Developing a simple real-time bearingfaultdetection algorithm is essential for small cooling fan motors, ensuring power electronics systems and data center stability. Our study introduces a real-time, low-resource a...
详细信息
Developing a simple real-time bearingfaultdetection algorithm is essential for small cooling fan motors, ensuring power electronics systems and data center stability. Our study introduces a real-time, low-resource algorithm for microcontrollers, utilizing motor current data to detect the most common bearingfaults in these applications. Notably, small motors exhibit significant current changes due to lubrication and contamination issues, unlike larger motors, where such changes are minimal. We comprehensively assess distributedbearingfaults stemming from lubrication and contamination across seven motors under various conditions. These motors are tested under both no-load and fan-load scenarios at ten different speeds, with controlled aging of motor bearings. Key time-domain features, such as Root Mean Square (RMS), peak values, and crest factors of motor current, are scrutinized to create our proposed algorithm. We rigorously evaluate sensitivity, false detection scenarios, and compare our algorithm to a machine learning model. In practical experiments using the TI F280049 microcontroller, our algorithm outperforms, demanding minimal instruction cycles and memory resources. Achieving an accuracy rate exceeding 92% and consistently demonstrating an F1 score of over 90%, the algorithm is proven to be a robust and practical solution for precisely and rapidly detecting distributedbearingfaults in small cooling fan motors.
distributedbearingfaults are highly common in industrial applications and display unpredictable vibration patterns impeding their detection. These faults stem from issues such as lubrication deficiencies, contaminat...
详细信息
distributedbearingfaults are highly common in industrial applications and display unpredictable vibration patterns impeding their detection. These faults stem from issues such as lubrication deficiencies, contamination, electrical erosion, roughness of the bearing surface, or the propagation of localized faults. This study aims to detect distributedbearingfaults by utilizing a multisensory approach consisting of current, accelerometer, and fluxgate sensors. A novel 2-D deep learning framework is proposed, leveraging signals from six distinct sources, including three-axis vibration signals, stray magnetic flux signal, and two-phase current signals. Data are collected from 3- and 10-hp induction motors at 50 operational points, spanning ten speed levels and five torque levels. These six signals are transformed into matrices and combined to create a comprehensive matrix that provides an overall depiction of the bearing condition. The proposed deep learning architecture employs a 2-D convolutional model, which takes 2-D images as input and determines the bearing status. To evaluate the system's robustness, the data are divided into training and testing sets. The proposed model demonstrates remarkable effectiveness in detecting distributedbearingfaults, achieving an impressive accuracy rate of 99.92%. Furthermore, a comprehensive comparison is provided, highlighting the impact of using various sets of inputs as sources for the deep learning model on the accuracy rate for each set. Through the analysis of the obtained results, a clear conclusion can be drawn: the model performs at its best when all six input sources are utilized.
暂无评论