Real time monitoring method for temperature overheating of supercritical thermal power unit

2021 ◽  
Author(s):  
Hongliang Wu
Keyword(s):  
Real Time ◽  
Power Unit ◽  
Thermal Power ◽  
Real Time Monitoring ◽  
Monitoring Method

A measurement method for the overturning coefficient of high-speed trains passing through complex terrain sections under strong wind conditions

2019 ◽  
Vol 234 (8) ◽  
pp. 885-895
Author(s):  
Zhaijun Lu ◽  
Weijia Huang ◽  
Mu Zhong ◽  
Dongrun Liu ◽  
Tian Li ◽  
...  
Keyword(s):  
Real Time ◽  
Complex Terrain ◽  
High Speed ◽  
Suspension System ◽  
Strong Wind ◽  
Real Time Monitoring ◽  
Monitoring Method ◽  
High Speed Trains ◽  
Strong Winds ◽  
Pass Through

Real-time monitoring of overturning coefficients is very important for ensuring the safety of high-speed trains passing through complex terrain sections under strong wind conditions. In recent years, the phenomenon of “car swaying” that occurs when trains pass through the complex terrain has brought new challenges to ensuring the safety and riding comfort of passengers. In China, more and more high-speed trains are facing strong wind environments when running in complex terrain sections. However, due to the limitation of objective conditions, so far, only a few economical and effective methods of measurement have been developed that are suitable for real-time monitoring of the overturning coefficient of commercial vehicles. Therefore, considering the applicability and universality of such a monitoring method, this study presents a method for measuring the overturning coefficient of trains using the primary suspension system under strong winds. A vehicle test was carried out to verify the accuracy of the method. The results show that after correction, the overturning coefficient obtained from the primary suspension system is generally consistent with the overturning coefficient obtained from the instrumented wheelset. The method of measuring the overturning coefficient of trains in strong wind environments with the primary suspension system is, thus, proven feasible.

Stress Real-Time Monitoring of Marine Derrick Based on Environmental Loads

2012 ◽  
Vol 226-228 ◽  
pp. 2128-2131
Author(s):  
Tao Huang ◽  
Jun Pu Wang ◽  
Fu Wan ◽  
Shao Wei Chen ◽  
Yao Dong
Keyword(s):  
Finite Element ◽  
Real Time ◽  
Engineering Application ◽  
Design Feature ◽  
Environmental Load ◽  
Real Time Monitoring ◽  
Element Analysis ◽  
Monitoring Method ◽  
Environmental Loading ◽  
Finite Element Software

Aiming at the design feature and the operating of K-type derrick, an effective method used to stress real-time monitoring consider environmental loading is proposed. The challenge to the South China Sea offshore drilling derrick is a illustration. The derrick corrosion and wall thinning conditions is be considered, then using finite element software analyze derrick structure of static analysis, get the higher force about main member of derrick. Based on finite element analysis, select the key parts layout of measuring points, then monitor derrick stress under nine storms environmental loading and extreme work condition. The measured data results show that: the nine storms environmental load affect capacity of drilling significantly, the different parts of the main member stress have the different degrees of influence by environmental load, the maximum can reach 50.8%. This real-time monitoring method of stress, can protect the safety of marine operations, has a certain value of engineering application.

Mo1540 The New Real-Time Monitoring Method Using Capsule Endoscopy for Gastrointestinal Bleeding After Endoscopic Treatment

2014 ◽  
Vol 79 (5) ◽  
pp. AB476
Author(s):  
Hayato Sasaki ◽  
Minori Matsumoto ◽  
Yasuo Kakugawa ◽  
Yutaka Saito ◽  
Taku Sakamoto ◽  
...  
Keyword(s):  
Gastrointestinal Bleeding ◽  
Real Time ◽  
Capsule Endoscopy ◽  
Endoscopic Treatment ◽  
Real Time Monitoring ◽  
Monitoring Method

Study of a New Real-Time Monitoring Method on the Underground Mining Activities

2012 ◽  
Vol 229-231 ◽  
pp. 1402-1405
Author(s):  
Xiu Zhi Meng ◽  
Zeng Zhi Zhang ◽  
Zong Sheng Wang
Keyword(s):  
Real Time ◽  
Underground Mining ◽  
Energy Eigenvalue ◽  
Mining Area ◽  
P Wave ◽  
Signal Acquisition ◽  
Mining Activities ◽  
Real Time Monitoring ◽  
P Waves ◽  
Monitoring Method

This paper presents a new real-time monitoring method based on the explosion source location technique on the underground mining activities in the situation the state can not achieve the full uninterrupted supervision because of the backward monitoring tools and equipment. The supervise mode results in some small coal mines in the profit-driven to ultra-layer or cross-border mining which causes a many of safety accidents. The five acceleration vibration sensors buried underground in the mining area pick up blasting vibration waves coming from blasting tunneling. Every signal acquisition sub-station deals with the according sensor output signals by using wavelet transform to identify the P waves and using energy eigenvalue method to determine the arriving time of P wave to the sensor, then translates the sensor’s spatial and temporal parameters to the principal computer. The principal computer locates the explosion source by the Geiger algorithm and displays the explosion source’s spatial message in the mine’s electronic map. The method is feasible and the positioning horizontal error is less than 10m by field-proven.

scholarly journals Research on a Real-Time Monitoring Method for the Wear State of a Tool Based on a Convolutional Bidirectional LSTM Model

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1233 ◽  
Author(s):  
Chen ◽  
Xie ◽  
Yuan ◽  
Huang ◽  
Li
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Tool Wear ◽  
Real Time ◽  
Original Data ◽  
Attention Mechanism ◽  
Cnc Machining ◽  
Numerical Control ◽  
Real Time Monitoring ◽  
Monitoring Method

To monitor the tool wear state of computerized numerical control (CNC) machining equipment in real time in a manufacturing workshop, this paper proposes a real-time monitoring method based on a fusion of a convolutional neural network (CNN) and a bidirectional long short-term memory (BiLSTM) network with an attention mechanism (CABLSTM). In this method, the CNN is used to extract deep features from the time-series signal as an input, and then the BiLSTM network with a symmetric structure is constructed to learn the time-series information between the feature vectors. The attention mechanism is introduced to self-adaptively perceive the network weights associated with the classification results of the wear state and distribute the weights reasonably. Finally, the signal features of different weights are sent to a Softmax classifier to classify the tool wear state. In addition, a data acquisition experiment platform is developed with a high-precision CNC milling machine and an acceleration sensor to collect the vibration signals generated during tool processing in real time. The original data are directly fed into the depth neural network of the model for analysis, which avoids the complexity and limitations caused by a manual feature extraction. The experimental results show that, compared with other deep learning neural networks and traditional machine learning network models, the model can predict the tool wear state accurately in real time from original data collected by sensors, and the recognition accuracy and generalization have been improved to a certain extent.

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