###### Abstract
When the inner and outer rings rotate relative to each other, the rolling elements roll within the tracks between the inner and outer rings, with their shape, size, and quantity directly affecting the performance and lifespan of the rolling bearing. The cage ensures an even distribution of the rolling elements. Based on the shape of the rolling elements, rolling bearings can be further classified, with commonly used rolling elements including balls, short cylindrical rollers, long cylindrical rollers, spiral rollers, tapered rollers, spherical rollers, and needle rollers. Each type is optimized for specific application scenarios. For instance, deep groove ball bearings are widely used in medium- to high-speed rotating situations due to their ability to withstand radial loads and certain axial loads, while cylindrical roller bearings are suitable for bearing larger radial loads, making them appropriate for heavy machinery. The concept of bearing detection can be traced back to the early 20 th century. Initially, methods for detecting bearing faults relied on manual inspections, where engineers assessed the operational status of the equipment through visual, auditory, and tactile observations. In the 1920s and 1930s, simple mechanical tools began to be used to assist in detection. By the 1940s, basic vibration measurement instruments were employed to record vibration conditions and conduct preliminary vibration analysis. During the 1950s and 1960s, with the advancement of electronic technology, fundamental sensors were utilized to capture bearing condition data. Signal processing techniques such as Fast Fourier Transform emerged, leading to the application of spectral analysis techniques in bearing fault detection. In the 1970s, with the development of digital signal processing technology, data processing transitioned from qualitative to quantitative. The 1980s saw the emergence of artificial intelligence methods alongside advancements in computer technology. In the 1990s, as multi-sensor fusion technology matured and data processing capabilities improved, detection methods evolved towards multimodal integration. In the early 21 st century, machine learning technologies, such as support vector machines, began to be widely applied. From 2010 to the present, deep learning technologies have continued to develop, with intelligent detection that integrates edge computing, big data, and large models becoming prevalent in industrial applications. In the future, with advancements in sensor technology and signal processing techniques, methods for detecting bearing faults will continuously progress towards integration and intelligence. In 2024, Texas Instruments utilized integrated microcontrollers to achieve real-time system fault detection, primarily for monitoring motor bearing anomalies. The integrated microcontroller operates convolutional neural network models using integrated neural network processing units, aiding in reducing latency and power consumption during fault monitoring. Since convolutional neural network models can autonomously learn the characteristics of different types of signals from raw sensor data, such as motor vibration signals, solar DC currents, or battery voltage and current, they are particularly well suited for sensor data analysis in fault detection and predictive maintenance. Integrating edge AI capabilities into the same integrated microcontroller used for managing real-time control not only optimizes system design but also enhances overall performance: the accuracy of fault detection reaches as high as 99%. This is a reflection of the current state of industrial bearing fault detection, where the integration of sensor technology and artificial intelligence signifies the value of interdisciplinary collaboration. The development of new technologies promotes the intelligent detection of bearing faults. The use of various types of data indicates the application of multimodal fusion; furthermore, the fusion methods can also utilize the output of one method as the input or reference for another method.