A rotating machinery fault diagnosis method based on multi-scale dimensionless indicators and random forests

Fault diagnosis methods based on dimensionless indicators have long been studied for rotating machinery. However, traditional dimensionless indicators frequently suffer a low accuracy of fault diagnosis for nonlinear and non-stationary dynamic signals of rotating machinery. In this paper, we propose an effective fault diagnosis method based on multi-scale dimensionless indicator (MSDI) and random forests. In the proposed method, the real-time vibration signals are first processed by the variational mode decomposition and then six types of MSDI are constructed based on the decomposed signals. Through utilizing the Fisher criterion, several top ranked MSDIs are selected as fault features. Based on the selected MSDIs, the random forests model is applied to determine fault types. To verify the superiority of the proposed method, several experiments on fault diagnosis are conducted on a centrifugal multi-level impeller blower. The results demonstrate that the proposed method can successfully identify different fault types and the average accuracy can reach 95.58%. In contrast with traditional dimensionless indicators based methods, the proposed method can improve the fault diagnosis accuracy by 7.25% and outperforms other techniques such as back propagation neural network, support vector machine and extreme learning machine. These results indicate that the MSDI can effectively solve the deficiency of the traditional dimensionless indicator, and has stronger distinguishing ability for the fault types.