Patent ID: 11860720
Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 22:
23. A method, comprising:
receiving historical feature data relating to a plurality of nodes in a system;
generating a machine learning (ML) model for a node of the plurality of nodes,
wherein the ML model is trained to predict a plurality of future feature values for the node based on at least a portion of the historical feature data, and
wherein the ML model is a non-linear neural network;

generating a causal graph for the plurality of nodes using a feature selection mechanism within the ML model, wherein the feature selection mechanism comprises a regularization term encouraging sparsity of nodes in selected features in the ML model; and
identifying a root cause for a fault in a first node of the plurality of nodes in the system using the generated causal graph, comprising:
using the ML model through counter factual reasoning, wherein for each candidate node a degree of its causal association to the fault may be determined by evaluating a difference between: (i) feature values for the first node predicted by the ML model using actual observed values of features prior to the fault and (ii) feature values for the first node predicted by the ML model using actual observed values, except that feature values relating to the candidate node are replaced by normal values predicted using earlier data.