An ML Software Solution for the Root Cause Analysis of Industrial Plant Faults. Root cause analysis (RCA) is a method of problem solving used for identifying the root causes of faults or problems. RCA is widely used in industrial process control, IT operations,
health industry and accident analysis in aviation, nuclear plants and rail transport. Without delving in the intricate details of speciﬁc problems, several general conditions can make RCA more diﬃcult than it may appear at ﬁrst sight. It is generally not possible, in practice, to monitor everything manually. There may be more than one root causes for a given problem and causal graphs often have many levels which makes the causal graph very diﬃcult to establish and query.
We developed an AI based solution of ﬁnding root cause and most inﬂuential path of the fault in industrial plants by learning the structure of causal Bayesian network from plant-data/sensor measurements and then using causal inference for ﬁnding causal eﬀects and path speciﬁc eﬀects. A ﬂexible and modular solution is designed for online monitoring of plants, predictive maintenance, ﬁnding the eﬀects of changing the values of control nodes on other nodes in the plant. Solution is ﬂexible enough to handle latent variables and works with both categorical and numeric variables/nodes.