Abbasi Muhammad Asim, Huang Shiping, Khan Aadil Sarwar
School of Civil Engineering and Transportation, South China University of Technology, 510641, Guangzhou, China; China-Singapore International Joint Research Institute, 510555, Guangzhou, China.
School of Civil Engineering and Transportation, South China University of Technology, 510641, Guangzhou, China; China-Singapore International Joint Research Institute, 510555, Guangzhou, China; School of Civil Engineering, Lanzhou Jiaotong University, 730070, Lanzhou, China.
ISA Trans. 2025 Jan;156:61-69. doi: 10.1016/j.isatra.2024.11.008. Epub 2024 Nov 20.
The article presents a framework for fault detection and classification to monitor the condition of motor bearings under multiple operating conditions. The condition monitoring of motor bearings is crucial for failure prevention, as bearings are prone to failure in challenging working environments. Intelligent fault diagnosis methods driven by deep learning and model-based approaches have been widely adopted to address these concerns. However, accurately diagnosing bearing faults across varying conditions and identifying multiple fault types remains challenging. The article proposes a multitask fault detection and classification approach for health monitoring using the HUST motor bearings dataset. The evaluation using HUST motor bearing datasets demonstrates robust performance across diverse operating conditions and in the presence of multiple faults. The HUST dataset is valuable for bearing fault diagnosis due to its diverse operating conditions and inclusion of multiple fault types, offering a realistic representation of fault scenarios derived from real bearing experiments. This methodology enhances the safety and reliability of mechanical equipment, with adaptability to various rotating scenarios.
本文提出了一个故障检测与分类框架，用于在多种运行条件下监测电机轴承的状态。电机轴承的状态监测对于预防故障至关重要，因为轴承在具有挑战性的工作环境中容易出现故障。由深度学习驱动的智能故障诊断方法和基于模型的方法已被广泛采用来解决这些问题。然而，在不同条件下准确诊断轴承故障并识别多种故障类型仍然具有挑战性。本文提出了一种使用HUST电机轴承数据集进行健康监测的多任务故障检测与分类方法。使用HUST电机轴承数据集进行的评估表明，该方法在各种运行条件下以及存在多种故障时都具有强大的性能。HUST数据集因其多样的运行条件和包含多种故障类型，对于轴承故障诊断具有重要价值，它提供了从实际轴承实验中得出的故障场景的真实表征。这种方法提高了机械设备的安全性和可靠性，并且适用于各种旋转场景。
