| dc.contributor.advisor | Donovan, John | en |
| dc.contributor.author | Lannon, Olivia | en |
| dc.date.accessioned | 2017-03-21T11:11:05Z | |
| dc.date.available | 2017-03-21T11:11:05Z | |
| dc.date.issued | 2009-08 | |
| dc.identifier.citation | Lannon, O. (2009). Application of multivariate statistical process control to fuel cell manufacturing. MSc, Institute of Technology, Sligo | en |
| dc.identifier.other | MSc | en |
| dc.identifier.uri | https://research.thea.ie/handle/20.500.12065/691 | |
| dc.description | Univariate statistical control charts, such as the Shewhart chart, do not satisfy the
requirements for process monitoring on a high volume automated fuel cell
manufacturing line. This is because of the number of variables that require monitoring.
The risk of elevated false alarms, due to the nature of the process being high volume,
can present problems if univariate methods are used. Multivariate statistical methods are
discussed as an alternative for process monitoring and control.
The research presented is conducted on a manufacturing line which evaluates the
performance of a fuel cell. It has three stages of production assembly that contribute to
the final end product performance. The product performance is assessed by power and
energy measurements, taken at various time points throughout the discharge testing of
the fuel cell.
The literature review performed on these multivariate techniques are evaluated
using individual and batch observations. Modern techniques using multivariate control
charts on Hotellings T2 are compared to other multivariate methods, such as Principal
Components Analysis (PCA).
The latter, PCA, was identified as the most suitable method. Control charts such
as, scores, T2 and DModX charts, are constructed from the PCA model. Diagnostic
procedures, using Contribution plots, for out of control points that are detected using
these control charts, are also discussed. These plots enable the investigator to perform
root cause analysis. Multivariate batch techniques are compared to individual
observations typically seen on continuous processes. Recommendations, for the
introduction of multivariate techniques that would be appropriate for most high volume
processes, are also covered. | en |
| dc.format | PDF | en |
| dc.language.iso | en | en |
| dc.subject | Production engineering | en |
| dc.subject | Process control - Statistical methods | en |
| dc.subject | Fuel cells | en |
| dc.subject | Multivariate analysis | en |
| dc.title | Application of multivariate statistical process control to fuel cell manufacturing | en |
| dc.type | Master thesis (research) | en |
| dc.publisher.institution | Institute of Technology, Sligo | en |
| dc.rights.access | Creative Commons Attribution-NonCommercial-NoDerivs | en |
| dc.subject.department | Mechanical and Electronic Engineering ITS | en |
| dc.subject.department | Quality Assurance ITS | en |
