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Many industries are keenly interested in detecting and classifying faults before systems are sent to the customer or fail in use. A common approach is measuring the vibration of the machine and then using a classifier to check whether a fault is present. However, this process is difficult to automate because accelerometers are applied to the unit under test and are sometimes difficult to install and maintain due to complicated surface conditions. Accurate contact-based sensing is difficult when trying to check each rotating machinery assembly product during end-of-line quality control examinations or when evaluating the machine health of pre-installed rotating machinery. A deep learning-based fault classification system using both scalar and vector acoustic signals is a promising alternative that can replace the traditional error-prone, contact-based methods. Acoustic sound pressure and particle velocity measurements capture the directional fault signature of the mechanical defects in electric motors, and a one-dimensional convolutional neural networks (1D-CNNs) approach is proposed to process raw sensing data and eliminate the need for manual feature extraction. An experimental case study is performed to test the proposed 1D-CNN based fault classification on three different mechanically faulty electric motors across a variety of speeds. The results from acoustic pressure and particle velocity signals are compared against those from accelerometer signals. The experimental study confirms the feasibility of the proposed 1D-CNN on acoustic signals to be an excellent replacement for contact-based methods when assessing and classifying the machine fault condition. © 2025 Institute of Noise Control Engineering.