Root cause analysis using artificial intelligence

Chigurupati, Asha; Lassar, Noah

doi:10.1109/ram.2017.7889651

Cited by 12 publications

(5 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this category, the main representatives are the Bayesian and Hybrid Bayesian networks. A Diagnostic Hybrid Bayesian Network is built in (Chigurupati and Lassar, 2017) to model the cause-effect relationship between the degradation parameters (cause) and failure modes (effect) that occur in order to capture the causesymptom relationship within the examined hardware system. The required step of assigning conditional probabilities for building up the Bayesian network topology is accomplished with the deployment of the linearly varying Weibull and Lognormal distributions.…”

Section: Resultsmentioning

confidence: 99%

A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing

Παπαγεωργίου

Theodosiou

Rapti

et al. 2022

Front. Manuf. Technol.

View full text Add to dashboard Cite

The identification of defect causes plays a key role in smart manufacturing as it can reduce production risks, minimize the effects of unexpected downtimes, and optimize the production process. This paper implements a literature review protocol and reports the latest advances in Root Cause Analysis (RCA) toward Zero-Defect Manufacturing (ZDM). The most recent works are reported to demonstrate the use of machine learning methodologies for root cause analysis in the manufacturing domain. The popularity of these technologies is then summarized and presented in the form of visualizing graphs. This enables us to identify the most popular and prominent methods used in modern industry. Although artificial intelligence gains more and more attraction in smart manufacturing, machine learning methods for root cause analysis seem to be under-explored. The literature survey revealed that only limited reviews are available in the field of RCA towards zero-defect manufacturing using AI and machine learning; thus, it attempts to fill this gap. This work also presents a set of open challenges to determine future developments.

show abstract

Section: Resultsmentioning

confidence: 99%

A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing

Παπαγεωργίου

Theodosiou

Rapti

et al. 2022

Front. Manuf. Technol.

View full text Add to dashboard Cite

show abstract

“…Both approaches are not scalable in large contexts where structured data is not available and manual preparation is not possible for several thousand workflows. An interesting approach in this context is provided by Chigurupati and Lassar [11], who presented a way to use a Bayesian network in order to model probabilities of failure paths for hardware.…”

Section: Single Pane Root Cause Analysismentioning

confidence: 99%

Enhancing Continuous Integration Systems with Assisted Root Cause Analysis

Bonic¹,

Bonic²,

Schmid³

2023

Preprint

View full text Add to dashboard Cite

<h4>Contribution</h4> <p>We provide initial algorithms and a framework leading to assisted root cause analysis through a modular architecture including collection, identification, analysis, and presentation steps. Arcalog, our proposed framework, creates pre-structured data from vast heterogeneous datasets automatically, enriches the data with additional information from the CI system, and adds fine-grained default and user-defined labels that support the root cause analysis of failures.</p> <h4>Background</h4> <p>Projects spanning hundreds of thousands of lines of code and several thousand daily continuous integration workflows cannot rely on manual prelabeling and qualitative interviews to generate meaningful improvements to broken CI job runs.</p> <h4>Evaluation</h4> <p>We evaluated our approach by measuring manual root cause analysis times over several CI jobs. The data we used is publicly available via the <a href="https://kubernetes.io/" target="_blank">Kubernetes</a> and <a href="https://www.redhat.com/en/technologies/cloud-computing/openshift" target="_blank">OpenShift</a> projects, allowing every researcher to continue and reproduce our work.</p> <h4>Community</h4> <p>In order to create reproducible workflows and improve debugging together, we have created the open Arcalot community. Join our round table, suggest enhancements, and vote on the next roadmap items!</p>

show abstract

“…Both approaches are not scalable in large contexts where structured data is not available and manual preparation is not possible for several thousand workflows. An interesting approach in this context is provided by Chigurupati and Lassar [12], who presented a way to use a Bayesian network in order to model probabilities of failure paths for hardware.…”

Section: Single Pane Root Cause Analysismentioning

confidence: 99%

Enhancing Continuous Integration Systems with Assisted Root Cause Analysis

Bonic¹,

Bonic²,

Schmid³

2023

Preprint

View full text Add to dashboard Cite

<h4>Contribution</h4> <p>We provide initial algorithms and a framework leading to assisted root cause analysis through a modular architecture including collection, identification, analysis, and presentation steps. Our proposed framework creates pre-structured data from vast heterogeneous datasets automatically, enriches the data with additional information from the CI system, and adds fine-grained default and user-defined labels that support the root cause analysis of failures.</p> <h4>Background</h4> <p>Projects spanning hundreds of thousands of lines of code and several thousand daily continuous integration workflows cannot rely on manual prelabeling and qualitative interviews to generate meaningful improvements to broken CI job runs.</p> <h4>Evaluation</h4> <p>We evaluated our approach by measuring manual root cause analysis times over several CI jobs. The data we used is publicly available via the <a href="https://kubernetes.io/" target="_blank">Kubernetes</a> and <a href="https://www.redhat.com/en/technologies/cloud-computing/openshift" target="_blank">OpenShift</a> projects, allowing every researcher to continue and reproduce our work.</p>

show abstract

Root cause analysis using artificial intelligence

Cited by 12 publications

References 1 publication

A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing

A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing

Enhancing Continuous Integration Systems with Assisted Root Cause Analysis

Enhancing Continuous Integration Systems with Assisted Root Cause Analysis

Contact Info

Product

Resources

About