scholarly journals Supervised Distributed Multi-Instance and Unsupervised Single-Instance Autoencoder Machine Learning for Damage Diagnostics with High-Dimensional Data—A Hybrid Approach and Comparison Study

Computers ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 34
Author(s):  
Stefan Bosse ◽  
Dennis Weiss ◽  
Daniel Schmidt
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Damage Detection ◽  
High Reliability ◽  
Hybrid Approach ◽  
Sensor Data ◽  
Detection Accuracy ◽  
Low Resource ◽  
Damage Diagnostics ◽  
Single Instance

Structural health monitoring (SHM) is a promising technique for in-service inspection of technical structures in a broad field of applications in order to reduce maintenance efforts as well as the overall structural weight. SHM is basically an inverse problem deriving physical properties such as damages or material inhomogeneity (target features) from sensor data. Often models defining the relationship between predictable features and sensors are required but not available. The main objective of this work is the investigation of model-free distributed machine learning (DML) for damage diagnostics under resource and failure constraints by using multi-instance ensemble and model fusion strategies and featuring improved scaling and stability compared with centralised single-instance approaches. The diagnostic system delivers two features: A binary damage classification (damaged or non-damaged) and an estimation of the spatial damage position in case of a damaged structure. The proposed damage diagnostics architecture should be able to be used in low-resource sensor networks with soft real-time capabilities. Two different machine learning methodologies and architectures are evaluated and compared posing low- and high-resolution sensor processing for low- and high-resolution damage diagnostics, i.e., a dedicated supervised trained low-resource and an unsupervised trained high-resource deep learning approach, respectively. In both architectures state-based recurrent artificial neural networks are used that process spatially and time-resolved sensor data from experimental ultrasonic guided wave measurements of a hybrid material (carbon fibre laminate) plate with pseudo defects. Finally, both architectures can be fused to a hybrid architecture with improved damage detection accuracy and reliability. An extensive evaluation of the damage prediction by both systems shows high reliability and accuracy of damage detection and localisation, even by the distributed multi-instance architecture with a resolution in the order of the sensor distance.

A machine-learning approach for damage detection in aircraft structures using self-powered sensor data

2017 ◽  
Author(s):  
Hadi Salehi ◽  
Saptarshi Das ◽  
Shantanu Chakrabartty ◽  
Subir Biswas ◽  
Rigoberto Burgueño
Keyword(s):  
Machine Learning ◽  
Damage Detection ◽  
Sensor Data ◽  
Learning Approach ◽  
Aircraft Structures ◽  
Machine Learning Approach ◽  
Self Powered

Global bridge damage detection using multi-sensor data based on optimized functional echo state networks

2020 ◽  
pp. 147592172094820
Author(s):  
Jingpei Dan ◽  
Wending Feng ◽  
Xia Huang ◽  
Yuming Wang
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Damage Detection ◽  
Structural Damage ◽  
Sensor Data ◽  
Classification Algorithms ◽  
Echo State Network ◽  
Time Series Classification ◽  
Echo State Networks ◽  
Bridge Damage

While machine learning has been increasingly incorporated into structural damage detection, most existing methods still rely on hand-crafted damage features. For a given structure, the performance of detection is heavily impacted by the quality of features, and choosing the optimal features may be difficult and time-consuming. Various time series classification algorithms studied in machine learning are able to classify structural responses into damage conditions without feature engineering; however, most of them only deal with univariate time series classification and are either inapplicable or ineffective on multivariate (i.e. multi-dimensional) data, thus unable to fully utilize all sensors available on real bridges. To address these limitations, we propose a global bridge damage detection method based on multivariate time series classification with optimized functional echo state networks. In this method, data from multiple sensors are directly used as inputs without feature extraction. Training of the functional echo state network is simple and straightforward, and by leveraging the nonlinear mapping capacity and dynamic memory of functional echo state network, the separability of different classes, that is, classifying accuracy is enhanced compared to conventional classification algorithms. Furthermore, hyperparameters of the functional echo state network are automatically optimized with particle swarm optimization algorithm, which further improves the accuracy while saving the cost of manual tuning. Experimental results on two classical data sets show that functional echo state network achieves high and stable accuracy, which indicate that our method can detect global bridge structural damage efficiently by analyzing multiple sensor data, and is prospected to be applied in real bridge structural health monitoring systems.

scholarly journals Innovative methods for earthquake damage detection and classification using airborne observation of critical infrastructures (project LOKI)

2021 ◽  
Author(s):  
Julia Kohns ◽  
Vivien Zahs ◽  
Tahira Ullah ◽  
Danijel Schorlemmer ◽  
Cecilia Nievas ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Damage Detection ◽  
Emergency Response ◽  
Point Clouds ◽  
Mission Planning ◽  
Exposure Model ◽  
Critical Infrastructures ◽  
Innovative Methods ◽  
Building Level

<p>Earthquakes play a major role worldwide regarding economic and social consequences. In the event of an earthquake, many lives are at risk and the impact on the built and natural environment may be significant. Until now, estimations of damage and losses and the assessment of the stability of buildings are, however, only available several days to months after the event and are often based on the subjective assessment of experienced engineers.</p><p>For the effective planning of rescue measures and the best possible use of available resources, a fast, (semi-)automatic and accurate detection of the situation and an objective assessment of damage to critical infrastructures is indispensable. This requires a combination of innovative methods and technologies (UAVs, Machine Learning and Crowdsourcing combined with earthquake engineering knowledge) covering a wide range of spatial and temporal scales.</p><p>The interdisciplinary system LOKI (www.uni-heidelberg.de/loki) consists of the following procedure: After the occurrence of an earthquake, an initial damage forecast is made within a few minutes based on the Global Dynamic Exposure model and integrated vulnerability functions in combination with the ground-motion field to identify areas with potential high/low damage. Missing building footprints and required building information are recorded via a crowdsourcing approach to complete the OpenStreetMap building database, which serves as input to the exposure model. In parallel, mission plans for overview flights are created and transferred to fixed-wing UAVs, which record low to medium-resolution photos and 3D point clouds of the entire affected area. These data are used for damage detection, in which a binary distinction is made at building level between visible and non-visible damage using Machine Learning approaches. Thus, after a few hours, first orthophotos and the location of potentially damaged buildings can already be transmitted to emergency response teams. Thereafter, mission planning focuses on the capture of high-resolution 3D information of individual buildings. Fleets of multicopter drones provide highly detailed 3D imagery following mission plans that can be modified in real time by the emergency response teams. The mission planning algorithms support prioritization of specific areas or buildings for data acquisition, so that rescue measures can be optimally supported. The acquired high-resolution images and point clouds serve as input for damage classification, which is carried out per building using a combination of automatic procedures and Micro-Mapping. This offers the possibility to combine the advantages of fast automated procedures with the human ability to visually interpret details. Potential global and building material-related damage characteristics, which are based on observations of previous earthquakes, are included in a damage catalogue and allow building damage to be classified into five damage grades. In an iterative process, a timely and objective building-level classification of damage with an indication of the reliability of the specified degree of damage is achieved.</p><p>The integration of various disciplines and the combination of different concepts and technologies allows supporting disaster relief in different temporal and spatial resolutions with timely and reliable information on earthquake-induced damage.</p>

Optimal Placement of Sensors for Damage Characterization Using Genetic Algorithms

2007 ◽  
Vol 334-335 ◽  
pp. 1033-1036 ◽  
Author(s):  
Yun Ju Yan ◽  
Huan Guo Chen ◽  
Jie Sheng Jiang
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
High Reliability ◽  
Damage Index ◽  
Health Assessment ◽  
Optimization Procedure ◽  
Piezoelectric Sensor ◽  
Sensor Data ◽  
Optimal Placement ◽  
Minimum Number

Sensor data are the basis for health assessment of complex structural systems. Careful selection and logical layout of sensors is critical to enable the high reliability of system health assessment. This paper presents a methodology how to use a minimum number of sensors, and what locations of them should be placed, so that the voltage signals received from the sensor can be used to detect both presence and extent of damage. In this study, an optimization procedure is developed using Genetic Algorithm (GA) to determine the location of piezoelectric sensor for damage detection in a composite wingbox. A new damage index using all differences in voltage signals decomposed by wavelet transform is proposed. Results show that the proposed method is available at determining number and location of sensors for structural damage detection using piezoelectric patch sensors.

scholarly journals Detecting Driver Drowsiness with Multi-Sensor Data Fusion Combined with Machine Learning

2021 ◽  
Author(s):  
Hovannes Kulhandjian
Keyword(s):  
Machine Learning ◽  
Data Fusion ◽  
Traffic Safety ◽  
Head Movement ◽  
High Reliability ◽  
Research Work ◽  
Visual Surveillance ◽  
Sensor Data ◽  
Highway Traffic ◽  
National Highway Traffic Safety

In this research work, we develop a drowsy driver detection system through the application of visual and radar sensors combined with machine learning. The system concept was derived from the desire to achieve a high level of driver safety through the prevention of potentially fatal accidents involving drowsy drivers. According to the National Highway Traffic Safety Administration, drowsy driving resulted in 50,000 injuries across 91,000 police-reported accidents, and a death toll of nearly 800 in 2017. The objective of this research work is to provide a working prototype of Advanced Driver Assistance Systems that can be installed in present-day vehicles. By integrating two modes of visual surveillance to examine a biometric expression of drowsiness, a camera and a micro-Doppler radar sensor, our system offers high reliability over 95% in the accuracy of its drowsy driver detection capabilities. The camera is used to monitor the driver’s eyes, mouth and head movement and recognize when a discrepancy occurs in the driver's blinking pattern, yawning incidence, and/or head drop, thereby signaling that the driver may be experiencing fatigue or drowsiness. The micro-Doppler sensor allows the driver's head movement to be captured both during the day and at night. Through data fusion and deep learning, the ability to quickly analyze and classify a driver's behavior under various conditions such as lighting, pose-variation, and facial expression in a real-time monitoring system is achieved.

APPLICATION OF THE EARLY DAMAGE DIAGNOSTICS TECHNIQUE TO EXAMINATION OF THE AVIATION PANEL

2019 ◽  
Vol 85 (6) ◽  
pp. 53-63 ◽  
Author(s):  
I. E. Vasil’ev ◽  
Yu. G. Matvienko ◽  
A. V. Pankov ◽  
A. G. Kalinin
Keyword(s):  
Acoustic Emission ◽  
Damage Detection ◽  
High Speed ◽  
Early Stage ◽  
Video Data ◽  
High Speed Video ◽  
Damage Diagnostics ◽  
Subsurface Defect ◽  
Sensitive Coating ◽  
Early Damage

The results of using early damage diagnostics technique (developed in the Mechanical Engineering Research Institute of the Russian Academy of Sciences (IMASH RAN) for detecting the latent damage of an aviation panel made of composite material upon bench tensile tests are presented. We have assessed the capabilities of the developed technique and software regarding damage detection at the early stage of panel loading in conditions of elastic strain of the material using brittle strain-sensitive coating and simultaneous crack detection in the coating with a high-speed video camera “Video-print” and acoustic emission system “A-Line 32D.” When revealing a subsurface defect (a notch of the middle stringer) of the aviation panel, the general concept of damage detection at the early stage of loading in conditions of elastic behavior of the material was also tested in the course of the experiment, as well as the software specially developed for cluster analysis and classification of detected location pulses along with the equipment and software for simultaneous recording of video data flows and arrays of acoustic emission (AE) data. Synchronous recording of video images and AE pulses ensured precise control of the cracking process in the brittle strain-sensitive coating (tensocoating)at all stages of the experiment, whereas the use of structural-phenomenological approach kept track of the main trends in damage accumulation at different structural levels and identify the sources of their origin when classifying recorded AE data arrays. The combined use of oxide tensocoatings and high-speed video recording synchronized with the AE control system, provide the possibility of definite determination of the subsurface defect, reveal the maximum principal strains in the area of crack formation, quantify them and identify the main sources of AE signals upon monitoring the state of the aviation panel under loading P = 90 kN, which is about 12% of the critical load.

Hybrid Approach to Sentiment Analysis based on Syntactic Analy- sis and Machine Learning

2010 ◽  
Vol 14 (2) ◽  
pp. 159-181
Author(s):  
MUNPYO HONG ◽  
MIYOUNG SHIN ◽  
Shinhye Park ◽  
Hyungmin Lee
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Hybrid Approach

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>

scholarly journals Automatic Identification of Upper Extremity Rehabilitation Exercise Type and Dose Using Body-Worn Sensors and Machine Learning: A Pilot Study

2021 ◽  
pp. 158-166
Author(s):  
Noah Balestra ◽  
Gaurav Sharma ◽  
Linda M. Riek ◽  
Ania Busza
Keyword(s):  
Machine Learning ◽  
Upper Extremity ◽  
Sensor Data ◽  
Inpatient Setting ◽  
Accelerometer Data ◽  
Data Set ◽  
Machine Learning Classification ◽  
Exercise Type ◽  
Exercise Dose ◽  
Rehabilitation Exercises

<b><i>Background:</i></b> Prior studies suggest that participation in rehabilitation exercises improves motor function poststroke; however, studies on optimal exercise dose and timing have been limited by the technical challenge of quantifying exercise activities over multiple days. <b><i>Objectives:</i></b> The objectives of this study were to assess the feasibility of using body-worn sensors to track rehabilitation exercises in the inpatient setting and investigate which recording parameters and data analysis strategies are sufficient for accurately identifying and counting exercise repetitions. <b><i>Methods:</i></b> MC10 BioStampRC® sensors were used to measure accelerometer and gyroscope data from upper extremities of healthy controls (<i>n</i> = 13) and individuals with upper extremity weakness due to recent stroke (<i>n</i> = 13) while the subjects performed 3 preselected arm exercises. Sensor data were then labeled by exercise type and this labeled data set was used to train a machine learning classification algorithm for identifying exercise type. The machine learning algorithm and a peak-finding algorithm were used to count exercise repetitions in non-labeled data sets. <b><i>Results:</i></b> We achieved a repetition counting accuracy of 95.6% overall, and 95.0% in patients with upper extremity weakness due to stroke when using both accelerometer and gyroscope data. Accuracy was decreased when using fewer sensors or using accelerometer data alone. <b><i>Conclusions:</i></b> Our exploratory study suggests that body-worn sensor systems are technically feasible, well tolerated in subjects with recent stroke, and may ultimately be useful for developing a system to measure total exercise “dose” in poststroke patients during clinical rehabilitation or clinical trials.

Sign in / Sign up

Export Citation Format

Share Document