This paper mainly discusses the on-line monitoring and intelligent diagnosis technology in the production process of hydropower forging. According to the characteristics and needs of hydropower forging production, this paper introduces how to make monitoring plan and select appropriate monitoring indicators, and how to use machine learning algorithm and artificial intelligence technology to extract fault information from monitoring data and give diagnostic suggestions. Finally, combined with the actual case, the application advantages and prospects of online monitoring and intelligent diagnosis technology in the production process of hydropower forgings are analyzed.
Hydropower forging is an important part in hydraulic engineering, its quality and performance directly affect the safety and operation of hydraulic engineering. In the production process, hydropower forgings need to go through multiple processes and complex process treatment, which is prone to various defects and failures. In order to ensure production quality and improve production efficiency, it is necessary to use online monitoring and intelligent diagnosis technology to carry out real-time monitoring and data analysis of production process.
Online monitoring technology refers to the real-time monitoring and data acquisition of the production process through sensors, meters and other equipment, to discover and deal with potential problems in time. In the production process of hydropower forgings, the indicators that need to be monitored mainly include temperature, pressure, flow, vibration and so on. The changes of these indicators directly affect the production process and product quality, so it is necessary to formulate a reasonable monitoring plan and select the appropriate monitoring indicators.
Monitor the development of plans
It is necessary to consider many factors such as production process, equipment characteristics and process requirements to make monitoring plan. First, it is necessary to identify the key processes and equipment that need to be monitored, and the indicators that need to be monitored for each process and equipment. Secondly, it is necessary to select the appropriate sensor and instrument equipment according to the characteristics and requirements of the monitoring indicators. Finally, it is necessary to develop the process and method of data collection and analysis to ensure the accuracy and real-time of data.
Selection of monitoring indicators
The representativeness and sensitivity of the indicators should be considered in selecting the appropriate monitoring indicators. Representativeness means that the indicator can reflect the state and change of the production process, and sensitivity means that the indicator can find potential problems and failures in time. In the production process of hydropower forgings, the indicators that need to be monitored include temperature, pressure, flow, vibration and so on. The change of these indicators directly affects the production process and product quality, so it is necessary to choose the appropriate sensor and instrument equipment for monitoring.
Intelligent diagnosis technology refers to the use of machine learning algorithms and artificial intelligence technology to analyze and process monitoring data, extract fault information and give diagnostic suggestions. In the production process of hydropower forgings, intelligent diagnosis technology can help enterprises find and deal with potential problems in time, improve production efficiency and product quality.
Fault information extraction
It is necessary to use appropriate algorithms and methods to extract fault information from monitoring data. Commonly used algorithms include time domain analysis, frequency domain analysis, wavelet analysis and so on. Through these algorithms, the monitoring data can be processed and analyzed, and the characteristic information related to the fault can be extracted. For example, through the analysis of vibration signals, abnormal vibrations and faults in the operation of equipment can be found.
The giving of diagnostic recommendations
Based on the extracted fault information, appropriate algorithms and methods can be used to give diagnosis suggestions. Common methods include rule-based expert system, statistics-based pattern recognition, neural network-based fault diagnosis, etc. These methods can provide diagnosis suggestions and handling measures according to different fault characteristics and information. For example, the fault diagnosis method based on neural network can be used to analyze and process the extracted fault information, and give corresponding diagnosis suggestions and treatment measures.
In practical applications, online monitoring and intelligent diagnosis technology can help enterprises find and deal with potential problems in time, improve production efficiency and product quality. Here is a practical application case:
Online monitoring and intelligent diagnosis technology are used to monitor and analyze the production process in real time. Through the monitoring and analysis of temperature, pressure, flow, vibration and other indicators, it is found that there are abnormal vibration and noise in the operation of a certain equipment. The fault diagnosis method based on neural network is used to analyze and deal with the problem, and it is found that the bearing of the equipment has some problems such as wear and loosening. The enterprise takes timely measures to maintain and maintain the equipment to avoid potential failures and production accidents.
On-line monitoring and intelligent diagnosis technology have important application value and prospect in the production process of hydropower forging. By making a reasonable monitoring plan and selecting appropriate monitoring indicators, potential problems and failures can be found and dealt with in time. At the same time, machine learning algorithm and artificial intelligence technology are used to analyze and process the monitoring data, from which fault information can be extracted and diagnostic suggestions are given. In the future, with the continuous development and progress of science and technology, the application of online monitoring and intelligent diagnosis technology in the field of hydropower forging production will be more extensive and in-depth.
