USE OF IIoT TECHNOLOGIES IN ENGINE MONITORING AND CONTROL SYSTEMS

Determining the operating parameters of engine systems is very important to ensure its long-term reliability. Modern power plants use a large number of modern electronic components (sensors, controllers, etc.). All systems generate a huge volume of data that must be tracked, processed and analyzed, which in turn creates calls to gateways and servers. However, the current state of development of microcontrollers and their power allow the use of fog computing, which can lead to more efficient use of both the power plant and data transmission systems. Also, the use of modern microcontrollers and fog computing allows modernizing old power plants with minimal effort and creating new information nodes for monitoring the state of nodes and engine systems and easily combining them with systems already existing on the engine. The selection of a microcontroller and justification of its type for the modernization of the fuel system of a diesel engine are considered. Rapid processes occur in the high-pressure fuel system of diesel engines, which can be determined by changes in fuel pressure. For detailed measurement of processes in the fuel system, measurements should be made with a frequency of at least 24 kHz, i.e.: if the crankshaft rotation frequency is 4000 min-1, then 24,000 measurements should be made every second (when registering data after 1 degree of crankshaft rotation). Another indicator that imposes its requirements is the bit rate of the analog-to-digital converter (ADC), which depends on the range of recorded values. For example, an ADC with a resolution of 8 bits is capable of outputting 256 discrete values (0…255). That is, the higher the bit rate, the more sensitive the converter is to signal changes, and 8 bits may not be enough. Thus, even a small number of sensors can generate a huge amount of data. Transmitting large amounts of data is irrational and can lead to data loss and "clogging" of transmission channels, which challenges the computational performance of the microprocessor.