Machine Learning-Based Automatic Fault Diagnosis Method for Operating Systems
DOI:
https://doi.org/10.53469/wjimt.2024.07(02).12Keywords:
Machine learning, Operating systems, Fault diagnosis, Hidden Markov models, Self-organizing competitive neural networksAbstract
In order to improve the stability, security and service quality of the operating system, an automatic fault diagnosis method based on machine learning is proposed in this paper. Firstly, using the fault detection of Tesla system as the background, AR model coefficient is taken as the feature of the fault system, and the influence of different state number and different mixed Gaussian number on the classification of hidden Markov model is explored. Secondly, the maximum likelihood estimation method is used to update the model parameters step by step, and the probability density function of the observed values is calculated to realize fault detection and diagnosis. Furthermore, a self-organizing competitive neural network is introduced to simplify the system into a running index and event substitution graph by using similarity graph, and faults are described as event sequence according to event time correlation. Finally, the ranking method is used to identify the key events and clear the fault mode, so as to realize the automatic fault diagnosis of the operating system. The method proposed in this study provides a new idea and method for the effective diagnosis of operating system faults.
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