# Fault Detection and Isolation of MEMS IMU Array Based on WOA-MVMD-GLT

## TL;DR

This paper introduces a new method for detecting and isolating faults in MEMS IMU arrays using a combination of optimization and signal processing techniques.

## Contribution

A novel fault detection and isolation method called WOA-MVMD-GLT is proposed for MEMS IMU arrays.

## Key findings

- The proposed WOA-MVMD-GLT method reduces false alarm and false isolation rates in MEMS IMU fault detection.
- MVMD is used to extract signal features, while GLT is used for fault detection and isolation.

## Abstract

The stable and accurate output of the inertial measurement unit array (IMU) of a micro-electro-mechanical system (MEMS) is the key to ensuring the data fusion of the MEMS IMU array. However, due to the large number of MEMS IMUs contained in the MEMS IMU array, it is susceptible to interference and has difficulty avoiding failures. The output of the MEMS IMU contains noise, outliers, and other related errors, which can seriously lead to low fault detection and isolation accuracy in the MEMS IMU. In this study, a new method of fault detection and isolation based on multivariate variational mode decomposition (MVMD), a whale optimization algorithm (WOA), and a generalized likelihood test (GLT) is proposed, which is called WOA-MVMD-GLT. Firstly, a multi-index fitness function WOA is proposed to optimize the parameters of MVMD. Secondly, MVMD is used to extract the features of the MEMS IMU’s signals. Finally, a GLT is used to construct a fault detection function and a fault isolation function to detect and isolate the faults of gyroscopes and accelerometers. The experimental results show that the method proposed in this paper can significantly reduce the false alarm rate and false isolation rate.