Toward Efficient and Interpretative Rolling Bearing Fault Diagnosis via Quadratic Neural Network With Bi-LSTM

With the widespread application of deep learning in Internet of Things (IoT), remarkable achievements have been made especially in rolling bearing fault diagnosis in rotating machinery. However, such complex models commonly have high demand for a large number of parameters and computational resources, and with insufficient interpretability, which restrict their extensive application in real-world industrial applications. To improve efficiency and interpretability, this study innovatively fuses a quadratic neural network (QNN) with a bidirectional long and short-term memory network (Bi-LSTM) to develop a novel hybrid model for quick and accurate diagnosis of rolling bearing faults. The results show that the…

• NN
  National Natural Science Foundation of China

  Awards: 61963018, 20212ACB202004, 52165012