Status quo of remote online monitoring and intelligent diagnosis system for petroleum and petrochemical equipment

With the increasing number of petroleum and petrochemical equipment and unit components, the testing of the safety performance of the equipment will become an indispensable part.

The healthy state of each component directly affects the working condition of petroleum and petrochemical equipment, once the component is damaged, it will work in an abnormal state, affecting the quality and efficiency of production, if it deteriorates further, it will lead to equipment damage, causing a series of chain reactions, resulting in enterprise shutdown, causing economic losses and casualties. To improve the safety and economic benefits of petroleum and petrochemical equipment, the working status of petroleum and petrochemical equipment is monitored online, to achieve the purpose of early detection and early repair of equipment failures, and to prevent serious consequences.

The traditional petroleum and petrochemical equipment monitoring instrument obtains the equipment working signal through the sensor, and then discretizes and collects the signal through the acquisition card, enters the collected discrete signal into the computer memory, and finally carries out a series of mathematical transformations on the obtained discrete signal through the monitoring algorithm program embedded in the instrument, to obtain more obvious fault characteristics and use this as the basis for manual fault discrimination.

Among them, the most widely used of many monitoring algorithms is the vibration monitoring method. Under normal circumstances, although traditional monitoring instruments have high accuracy and reliability, they have problems such as high prices, large volumes, complex operations, and difficult-to-update functions. The advent of virtual instruments enables users to design the functions of the instruments according to their own needs and uses virtual instruments to realize online monitoring and fault diagnosis of petroleum and petrochemical equipment, which not only increases the application functions and work efficiency of the instruments, significantly reduces the research and development cycle of the instruments, but also has the advantages of high flexibility, high reusability, minimizing the scale of the test system, easy to upgrade and maintain, and reducing the development cost of the system.

Very few test systems on the market have intelligent diagnostic capabilities, and with the continuous development of machine learning, deep learning, and hardware capabilities, it is possible to design virtual instruments with intelligent diagnostic capabilities. The existence of an intelligent diagnosis function of the instrument can effectively alleviate the staff’s dependence on manual judgment, and the intelligent diagnostic instrument does not require domain knowledge, which can enable the equipment operator to monitor the working status of the equipment. Common intelligent diagnosis methods include intelligent diagnosis methods based on feature extraction combined with shallow neural networks and intelligent diagnosis methods based on deep learning.

The development process of the condition monitoring system can be divided into three stages: the first stage is the regular inspection of the offline analysis system, a system can monitor multiple pieces of equipment, easy to operate, but it requires a lot of manpower and material costs; The second stage is a stand-alone online monitoring system, with a computer as the main body for online monitoring and diagnosis, the system has a strong online monitoring and diagnosis function, but the performance requirements for the computer and collection device and other hardware are relatively high. Due to the closed characteristics of the system, it is difficult to share equipment monitoring information, which is only suitable for monitoring equipment in a small area, and is not suitable for large-scale use; At this stage, it has developed into a remote online monitoring and diagnosis system and uses network technology to realize decentralized data collection and centralized monitoring and management of equipment.

Equipment remote online monitoring technology mainly includes wired collection, remote monitoring and wireless collection, remote monitoring. The former mainly collects the working signal of the device through USB, serial port, and other communication methods performs algorithm analysis on the data transmitted to the cloud through the protocol, and displays the results in the monitoring system. The latter realizes the wireless transmission of equipment working signal data through the acquisition function of integrated sensors and wireless communication technology. Compared with wired remote communication, wireless remote communication technology avoids the layout of cable lines, and has the characteristics of low installation cost and long transmission distance.

The span between petroleum and petrochemical equipment and equipment is large, the measurement points are scattered, and the cost of cable laying and maintenance is too high, and wireless remote communication technology instead of wired acquisition can make up for this shortcoming. Remote communication is mainly divided into three layers, and the IoT networking technologies applied to the physical layer and data link layer mainly include WIFI, Bluetooth, ZigBee, LORA, 4G, 5G, and NB-IoT. WIFI and Bluetooth technology have been widely used in all walks of life, but there are disadvantages such as large power consumption and close transmission distance; ZigBee technology has slightly improved transmission distance, with low power consumption and low cost and other advantages, but it is still difficult to achieve long-distance transmission, and there are shortcomings such as poor penetration; LORA technology can complete kilometers-level long-distance transmission tasks, low power consumption, high security, but its transmission speed is slow; 4G/5G technology has strong mobility and long transmission distance, but its cost is too high. NB-IoT has the advantages of low power, ultra-long-distance transmission, and strong mobility, but it requires base station support. The network layer and transport layer mainly includes TCP, UDP, IPv4, and IPv6; the application layer protocol mainly includes CoAP, MQTT, and HTTP technology. CoAP technology is based on the UDP protocol and can interact with low-power acquisition and sending devices; MQTT and HTTP are based on the client/server model, and the data transmission is realized through the client establishing a persistent connection TCP.

Remote, Wireless Monitoring for the Oil & Gas Industry – YouTube