Predictive Maintenance Systems for Lubricants

A method, system, and panel for monitoring the condition of lubricating oil in industrial equipment using a combination of oil sensors, laboratory analyses, and equipment data. The method involves continuously monitoring oil parameters using sensors, analyzing lab oil samples, and integrating equipment data. AI tools identify when equipment is operating within limits versus when issues are starting. The method provides earlier and more accurate failure prediction compared to just lab analyses or sensors alone. The system includes a pressurized oil panel with sensors, a forwarding means to send data, a data integration and AI system, and a web app for visualization.

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Online evaluation and optimization method for hydroelectric generator bearing lubricating oil quality, comprising: circulating lubricating oil through the generator bearings; monitoring and evaluating lubricating oil quality indicators in real-time; and performing online purification of metal particles, sediments, and moisture based on evaluation results.

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Lubricants used in machines degrade with time. This may be caused by load, contamination and operating environment. Proper monitoring and analysis of the state of the lubricant is required to keep the machine in good condition and to minimize the cost of maintenance. This research is concerned with the application of lubrication standards to ensure proper operation of rotating equipment. The methods outlined include monitoring, analysis and comparison with lubricant performance standards. The method when rightly applied can signal wear, level of contamination and chemistry of the lubricant. Viscosity monitoring and analysis is useful to determine the state of the lubricant and hence the health of the machine, which is useful in taking maintenance decisions. With proper monitoring and analysis of the lubricant in rotating equipment, safety is ensured in addition to reduction of unforeseen failures and the cost of maintenance of the machines.

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This article is an overview of new trends in oil condition monitoring and the impact of Industry 4.0 on the monitoring of lubricants in real time. New solutions are being introduced into this industry with new advantages in the development of artificial intelligence, as well as machine learning and sensor technologies that are applicable for data-based services. This is called predictive maintenance, which changes the traditional routine maintenance and provides a deep understanding of the operation of equipment. Constant monitoring of the condition of lubricants during operation is necessary to maintain the quality of the equipment in real time since non-operational monitoring can reduce operating costs by eliminating equipment downtime during daily diagnostics. Keywords: lubricating oils, hydraulic fluids, maintenance, operating conditions, sensors

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Lubricant condition monitoring has been widely applied for equipment diagnostics and prognostics under condition-based maintenance (CBM). The main purposes of using lubricant are to fulfil requirement of machinery performance and indicate health status of equipment, which imply that the condition of lubricant can not only be used to predict future performance but also diagnose mechanical failures. However, there is deficiency of functional knowledge in existing lubricant information analysis methods, hence the results are usually hard to be interpreted as effective maintenance decisions. This paper presents an approach based on multilevel flow modeling (MFM) to represent the lubricant functions and establish function structure of mechanical equipment that can clearly show the relationship between lubricant and the other components in a functional perspective. The causal feature of MFM allows to utilize a function model to diagnose the possible causes of abnormal lubricant condition and predict further influences. The approach is demonstrated by a hydrodynamic bearing system.

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A lubrication distribution assembly that optimizes lubricant delivery by automatically detecting piston stroke length and adjusting operating parameters accordingly. The assembly includes a piston-based metering device, a sensor that measures piston stroke length over multiple cycles, and a controller that sets operating points based on the measured stroke length to ensure consistent lubricant delivery.

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A device and method for determining lubrication parameters using artificial intelligence. The method trains a model on input data and setpoint parameters, then uses the trained model to predict lubrication parameters from indirect measurements. The device comprises processors and memory for implementing the trained model.

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A real-time lubricant monitoring system for machines that eliminates the need for regular sampling and analysis. The system comprises a sensor installed in the lubricant flow, a data hub for collecting and storing sensor data, and a data processor that continuously monitors lubricant properties against predetermined thresholds. The system can also incorporate vibration monitoring and alert the user when maintenance is required, enabling proactive maintenance and preventing costly damage.

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Determining the remaining useful life of an oil filter in machinery like gearboxes, turbines, and engines to optimize filter change intervals. The method involves diverting a representative flow of oil from between the pump and filter, measuring the flow rate at two times, and calculating the remaining useful life as a function of the flow rate difference. This allows estimating filter life based on real-time flow instead of just runtime.

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Device for predicting future state of a central lubrication system, comprising sensors to monitor system parameters, a processing unit to analyze the data, and an output unit to display the predicted state. The processing unit determines the current system state and uses historical data to estimate future system behavior over a specified time period.

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In predictive maintenance, interventions are dependent on information from the monitoring of parameters, their main advantage is the reduction of periodic stops, increasing the time of availability of equipment and decreasing costs. Subjects such as wear, lubrication and surfaces with relative movement are studied by tribology. The purpose of this article was to investigate the relationship between tribology and predictive maintenance. The basis for scientific foundation was bibliographic research in studies by different authors. Thus, it is concluded that tribology has significant relevance in performing predictive maintenance, especially in view of the analysis of lubricating oil and wear of particles. These analyzes and correct implementation were considered, being a fundamental point for companies that seek to increase their competitiveness. However, scarcity was observed in practical case studies, which demonstrates that the relationship between maintenance and tribological factors needs to be studied more deeply.

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Intelligent lubricating oil analysis is a procedure in condition-based maintenance (CBM) of diesel vehicle fleets. Together with diagnostic and failure prediction processes, it composes a data-driven vehicle maintenance structure that helps a fleet manager making decisions on a particular breakdown. The monitoring or controlof lubricating oils in diesel engines can be carried out in different ways and following different methodologies. However, the list of studies related to automatic lubricant analysis as methods for determining the degradation rate of automotive diesel engines is short. In this paper we present an intelligent data analysis from 5 different vehicles to evaluate whether the variables collected make it possible to determine the operating condition of lubricants. The results presented show that the selected variables have the potential to determine the operating condition, and that they are highly related with the lubricant condition.We also evaluate the inclusion of new variables engineered from raw data for a better determination of the operating condition. One of su... Read More

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In industry, owners, engineers and workers have struggled with lubricant degradation and its effects on their equipment. The purpose of Lubrication Degradation Mechanisms: A Complete Guide is to help personnel to understand the reasons behind the degradation of their lubricant, determine methods to identify the onset of degradation and reduce or eliminate lubricant degradation within their equipment. One of the most common forms of lubricant degradation is oxidation. However, this is not the only method by which a lubricant degrades. By understanding the differences between degradation patterns, personnel can employ specific tasks / tests to aid in their identification of the type of degradation and the factors responsible. The aim of this book is to educate facility personnel on the methods of degradation and ways in which it can be reduced or eliminated while keeping an eye on the cost of operation.

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Method and device for determining lubricating oil life in mechanical equipment, comprising: determining used oil mileage in each time interval based on operating conditions and mileage; superimposing used oil mileage to obtain cumulative mileage; and determining remaining oil life based on cumulative mileage and standard mileage.

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Anomaly detection device and method that detects equipment abnormalities based on a judgment standard tailored to the type of lubricating oil used in moving parts. The device compares operational data against threshold values that are dynamically adjusted according to the lubricating oil type, enabling more accurate abnormality detection and reducing false alarms.

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