Prognostics and health management is an engineering discipline that aims to support system operation while ensuring maximum safety and performance. Prognostics is a key step of this framework, focusing on developing effective maintenance policies based on predictive methods. Traditionally, prognostics models forecast the degradation process using regression techniques that approximate a mapping function from input to continuous remaining useful life estimates. These models are typically of high complexity and low interpretability. Classification approaches are an alternative solution to these types of models. We propose a predictive classification model that translates the input into discrete output variables instead of mapping the input to a single remaining useful life estimate. Each discrete output variable corresponds to a range of remaining useful life values. In other words, each output class variable represents the likelihood or risk of failure within a specific time range. We apply this model to a real-world case study involving the unscheduled and scheduled removals of a set of engine bleed valves from a fleet of Boeing 737 aircraft. The model can reach an area under the (micro-average) receiver operating characteristic curve of 72%. Our results suggest that the proposed multiclass gated recurrent unit network can provide valuable information about the different fault stages (corresponding to intervals of residual lives) of the studied valves.

Developing Health Indicators (HI) is a crucial aspect of prognostics and health management of complex systems. Previous research has demonstrated the benefits of accurately determining the HI, which can lead to better performance of prognostic models. However, the existing methodologies for determining HI in complex systems are mostly semi-supervised and rely on assumptions that may not hold in real-world scenarios. The existing methods usually involve using a reference set of healthy sensor readings or run-to-failure data to infer HI. But the unsupervised inference of HI from sensor readings, which is challenging in scenarios where diverse operating conditions can mask the effect of degradation on sensor readings, has not been extensively researched. In this paper, we propose a novel physics-informed unsupervised model for determining HI. Unlike previous methods, constrained by assumptions, the proposed method uses prior general knowledge about degradation to infer HI, thereby eliminating the need for a reference set of healthy sensor readings. The proposed unsupervised model is an Autoencoder that incorporates constraints on its latent space to ensure consistency with knowledge about degradation. We assess the efficacy of the proposed model by analyzing a prevalent prognostic case study, specifically the turbofan engine dataset (N-CMAPSS). Our analysis considers the model's sensitivity to data availability and the resulting Health Index's quality, including trendability and monotonicity. Additionally, we investigate the impact of incorporating the Health Index in predicting Remaining Useful Life (RUL). We demonstrate that our proposed method generates a Health Index that exhibits greater monotonicity and trendability than the current state-of-the-art semi-supervised approach. Moreover, our approach for identifying the Health Index leads to enhanced prognostic performance compared to the existing semi-supervised approach.