Liner Wear Prediction Using Bayesian Regression Models and Clustering
Chutes, bins, and hoppers are critical assets in bulk commodity handling. Sacrificial wear liners are employed to protect these assets from abrasive wear. An essential maintenance challenge is optimising the timing of liner replacements. Traditionally, episodic human inspections have been in place,...
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| Language: | English |
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The Prognostics and Health Management Society
2025-03-01
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| Series: | International Journal of Prognostics and Health Management |
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| Online Access: | https://papers.phmsociety.org/index.php/ijphm/article/view/4266 |
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| author | Jacob Van Den Broek Melinda Hodkiewicz Adriano Polpo |
| author_facet | Jacob Van Den Broek Melinda Hodkiewicz Adriano Polpo |
| author_sort | Jacob Van Den Broek |
| collection | DOAJ |
| description | Chutes, bins, and hoppers are critical assets in bulk commodity handling. Sacrificial wear liners are employed to protect these assets from abrasive wear. An essential maintenance challenge is optimising the timing of liner replacements. Traditionally, episodic human inspections have been in place, but now, real-time wireless IoT sensing systems that measure liner thickness are being used. We propose a novel approach to estimate the remaining useful chute liner life. Instead of linear extrapolation based on individual sensor wear rates (commonly used in industry), we leverage a Clustered Bayesian Hierarchical Modeling (BHM). Two models are developed: Model 1 (Cluster Exemplar) uses parameters from the closest cluster exemplar, while Model 2 (Spatial and Temporal BHM) incorporates data from the active sensor, with prior distribution informed by Model 1. Data are drawn from a single hopper with 88 sensors, 20 of which reached their end-of-life threshold. Both Model 1 and Model 2 outperform the industry regression approach, significantly reducing overprediction. Notably, Model 2 predicts remaining useful life within 95% credible intervals and identifies anomalous sensor performance. This innovative use of historical and adjacent sensor data enhances wear degradation prediction, contributing valuable insights to the literature. |
| format | Article |
| id | doaj-art-0f74bc965ace4e2eacd78fadfd482de6 |
| institution | OA Journals |
| issn | 2153-2648 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | The Prognostics and Health Management Society |
| record_format | Article |
| series | International Journal of Prognostics and Health Management |
| spelling | doaj-art-0f74bc965ace4e2eacd78fadfd482de62025-08-20T01:48:49ZengThe Prognostics and Health Management SocietyInternational Journal of Prognostics and Health Management2153-26482025-03-01161111https://doi.org/10.36001/ijphm.2025.v16i1.4266Liner Wear Prediction Using Bayesian Regression Models and ClusteringJacob Van Den Broek0Melinda Hodkiewicz1Adriano Polpo2https://orcid.org/0000-0002-5959-1808School of Physics, Mathematics and Computing, University of Western Australia, Perth, WA, 6009, AustraliaSchool of Engineering, University of Western Australia, Perth, WA, 6009, AustraliaUniversity of Western AustraliaChutes, bins, and hoppers are critical assets in bulk commodity handling. Sacrificial wear liners are employed to protect these assets from abrasive wear. An essential maintenance challenge is optimising the timing of liner replacements. Traditionally, episodic human inspections have been in place, but now, real-time wireless IoT sensing systems that measure liner thickness are being used. We propose a novel approach to estimate the remaining useful chute liner life. Instead of linear extrapolation based on individual sensor wear rates (commonly used in industry), we leverage a Clustered Bayesian Hierarchical Modeling (BHM). Two models are developed: Model 1 (Cluster Exemplar) uses parameters from the closest cluster exemplar, while Model 2 (Spatial and Temporal BHM) incorporates data from the active sensor, with prior distribution informed by Model 1. Data are drawn from a single hopper with 88 sensors, 20 of which reached their end-of-life threshold. Both Model 1 and Model 2 outperform the industry regression approach, significantly reducing overprediction. Notably, Model 2 predicts remaining useful life within 95% credible intervals and identifies anomalous sensor performance. This innovative use of historical and adjacent sensor data enhances wear degradation prediction, contributing valuable insights to the literature.https://papers.phmsociety.org/index.php/ijphm/article/view/4266bayesian hierarchicaliotchuteinstrumented boltlinerwear prediction |
| spellingShingle | Jacob Van Den Broek Melinda Hodkiewicz Adriano Polpo Liner Wear Prediction Using Bayesian Regression Models and Clustering International Journal of Prognostics and Health Management bayesian hierarchical iot chute instrumented bolt liner wear prediction |
| title | Liner Wear Prediction Using Bayesian Regression Models and Clustering |
| title_full | Liner Wear Prediction Using Bayesian Regression Models and Clustering |
| title_fullStr | Liner Wear Prediction Using Bayesian Regression Models and Clustering |
| title_full_unstemmed | Liner Wear Prediction Using Bayesian Regression Models and Clustering |
| title_short | Liner Wear Prediction Using Bayesian Regression Models and Clustering |
| title_sort | liner wear prediction using bayesian regression models and clustering |
| topic | bayesian hierarchical iot chute instrumented bolt liner wear prediction |
| url | https://papers.phmsociety.org/index.php/ijphm/article/view/4266 |
| work_keys_str_mv | AT jacobvandenbroek linerwearpredictionusingbayesianregressionmodelsandclustering AT melindahodkiewicz linerwearpredictionusingbayesianregressionmodelsandclustering AT adrianopolpo linerwearpredictionusingbayesianregressionmodelsandclustering |