A Novel Methodology for Fault Identification of Multi-stage Manufacturing Process Using Product Quality Measurement

Xiaorui Tong, Hossein D. Ardakani, David Siegel, Ellen Gamel, and Jay Lee
Publication Target: 
IJPHM
Publication Issue: 
1
Submission Type: 
Full Paper
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ijphm_17_007.pdf4.78 MBMay 14, 2017 - 6:53am

Data-driven modeling and fault detection of multi-stage manufacturing processes remain challenging due to the increasing complexity of the manufacturing process, the lack of structural data, data multi-dimensionality, and the additional difficulty when dealing with large data sets. The implementation of add-on sensors and establishing data acquisition, transfer, storage and analysis has the potential to facilitate advanced data modeling techniques. However, besides the associated costs, dealing with high-volume multi-dimensional data sets can be a major challenge. This paper presents a novel methodology for early fault identification of multi-stage manufacturing processes using a statistical approach. The major advantage of the proposed methodology is its reliance on only the product quality measurements and basic product manufacturing records, given the presence of peer sets. This leads to a feasible fault identification solution in a sensor-less environment without investing costly data collection systems. The developed methodology transforms the end-of-process quality measurements to a process performance metric based on a density-based statistical approach and a peer-to-peer comparison of the machines at one stage of the process. This approach allows one to be more proactive and identify the problematic machines that could be affecting product quality. A case study in an actual multi-stage manufacturing process is used to demonstrate the effectiveness of the developed methodology.

Publication Year: 
2017
Publication Volume: 
8
Publication Control Number: 
007
Page Count: 
10
Submission Keywords: 
fault diagnosis
multi-stage manufacturing process
product quality measurement
industrial big data analytics
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Submitted by: 
  
 
 
 

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