Centrifugal pumps have a wide range of applications in industrial and municipal water affairs. During the use of centrifugal pumps, failures such as bearing wear, blade damage, impeller imbalance, shaft misalignment, cavitation, water hammer, etc., often occur. It is of great importance to use smart sensors and digital Internet of Things (IoT) systems to monitor the real-time operating status of pumps and predict potential failures for achieving predictive maintenance of pumps and improving the intelligence level of machine health management. Firstly, the common fault forms of centrifugal pumps and the characteristics of vibration signals when a fault occurs are introduced. Secondly, the centrifugal pump monitoring IoT system is designed. The system is mainly composed of wireless sensors, wired sensors, data collectors, and cloud servers. Then, the microelectromechanical system (MEMS) chip is used to design a wireless vibration temperature integrated sensor, a wired vibration temperature integrated sensor, and a data collector to monitor the running state of the pump. The designed wireless sensor communicates with the server through Narrow Band Internet of Things (NB-IoT). The output of the wired sensor is connected to the data collector, and the designed collector can communicate with the server through 4G communication. Through cloud-side collaboration, real-time monitoring of the running status of centrifugal pumps and intelligent diagnosis of centrifugal pump faults are realized. Finally, on-site testing and application verification of the system was conducted. The test results show that the designed sensors and sensor application system can make good use of the centrifugal pump failure mechanism to automatically diagnose equipment failures. Moreover, the diagnostic accuracy rate is above 85% by using the method of wired sensor and collector. As a low-cost and easy-to-implement solution, wireless sensors can also monitor gradual failures well. The research on the sensors and pump monitoring system provides feasible methods and an effective means for the application of centrifugal pump health management and predictive maintenance.
Water jet peening (WJP) is a mechanical surface strengthening process, which can improve the residual stress (RS) of the peened surface and then improve the fatigue life of components. In this paper, erosion experiments are conducted to investigate the influence of peening parameters on erosion. On this basis, RSs induced by WJP are studied in relation to the peening parameters. In addition, the coupled Eulerian–Lagrangian (CEL) technique is used to model and simulate the dynamic impact process of WJP on Al6061-T6. The influence of peening parameters such as jet pressure p, jet traverse velocity vf, and the number of water jet pass n on the modification of residual stress field (RSF) is examined by simulation and experiment. The influence of incidence angle α and water jet diameter d on RSF is also investigated by simulation. Results show that compressive RS σcrs is a result of the action of water-hammer pressure alone. Furthermore, σcrs increases with an increase in p, n and α. The optimal peening parameters for Al6061-T6 are found to be p = 60 MPa, vf = 2000 mm/min, n = 4, α = 90 deg and d = 2.0 mm. Finally, the depth of compressive RS layer D0 increases greatly with an increase in water jet diameter d and can reach 984 μm.
Abrasive waterjet (AWJ) peening can be used for metal surface strengthening by introducing near-surface plastic strain and compressive residual stress. The present studies seldom focus on residual stress by AWJ peening of targets with different geometrical features. In fact, those targets usually exist on some machine parts including gear roots, shaft shoulders, and stress concentration areas. According to Hertz theory of contact and Miao's theoretical model for predicting residual stress of flat surface, this paper developed a theoretical model for investigating residual stress of targets with different geometrical features including concave arc surface, concave sphere surface, convex arc surface, and sphere surface. AWJ peening of targets with different geometrical features and different radii of Gaussian curved surface was simulated by abaqus. Theoretical results were consistent with numerical simulation results and published experimental results (H. Y. Miao, S. Larose, et al., 2010, “An analytical approach to relate shot peening parameters to Almen intensity,” Surf. Coat. Technol., 205, pp. 2055–2066; Cao et al., 1995, “Correlation of Almen arc height with residual stresses in shot peening process”, Mater. Sci. Technol. 11, pp. 967–973.), which will be helpful for predicting residual stress of gear roots, shaft shoulders, and stress concentration areas after AWJ peening. The research results showed that under the same peening parameters, σmax, σtop, dmax, and dbottom in concave surface (including concave arc surface and concave sphere surface) were the maximum; σmax, σtop, dmax, and dbottom in convex surface (including convex arc surface and sphere surface) were the minimum; for concave surface, σtop, σmax, dbottom, and dmax decreased, respectively, with target radius; for convex surface, σtop, σmax, dbottom, and dmax increased, respectively, with target radius.
In the process of quantum information processing, decoherence effect caused by the coupling between system and its environment will no doubt lead to the error of quantum information stored in the system. To decrease the influence of decoherence effect, decoherence-free subspace (DFS) is introduced. In this work, several schemes to convert the polarized-entangled Knill-Laflamme-Milburn (KLM) states into Greenberger-Horne-Zeilinger (GHZ) states in DFS by using the weak cross-Kerr nonlinearity are proposed. Numerical analysis shows that the proposed schemes have high fidelity and success probability. Due to the unique applications of multi-qubit quantum entangled states in quantum information processing and the robust feature of DFS, the proposed schemes may play positive roles in the future development of quantum communication and quantum information processing tasks.
In order to meet the requirements of online condition monitoring and automatic fault diagnosis of plants, the leakage errors of spectra in discrete spectrum analysis are corrected automatically. Aiming at the leakage errors in spectra of signals windowed by rectangular window, a automatic correcting method of leakage errors is presented based on the extremum searching method(ESM) and the energy centrobaric correction method(ECCM). The steps of the method are: firstly, compute the Discrete Fourier Transform (DFT) of the signal by Fast Fourier Transform (FFT) method and get the amplitude spectrum of it; secondly, find the all points needed to be corrected by the extremum searching method; thirdly, correct the frequencies, amplitudes and phases of the corresponding points by energy centrobaric correction method; finally, get the corrected signal by Inverse Fast Fourier Transform (IFFT) method. The simulation results certify that the leakage errors of spectra after being corrected can be controlled within 3% when the minimal difference in neighbouring frequency components is greater than 10 times of the frequency resolution. This correcting method can meet the requirements of engineering practice.
The misalignment of gear coupling is the most commonly observed fault in industry fields. It can cause great harm. In order to find out its physical mechanism and dynamic charactersic, a new Finite Element Modal of misaligned rotor-bearing system with gear coupling is presented in this paper. By considering the forces and moments which applied on rotor in two typical misaligned conditions, i.e. parallel and angular misalignment, the dynamic equation is established. Through simulation and dynamic analysis of two-span rotor system with misalignment, quantitive relationship between the misalignment and steady-state response is obtained. Meanwhile, an investigation on phase of twice rotational speed freqency (2X) reveals that in parallel misaligned condition, the phases of two sides of coupling are same, while in angular misalignment condition the phases are opposite (almost 180°difference). This feature can be used as an important reference when confirming a misalignment fault.
In modern industry, plant equipment must be maintained effectively to guarantee safety operation. An intelligent instrument for plant equipment state inspection and safety evaluation was developed based on embedded technology. Firstly, the overall structure of the instrument system was introduced. Then the mainboard circuits were designed based on ARM core microprocessor. The control logics of the dynamic machine signals acquisition subsystem were designed by using of a complex programmable logic device. After that, the embedded operating system was transplanted and device drivers were programmed. Finally, the functions of the applications were stated. The designed instrument can meet the needs of the vibration acquisition of the rotating machines satisfactorily and provide effective means for equipment maintenance.
To solve the problem of “under-maintenance” and “over-maintenance” in the daily maintenance of equipment, the predictive maintenance method based on the running state of equipment has shown great advantages, and fault prediction is an important part of predictive maintenance. First, the spectrum information of the equipment is extracted by the Hilbert–full-vector spectrum as the input of fault prediction. Compared with the traditional spectrum, this spectrum information fuses the signals of two sensors in the same section of the device, which can reflect the actual operational state of the device more comprehensively. Then, the temporal convolutional network is used to predict the amplitudes of different feature frequencies, and the double-layer attention mechanism is introduced to mine the correlation between the corresponding amplitudes of different feature frequencies and between the data at different historical moments, to highlight the more important influencing factors. In this way, the prediction accuracy of the model for the amplitude corresponding to the feature frequency of concern is improved. Finally, experimental verification is carried out on the XJTU-SY dataset. The results show that the TCDAN model proposed in this paper is significantly superior to TCN, GRU, BiLSTM, and LSTM, which can provide a more effective decision-making basis for the predictive maintenance of equipment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.