# A Transferable Autologistic Model for Predicting Rare Failures in Heterogeneous Equipment

arXiv: https://arxiv.org/abs/2608.06695 · HTML: https://mlsanity.com/paper/2608.06695/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 47 references that are in the map.
- First posted on arXiv 7 Aug 2026.

## Builds on (most cited first, 5 of 5)

- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Recurrent Neural Networks for Multivariate Time Series with Missing Values](https://mlsanity.com/paper/1606.01865/) (2016)
- [Target-Embedding Autoencoders for Supervised Representation Learning](https://mlsanity.com/paper/2001.08345/) (2020)
- [Multivariate time series models for mixed data](https://mlsanity.com/paper/2104.01067/) (2021)
- [Prediction of rare events in the operation of household equipment using co-evolving time series](https://mlsanity.com/paper/2312.09410/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Synthetic Multivariate Refrigerator Time-Series Dataset for Predictive Maintenance](https://mlsanity.com/paper/2609.22229/) (2026)

## Abstract (arXiv)

Predicting failures before they occur remains a major challenge in predictive maintenance, particularly when failures are rare, when equipment of the same family differ in sensor configurations, and when the goal is anticipation rather than diagnosis of an already observed fault. This paper proposes a common-to-target probabilistic model that learns shared failure-related patterns across a family of heterogeneous equipment and adapts parsimoniously to target equipment. The model explicitly accounts for sensor heterogeneity, operating context, and degradation dynamics to produce calibrated failureprobability estimates suitable for maintenance planning. Its performance is evaluated on a synthetic refrigerator dataset comprising 27 simulated refrigerators with varying sensor configurations, operating conditions, and failure types, providing a controlle