hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Discovering Premature Replacements in Predictive Maintenance Time-to-Event Data
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0001-9416-5647
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0001-5163-2997
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0002-3034-6630
Halmstad University, School of Information Technology, Center for Applied Intelligent Systems Research (CAISR).ORCID iD: 0000-0003-3272-4145
Show others and affiliations
2023 (English)In: Proceedings of the Asia Pacific Conference of the PHM Society 2023 / [ed] Takehisa Yairi; Samir Khan; Seiji Tsutsumi, New York: The Prognostics and Health Management Society , 2023, Vol. 4Conference paper, Published paper (Refereed)
Abstract [en]

Time-To-Event (TTE) modeling using survival analysis in industrial settings faces the challenge of premature replacements of machine components, which leads to bias and errors in survival prediction. Typically, TTE survival data contains information about components and if they had failed or not up to a certain time. For failed components, the time is noted, and a failure is referred to as an event. A component that has not failed is denoted as censored. In industrial settings, in contrast to medical settings, there can be considerable uncertainty in an event; a component can be replaced before it fails to prevent operation stops or because maintenance staff believe that the component is faulty. This shows up as “no fault found” in warranty studies, where a significant proportion of replaced components may appear fault-free when tested or inspected after replacement.

In this work, we propose an expectation-maximization-like method for discovering such premature replacements in survival data. The method is a two-phase iterative algorithm employing a genetic algorithm in the maximization phase to learn better event assignments on a validation set. The learned labels through iterations are accumulated and averaged to be used to initialize the following expectation phase. The assumption is that the more often the event is selected, the more likely it is to be an actual failure and not a “no fault found”.

Experiments on synthesized and simulated data show that the proposed method can correctly detect a significant percentage of premature replacement cases.

Place, publisher, year, edition, pages
New York: The Prognostics and Health Management Society , 2023. Vol. 4
Series
Proceedings of the Asia Pacific Conference of the PHM Society, E-ISSN 2994-7219
Keywords [en]
Survival Analysis, Predictive Maintenance, Early Replacements, Genetic Algorithms
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hh:diva-52105DOI: 10.36001/phmap.2023.v4i1.3609OAI: oai:DiVA.org:hh-52105DiVA, id: diva2:1814294
Conference
4th Asia Pacific Conference of the Prognostics and Health Management, Tokyo, Japan, September 11-14, 2023
Funder
Knowledge Foundation, 20200001
Note

Som manuscript i avhandling/As manuscript in thesis.

Available from: 2023-11-23 Created: 2023-11-23 Last updated: 2023-12-19Bibliographically approved
In thesis
1. Machine Learning Survival Models: Performance and Explainability
Open this publication in new window or tab >>Machine Learning Survival Models: Performance and Explainability
2023 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Survival analysis is an essential statistics and machine learning field in various critical applications like medical research and predictive maintenance. In these domains understanding models' predictions is paramount. While machine learning techniques are increasingly applied to enhance the predictive performance of survival models, they simultaneously sacrifice transparency and explainability. 

Survival models, in contrast to regular machine learning models, predict functions rather than point estimates like regression and classification models. This creates a challenge regarding explaining such models using the known off-the-shelf machine learning explanation techniques, like Shapley Values, Counterfactual examples, and others.   

Censoring is also a major issue in survival analysis where the target time variable is not fully observed for all subjects. Moreover, in predictive maintenance settings, recorded events do not always map to actual failures, where some components could be replaced because it is considered faulty or about to fail in the future based on an expert's opinion. Censoring and noisy labels create problems in terms of modeling and evaluation that require to be addressed during the development and evaluation of the survival models.

Considering the challenges in survival modeling and the differences from regular machine learning models, this thesis aims to bridge this gap by facilitating the use of machine learning explanation methods to produce plausible and actionable explanations for survival models. It also aims to enhance survival modeling and evaluation revealing a better insight into the differences among the compared survival models.

In this thesis, we propose two methods for explaining survival models which rely on discovering survival patterns in the model's predictions that group the studied subjects into significantly different survival groups. Each pattern reflects a specific survival behavior common to all the subjects in their respective group. We utilize these patterns to explain the predictions of the studied model in two ways. In the first, we employ a classification proxy model that can capture the relationship between the descriptive features of subjects and the learned survival patterns. Explaining such a proxy model using Shapley Values provides insights into the feature attribution of belonging to a specific survival pattern. In the second method, we addressed the "what if?" question by generating plausible and actionable counterfactual examples that would change the predicted pattern of the studied subject. Such counterfactual examples provide insights into actionable changes required to enhance the survivability of subjects.

We also propose a variational-inference-based generative model for estimating the time-to-event distribution. The model relies on a regression-based loss function with the ability to handle censored cases. It also relies on sampling for estimating the conditional probability of event times. Moreover, we propose a decomposition of the C-index into a weighted harmonic average of two quantities, the concordance among the observed events and the concordance between observed and censored cases. These two quantities, weighted by a factor representing the balance between the two, can reveal differences between survival models previously unseen using only the total Concordance index. This can give insight into the performances of different models and their relation to the characteristics of the studied data.

Finally, as part of enhancing survival modeling, we propose an algorithm that can correct erroneous event labels in predictive maintenance time-to-event data. we adopt an expectation-maximization-like approach utilizing a genetic algorithm to find better labels that would maximize the survival model's performance. Over iteration, the algorithm builds confidence about events' assignments which improves the search in the following iterations until convergence.

We performed experiments on real and synthetic data showing that our proposed methods enhance the performance in survival modeling and can reveal the underlying factors contributing to the explainability of survival models' behavior and performance.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2023. p. 25
Series
Halmstad University Dissertations ; 108
Keywords
Survival Analysis, Explainable Artificial Intelligence, Survival Patterns, Counterfactual Explanations, Evaluation Metrics, Concordance Index
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-52269 (URN)978-91-89587-30-4 (ISBN)978-91-89587-29-8 (ISBN)
Presentation
2024-01-18, Wigforss, Hus J, Kristan IV:s väg 3, Halmstad, 09:00 (English)
Opponent
Supervisors
Available from: 2023-12-19 Created: 2023-12-18 Last updated: 2024-02-01Bibliographically approved

Open Access in DiVA

fulltext(1267 kB)65 downloads
File information
File name FULLTEXT01.pdfFile size 1267 kBChecksum SHA-512
d5161a11f21c7bf62048baca3398a18af53113d8dadec6e07e382070358fd3f153744e3c03dcc638a552e800bf3e6700b1bae09e6b5eac7dde17e227182ae954
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records

Alabdallah, AbdallahRögnvaldsson, ThorsteinnFan, YuantaoPashami, SepidehOhlsson, Mattias

Search in DiVA

By author/editor
Alabdallah, AbdallahRögnvaldsson, ThorsteinnFan, YuantaoPashami, SepidehOhlsson, Mattias
By organisation
Center for Applied Intelligent Systems Research (CAISR)
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 65 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 483 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf