A new Health Index for RUL estimation of MEMS sensors using dimensionality reduction and Artificial Neural Networks

The accurate estimation of the Remaining Useful Life (RUL) of Micro-Electro-Mechanical Systems (MEMS) sensors is crucial for enabling effective condition-based maintenance (CBM) and improving system reliability. This is of vital importance in planning machinery replacements before their actual fault, reducing downtime and thus greatly optimizing costs. However, unlike industrial components (such as bearings, rotating machinery, or power devices), current prognostic strategies for MEMS sensors are limited due to the lack of suitable health indexes capable of describing the degradation path of the sensor’s metrological performances. To address this gap, this paper proposes a novel Health Index for MEMS accelerometers based on static accelerometer outputs by combining time-domain and frequency-domain features using a dimensionality reduction technique. By applying dimensionality reduction, the method captures the essential characteristics of sensor degradation, which are then used to train a Deep Learning-based model for predicting the Remaining Useful Life sufficiently in advance to allow for appropriate maintenance. This paper aims to fill an important gap by providing an innovative solution for MEMS sensor prognostics, with broad implications for industrial sectors reliant on precision sensing technologies. The performance of the proposed health indicator is validated through experimental data, demonstrating its ability to enhance the accuracy of MEMS sensor prognostics. Using data from the proposed accelerated life test and the Arrhenius degradation model, the RUL prediction based on the proposed health indicator has been performed at various operating temperatures, including typical conditions of industrial environments with high thermal stress.