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Learning to Learn HVAC Failures: Layering ML Experiments in the Absence of Ground Truth
4th International Conference on Reliability, Safety and Security of Railway Systems, RSSRail 2022 ; 13294 LNCS:95-111, 2022.
Article in English | Scopus | ID: covidwho-1877757
ABSTRACT
Passenger comfort systems such as Heating, Ventilation, and Air-Conditioning units (HVACs) usually lack the data monitoring quality enjoyed by mission-critical systems in trains. But climate change, in addition to the high ventilation standards enforced by authorities due to the COVID pandemic, have increased the importance of HVACs worldwide. We propose a machine learning (ML) approach to the challenge of failure detection from incomplete data, consisting of two

steps:

1. human-annotation bootstrapping, on a fraction of temperature data, to detect ongoing functional loss and build an artificial ground truth (AGT);2. failure prediction from digital-data, using the AGT to train an ML model based on failure diagnose codes to foretell functional loss. We exercise our approach in trains of Dutch Railways, showing its implementation, ML-predictive capabilities (the ML model for the AGT can detect HVAC malfunctions online), limitations (we could not foretell failures from our digital data), and discussing its application to other assets. © 2022, Springer Nature Switzerland AG.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 4th International Conference on Reliability, Safety and Security of Railway Systems, RSSRail 2022 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 4th International Conference on Reliability, Safety and Security of Railway Systems, RSSRail 2022 Year: 2022 Document Type: Article