This paper presents a convolutional neural network (CNN) based approach for fault
diagnosis of rotating machinery. The proposed approach incorporates sensor fusion by …

Fault diagnosis is critical to ensure the safety and reliable operation of rotating machinery.
Most methods used in fault diagnosis of rotating machinery extract a few feature values from …

Fault diagnosis of rotating machinery plays a significant role in the reliability and safety of
modern industrial systems. The traditional fault diagnosis methods usually need manually …

Fault diagnosis has a great significance in preventing serious failures of rotating machinery
and avoiding huge economic losses. The performance of the existing fault diagnosis …

Currently, convolutional neural networks (CNNs) have shown great potential in the field of
rotating machinery fault diagnosis. To maximize accuracy, the network architecture of novel …

Because multi-fault vibration signals in rotating machinery are often more complicated than
single faults, human-designed fault feature sets are not yet able to respond adequately to …

Intelligent mechanical fault diagnosis algorithms based on deep learning have achieved
considerable success in recent years. However, degradation of the diagnostic accuracy and …

Intelligent fault diagnosis methods based on deep learning becomes a research hotspot in
the fault diagnosis field. Automatically and accurately identifying the incipient micro-fault of …

This paper proposed a novel fault recognition method for rotating machinery on the basis of
multi-sensor data fusion and bottleneck layer optimized convolutional neural network (MB …

Intelligent machinery fault diagnosis system has been receiving increasing attention recently
due to the potential large benefits of maintenance cost reduction, enhanced operation safety …