# A Survey on Predictive Maintenance for Industry 4.0

arXiv: https://arxiv.org/abs/2002.08224 · HTML: https://mlsanity.com/paper/2002.08224/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2020.

## Built on by (most cited first, 2 of 2)

- [Intrinsic Anomaly Detection for Multi-Variate Time Series](https://mlsanity.com/paper/2206.14342/) (2022)
- [Learning representations with end-to-end models for improved remaining useful life prognostics](https://mlsanity.com/paper/2104.05049/) (2021)

## Abstract (arXiv)

Production issues at Volkswagen in 2016 lead to dramatic losses in sales of up to 400 million Euros per week. This example shows the huge financial impact of a working production facility for companies. Especially in the data-driven domains of Industry 4.0 and Industrial IoT with intelligent, connected machines, a conventional, static maintenance schedule seems to be old-fashioned. In this paper, we present a survey on the current state of the art in predictive maintenance for Industry 4.0. Based on a structured literate survey, we present a classification of predictive maintenance in the context of Industry 4.0 and discuss recent developments in this area.