## 1 Introduction
This study demonstrates the system's effectiveness in detecting early fatigue failure in rolling bearings under lubrication conditions. Insufficient lubrication of gearboxes in industrial motors shortens their lifespan and can lead to catastrophic failures and losses. One paper [6] proposed diagnosing the lack of lubrication in gear oil systems using motor current signature analysis (MCSA). The study proposed, investigated, and experimentally validated two new technologies for diagnosing inadequate lubrication in gearmotor systems based on MCSA. Gearboxes are critical components in various industrial applications, where gear wear over time can lead to significant failures and unexpected downtime. Vibration analysis is commonly used to monitor and diagnose gear wear, but its application is limited due to the complexity of vibration characteristics caused by gear wear propagation. A review paper [7] summarizes the progress in vibration-based gear wear monitoring, exploring the relationship between gear surface features and vibration characteristics and offering recommendations for future research in this field to improve predictive maintenance and remaining useful life predictions. The use of artificial intelligence in fault diagnosis is the subject of another study [8]. In that work, an attempt was made to systematically review the progress of machine learning theories in fault diagnosis from zero to future perspectives. Another paper [9] focuses on vibration monitoring in rotating machinery, enabling effective diagnostics through specific patterns in vibration signals. A systematic analysis of various methodologies for signal measurement, preprocessing, feature selection, and fault diagnosis highlights the latest research trends and innovations in this field, offering recommendations for future studies. Furthermore, one paper [10] investigates the structure design and technology of a centralized lubrication system with a distributed structure, addressing the shortcomings of traditional systems, such as poor generality and low measurement precision. It proposes reliable digital detection methods for oil pressure, flow, and temperature, significantly improving lubrication performance when applied in industrial settings. Another paper [11] deals with problems with intelligent lubrication systems for construction machinery and creates a performance evaluation system for intelligent lubrication systems for construction machinery by analyzing and selecting appropriate evaluation indices. In another paper [12], a computer program was developed for the analysis of engine lubrication systems, providing mathematical models of oil flow through a hydraulic tappet, as well as models of oil flow and plain bearings. Proper maintenance of industrial robots is closely linked to their lifespan. How can you ensure that your industrial robots \"live\" as long as possible? First and foremost, we focus on the most common faults or symptoms observed in robotic systems, such as leakage in gear mechanisms, increased vibrations, positioning errors, irregular movement, oscillations, noise, and robot malfunctions. In technical practice, we often encounter the need to address various operational issues, with our attention typically focusing on system errors, software malfunctions, or deficiencies in control processes. However, we rarely give enough attention to fundamental elements, such as the condition of the lubricant, specifically, its cleanliness, quality, or suitability for the specific application. The aforementioned faults and negative symptoms are often a result of malfunctioning, improper, or insufficient lubrication. The degradation of the lubricant due to an increased presence of mechanical contaminants leads to failures in the lubrication system and the emergency shutdown of the robot. These parameters can significantly impact the reliability and lifespan of technical equipment.