scholarly journals Fault Detection and Isolation for Redundant Inertial Measurement Unit under Quantization

2018 ◽  
Vol 8 (6) ◽  
pp. 865 ◽  
Author(s):  
Tong Zhang ◽  
Fenfen Wang ◽  
Wenxing Fu
Keyword(s):  
Fault Detection ◽  
Inertial Measurement Unit ◽  
Fault Detection And Isolation ◽  
Measurement Unit ◽  
Inertial Measurement

scholarly journals A Deep Neural Network-Based Fault Detection Scheme for Aircraft IMU Sensors

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Yiming Zhang ◽  
Hang Zhao ◽  
Jinyi Ma ◽  
Yunmei Zhao ◽  
Yiqun Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Inertial Measurement Unit ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Measurement Unit ◽  
Short Term ◽  
Detection Scheme ◽  
Inertial Measurement ◽  
Long Short Term Memory

A new fault detection scheme for aircraft Inertial Measurement Unit (IMU) sensors is developed in this paper. This scheme adopts a deep neural network with a CNN-LSTM-fusion architecture (CNN: convolution neural network; LSTM: long short-term memory). The fault detection network (FDN) developed in this paper is irrelative to aircraft model or flight condition. Flight data is reformed into a 2D format for FDN input and is mapped via the net to fault cases directly. We simulate different aircrafts with various flight conditions and separate them into training and testing sets. Part of the aircrafts and flight conditions appears only in the testing set to validate robustness and scalability of the FDN. Different architectures of FDN are studied, and an optimized architecture is obtained via ablation studies. An average detecting accuracy of 94.5% on 20 different cases is achieved.

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.

scholarly journals Automatically evaluating balance using machine learning and data from a single inertial measurement unit

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Measurement Unit ◽  
Balance Performance ◽  
Inertial Measurement ◽  
Self Assessments

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

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