Machine and component residual life estimation through the application of neural networks

dc.contributor.authorHerzog, M.A.
dc.contributor.authorMarwala, T.
dc.contributor.authorHeyns, P.S. (Philippus Stephanus)
dc.contributor.emailStephan.Heyns@up.ac.zaen_US
dc.date.accessioned2010-08-26T13:12:21Z
dc.date.available2010-08-26T13:12:21Z
dc.date.issued2009-02
dc.description.abstractThis paper concerns the use of neural networks for predicting the residual life of machines and components. In addition, the advantage of using condition-monitoring data to enhance the predictive capability of these neural networks was also investigated. A number of neural network variations were trained and tested with the data of two different reliability-related datasets. The first dataset represents the renewal case where the failed unit is repaired and restored to a good-as-new condition. Data were collected in the laboratory by subjecting a series of similar test pieces to fatigue loading with a hydraulic actuator. The average prediction error of the various neural networks being compared varied from 431 to 841 s on this dataset, where test pieces had a characteristic life of 8971 s. The second dataset was collected from a group of pumps used to circulate a water and magnetite solution within a plant. The data therefore originated from a repaired system affected by reliability degradation. When optimized, the multi-layer perceptron neural networks trained with the Levenberg–Marquardt algorithm and the general regression neural network produced a sum-of-squares error within 11.1% of each other for the renewal dataset. The small number of inputs and poorly mapped input space on the second dataset meant that much larger errors were recorded on some of the test data. The potential for using neural networks for residual life prediction and the advantage of incorporating condition-based data into the model was nevertheless proven for both examples.en_US
dc.identifier.citationHerzog, MA, Marwala, T & Heyns, PS 2009, ‘Machine and component residual life estimation through the application of neural networks’, Reliability Engineering and System Safety, vol. 94, no. 2, pp. 479-489. [www.elsevier.com/locate/ress]en_US
dc.identifier.issn0951-8320
dc.identifier.other10.1016/j.ress.2008.05.008
dc.identifier.urihttp://hdl.handle.net/2263/14749
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.rightsElsevieren_US
dc.subjectNeural networksen_US
dc.subjectCondition-monitoring dataen_US
dc.subjectResidual lifeen_US
dc.subject.lcshNeural networks (Computer science)
dc.subject.lcshMachine designen
dc.subject.lcshMachinery -- Monitoringen
dc.subject.lcshMachinery, Dynamics ofen
dc.titleMachine and component residual life estimation through the application of neural networksen_US
dc.typePostprint Articleen_US

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Herzog_Machine(2010).pdf
Size:
528.33 KB
Format:
Adobe Portable Document Format
Description:
Postprint Article

License bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
license.txt
Size:
2.44 KB
Format:
Item-specific license agreed upon to submission
Description: