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Vision-Based Fault Diagnostics Using Explainable Deep Learning With Class Activation Maps
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DOI：10.1109/ACCESS.2020.3009852.png)
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摘要
En 中文
In the era of the fourth industrial revolution (Industry 4.0) and the Internet of Things (IoT), real-time data is enormously collected and analyzed from mechanical equipment. By classifying and characterizing the measured signals, the fault condition of mechanical components could be identified. However, most current health monitoring techniques utilize time-consuming and labor-intensive feature engineering, i.e., feature extraction and selection, that are carried out by experts. This paper, on the contrary, deals with an automatic diagnosis method of machine monitoring using a convolutional neural network (CNN) with class activation maps (CAM). A class activation map enables us to discriminate the fault region in the images, thus allowing us to localize the fault precisely. The goal of the paper is to demonstrate how CNN and CAM could be employed to real-world vibration video to characterize the machine's status, representing normal or fault conditions. The performance of the proposed model is validated with a base-excited cantilever beam dataset and a water pump dataset. This paper presents a novel industrial application by developing a promising method for automatic machine condition-based monitoring.
Keyword:
Feature extraction
Vibrations
Fault diagnosis
Artificial neural networks
Machine learning
Mechanical sensors
Convolutional neural network
class activation maps
discriminative region
fault detection
mechanical component
explainable AI
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
K
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