Search for: All records

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. 

Early identification of rotating machinery faults is crucial to avoid catastrophic failures upon installation. Contact-based vibration acquisition approaches are traditionally used for the purpose of machine health monitoring and end-of-line quality control. In complex working conditions, it can be difficult to perform an accurate accelerometer based vibration test. Acoustic signals (sound pressure and particle velocity) also contain important information about the operating state of mechanical equipment and can be used to detect different faults. A deep learning approach, namely one-dimensional Convolution Neural Networks (1D-CNN) can directly process raw time signals thereby eliminating the human dependance on fault feature extraction. An experimental research study is conducted to test the proposed 1D-CNN methodology on three different electric motor faults. The results from the study indicate that the fault detection performance from the new acoustic-based method is very effective and thus can be a good replacement to the conventional accelerometer-based methods for detection and diagnosis of mechanical faults in electric motors.