Bearing Metrics for Health Monitoring of Machine Tool Linear Axes

2019 ◽  
Author(s):  
Gregory W. Vogl ◽  
Brian C. Galfond ◽  
N. Jordan Jameson
Keyword(s):  
Standard Deviation ◽  
Inertial Measurement Unit ◽  
Ball Bearing ◽  
Geometric Error ◽  
Rotating Machinery ◽  
Measurement Unit ◽  
Ball Bearings ◽  
Statistical Population ◽  
Inertial Measurement ◽  
Signal Features

Abstract Diagnostics and prognostics of rotating machinery ball bearings is quite mature with an abundance of available methods and algorithms. However, extending these algorithms to other ball bearing applications is challenging and may not yield usable results. This work used a linear axis to study the ability of an inertial measurement unit (IMU), along with nine signal features, to measure changes in geometric error motions due to induced faults on the recirculating ball bearings of two carriage trucks. The IMU data was analyzed with the nine features used for rotating machinery systems, including root-mean-square, standard deviation, and kurtosis. For each stage of degradation, the statistical population and median value of each feature were compared against the population and median for no degradation, to monitor feature changes due to ball damage. Correlation analyses revealed an ability of the standard deviation feature to detect statistically significant changes as small as 0.05 micrometers or 0.5 microradians, corresponding to a total damaged surface area of truck balls of less than 0.1 percent.

scholarly journals Pendeteksi Sikap pada Model Wahana Terbang menggunakan Inertial Measurement Unit

2015 ◽  
Vol 3 (1) ◽  
pp. 16
Author(s):  
NANDANG TARYANA ◽  
DECY NATALIANA ◽  
ALFIE RIZKY ANANDA
Keyword(s):  
Standard Deviation ◽  
Measurement System ◽  
Tilt Angle ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Model Aircraft

ABSTRAKPenelitian ini membahas tentang aplikasi dari sensor gyroscope dan accelerometer yang merupakan komponen penyusun alat ukur inersial (inertial measurement unit) untuk mendeteksi sikap (attitude) pada wahana terbang tanpa awak. Sikap (attitude) memberikan 3 (tiga) informasi yaitu roll, pitch dan yaw. Penelitian ini bertujuan untuk melihat apakah alat pendeteksi sikap (attitude) sudah layak atau tidak digunakan pada wahana terbang yang akan di-modelkan yaitu dengan cara merancang sistem pengukuran/pendeteksi serta monitoring sikap (attitude) menggunakan software LabVIEW. Metode yang digunakan untuk mendeteksi kemiringan attitude merupakan pengabungan hasil pengukuran dari gyroscope dan accelerometer. Pengujian alat pendeteksi sikap dilakukan dengan cara mensimulasikan kinematika pergerakan wahana terbang. Berdasarkan hasil pengujian, alat pendeteksi sikap sudah layak digunakan pada wahana terbang Hal ini sesuai dengan simpangan rata – rata yang diperoleh dari hasil pengukuran rotasi pada sumbu x (roll) sebesar 0,58 o, rotasi pada sumbu y (pitch) sebesar 0,53 o dan rotasi pada sumbu z (yaw) sebesar 7,64 o.Kata kunci:  Gyroscope, Accelerometer, Inertial Measurement Unit, attitude ABSTRACTThis journal elaborate the aplication of a gyroscope and accelerometer from an inertial measurement unit (IMU) for sensing attitude on an aircraft. Attitude give 3 (three) basic informations, that information are roll, pitch and yaw. The purpose of this journal is to analys if the attitude sensing device are suitble to be used on a model aircraft. This journal are designing measurement system and monitoring using software from LabVIEW. The method used to detect roll, pitch and yaw is combination from the measurement of gyroscope and accelerometer. The testing of the attitude sensing device by simulating the kinematics of an aircraft. The results shows that the attude sensing device are qualified for sensing tilt angle for x-axis (roll) with standard deviation 0,58 o, sensing tilt angle for y-axis (pitch) with standard deviation 0.53 o and sensing tilit angle for z (axis) with standar deviation 7,64 o.Keyword:  Gyroscope, Accelerometer, Inertial Measurement Unit, attitude  

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.

scholarly journals Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4767
Author(s):  
Karla Miriam Reyes Leiva ◽  
Milagros Jaén-Vargas ◽  
Benito Codina ◽  
José Javier Serrano Olmedo
Keyword(s):  
Inertial Measurement Unit ◽  
Visually Impaired ◽  
Inertial Sensors ◽  
Assistive Technologies ◽  
Biomechanical Analysis ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Visually Impaired People ◽  
Impaired People ◽  
Application Fields

A diverse array of assistive technologies have been developed to help Visually Impaired People (VIP) face many basic daily autonomy challenges. Inertial measurement unit sensors, on the other hand, have been used for navigation, guidance, and localization but especially for full body motion tracking due to their low cost and miniaturization, which have allowed the estimation of kinematic parameters and biomechanical analysis for different field of applications. The aim of this work was to present a comprehensive approach of assistive technologies for VIP that include inertial sensors as input, producing results on the comprehension of technical characteristics of the inertial sensors, the methodologies applied, and their specific role in each developed system. The results show that there are just a few inertial sensor-based systems. However, these sensors provide essential information when combined with optical sensors and radio signals for navigation and special application fields. The discussion includes new avenues of research, missing elements, and usability analysis, since a limitation evidenced in the selected articles is the lack of user-centered designs. Finally, regarding application fields, it has been highlighted that a gap exists in the literature regarding aids for rehabilitation and biomechanical analysis of VIP. Most of the findings are focused on navigation and obstacle detection, and this should be considered for future applications.

scholarly journals Early Detection of Freezing of Gait during Walking Using Inertial Measurement Unit and Plantar Pressure Distribution Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2246
Author(s):  
Scott Pardoel ◽  
Gaurav Shalin ◽  
Julie Nantel ◽  
Edward D. Lemaire ◽  
Jonathan Kofman
Keyword(s):  
Early Detection ◽  
Plantar Pressure ◽  
Inertial Measurement Unit ◽  
Binary Classification ◽  
Freezing Of Gait ◽  
Pressure Sensors ◽  
Measurement Unit ◽  
Distribution Data ◽  
Inertial Measurement ◽  
Plantar Pressure Distribution

Freezing of gait (FOG) is a sudden and highly disruptive gait dysfunction that appears in mid to late-stage Parkinson’s disease (PD) and can lead to falling and injury. A system that predicts freezing before it occurs or detects freezing immediately after onset would generate an opportunity for FOG prevention or mitigation and thus enhance safe mobility and quality of life. This research used accelerometer, gyroscope, and plantar pressure sensors to extract 861 features from walking data collected from 11 people with FOG. Minimum-redundancy maximum-relevance and Relief-F feature selection were performed prior to training boosted ensembles of decision trees. The binary classification models identified Total-FOG or No FOG states, wherein the Total-FOG class included data windows from 2 s before the FOG onset until the end of the FOG episode. Three feature sets were compared: plantar pressure, inertial measurement unit (IMU), and both plantar pressure and IMU features. The plantar-pressure-only model had the greatest sensitivity and the IMU-only model had the greatest specificity. The best overall model used the combination of plantar pressure and IMU features, achieving 76.4% sensitivity and 86.2% specificity. Next, the Total-FOG class components were evaluated individually (i.e., Pre-FOG windows, Freeze windows, transition windows between Pre-FOG and Freeze). The best model detected windows that contained both Pre-FOG and FOG data with 85.2% sensitivity, which is equivalent to detecting FOG less than 1 s after the freeze began. Windows of FOG data were detected with 93.4% sensitivity. The IMU and plantar pressure feature-based model slightly outperformed models that used data from a single sensor type. The model achieved early detection by identifying the transition from Pre-FOG to FOG while maintaining excellent FOG detection performance (93.4% sensitivity). Therefore, if used as part of an intelligent, real-time FOG identification and cueing system, even if the Pre-FOG state were missed, the model would perform well as a freeze detection and cueing system that could improve the mobility and independence of people with PD during their daily activities.

scholarly journals Trunk Posture during Manual Materials Handling of Beer Kegs

2021 ◽  
Vol 18 (14) ◽  
pp. 7380
Author(s):  
Colleen Brents ◽  
Molly Hischke ◽  
Raoul Reiser ◽  
John Rosecrance
Keyword(s):  
Small Businesses ◽  
Inertial Measurement Unit ◽  
Measurement Unit ◽  
Low Back ◽  
Inertial Measurement ◽  
Materials Handling ◽  
Kinematic Measurement ◽  
Manual Materials Handling ◽  
Trunk Posture ◽  
Data Design

Craft brewing is a rapidly growing industry in the U.S. Most craft breweries are small businesses with few resources for robotic or other mechanical-assisted equipment, requiring work to be performed manually by employees. Craft brewery workers frequently handle stainless steel half-barrel kegs, which weigh between 13.5 kg (29.7 lbs.) empty and 72.8 kg (161.5 lbs.) full. Moving kegs may be associated with low back pain and even injury. In the present study, researchers performed a quantitative assessment of trunk postures using an inertial measurement unit (IMU)-based kinematic measurement system while workers lifted kegs at a craft brewery. Results of this field-based study indicated that during keg handling, craft brewery workers exhibited awkward and non-neutral trunk postures. Based on the results of the posture data, design recommendations were identified to reduce the hazardous exposure for musculoskeletal disorders among craft brewery workers.

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