scholarly journals IMU Fault Detection Based on -CUSUM

2012 ◽  
Vol 2012 ◽  
pp. 1-15 ◽  
Author(s):  
Élcio Jeronimo de Oliveira ◽  
Hélio Koiti Kuga ◽  
Ijar Milagre da Fonseca
Keyword(s):  
Fault Detection ◽  
Impulsive Noise ◽  
Median Filter ◽  
Fault Detection And Isolation ◽  
Chi Square ◽  
Micro Electro Mechanical Systems ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Threshold Criteria ◽  
Data Acquisition Process

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.

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

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
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.

Abnormalities Detection of IMU Based on PCA in Motion Monitoring

2012 ◽  
Vol 224 ◽  
pp. 533-538 ◽  
Author(s):  
Jing Zhou ◽  
Steven Su ◽  
Ai Huang Guo ◽  
Wei Dong Chen
Keyword(s):  
Measurement Method ◽  
Degrees Of Freedom ◽  
Inertial Sensors ◽  
Principal Component ◽  
Body Sensor Networks ◽  
Remote Measurement ◽  
Micro Electro Mechanical Systems ◽  
Inertial Measurement ◽  
Multiple Degrees Of Freedom ◽  
Measurement Units

Inertial measurement units (IMU) are used as an affordable and effective remote measurement method for health monitoring in body sensor networks (BSNs) based on tracking people’s daily motions and activities. These inertial sensors are mostly micro-electro-mechanical systems with a combination of multi-axis combinations of precision gyroscopes, accelerometers, and magnetometers to sense multiple degrees of freedom (DoF).Unfortunately in the process of motion monitoring actual sensor outputs may contain some abnormalities, which might result in the misinterpretations of activities. In this paper, we use Principal component analysis (PCA) combined with Hotelling’s T2 and SPE statistic to detect abnormal data in the process of motion monitoring with IMU to ensure the reliability and accuracy in application. The simulated results prove this method is effective and feasible.

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