Abstract: Fault root cause analysis is to find the occurrence cause of specific problems,faults and events,becoming the important technique for origin tracing in several paradigms.However,existing methods still cannot satisfy practical requirements of efficiency,accuracy and stability.BN(Bayesian network) is used as the knowledge framework for representing and inferring the depen-dencies among relevant attributes.Specifically,the vector quantized variational autoencoder algorithm for attribute reduction is proposed at first.Then,the α-BIC scoring metric is adopted to learn RCBN efficiently.Following,efficient inferences in RCBN are implemented by BN embedding by calculating the probabilities of fault occurrence for given causes.Finally,the Blame mechanism in causal model is adopted to evaluate the contribution of causes w.r.t.given faults and fulfill fault root cause analysis.Experimental results on 3 public datasets and 3 synthetic datasets show that the average accuracy and efficiency of the proposed fault detection are better than current representative methods,such that the precision is 7% higher and the running time is 60% faster than the comparison methods.

Key words: Fault root cause analysis, Bayesian network, Vector quantized variational autoencoder, Bayesian information criterion, Root cause contribution