Self-validating high-g accelerometers through data-driven methods

Abstract

In the aerospace industry, pyroshock testing is an indispensable step in designing space electronics. Yet, damages in high-g accelerometers, the core measuring instruments in pyroshock test systems, could result in various failures of pyroshock tests. To ensure the reliability of pyroschock tests for space electronics, a machine learning system is proposed to perform self-validation for high-g accelerometers. In this work, self-validation refers to the capability of identifying five key parameters, namely the validated shock signal, the validated uncertainty, the measurement status, the raw shock signal and the fault type, synchronously during measuring shock signals in pyroshock tests. To achieve the highest performance, we accomplish these tasks through combining an ensemble learning model and a deep neural network (DNN). The ensemble learning model, which integrates several k-nearest neighbors with different k values, is used to identify the sensors’ health conditions from their measurements and diagnose their fault types synchronously if damaged. The DNN, a deep autoencoder-based neural network, is designed to correct corrupted measurements through constructing the mapping between faulty signals and their corresponding reference counterparts. Experimental results show that the proposed machine learning system is capable of not only accurately identifying the health conditions and fault types of the damaged high-g accelerometers from their measurements, but also recovering the corrupted shock signals to a large extent, and, meanwhile, outputting the five self-validation parameters.