Online lubrication monitoring to detect lubrication and equipment abnormalities in a timely manner

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For chemical enterprises, equipment management is extremely important; especially in the current era of intelligent manufacturing, the level of equipment management determines the efficiency of production as well as the competitiveness of the enterprise. Over the past few decades, high-quality spare parts (gears, bearings, lubricants, etc.) have led to reduced overall maintenance costs and increased productivity ； However, in most mature enterprises today, there is less and less room for improvement. With the rapid development of technologies such as the Internet of Things and sensors, predictive maintenance will be key to enhancing the level of equipment management! As the saying goes, lubricant is the “blood” of industrial equipment. Many parameters in lubricants not only indicate the condition of the oil itself but also reflect the operating condition of the equipment (such as abnormal wear and contamination). In traditional lubrication management and maintenance, equipment managers rely primarily on routine inspections, periodic testing, and scheduled repairs. As shown in the example below, there is a certain \"time lag\" associated with the blue line: the oil quality test reports reflect the conditions from the past. If any problems arise during the period between tests, it can lead to significant losses or even unplanned shutdowns of the equipment. Lubrication inspections rely more on visual inspection and the detection of noise; by the time a problem is identified, it is already at the end of the equipment’s operational life, and the damage is irreversible. By using online monitoring (green curve), real-time alerts can be provided, enabling equipment managers to quickly locate and resolve issues, thereby reducing the overall costs associated with equipment management and maintenance. Currently, with the development of sensor technology both domestically and internationally, as well as the industrial Internet, we have obtained the Lubrication System Health Index (LHI) through weighted algorithms and fitting. In practical applications, specific health curves can be determined through specialized characterization or by fitting them using at least three parameters. This helps equipment maintenance personnel take appropriate actions at different stages, thereby making equipment management safer, more efficient, and more intelligent. For detailed applications and solutions, please contact Yuweijiang (13918960457) promptly. About Us: Previ Workshop focuses on the online monitoring and predictive maintenance of industrial equipment. It is committed to using the industrial Internet to help industrial enterprises accelerate their digital transformation, thereby making equipment management and maintenance safer, more efficient, and smarter. Our core team comes from well-known domestic and international companies such as ExxonMobil and Sany Heavy Industry; we place greater emphasis on developing algorithms based on practical applications, aiming to solve real-world problems rather than merely presenting data.