Self-adaptive single and simultaneous fault diagnosis for rotating machinery via redefined signal quality indicator and parallel ensemble network

Rotating machinery fault diagnosis plays a crucial role in industrial applications. However, existing methods face tremendous challenges in dealing with nonlinear noisy signals and intricate simultaneous-fault scenario. Dedicated to address this issue, a neoteric compound fault diagnosis method is proposed by using redefined signal quality indicator (RSQI) and parallel ensemble network. In this paper, RSQI is devised to eliminate noise components, and it can balance the noise reduction and signal fidelity. By further exploring the functionality of light gradient boosting machines (LGBM), parallel ensemble network containing two heterogeneous LGBMs is constructed. One is used to identify fault numbers, and the other is used for the single or simultaneous-fault scenario recognition. The proposed network is self-adaptive to the precious nature of the issue without user intervention for empirical threshold decision, and the two heterogeneous LGBMs can concurrently execute for responding to the diagnostic task in real time. Finally, two experimental studies are conducted to validate the proposed method. The experimental results of five multi-criteria decision-making (MCDM) methods indicate that the proposed method is competitive in the classification performance and algorithm robustness.