Wavelet packet decomposition

Network packet

Quantum mechanics

Algorithm

Computer security

Entropy (arrow of time)

Computer science

Classical mechanics

Computer network

Wavelet transform

Thermodynamics

Physics

Nonlinear system

Pattern recognition (psychology)

Artificial intelligence

Moment (physics)

Deep learning

Bearing (navigation)

Wavelet

Knowledge-Based Systems

Early fault prognosis of bearing is a very meaningful yet challenging task to improve the security of rotating machinery. For this purpose, a novel method based on enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy is proposed in this paper. First, complex wavelet packet energy moment entropy is defined as a new monitoring index to characterize bearing performance degradation. Second, deep gated recurrent unit network is constructed to capture the nonlinear mapping relationship hidden in the defined monitoring index. Finally, a modified training algorithm based on learning rate decay strategy is developed to enhance the prognosis capability of the constructed deep model. The proposed method is applied to analyze the simulated and experimental signals of bearing. The results demonstrate that the proposed method is more superior in sensibility and accuracy to the existing methods.

Enhanced deep gated recurrent unit and complex wavelet packet energy moment entropy for early fault prognosis of bearing