Rolling bearing fault detection using symmetrized dot pattern indices and feedforward neural network

Fault detection (FD) is of primary importance for maintenance of mechanical systems. In recent years, the symmetrized dot pattern (SDP) technique has been increasingly applied in this context. This article introduces a new approach to SDP based on new indices derived from SDP transformed vibrational signals. The indices characterize the density, orientation and curvature of the distribution of ‘snowflake’ diagrams, and they were used as inputs for a feedforward neural network (FNN) for FD of bearings as an application of the new approach. The validity of the technique was demonstrated through its application on two public rolling bearing datasets, thereby substantiating its generalizability across a range of fault types and operating conditions. Results demonstrate high classification accuracy, low false positives rates and low computational costs suitable for real-time implementation. The new SDP and FNN approach was also compared to the classical convolutional neural network-based approach: the new method does not require the images, achieves better performance and has a low computational cost compared to the classical one. Finally, the proposed approach is compared with the most modern techniques demonstrating its validity.