Similarity index of the STFT-based health diagnosis of variable speed rotating machines

Fault diagnosis and health monitoring of industrial rotating machines are of paramount importance for ensuring the reliability, safety, and efficiency of modern industrial operations. This paper proposes a Short-Time Fourier Transform (STFT)-based fault diagnosis approach for industrial rotating machinery. In this proposed model, the STFT of the reference vibration signals is evaluated and compared with the STFT of the other testing vibration signals to diagnose the fault types. Three different similarity operators: Euclidean distance, cosine similarity, and structural similarity are used to conclude the similarity index between the reference signal and test signal. By using variable speed vibration data with different fault types, the proposed model can better simulate real- world conditions and improve the accuracy and effectiveness of fault diagnosis. The results from the confusion matrices, heat maps, and t-SNE plots demonstrate the effectiveness of the proposed method for fault diagnosis and monitoring of variable-speed rotating machines using vibration signals. It is concluded that the structural similarity index proved to be a promising approach for accurate fault diagnosis in variable-speed rotating machines. The results are also compared with the existing approaches in the literature and it was concluded that the proposed model attains the highest accuracy for the variable speed rotating machines.