Abstract
Given the sophisticated technologies that modern industrial organizations are equipped with, monitoring and diagnostics of equipment condition are critical tasks. The current study aims to develop an improved diagnostic system for industrial equipment in the oil and gas industry using Schneider Electric M241 and M340 programmable logic controllers (PLCs). The first step in this process is to analyze the faults that occur during equipment operation, as well as to study the signal processing methods used in the oil and gas industry. The second step is to use PLCs for automated data collection, parameter monitoring and diagnostics of equipment condition. This approach allows for real-time control of key technological processes, reducing the probability of failures and increasing the reliability of production equipment. The study examined the impact of various data processing strategies on the efficiency of industrial equipment diagnostics. PLC data collection and analysis methods were considered, including continuous parameter monitoring, threshold control and trigger events. Based on these methods, diagnostic algorithms were developed and implemented in the EcoStruxure Machine Expert and EcoStruxure Control Expert, which provide automatic fault detection and alarm generation.