Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization

By Feng Jia, Yaguo Lei, Na Lu, Saibo Xing. Mechanical systems and signal processing, 2018

547 citations recorded by the source corpus.

Abstract Deep learning has attracted attentions in intelligent fault diagnosis of machinery because it allows a deep network to accomplish the tasks of feature learning and fault classification automatically. Among deep learning models, convolutional neural networks (CNNs) are able to learn features from mechanical vibration signals and thus several studies have applied CNNs in intelligent fault diagnosis of machinery. However, these studies suffer from the following weaknesses. (1) The imbalanced distribution of machinery health conditions is not considered. (2) What CNNs have learned is not clear. Therefore, in this paper, a framework called deep normalized convolutional neural network (DNCNN) is proposed for imbalanced fault classification of machinery to overcome the first weakness. Meanwhile, neuron activation maximization (NAM) algorithm is developed to handle the second weakness. To verify the proposed methods, three bearing datasets containing single faults and compound faults are constructed with different imbalanced degrees. The classification accuracies of the three datasets demonstrate that DNCNN is able to deal with the imbalanced classification problem more effectively than the commonly used CNNs. By analyzing the kernels of the convolutional layers of DNCNN via NAM algorithm, we find that these kernels act as filters and they become complex when the layers go deeper. This result may help us understand what DNCNN has learned in intelligent fault diagnosis of machinery.

Subfields: Control and Systems Engineering, Industrial and Manufacturing Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/eedfdb9f46933fc87132bb52dd509fd23741a5bf

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. Xuefeng Chen
2. Chuang Sun
3. Qiuhua Miao
4. Yudong Cao
5. Yifei Ding
6. Robert X. Gao
7. Ming Zhao
8. Jing Lin

In Industrial and Manufacturing Engineering

1. Robert X. Gao
2. Jianbo Yu

In Mechanical Engineering

1. Qiuhua Miao
2. Yudong Cao
3. Yifei Ding
4. Robert X. Gao
5. Ming Zhao
6. Jing Lin
7. Minping Jia
8. Chuang Sun

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): “[15] employed a deep normalized convolutional neural network (NCNN) for imbalanced fault classification of machinery and analyzed its mechanism via visualization.”
• WaveletKernelNet: An Interpretable Deep Neural Network for Industrial Intelligent Diagnosis (2019, background): “Jia [24] proposed a novel 1D-CNN framework for the fault diagnosis of three imbalanced bearing datasets.”
• A comprehensive review on convolutional neural network in machine fault diagnosis (2020, methodology): “echanical data for fault classification. Qian et al. [111] constructed an adaptive overlapping CNN for bearing fault diagnosis, in which the raw vibration signals were used to train model. Jia et al. [112] proposed a normalized CNN for imbalanced fault classification of machinery. In this method, a ”
• Remaining Useful Life Prediction Based on a Double-Convolutional Neural Network Architecture (2019, unknown): “A deep normalized CNN is proposed in [23] for imbalanced fault clas-sification of machinery.”