Understanding Overlap in Automatic Root Cause Analysis in Manufacturing Using Causal Inference

Jose L. Borges,Vera L. Migueis,Eduardo E. Oliveira

doi:10.1109/access.2021.3139199

Jose L. Borges, Vera L. Migueis + Show 1 more

Open Access

https://doi.org/10.1109/access.2021.3139199

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Abstract

Overlap has been identified in previous works as a significant obstacle to automated diagnosis using data mining algorithms, since it makes it impossible to discern how each machine influences product quality. Several solutions that handle overlap have been proposed, but the final result is a list of potential overlapped root causes. The goal of this paper is to develop a solution resilient to overlap that can determine the true root cause from a list of possible root causes, when possible, and determine the conditions in which it is possible to identify the root causes. This allows for a better understanding of overlap, and enables the development of a fully automatic root cause analysis for manufacturing. To do so, we propose an automatic root cause analysis approach that uses causal inference and <i>do</i> calculus to determine the true root cause. The proposed approach was validated on simulated and real case-study data, and allowed for an estimation of the effect of a product passing through a certain machine while disregarding the effect of overlap, in certain conditions. The results were on par with the state-of-the-art solutions capable of handling overlap. The contributions of this paper are a graphical definition of overlap, the identification of the conditions in which is possible to overcome the effect of overlap, and a solution that can present a single true root cause when such conditions are met.

Highlights

Manufacturing is highly competitive (Choudhary et al 2009) and managing manufacturing operations can be very complex
The interventional queries and Positive Mutual Information (PMI) values were computed for all three tuples, and these values were checked to see if the true root cause node was the one with the single highest value
As the PMI approach represents the best results from previous works, we can say that the proposed approach based on causal inference achieves a similar performance than the best of the previous methods

Summary

Introduction

Manufacturing is highly competitive (Choudhary et al 2009) and managing manufacturing operations can be very complex. Manufacturing companies should solve their operational problems efficiently and permanently in order to remain competitive. A manufacturing process is defined by a sequence of steps that products go through. In this illustrative example of a manufacturing process with three steps, P1 was already processed and is being monitored, P2 is being processed in Machine 3, and P3 is waiting to be processed before Step B. The root cause of the problem is in Step A, Machine 1

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