scholarly journals Bayesian network-based root cause analysis and fault pathway identification in complex industrial processes

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Kalyani Zope ◽  
Tanmaya Singhal ◽  
Sri Harsha Nistala ◽  
Venkataramana Runkana
Keyword(s):  
Bayesian Networks ◽  
Sensor Data ◽  
Continuous Variables ◽  
Industrial Systems ◽  
Causal Maps ◽  
Root Cause ◽  
Industrial Operations ◽  
Plant Personnel ◽  
Discretization Technique ◽  
Root Cause Identification

Real-time root cause identification (RCI) of faults or abnormal events in industries gives plant personnel the opportunity to address the faults before they progress and lead to failure. RCI in industrial systems must deal with their complex behavior, variable interactions, corrective actions of control systems and variability in faulty behavior. Bayesian networks (BNs) is a data-driven graph-based method that utilizes multivariate sensor data generated during industrial operations for RCI. Bayesian networks, however, require data discretization if data contains both discrete and continuous variables. Traditional discretization techniques such as equal width (EW) or equal frequency (EF) discretization result in loss of dynamic information and often lead to erroneous RCI. To deal with this limitation, we propose the use of a dynamic discretization technique called Bayesian Blocks (BB) which adapts the bin sizes based on the properties of data itself. In this work, we compare the effectiveness of three discretization techniques, namely EW, EF and BB coupled with Bayesian Networks on generation of fault propagation (causal) maps and root cause identification in complex industrial systems. We demonstrate the performance of the three methods on the industrial benchmark Tennessee-Eastman (TE) process.  For two complex faults in the TE process, the BB with BN method successfully diagnosed correct root causes of the faults, and reduced redundancy (up to 50%) and improved the propagation paths in causal maps compared to other two techniques.

Analysis of Leakage Failures in Flash Memory Devices and Root Cause Identification

2000 ◽  
Author(s):  
J. N. C. de Luna ◽  
M. O. del Fierro ◽  
J. L. Muñoz
Keyword(s):  
Flash Memory ◽  
Electromagnetic Interference ◽  
Yield Loss ◽  
High Amplitude ◽  
Physical Analysis ◽  
Input Buffer ◽  
Automated Test Equipment ◽  
Root Cause ◽  
Root Cause Identification ◽  
Test Process

Abstract An advanced flash bootblock device was exceeding current leakage specifications on certain pins. Physical analysis showed pinholes on the gate oxide of the n-channel transistor at the input buffer circuit of the affected pins. The fallout contributed ~1% to factory yield loss and was suspected to be caused by electrostatic discharge or ESD somewhere in the assembly and test process. Root cause investigation narrowed down the source to a charged core picker inside the automated test equipment handlers. By using an electromagnetic interference (EMI) locator, we were able to observe in real-time the high amplitude electromagnetic pulse created by this ESD event. Installing air ionizers inside the testers solved the problem.

A New Approach for SRAM Soft Defect Root Cause Identification

2007 ◽  
Author(s):  
Peter Egger ◽  
Stefan Müller ◽  
Martin Stiftinger
Keyword(s):  
Integrated Circuits ◽  
Top Down ◽  
Feature Size ◽  
Design Rules ◽  
New Approach ◽  
Root Cause ◽  
Wafer Fab ◽  
Sram Cell ◽  
Tcad Simulation ◽  
Root Cause Identification

Abstract With shrinking feature size of integrated circuits traditional FA techniques like SEM inspection of top down delayered devices or cross sectioning often cannot determine the physical root cause. Inside SRAM blocks the aggressive design rules of transistor parameters can cause a local mismatch and therefore a soft fail of a single SRAM cell. This paper will present a new approach to identify a physical root cause with the help of nano probing and TCAD simulation to allow the wafer fab to implement countermeasures.

Bayesian networks based rare event prediction with sensor data

2009 ◽  
Vol 22 (5) ◽  
pp. 336-343 ◽  
Author(s):  
Seong-Pyo Cheon ◽  
Sungshin Kim ◽  
So-Young Lee ◽  
Chong-Bum Lee
Keyword(s):  
Bayesian Networks ◽  
Rare Event ◽  
Sensor Data ◽  
Event Prediction

Advancement in Data Engineering and Feature Processing Workflow by Using Deep Learning Techniques for the Automation of ESP Failure Root Cause Analyses

2021 ◽  
Author(s):  
Saniya Karnik ◽  
Navya Yenuganti ◽  
Bonang Firmansyah Jusri ◽  
Supriya Gupta ◽  
Prasanna Nirgudkar ◽  
...  
Keyword(s):  
Deep Learning ◽  
Failure Analysis ◽  
Language Processing ◽  
Turnaround Time ◽  
Process Efficiency ◽  
Root Cause ◽  
Feature Processing ◽  
Learning Techniques ◽  
Electrical Submersible Pump ◽  
Root Cause Identification

Abstract Today, Electrical Submersible Pump (ESP) failure analysis is a tedious, human-intensive, and time-consuming activity involving dismantle, inspection, and failure analysis (DIFA) for each failure. This paper presents a novel artificial intelligence workflow using an ensemble of machine learning (ML) algorithms coupled with natural language processing (NLP) and deep learning (DL). The algorithms outlined in this paper bring together structured and unstructured data across equipment, production, operations, and failure reports to automate root cause identification and analysis post breakdown. This process will result in reduced turnaround time (TAT) and human effort thus drastically improving process efficiency.

Root cause identification approach using decomposition of QFD and extended RPN for product manufacturing reliability degradation

2021 ◽  
pp. 1748006X2110436
Author(s):  
Anqi Zhang ◽  
Yihai He ◽  
Chengcheng Wang ◽  
Jishan Zhang ◽  
Zixuan Zhang
Keyword(s):  
Quality Function Deployment ◽  
Degradation Mechanism ◽  
Product Reliability ◽  
Root Cause ◽  
Reliability Characteristics ◽  
Identification Technique ◽  
Reliability Control ◽  
Product Manufacturing ◽  
Identification Approach ◽  
Root Cause Identification

Reliability is reflected in product during manufacturing. However, due to uncontrollable factors during production, product reliability may degrade substantially after manufacturing. Thus, root cause analysis is important in identifying vulnerable parameters to prevent the product reliability degradation in manufacturing. Therefore, a novel root cause identification approach based on quality function deployment (QFD) and extended risk priority number (RPN) is proposed to prevent the degradation of product manufacturing reliability. First, the connotation of product manufacturing reliability and its degradation mechanism are expounded. Second, the associated tree of the root cause of product manufacturing reliability degradation is established using the waterfall decomposition of QFD. Third, the classic RPN is extended to focus on importance to reliability characteristics, probability, and un-detectability. Furthermore, fuzzy linguistic is adopted and the integrated RPN is calculated to determine the risk of root causes. Therefore, a risk-oriented root cause identification technique of product manufacturing reliability degradation is proposed using RPN. Finally, a root cause identification of an engine component is presented to verify the effectiveness of this method. Results show that the proposed approach can identify the root cause objectively and provide reference for reliability control during production.

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