Fault Detection and Isolation in Inertial Measurement Units Based on ?2-CUSUM and Wavelet Packet

The problem of fault detection and isolation (FDI) on inertial measurement units (IMUs) has received great attention in the last years, mainly with growing use of IMU strapdown platforms using fiber optic gyros (FOG) or micro electro mechanical systems (MEMSs). A way to solve this problem makes use of sensor redundancy and parity vector (PV) analysis. However, the actual sensor outputs can include some anomalies, as impulsive noise which can be associated with the sensors itself or data acquisition process, committing the elementary threshold criteria as commonly used. Therefore, to overcome this problem, in this work, it is proposed an algorithm based on median filter (MF) for prefiltering and chi-square cumulative sum (-CUSUM) only for fault detection (FD) applied to an IMU composed by four FOGs.

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Fault Detection and Isolation in Inertial Measurement Units Based on -CUSUM and Wavelet Packet

Mathematical Problems in Engineering ◽

10.1155/2013/869293 ◽

2013 ◽

Vol 2013 ◽

pp. 1-10 ◽

Cited By ~ 5

Author(s):

Élcio Jeronimo de Oliveira ◽

Ijar Milagre da Fonseca ◽

Hélio Koiti Kuga

Keyword(s):

Fault Detection ◽

Wavelet Packet ◽

Median Filter ◽

Principal Component ◽

Fault Isolation ◽

Fault Detection And Isolation ◽

Measurement Unit ◽

Short Delay ◽

Inertial Measurement ◽

Low Level

The aim of this paper is to present a fault detection algorithm (FDI) based on signal processing techniques developed for an inertial measurement unit (IMU) with minimal redundancy of fiber optic gyros. In this work the recursive median filter is applied in order to remove impulses (outliers) arising from data acquisition process and parity vector operations, improving the fault detection and isolation performance. The FDI algorithm is divided into two blocks: fault detection (FD) and fault isolation (FI). The FD part of the algorithm is used to guarantee the reliability of the isolation part and is based on parity vector analysis using -CUSUM algorithm. The FI part is performed using parity space projection of the energy subbands obtained from wavelet packet decomposition. This projection is an extension of clustering analysis based on singular value decomposition (SVD) and principal component analysis (PCA). The results of the FD and FI algorithms have shown the effectiveness of the proposed method, in which the FD algorithm is capable of indicating the low-level step bias fault with short delay and a high index of correct decisions of the FI algorithm also with low-level step bias fault.

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three-channel filter theory for fault detection and isolation of redundant inertial measurement unit

10.26226/morressier.59c106e9d462b80292389d8b ◽

2017 ◽

Author(s):

Tong ZHANG

Keyword(s):

Fault Detection ◽

Inertial Measurement Unit ◽

Fault Detection And Isolation ◽

Measurement Unit ◽

Inertial Measurement ◽

Filter Theory ◽

Channel Filter

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Inertial Sensors and Inertial Measurement Units

Pedestrian Inertial Navigation with Self‐Contained Aiding ◽

10.1002/9781119699910.ch2 ◽

2021 ◽

pp. 17-36

Keyword(s):

Inertial Sensors ◽

Inertial Measurement Units ◽

Inertial Measurement ◽

Measurement Units

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Sensor Fusion To Improve State Estimate Accuracy Using Multiple Inertial Measurement Units

2021 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL) ◽

10.1109/inertial51137.2021.9430484 ◽

2021 ◽

Author(s):

Ujjval N. Patel ◽

Imraan A. Faruque

Keyword(s):

Sensor Fusion ◽

Inertial Measurement Units ◽

State Estimate ◽

Inertial Measurement ◽

Measurement Units ◽

Improve State

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Analysis of ergonomic and unergonomic human lifting behaviors by using Inertial Measurement Units

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2017-0002 ◽

2017 ◽

Vol 3 (1) ◽

pp. 7-10 ◽

Cited By ~ 1

Author(s):

Jan Kuschan ◽

Henning Schmidt ◽

Jörg Krüger

Keyword(s):

Quality Of Life ◽

Machine Learning ◽

Angular Velocity ◽

Musculoskeletal System ◽

Occupational Diseases ◽

Inertial Measurement Units ◽

Inertial Measurement ◽

Measurement Units ◽

Main Components

Abstract:This paper presents an analysis of two distinct human lifting movements regarding acceleration and angular velocity. For the first movement, the ergonomic one, the test persons produced the lifting power by squatting down, bending at the hips and knees only. Whereas performing the unergonomic one they bent forward lifting the box mainly with their backs. The measurements were taken by using a vest equipped with five Inertial Measurement Units (IMU) with 9 Dimensions of Freedom (DOF) each. In the following the IMU data captured for these two movements will be evaluated using statistics and visualized. It will also be discussed with respect to their suitability as features for further machine learning classifications. The reason for observing these movements is that occupational diseases of the musculoskeletal system lead to a reduction of the workers’ quality of life and extra costs for companies. Therefore, a vest, called CareJack, was designed to give the worker a real-time feedback about his ergonomic state while working. The CareJack is an approach to reduce the risk of spinal and back diseases. This paper will also present the idea behind it as well as its main components.

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