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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.

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