This research aims to investigate and experiment on a state-of-the-art problem to treat the non-monotonic behavior of fault progression trends in predictive maintenance. Well-established algorithms and literature are researched for fault progression prognostics, however, not considerable attention has been given to
monotonic constraints at a preprocessing stage. A non-monotonic trend carries complex information which has outliers and nonessential signal values. The goal of the project is to motivate the usage of monotonic constraints to treat non-monotonic signals of a degrading component. The problem is presented as follows: Determining if the monotonic constrained method at a preprocessing step shall assist prognostics to estimate the remaining useful life of a component accurately. To explore this research, a monotonic constraint - Average Conditional Displacement (ACD) is used at the preprocessing step of a model, in comparison with regular preprocessing methods. The model is experimented on the NASAs simulated C-MAPSS datasets of turbofan engine and modelled with two prognostics algorithms. The model performance is measured
with performance metrics. The results showcase that by treating non-monotonic trends with monotonic constraints does improve the prognostics. However, they are not significantly advanced compared to other preprocessing steps.