Automatic sensor-based detection of motor failures such as bearing faults is crucial for predictive maintenance in various industries. Numerous methodologies have been developed over the years to detect bearing faults. Despite the appearance of numerous different approaches for diagnosing faults in motors have been proposed, vibration-based methods have become the de facto standard and the most commonly used techniques. However, acquiring reliable vibration signals, especially from rotating machinery, can sometimes be infeasibly difficult due to challenging installation and operational conditions (e.g., variations on accelerometer locations on the motor body), which will not only alter the signal patterns significantly but may also induce severe artifacts. Moreover, sensors are costly and require periodic maintenance to sustain a reliable signal acquisition. To address these drawbacks and void the need for vibration sensors, in this study, we propose a novel sound-to-vibration transformation method that can synthesize realistic vibration signals directly from the sound measurements regardless of the working conditions, fault type, and fault severity. As a result, using this transformation, the data acquired by a simple sound recorder, e.g., a mobile phone, can be transformed into the vibration signal, which can then be used for fault detection by a pre-trained model. The proposed method is extensively evaluated over the benchmark Qatar University Dual-Machine Bearing Fault Benchmark dataset (QU-DMBF), which encapsulates sound and vibration data from two different machines operating under various conditions. Experimental results show that this novel approach can synthesize such realistic vibration signals that can directly be used for reliable and highly accurate motor health monitoring.

翻译：自动化传感器检测电机故障（如轴承缺陷）对于各行业的预测性维护至关重要。多年来，研究者已开发出多种检测轴承故障的方法。尽管涌现出众多不同的电机故障诊断方案，基于振动信号的方法已成为事实上的行业标准，也是最常用的技术。然而，获取可靠的振动信号（尤其针对旋转机械）常因安装与运行条件的限制（如加速度计在电机壳体上的位置变化）而极其困难，这不仅会显著改变信号模式，还可能引入严重伪影。此外，传感器成本高昂且需定期维护以维持稳定的信号采集。为解决上述问题并摒弃振动传感器需求，本研究提出一种新颖的声-振变换方法，可在任意工况、故障类型及严重程度下，直接从声音测量信号合成真实的振动信号。通过这种变换，由简单录音设备（如手机）采集的数据可转化为振动信号，进而用于预训练模型的故障检测。该方法在卡塔尔大学双电机轴承故障基准数据集（QU-DMBF）上进行了全面评估，该数据集包含两台不同工况下电机的声-振数据。实验结果表明，该方法合成的振动信号具备高度真实性，可直接用于可靠且高精度的电机健康监测。