Real-time fault monitoring method of charging pile based on improved RNN neural network

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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An engineering design of real-time fault monitoring method for power-on-wheel electric vehicles

2015 6th International Conference on Automation, Robotics and Applications (ICARA) ◽

10.1109/icara.2015.7081118 ◽

2015 ◽

Author(s):

Ziming Qi ◽

Kuang Ma ◽

Nicholas Sargeant ◽

William Phipps

Keyword(s):

Engineering Design ◽

Real Time ◽

Electric Vehicles ◽

Monitoring Method ◽

Fault Monitoring

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Automatic Implementation of a Self-Adaption Non-Intrusive Load Monitoring Method Based on the Convolutional Neural Network

Processes ◽

10.3390/pr8060704 ◽

2020 ◽

Vol 8 (6) ◽

pp. 704 ◽

Cited By ~ 1

Author(s):

Xin Wu ◽

Dian Jiao ◽

Yu Du

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Real Time ◽

Load Identification ◽

Neural Network Models ◽

Monitoring Method ◽

Line Load ◽

The Real ◽

Load Monitoring ◽

On Line

Non-intrusive load monitoring (NILM) is an effective way to achieve demand-side measurement and energy efficiency optimization. This paper studies a method of non-intrusive on-line load monitoring under a high-frequency mode of electric data acquisition, which enables the NILM to be automated and in real-time, including the short-term construction of a dynamic signature library and continuous on-line load identification. Firstly, in the short initial operation phase, load separation and category determination are carried out to construct the load waveform library of the monitoring user. Then, the continuous load monitoring phase begins. Based on the data of each user’s signature library, the decomposition waveforms are classified by convolutional neural network models that are constructed to be suitable for each signature library in order to realize load identification. The real-time power consumption status of the load can be obtained continuously. In this paper, the electricity data of actual users are collected and used to perform the experiments, which show that the proposed method can construct the load signature library adaptively for different users. Meanwhile, the classification of the convolutional neural network model based on a library constructed in actual operation ensures the real-time and accuracy of load monitoring.

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Power Loss Prediction for Aging Characteristics and Condition Monitoring for Parallel-Connected Power Modules Using Nonlinear Autoregressive Neural Network

Complexity ◽

10.1155/2019/8759873 ◽

2019 ◽

Vol 2019 ◽

pp. 1-10

Author(s):

Shengyou Xu ◽

Xin Yang ◽

Li Ran ◽

Minyou Chen ◽

Wei Lai

Keyword(s):

Neural Network ◽

Real Time ◽

Condition Monitoring ◽

Power Loss ◽

Junction Temperature ◽

Monitoring Method ◽

Current Sharing ◽

Power Modules ◽

The Impact ◽

Nonlinear Autoregressive

Power modules connected in parallel may have different electrothermal performance variances resulting from aging because of the nonuniform rate of degradation; different electrothermal performance variances mean different current sharing, different junction temperature, and power losses, which will directly influence the overall characteristics of them. Thus, it is essential to monitor the condition and evaluate the degradation grade to improve the reliability of large-scale power modules. In this paper, the impact of thermal resistance difference on current sharing, junction temperature, and power loss of parallel-connected power modules has been discussed and analyzed. Additionally, a methodology is proposed for condition monitoring and evaluation of the power modules without intruding them by recognizing the increase in external power loss due to internal degradation from aging. In this method, power modules are deemed as a whole system considering only external factors associated with them, all important electrical and thermal parameters are classified as the inputs, and power loss is considered as the output. Firstly, power dissipation is predicted by models using NARX (nonlinear autoregressive with exogenous input) neural network. Then, a monitoring method is illustrated based on the prediction model; a reasonable criterion for the error between the normal and the predicted real-time power loss is established. Finally, the real-time condition and the degradation grade of aging can be evaluated so that the operator can take suitable operating measures by means of this approach. Experimental results validated the effectiveness of the proposed methodology.

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Research of Fault Warning System of the Cooling Tower Fan Blade

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.42.183 ◽

2010 ◽

Vol 42 ◽

pp. 183-187 ◽

Cited By ~ 1

Author(s):

Jian Ping Chen ◽

Wei Dong

Keyword(s):

Real Time ◽

Cooling Tower ◽

Warning System ◽

Control Unit ◽

Pulse Signal ◽

Failure Diagnosis ◽

Signal Acquisition ◽

Monitoring Method ◽

Fault Monitoring ◽

Fan Blade

A new monitoring method is described on the basis of the technology of the cooling tower fan monitoring and failure diagnosis, the warning system uses microprogrammed control unit real-time monitoring the blade. A magnet was fixed on the tip of every blade and these magnets were uniformly distributed in the same plane. A Hall proximity switch was fixed on the air duct which was at the same plane with the magenet, and the Hall proximity switch was used as a signal acquisition sensor, and the pulse signal of the Hall proximity swith was collected through microprogrammed control unit. Using the timing and interrupt function of microprogrammed control unit, the pulse signal was analysed and processed, when the blade breaks down and deforms, the preprogramme shut down the fan and outputted a forecast alarm, so the blade accident would be prevented. It is proved that running state of the system is good by production and practice and the monitoring method can greatly improve the reliability of the fault monitoring and warning. The monitoring system has a wide application.

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