A Fault Diagnosis Algorithm for the Dedicated Equipment Based on the CNN-LSTM Mechanism

Abstract

Dedicated equipment, which is widely used in many different types of vehicles, is the core system that determines the combat capability of special vehicles. Therefore, assuring the normal operation of dedicated equipment is crucial. With the increase in battlefield complexity, the demand for equipment functions is increasing, and the complexity of dedicated equipment is also increasing. To solve the problem of fault diagnosis of dedicated equipment, a fault diagnosis algorithm based on CNN-LSTM was proposed in this paper. CNN and LSTM are used in the model adopted by the algorithm to extract spatial and temporal features from the data. CBAM is used to enhance the model’s accuracy in identifying faults for dedicated equipment. Data on dedicated equipment faults were obtained from a hardware-in-loop simulation platform to verify the model. It is demonstrated that the proposed fault diagnosis algorithm has high recognition ability for dedicated equipment by comparing it to other neural network models.

FLAM: Locating and Mitigating 5GC Network Failure with Knowledge Graphs in China Telecom’s Network

“…Various advanced techniques such as Bayesian networks [6]- [8], deep learning [9]- [11], long-short term memory [12] [13], have been proposed for intelligent filure diagnosis and localization in 5G networks. However, those purely data-driven localized methods have some limitations: (i) Poor interpretability, traditional machine learning (ML) models are difficult to interpret and act as a black box for O&M experts, those can't explain the reasoning behind failure-located results.…”