Autoencoder

Deep learning

Physics

Geology

Undersampling

Pattern recognition (psychology)

Polymer chemistry

Bearing (navigation)

Signature (topology)

Mathematics

Time domain

Entropy (arrow of time)

Algorithm

Filter (signal processing)

Artificial intelligence

Seismology

Fault (geology)

Chemistry

Geometry

Aliasing

Computer science

Quantum mechanics

Modal

Computer vision

Applied Intelligence

With the development of industry and technology, mechanical systems’ safety has strong relations with the diagnosis of bearing faults. Accurate fault diagnosis is essential for the safe and stable operation of rotating machinery. Most former research depends too much on the fault signal specificity and learning model’s choices. To overcome the disadvantages of lacking intrinsic mode function (IMF) modal aliasing, low degree of discrimination between data of different fault types, high computational complexity. This paper proposes a method that combines multi-scale weighted entropy morphological filtering (MWEMF) signal processing and bidirectional long-short term memory neural networks (Bi-LSTM). The developed rolling bearing fault diagnosis strategy is then implemented to different databases and potential models to demonstrate the greatly improved system’s ability to reconstruct the time-to-frequency domain characteristics of fault signature signals and reduce learning cost. After verification, the classification accuracy of the proposed model reaches 99%.

Bearing fault diagnosis based on combined multi-scale weighted entropy morphological filtering and bi-LSTM