Enhancing reliability through interpretability: A comprehensive survey of interpretable intelligent fault diagnosis in rotating machinery
This paper presents a comprehensive survey on interpretable intelligent fault diagnosis for
rotating machinery, addressing the challenge of the “black box” nature of machine learning …
rotating machinery, addressing the challenge of the “black box” nature of machine learning …
Learning signal temporal logic through neural network for interpretable classification
Machine learning techniques using neural networks have achieved promising success for
time-series data classification. However, the models that they produce are challenging to …
time-series data classification. However, the models that they produce are challenging to …
Multi-class Temporal Logic Neural Networks
Time-series data can represent the behaviors of autonomous systems, such as drones and
self-driving cars. The problem of binary and multi-class classification has received a lot of …
self-driving cars. The problem of binary and multi-class classification has received a lot of …
TLINet: Differentiable Neural Network Temporal Logic Inference
There has been a growing interest in extracting formal descriptions of the system behaviors
from data. Signal Temporal Logic (STL) is an expressive formal language used to describe …
from data. Signal Temporal Logic (STL) is an expressive formal language used to describe …
Research on Ship Fault Identification Based on Optimized Convolutional Neural Network Algorithm
Z Zhang, Y Zhao, F Chen - 2024 7th International Conference …, 2024 - ieeexplore.ieee.org
The identification of abnormal vibration noise of underwater ships is of great significance for
troubleshooting ship faults to prevent damage deterioration and ensure its vitality. Aiming at …
troubleshooting ship faults to prevent damage deterioration and ensure its vitality. Aiming at …