A Novel Methodology for Fault Identification of Multi-stage Manufacturing Process Using Product Quality Measurement
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 implementatio...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
The Prognostics and Health Management Society
2017-01-01
|
Series: | International Journal of Prognostics and Health Management |
Subjects: | |
Online Access: | https://papers.phmsociety.org/index.php/ijphm/article/view/2534 |
id |
doaj-b6f8d2394a66433283828a9abe3e7d85 |
---|---|
record_format |
Article |
spelling |
doaj-b6f8d2394a66433283828a9abe3e7d852021-07-02T20:42:25ZengThe Prognostics and Health Management SocietyInternational Journal of Prognostics and Health Management2153-26482153-26482017-01-0181doi:10.36001/ijphm.2017.v8i1.2534A Novel Methodology for Fault Identification of Multi-stage Manufacturing Process Using Product Quality MeasurementXiaorui Tong0Hossein D. Ardakani1David Siegel2Ellen Gamel3Jay Lee4NSF I/UCRC for Intelligent Maintenance Systems (IMS), University of Cincinnati, Cincinnati, OH 45221 USANSF I/UCRC for Intelligent Maintenance Systems (IMS), University of Cincinnati, Cincinnati, OH 45221 USANSF I/UCRC for Intelligent Maintenance Systems (IMS), University of Cincinnati, Cincinnati, OH 45221 USANSF I/UCRC for Intelligent Maintenance Systems (IMS), University of Cincinnati, Cincinnati, OH 45221 USANSF I/UCRC for Intelligent Maintenance Systems (IMS), University of Cincinnati, Cincinnati, OH 45221 USAData-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.https://papers.phmsociety.org/index.php/ijphm/article/view/2534fault diagnosismulti-stage manufacturing processproduct quality measurementindustrial big data analytics |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiaorui Tong Hossein D. Ardakani David Siegel Ellen Gamel Jay Lee |
spellingShingle |
Xiaorui Tong Hossein D. Ardakani David Siegel Ellen Gamel Jay Lee A Novel Methodology for Fault Identification of Multi-stage Manufacturing Process Using Product Quality Measurement International Journal of Prognostics and Health Management fault diagnosis multi-stage manufacturing process product quality measurement industrial big data analytics |
author_facet |
Xiaorui Tong Hossein D. Ardakani David Siegel Ellen Gamel Jay Lee |
author_sort |
Xiaorui Tong |
title |
A Novel Methodology for Fault Identification of Multi-stage Manufacturing Process Using Product Quality Measurement |
title_short |
A Novel Methodology for Fault Identification of Multi-stage Manufacturing Process Using Product Quality Measurement |
title_full |
A Novel Methodology for Fault Identification of Multi-stage Manufacturing Process Using Product Quality Measurement |
title_fullStr |
A Novel Methodology for Fault Identification of Multi-stage Manufacturing Process Using Product Quality Measurement |
title_full_unstemmed |
A Novel Methodology for Fault Identification of Multi-stage Manufacturing Process Using Product Quality Measurement |
title_sort |
novel methodology for fault identification of multi-stage manufacturing process using product quality measurement |
publisher |
The Prognostics and Health Management Society |
series |
International Journal of Prognostics and Health Management |
issn |
2153-2648 2153-2648 |
publishDate |
2017-01-01 |
description |
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. |
topic |
fault diagnosis multi-stage manufacturing process product quality measurement industrial big data analytics |
url |
https://papers.phmsociety.org/index.php/ijphm/article/view/2534 |
work_keys_str_mv |
AT xiaoruitong anovelmethodologyforfaultidentificationofmultistagemanufacturingprocessusingproductqualitymeasurement AT hosseindardakani anovelmethodologyforfaultidentificationofmultistagemanufacturingprocessusingproductqualitymeasurement AT davidsiegel anovelmethodologyforfaultidentificationofmultistagemanufacturingprocessusingproductqualitymeasurement AT ellengamel anovelmethodologyforfaultidentificationofmultistagemanufacturingprocessusingproductqualitymeasurement AT jaylee anovelmethodologyforfaultidentificationofmultistagemanufacturingprocessusingproductqualitymeasurement AT xiaoruitong novelmethodologyforfaultidentificationofmultistagemanufacturingprocessusingproductqualitymeasurement AT hosseindardakani novelmethodologyforfaultidentificationofmultistagemanufacturingprocessusingproductqualitymeasurement AT davidsiegel novelmethodologyforfaultidentificationofmultistagemanufacturingprocessusingproductqualitymeasurement AT ellengamel novelmethodologyforfaultidentificationofmultistagemanufacturingprocessusingproductqualitymeasurement AT jaylee novelmethodologyforfaultidentificationofmultistagemanufacturingprocessusingproductqualitymeasurement |
_version_ |
1721322747911471104 |