalization. In addition to processing big-volume periodically updated stream-like data, our model can capture event onset information and time-to-event information, incorporate time-varying features, provide insights of variable importance and have good prediction power. To the best of our knowledge, it is the first work combining sliding window technique with the random survival forest model. The model achieves remarkable performance and could be easily deployed to monitor patients in real time

Reliability Analysis Method of Aircraft Hatch Lock Mechanism with Multi-failure Modes

How to improve the reliability calculation efficiency of the aircraft cabin door lock mechanism closing system, reduce the calculation time and reduce the failure rate of the cabin door is an urgent problem to be solved. An aircraft hatch lock mechanism is chosen as an example, the simulation model of the mechanism is established through LMS, and the maximum hydraulic pressure failure and the time-failure modes are studied during the closing process of the lock mechanism. Considering the correlation between the two failure modes, based on the important sampling method and B-P neural network method, the reliability of the aircraft hatch lock mechanism with multiple failure modes is calculated. Comparing the simulation results of these two methods with the results of the traditional Monte Carlo method, it is concluded that the above two methods for calculating the reliability of the aircraft door lock mechanism are reasonable, and the error range is within 3%. The computational efficiency of the two methods is improved compared with the traditional methods. Among them, the B-P neural network method is more accurate and efficient than the important sampling method, and is more suitable for studying the reliability of the aircraft door lock mechanism

Fracture Propagation and Morphology Due to Non-Aqueous Fracturing: Competing Roles between Fluid Characteristics and In Situ Stress State

Non-aqueous or gaseous stimulants are alternative working fluids to water for hydraulic fracturing in shale reservoirs, which offer advantages including conserving water, avoiding clay swelling and decreasing formation damage. Hence, it is crucial to understand fluid-driven fracture propagation and morphology in shale formations. In this research, we conduct fracturing experiments on shale samples with water, liquid carbon dioxide, and supercritical carbon dioxide to explore the effect of fluid characteristics and in situ stress on fracture propagation and morphology. Moreover, a numerical model that couples rock property heterogeneity, micro-scale damage and fluid flow was built to compare with experimental observations. Our results indicate that the competing roles between fluid viscosity and in situ stress determine fluid-driven fracture propagation and morphology during the fracturing process. From the macroscopic aspect, fluid-driven fractures propagate to the direction of maximum horizontal stress direction. From the microscopic aspect, low viscosity fluid easily penetrates into pore throats and creates branches and secondary fractures, which may deflect the main fracture and eventually form the fracture networks. Our results provide a new understanding of fluid-driven fracture propagation, which is beneficial to fracturing fluid selection and fracturing strategy optimization for shale gas hydraulic fracturing operations