A Bayesian network approach to root cause diagnosis of process variations

By Sanorita Dey, J. Stori. 2005

182 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering, Mechanical Engineering, Strategy and Management

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Industrial and Manufacturing Engineering

1. J. V. Abellán-Nebot
2. Min Xie
3. Lei Huang
4. Bao-ping Cai
5. Fernando Romero Subirón

In Mechanical Engineering

1. J. V. Abellán-Nebot
2. Min Xie
3. Renjie Ji
4. Yonghong Liu
5. Lei Huang
6. Bao-ping Cai
7. Fernando Romero Subirón

In Strategy and Management

1. J. V. Abellán-Nebot
2. Min Xie
3. Lei Huang
4. Bao-ping Cai
5. Fernando Romero Subirón

Citations in the corpus

• Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network (2014, background): “Dey and Stori [21] developed and presented a process monitoring and diagnosis approach based on a Bayesian belief network for incorporating multiple process metrics from multiple sensor sources in sequential machining operations to identify the root cause of process variations and provide a probabil”
• Bayesian Networks in Fault Diagnosis (2017, methodology): “[39] S. Dey and J. A. Stori, “A Bayesian network approach to root cause diagnosis of process variations,” Int.”
• A review of machining monitoring systems based on artificial intelligence process models (2010, methodology): “Therefore, high-frequency components of cutting force are difficult to observe, and they are suitable only for detecting slow events or when a fast response is not essential [ 22 ].”