Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism

By Xiang Li, Wei Zhang, Qian Ding. Signal Processing, 2019

346 citations recorded by the source corpus.

Abstract In the recent years, deep learning-based intelligent fault diagnosis methods of rolling bearings have been widely and successfully developed. However, the data-driven method generally remains a “black box” to researchers and there is a gap between the emerging neural network-based methods and the well-established traditional fault diagnosis knowledge. This paper proposes a novel deep learning-based fault diagnosis method for rolling element bearings. Attention mechanism is introduced to assist the deep network to locate the informative data segments, extract the discriminative features of inputs, and visualize the learned diagnosis knowledge. Experiments on a popular rolling bearing dataset intuitively show the effectiveness of the proposed method, which is able to provide reliable diagnosis even with very few training data. The experimental results suggest this research offers a promising tool for intelligent fault diagnosis and provides effort in understanding the underlying mechanism of deep neural network.

Subfields: Control and Systems Engineering, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/6515a9bd750023d70b0aeb97ee87342a6a80fc31

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Chuang Sun
2. Xuefeng Chen
3. Ruqiang Yan
4. Qiuhua Miao
5. Yudong Cao
6. Yifei Ding
7. Shibin Wang
8. Minping Jia

In Mechanical Engineering

1. Qiuhua Miao
2. Yudong Cao
3. Yifei Ding
4. Shibin Wang
5. Minping Jia
6. Chuang Sun
7. Ruqiang Yan
8. Xuefeng Chen

In Mechanics of Materials

1. Xuefeng Chen
2. Ruqiang Yan
3. Chuang Sun
4. Qiuhua Miao
5. Yudong Cao
6. Yifei Ding
7. Minping Jia

Citations in the corpus

• A novel time–frequency Transformer based on self–attention mechanism and its application in fault diagnosis of rolling bearings (2021, methodology): “Nowadays, attention mechanism, which can associate di ff erent positions of a sequence to compute its unique representation, has now been successfully applied in a variety of tasks including natural language processing (NLP), computer vision (CV), and even fault diagnose [24].”
• Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study (2020, background): “Actually, some papers in intelligent fault diagnosis have noted this problem and attempted to propose some interpretable model [60, 61].”