Development of Quantitative Analytical Program for Corrosion Sensor Data

2021 ◽  
Vol 70 (2) ◽  
pp. 47-54
Author(s):  
Norikazu Fuse ◽  
Atsushi Naganuma ◽  
Yoshiharu Shumuta ◽  
Jun-ichi Tani ◽  
Yasuhiko Hori
Keyword(s):  
Sensor Data ◽  
Corrosion Sensor ◽  
Analytical Program

scholarly journals Corrosion Sensor Development for Condition-Based Maintenance of Aircraft

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Gino Rinaldi ◽  
Trisha Huber ◽  
Heather McIntosh ◽  
Les Lebrun ◽  
Heping Ding ◽  
...  
Keyword(s):  
Structural Integrity ◽  
Chloride Ion ◽  
Hydrogen Gas ◽  
Condition Based Maintenance ◽  
Ion Concentration ◽  
Sensor Data ◽  
Polymer Sensors ◽  
Corrosion Sensor ◽  
Commercial Off The Shelf ◽  
Maintenance Schedules

Aircraft routinely operate in atmospheric environments that, over time, will impact their structural integrity. Material protection and selection schemes notwithstanding, recurrent exposure to chlorides, pollution, temperature gradients, and moisture provide the necessary electrochemical conditions for the development and profusion of corrosion in aircraft structures. For aircraft operators, this becomes an important safety matter as corrosion found in a given aircraft must be assumed to be present in all of that type of aircraft. This safety protocol and its associated unscheduled maintenance requirement drive up the operational costs of the fleet and limit the availability of the aircraft. Hence, there is an opportunity at present for developing novel sensing technologies and schemes to aid in shifting time-based maintenance schedules towards condition-based maintenance procedures. In this work, part of the ongoing development of a multiparameter integrated corrosion sensor is presented. It consists of carbon nanotube/polyaniline polymer sensors and commercial-off-the-shelf sensors. It is being developed primarily for monitoring environmental and material factors for the purpose of providing a means to more accurately assess the structural integrity of aerospace aluminium alloys through fusion of multiparameter sensor data. Preliminary experimental test results are presented for chloride ion concentration, hydrogen gas evolution, humidity variations, and material degradation.

scholarly journals A Low Cost Sensor System for Monitoring and Predicting Corrosion of Long Term Spent Nuclear Fuel Storage

2021 ◽  
Author(s):  
Vikram M. Patel
Keyword(s):  
Nuclear Fuel ◽  
Spent Nuclear Fuel ◽  
Low Cost ◽  
Storage System ◽  
Sensor System ◽  
Sensor Data ◽  
Storage Facility ◽  
Corrosion Sensor ◽  
Fuel Storage ◽  
Permanent Storage

With the closing of the Yucca mountain storage facility, on-site storage of spent nuclear fuel at reactor sites has increased and will continue to increase until a permanent storage facility is prepared. Dry storage canisters are used to store spent nuclear fuel waste over long periods of time, but are susceptible to mechanical failure via corrosion. This dissertation presents a system to monitor the integrity of the storage canister. Sensor data fusion algorithms have been designed to predict the integrity of the storage system and provide feedback for preventative maintenance. The environmental conditions that lead to corrosion have been replicated and detected by the sensor system within an environmental chamber and the predictive model has been able to estimate the time till failure of a sacrificial corrosion sensor.

Auditory visualization of landmine detector sensor data

2009 ◽  
Author(s):  
Bradley M. Davis ◽  
Woodrow W. Winchester ◽  
Jason D. Zedlitz
Keyword(s):  
Sensor Data

Anomaly Detection in Sensor Data

2018 ◽  
Vol 18 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Jong-Min Kim ◽  
Jaiwook Baik
Keyword(s):  
Anomaly Detection ◽  
Sensor Data

Study on Fault Diagnosis of Planetary Gearbox in Unmanned Aerial Vehicle Using Multi sensor Data

2020 ◽  
Vol 20 (4) ◽  
pp. 332-342
Author(s):  
Hyung Jun Park ◽  
Seong Hee Cho ◽  
Kyung-Hwan Jang ◽  
Jin-Woon Seol ◽  
Byung-Gi Kwon ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Unmanned Aerial Vehicle ◽  
Sensor Data ◽  
Planetary Gearbox ◽  
Aerial Vehicle

A practical approach on non-regular sampling and universal demosaicing of raw image sensor data

2020 ◽  
Vol 2020 (1) ◽  
pp. 91-95
Author(s):  
Philipp Backes ◽  
Jan Fröhlich
Keyword(s):  
Image Quality ◽  
Source Code ◽  
Image Sensor ◽  
Sensor Data ◽  
Color Filter ◽  
Lowpass Filter ◽  
Sampling Errors ◽  
Regular Sampling ◽  
Single Sensor ◽  
Similar Image Quality

Non-regular sampling is a well-known method to avoid aliasing in digital images. However, the vast majority of single sensor cameras use regular organized color filter arrays (CFAs), that require an optical-lowpass filter (OLPF) and sophisticated demosaicing algorithms to suppress sampling errors. In this paper a variety of non-regular sampling patterns are evaluated, and a new universal demosaicing algorithm based on the frequency selective reconstruction is presented. By simulating such sensors it is shown that images acquired with non-regular CFAs and no OLPF can lead to a similar image quality compared to their filtered and regular sampled counterparts. The MATLAB source code and results are available at: http://github. com/PhilippBackes/dFSR

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

Analysis of Space Sensor Data.

1994 ◽  
Author(s):  
Andrew J. Mazzella ◽  
Delorey Jr. ◽  
Dennis E.
Keyword(s):  
Sensor Data
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