A data-driven modular architecture with denoising autoencoders for health indicator construction in a manufacturing process

TL;DR

This paper introduces ModularHI, a novel modular approach using denoising autoencoders to construct health indicators for manufacturing systems without requiring historical data, enabling degradation detection in SMEs.

Contribution

The paper presents ModularHI, a new data-driven, modular framework that constructs health indicators without historical data, suitable for small and medium enterprises.

Findings

Successfully detects system degradation in open datasets

Operates without needing historical data for model training

Provides a flexible, sensor-input-based health monitoring approach

Abstract

Within the field of prognostics and health management (PHM), health indicators (HI) can be used to aid the production and, e.g. schedule maintenance and avoid failures. However, HI is often engineered to a specific process and typically requires large amounts of historical data for set-up. This is especially a challenge for SMEs, which often lack sufficient resources and knowledge to benefit from PHM. In this paper, we propose ModularHI, a modular approach in the construction of HI for a system without historical data. With ModularHI, the operator chooses which sensor inputs are available, and then ModularHI will compute a baseline model based on data collected during a burn-in state. This baseline model will then be used to detect if the system starts to degrade over time. We test the ModularHI on two open datasets, CMAPSS and N-CMAPSS. Results from the former dataset showcase our system's ability to detect degradation, while results from the latter point to directions for further research within the area. The results shows that our novel approach is able to detect system degradation without historical data.