Electric motors are widely used in many industrial applications on account of stability, solidity and ease of use. Mechanical bearing faults have the highest statistical occurrence percentage among all of the motor fault types. Accurate and advance detection of the bearing faults is critical to avoid unpredicted breakdowns of electric motors. Through early detection of bearing faults, it would be possible to solve the problem at a lower cost by repairing and/or replacing relevant parts. Most of the fault detection works in the literature attempted to detect binary {healthy, faulty} motor fault case based on a single input. In this study, we propose an enhanced performance bearing fault diagnosis system based on multichannel, multilevel 1D-CNN classifier processing vibration data collected from multiple accelerometers mounted on bearings in a test bed. Effectiveness and feasibility of the proposed method are validated by applying it to the benchmark IMS bearing vibration dataset for inner race and rolling element faults and comparing the results with the conventional single-axis data-based fault detection.

由于稳定性，坚固性和易用性，电动机在许多工业应用中被广泛使用。在所有电机故障类型中，机械轴承故障的统计发生率最高。准确，提前地检测轴承故障对于避免电动机发生意外故障至关重要。通过及早发现轴承故障，可以通过维修和/或更换相关零件以较低的成本解决问题。文献中的大多数故障检测工作都试图基于单个输入来检测二进制{健康，故障}电动机故障情况。在这项研究中，我们提出了一种基于多通道的性能增强的轴承故障诊断系统，多级1D-CNN分类器处理从安装在测试台轴承上的多个加速度计收集的振动数据。该方法的有效性和可行性通过将其应用于基准IMS轴承振动数据集的内圈和滚动元件故障进行了验证，并将结果与​​传统的基于单轴数据的故障检测进行了比较。