scholarly journals Development of a condition monitoring system for compressor equipment with neural network data analysis

2019 ◽  
Vol 1399 ◽  
pp. 022058
Author(s):  
D K Zyryanov ◽  
V V Bukhtoyarov ◽  
N A Bukhtoyarova ◽  
V V Kukartsev ◽  
V S Tynchenko ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Analysis ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Network Data ◽  
Condition Monitoring System ◽  
Compressor Equipment

Research of Intelligent Substation Condition Monitoring Based on the Neural Network

2013 ◽  
Vol 325-326 ◽  
pp. 692-696
Author(s):  
Da Peng Chai ◽  
Qiang Qiang Xue ◽  
Ling Mei Wang ◽  
Xing Yong Zhao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Electric Power ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Power Equipment ◽  
The Neural Network ◽  
Condition Monitoring System ◽  
Artificial Neural Network Ann

The substation electric power equipment condition monitoring is the basis of intelligent substation. This paper analyzes the composition of the substation electric power equipment condition monitoring system and monitoring parameters, and with the transformer condition monitoring as an example, this paper proposes fault diagnosis methods of electric power equipment using artificial neural network(ANN).

scholarly journals Design of a Condition Monitoring System for Wind Turbines

Energies ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 464
Author(s):  
Jinje Park ◽  
Changhyun Kim ◽  
Minh-Chau Dinh ◽  
Minwon Park
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Offshore Wind ◽  
Offshore Wind Turbines ◽  
Diagnosis Model ◽  
Condition Monitoring System ◽  
Condition Diagnosis

Renewable energy is being adopted worldwide, and the proportion of offshore wind turbines is increasing. Offshore wind turbines operate in harsh weather conditions, resulting in various failures and high maintenance costs. In this paper, a condition diagnosis model for condition monitoring of an offshore wind turbine has been developed. The generator, main bearing, pitch system, and yaw system were selected as components subject to the condition monitoring by considering the failure rate and downtime of the wind turbine. The condition diagnosis model works by comparing real-time and predictive operating data of the wind turbine, and about four years of Supervisory Control and Data Acquisition (SCADA) data from a 2 MW wind turbine was used to develop the model. A deep neural network and an artificial neural network were used as machine learning to predict the operational data in the condition diagnosis model, and a confusion matrix was used to measure the accuracy of the failure determination. As a result of the condition monitoring derived by inputting SCADA data to the designed system, it was possible to maintain the failure determination accuracy of more than 90%. The proposed condition monitoring system will be effectively utilized for the maintenance of wind turbines.

Design of New Condition Monitoring System and Data Analysis in Large Forging Die Hydraulic Press

2011 ◽  
Vol 311-313 ◽  
pp. 1546-1550
Author(s):  
Hui Chen ◽  
Jian Ping Tan ◽  
Jin Li Gong ◽  
Zhao Qiang Shu ◽  
Xian Yue Cao ◽  
...  
Keyword(s):  
Data Analysis ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Working Conditions ◽  
Hydraulic Press ◽  
Contact Method ◽  
Large Forging ◽  
Monitoring Method ◽  
Condition Monitoring System ◽  
Safety Operation

The mechanical characteristics and extremely hard working conditions of large die hydraulic press are considered. The limitations of traditional condition monitoring method are analyzed. A new condition monitoring system which consists of a digital and non-contact method for measuring column stress and a digital method for measuring moving beam’s spatial altitude based on machine vision is proposed. The basic principle and composition of the system are introduced in detail. The system is applied in 300MN large forging hydraulic press, and large amounts of monitoring data are collected. Data analysis is carried out. The laws of column stress distribution in different working conditions are found out. At last, some recommendations are proposed which is helpful for the safety operation and maintenance of large hydraulic press.

scholarly journals Application of Multisensor Information Fusion Technology in the Measurement of Dynamic Machining Errors of Computer Numerical Control (CNC) Machine Tools

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaoping Li ◽  
Yonghong Deng ◽  
Xuezhe Li
Keyword(s):  
Neural Network ◽  
Machine Tool ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Error Compensation ◽  
Machine Tools ◽  
Cnc Machine Tools ◽  
Cnc Machine ◽  
Condition Monitoring System ◽  
The Mathematical Model

A CNC machine tool is process control equipment integrating machine, electricity, and liquid, which makes its fault diagnosis complex and special due to its own advanced, complex, and intelligent characteristics. Traditional diagnostic methods rely on the engineering experience of technical personnel, which incorporates human subjective factors, and can only perform qualitative analysis, resulting in low diagnostic efficiency. And through a single sensor to detect and diagnose the machine tool, the accuracy and credibility of the decision are low, and the system is also weak against interference. In this paper, we first summarize the composition and working principle of CNC machine tools and analyze the working condition signals generated by CNC machine tools and the sensors that collect the signals and decide to use a multisensor multisignal fusion-based approach to monitor the machine tool status. It is possible to obtain more effective and valuable information from the observed information through multiple sensors so that the goal of fusion can be achieved. In this paper, a multisensor fusion technique based on wavelet transform and neural network fusion is applied to a machine tool condition monitoring system. The theoretical basis of wavelet analysis and neural network is introduced, and the composition of the condition monitoring system and the process of applying multisensor fusion technology based on wavelet analysis and neural network in the condition monitoring system are given. A complete software and hardware system for online monitoring of CNC machine tools is established. In order to improve the accuracy of the mathematical model, the use of a neural network to fit the nonlinear data and the use of coarse set theory to simplify the relevant data can effectively solve the accurate establishment of the mathematical model in the error compensation method. The thermal error compensation method for CNC machine tools is proposed based on rough set theory, ant colony algorithm, and neural network. This paper first investigates the current development of error compensation technology for CNC machining centers, analyzes the various error sources of CNC machine tools, and finds out the influencing factors affecting the errors of CNC machine tools.

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