bluuebunny/crossref_metadata_2025 huggingface dataset:
Pelaksanaan Diversi Terhadap Perkara Tindak Pidana yang dilakukan oleh Anak
Authors: Nasrullah Narullah
Published: February 2021

Tujuan penelitian menganalisis pelaksanaan diversi terhadap perkara tindak pidana yang dilakukan oleh anak di Wilayah Hukum Gorontalo. Tipe penelitian ini adalah penelitian yuridis empiris dengan penekanan pada fakta yuridis yang ada di lapangan. Hasil penelitian menunjukkan bahwa hakikat pelaksanaan diversi dalam perkara tindak pidana yang dilakukan oleh anak yaitu diversi menekankan pada pemulihan kembali seperti pada keadaan yang semula, dilatarbelakangi keinginan untuk menghindari stigma anak sebagai pelaku kejahatan dan menghindari efek negatif terhadap jiwa dan perkembangan anak tersebut dalam keterlibatannya dalam sistem peradilan pidana. Pelaksanaan diversi di wilayah hukum provinsi Gorontalo belum efektif dikarenakan masih banyak perkara anak, ditangani secara prosedur formal dengan kata lain gagal atau tidak dilaksanakan diversi.

The research objective is to analyze the implementation of diversion of criminal cases committed by children in the Gorontalo Legal Area. This type of research is empirical juridical research with an emphasis on juridical facts in the field. The results showed that the nature of the implementation of diversion in criminal cases committed by children, namely diversion emphasizes recovery back to its original state, motivated by the desire to avoid the stigma of children as perpetrators of crime and avoid negative effects on the child's psyche and development in their involvement in the system. criminal justice. The implementation of diversion in the jurisdiction of Gorontalo province has not been effective because there are still many cases of children, handled in a formal procedure, in other words failing or not being carried out by diversion.
Support Vector Machine And K-Nearest Neighbor Based Liver Disease Classification Model
Authors: Assegie Tsehay Admassu
Published: February 2021

Machine-learning approaches have become greatly applicable in disease diagnosis and prediction process. This is because of the accuracy and better precision of the machine learning models in disease prediction. However, different machine learning models have different accuracy and precision on disease prediction. Selecting the better model that would result in better disease prediction accuracy and precision is an open research problem. In this study, we have proposed machine learning model for liver disease prediction using Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) learning algorithms and we have evaluated the accuracy and precision of the models on liver disease prediction using the Indian liver disease data repository. The analysis of result showed 82.90% accuracy for SVM and 72.64% accuracy for the KNN algorithm. Based on the accuracy score of SVM and KNN on experimental test results, the SVM is better in performance on the liver disease prediction than the KNN algorithm.
Two Mode DPM Equipped with an Automatic Leak Test Using MPX5050GP and MPXV4115VC6U Sensors
Authors: Rooswita Fita Florensa , Rahmawati Triana , Syaifudin Syaifudin
Published: February 2021

The calibration process aims to guarantee measurement results in accordance with established standards. One of the tools used for pressure calibration is the Digital Presure Meter, which is the function of this tool to measure pressure on the Sphygmomanometer and Suction Pump or other tools that use pressure parameters for measurement. This module uses the Arduino system as a control and processing of analog data into digital data in order to condition the output of the MPX5050GP sensor for positive pressure and MPXV4115VC6U sensor for vacum pressure, this module uses a 4x20 LCD character display and there is a selection of mmHg and Kpa units with fluctuating resolution 0.25. Also in this module there is also an automatic leak test feature for the Sphygmomanometer, the measurement results obtained an average error of 7.3 mmHg for sphymomanometer measurements, and for suction pumps less than 1.5 Kpa. From these results it was concluded that this module can be used for the measurement of tools that use positive pressure and negative pressure.
Design and Build A Photovoltaic and Vertical Savonious Turbine Power Plant as an Alternative Power Supply to Help Save Energy in Skyscrapers
Authors: Priyambodo Dadang , Anggara Trisna Nugraha
Published: February 2021

Indonesia is a tropical country, because its area that crosses the equator has enormous solar potential, with a radiation magnitude of 4.80 kWh / m2 / day / day or equivalent to 112,000 GWp. Meanwhile, the solar energy received by the earth is 1.74 x 1017 W / hour, of which about 1-2% is converted into wind energy. However, in terms of total energy potential, Indonesia only uses around 10 MWp of solar energy, the difference is that wind energy which is planned to reach 250 MW in 2025 only uses 1 MW of the total potential. Skyscrapers are places with enormous wind energy potential. Because if the height exceeds 150 m, the reference point of the building can be called a skyscraper, which will cause high wind speed at the top of the building. This phenomenon is caused by the influence of the friction force which suppresses the wind speed, therefore the higher the position, the higher the wind speed. With this potential, to provide additional power and help save energy in existing facilities in the building, a smart photovoltaic and vertical turbine prototype design was created. The prototype design is a combination of weathered turbines and solar panels, the reason for using this type of turbine is because it can rotate at low wind speed (low wind speed) and has a very simple structure. In addition, because it uses a vertical shaft, the generator and gearbox can be placed close to the ground, making maintenance easier. The working principle of this prototype is designed to use a turbine to convert wind energy on the roof of the building into electrical energy, then mix it with electrical energy converted from solar energy by solar panels, so that electrical energy will be more optimal.
Data-driven Application of PHM to Asset Strategies
Authors: Lukens Sarah , Markham Matt
Published: February 2021

There are many benefits from implementing a prognostics and health management (PHM) initiative in an industrial facility, such as realizing potentials from reducing unplanned downtime and increased asset efficiency. Many industrial companies would like to take advantage of PHM technologies and algorithms to meet their business objectives, but identifying how to get started can be a daunting challenge. The classical approach is to begin with a Reliability Centered Maintenance (RCM) program supported by failure modes and effects analysis (FMEA) where all possible failure modes, their risks, and mitigating actions are evaluated in the context of asset function. In this framework, application of PHM technologies is viewed as a maintenance strategy effective at mitigating certain failure modes in specific cases that are both feasible and costeffective. However, there are many challenges and limitations to traditional RCM where data-driven analytics embedded in these work processes can help overcome and/or automate. On the other hand, the use of data-driven approaches introduces new challenges surrounding available data, data quality, and identifying numerical methods that are scalable across large datasets. In this paper, we present a case study applied to historical maintenance data for identifying and prioritizing where to start a PHM initiative, and discuss the work processes and various challenges encountered when embedding data analytics in classical reliability approaches.
On Practical Aspects of Using RNNs for Fault Detection in Sparsely-labeled Multi-sensor Time Series
Authors: Gugulothu Narendhar , TV Vishnu , Gupta Priyanka , Malhotra Pankaj , Vig Lovekesh , Agarwal Puneet , Shroff Gautam
Published: February 2021

In this work, we attempt to address two practical limitations when using Recurrent Neural Networks (RNNs) as classifiers for fault detection using multi-sensor time series data: Firstly, there is a need to understand the classification decisions of RNNs. It is difficult for engineers to diagnose the faults when multiple sensors are being monitored at once. The faults detected by RNNs can be better understood if the sensors carrying the faulty signature are known. To achieve this, we propose a sensor relevance scoring (SRS) approach that scores each sensor based on its contribution to the classification decision by leveraging the hidden layer activations of RNNs. Secondly, lack of labeled training data due to infrequent faults (or otherwise) makes it difficult to train RNNs in a supervised manner. We pre-train an RNN on large unlabeled data via an autoencoder in an unsupervised manner, and then finetune the RNN for the fault detection task using small amount of labeled training data. Through experiments on a public gasoil heating loop dataset and a proprietary pump dataset, we demonstrate the efficacy of the proposed solutions, and show that i) SRS can help point to the sensors relevant for a fault, ii) large unlabeled data can be used to pre-train an RNNbased fault detector in an unsupervised manner in sparselylabeled scenarios, and iii) a purely unsupervised approach for fault detection (e.g. based on RNN-autoencoders) may not suffice when the number of sensors being monitored is large while the signature for fault is present in only a small subset of sensors.
Leak Detection, Localization, and Prognosis of High Pressure Fuel Delivery System
Authors: Sarwar Azeem , Lu Xiangxing
Published: February 2021

Spark Ignition Direct Injection (SIDI) technology enables better fuel economy and tail pipe emissions in vehicles equipped with gasoline engines. The SIDI technology relies on the system’s ability to deliver fuel at high pressures (20-40 MPa). Such high pressure systems are prone to leakage if subjected to excessive vibrations, improper fitting, or failure of pressure seals over time due to cyclical loading. Fuel leakage can directly affect the operation of the engine and can cause customer inconvenience. It, therefore, becomes very important to devise a scheme that can effectively diagnose and prognose such kind of system fault. In this report, algorithm development for diagnosis and prognosis of leaks in high pressure fuel delivery system is presented. In particular, pressure profile of fuel in the common rail at engine cranking and engine shutdown are studied to generate schemes for fault detection, fault isolation, and fault prediction. The developed results are equally applicable to direct injection diesel engines given their similarity of operating principles and components.
New Adaptive Prognostics Approach Based on Hybrid Feature Selection with Application to Point Machine Monitoring
Authors: Atamuradov Vepa , Medjaher Kamal , Dersin Pierre , Zerhouni Noureddine , Camci Fatih
Published: February 2021

This paper proposes a new adaptive prognostics approach consisting of hybrid feature selection and remaining-useful-life (RUL) estimation steps for railway point machines. In step-1, different time-domain based features are extracted and the best ones are selected by the hybrid feature selection method. Then, a degradation model is fitted to each of the selected features and the parameters are estimated. In step-2, the RUL of the component is predicted by using the proposed adaptive prognostics approach. The adaptive prognostics is based on the weighted likelihood combination of the estimated model parameters. The model parameters each of which estimated by curve fitting are used in the calculation of the likelihood probability weights. Then, an adaptive degradation model is built by using the weighted combination of the model parameter estimates and the component RUL is estimated. The proposed approach is validated on in-field point machine sliding-chair degradation and the results are discussed.
Acoustic Assessment of an End Mill for Analysis of Tool Wear
Authors: Alzahrani Abdullah M. , Liu Rui , Kolodziej Jason R.
Published: February 2021

This paper investigates and analyzes the wear on an end mill cutting tool. Current tool condition monitoring systems lack efficient and effective fault prediction and diagnosis. To achieve adoption in the machining industry, condition monitoring must be accurate, non-invasive and cost effective with a longer operational lifetime of tool wear desired. The concept in this work is to associate an acoustic signature to a tool wear condition. First, the application of a Short Time Fourier Transform (STFT) as a time-frequency method to the acoustic measurement that can provide more information throughout the milling operation and gives a better representation of the signal than the conventional methods. Then a support vector machine (SVM) technique is applied as a classification approach to improve condition monitoring of the tool wear. This preliminary research’s goal is to create an accurate prognostic tool to determine tool wear condition through a noninvasive means with the ability to retrofit existing mills with the developed technology. The methodology is validated by actual end mill operation of the cutting of an aluminum workpiece at various feed rates, spindle speeds, and cutting depth.
Development and Field Evaluation of Data-driven Whole Building Fault Detection and Diagnosis Strategy
Authors: Chen Yimin , Wen Jin
Published: February 2021

Faults, i.e., malfunctioned sensors, components, control, and systems, in a building have significantly adverse impacts on the building’s energy consumption and indoor environment. To date, extensive research has been conducted on the development of component level fault detection and diagnosis (FDD) for building systems, especially the Heating, Ventilating, and Air Conditioning (HVAC) system. However, for faults that have multi-system impacts, component level FDD tools may encounter high false alarm rate due to the fact that HVAC subsystems are often tightly coupled together. Hence, the detection and diagnosis of whole building faults is the focus of this study. Here, a whole building fault refers to a fault that occurs in one subsystem but triggers abnormalities in other subsystems and have significant adverse whole building energy impact. The wide adoption of building automation systems (BAS) and the development of machine learning techniques make it possible and cost-efficient to detect and diagnose whole building faults using data-driven methods. In this study, a whole building FDD strategy which adopts weather and schedule information based pattern matching (WPM) method and feature based Principal Component Analysis (FPCA) for fault detection, as well as Bayesian Networks (BNs) based method for fault diagnosis is developed. Fault tests are implemented in a real campus building. The collected data are used to evaluate the performance of the proposed whole building FDD strategies.
Safety Monitoring and Prognostics for Automatic Aircraft Take-off
Authors: Schumann Johann , Zollitsch Alexander W. , Mumm Nils , Holzapfel Florian
Published: February 2021

The take-off of an aircraft is one of the most dangerous flight phases, as failures and adverse environmental conditions can lead to a catastrophe. Should abnormal events occur during the roll phase, the crew or the flight computer has to make the decision if the take-off can be safely rejected and the aircraft can brake and come to a standstill on the runway, or if the take-off has to be attempted in any case.

This decision has to be made instantaneously upon the estimate of the current state of the aircraft, using available sensor data under noise and potential failure conditions. In order to do so, at any time during the roll phase, a prediction has to be made, if the aircraft can come to a safe stop within the boundaries of the runway.

In this paper, we formulate this decision making task as an online prognostics problem and develop a model-based architecture that allows us to perform a probabilistic prediction of the aircraft’s braking distance given the current aircraft state. We are using particle filter and Monte-Carlo based prediction algorithms. Because this task has to be performed in real-time on the on-board flight computer, computational resources are very restricted. We therefore propose several models of increasing fidelity, which have substantially different computational footprints and exhibit different levels of accuracy that can impose severe restrictions on the handling of uncertainties and on the failures that can be modeled.
Advanced Maintenance in Manufacturing
Authors: Thomas Douglas S
Published: February 2021

The costs of maintenance and the potential effect on maintenance costs from adopting predictive maintenance techniques is not well documented at the national level. A number of data items need to be collected to estimate the costs and losses associated with maintenance. This paper examines the current literature on maintenance costs as it relates to advanced maintenance techniques and discusses the feasibility of collecting data to measure the relevant costs and losses. Discussions with manufacturing maintenance personnel suggests that manufacturers are willing and able to provide estimates or approximations of the data needed for estimating the manufacturing costs/losses relevant to advanced maintenance techniques. However, some discussants expressed uncertainty about the willingness to provide some of the data. Some items were not tracked; however, most believed that an approximation could be provided in these cases. In order to estimate maintenance cost for the manufacturing industry as a whole , a sufficient sample size is needed. Depending on the standard deviation, confidence interval, and accepted margin of error, a needed sample size of 77 is estimated, but could reasonably be as low as 14.
Data Driven Health Monitoring Approach to Extending Small Sats Mission
Authors: Sun Fangzhou , Dubey Abhishek , Kulkarni Chetan S. , Mahadevan Nagbhushan , Luna Ali Guarneros
Published: February 2021

In the next coming years, the International Space Station (ISS) plans to launch several small-sat missions powered by lithium-ion battery packs. An extended version of such mission requires dependable, energy dense, and durable power sources as well as system health monitoring. Hence a good health estimation framework to increase mission success is absolutely necessary as the devices are subjected to high demand operating conditions. This paper describes a hierarchical architecture which combines data-driven anomaly detection methods with a fine-grained model-based diagnosis and prognostics architecture. At the core of the architecture is a distributed stack of deep neural network that detects and classifies the data traces from nearby satellites based on prior observations. Any identified anomaly is transmitted to the ground, which then uses model-based diagnosis and prognosis framework to make health state estimation. In parallel, periodically the data traces from the satellites are transported to the ground and analyzed using model-based techniques. This data is then used to train the neural networks, which are run from ground systems and periodically updated. The collaborative architecture enables quick data-driven inference on the satellite and more intensive analysis on the ground where often time and power consumption are not constrained. The current work demonstrates implementation of this architecture through an initial battery data set. In the future we propose to apply this framework to other electric and electronic components on-board the small satellites.
Efficient control and Fault Detection and Isolation in Building HVAC systems
Authors: Naug Avisek
Published: February 2021

My thesis work is focused on advancing the state of the art in smart building monitoring and control problems. A major part of my thesis will develop combined model- and data-driven models for the energy systems of buildings and apply them for energy monitoring and optimization, fault diagnosis, and fault-adaptive control. In past work, I have already developed initial machine learning-based models for energy optimization. In this paper, I am focusing on developing a framework using bond graphs to build a model of the HVAC system and understand how different factors affect the measurement variables of this system. This information will help us do subsequent fault diagnosis by studying the characteristics of the system behavior represented by the temporal causal graph. It will also provide the basis for fault-adaptive control of buildings
Health Management and Prognostics of Complex Structures and Systems
Authors: Li Guoyi
Published: February 2021

This Ph.D. research focuses on developing reliable and effective structural health monitoring and prognostic health management frameworks for complex structures and system. The ultrasonic guided wave is used as a robust interrogation tool for structural level monitoring; numerical simulation, sensing architecture, signal processing and damage localization technique are being developed. For system level management, data mining techniques, with high-fidelity simulation platform, are used to establish a reliable fault detection and prediction for in-air aircrafts.
MCATSS - End-To-End Mobile Cardiopulmonary Tolerance Score System
Authors: Aras Shravan , Dao Anh , Gniady Chris , Skaria Rinku , Khalpey Zain
Published: February 2021

Recently hospital re-admission rates have received national attention due to the rise in healthcare costs and poor clinical outcomes specially in case cardiac surgery patients. While there exists risk prediction model for discharging patients, few are utilized due several limitations such as poor discriminative ability, lack of discharge assessment and inadequate measurement of functional status. Further none of the current models present real-time actionable information to facilitate early identification and risk-stratification for patients. Hence in this paper we propose a Mobile Cardiopulmonary Tolerance Score System (MCATSS), which is a system that facilitates data collection using a series of automated tests that incorporate data acquisition from external sensors placed on the patient's body. MCATSS has been designed to reduce erroneous input collection, increase the ease of use for clinicians and provide detailed statistics for medical personals to assist them in determining the safe discharge of patients. Overall, MCATSS offers a platform for digital cross-talk of continuous, sensor- and input-driven data to clinicians at their fingertips and will improve patient care, communication and evidence-based practice while decreasing costs and readmission rates.
Continuous Time Bayesian Networks in Prognosis and Health Management of Centrifugal Pumps
Authors: Forrester Tyler , Harris Mark , Senecal Jacob , Sheppard John
Published: February 2021

This paper presents a novel method for performing risk-based prognosis and health management (rPHM) on centrifugal pumps. We present the rPHM framework and apply common modeling tools used in reliability and testability analysis---dependency (D) matrices and fault tree analysis---as a basis for constructing an underlying predictive model. We then introduce the mathematics of the Continuous Time Bayesian Network (CTBN), which is a probabilistic graphical model based on a factored Markov process that is designed to capture system evolution through time, and we explain how to apply a CTBN derived from D-matrices and fault trees to consider the impact of a set of faults common to centrifugal pumps on emerging hazards in the pump system. We demonstrate the utility of using CTBNs for rPHM analysis with two experiments showing the descriptive power of our modeling approach.
Risk-Based Dynamic Anisotropic Operational Safety Bound for Rotary UAV Traffic Control
Authors: Hu Jueming , Erzberger Heinz , Goebel Kai , Liu Yongming
Published: February 2021

This paper proposed a novel method to determine probabilistic operational safety bound for unmanned aircraft traffic management. The key idea is to implement probabilistic uncertainty quantification and design the operational safety bound shape considering UAV’s heading direction. Operational safety bound is used to identify a virtual geographic boundary to protect aircraft and to ensure airspace safety. The proposed operational safety bound is calculated as a function of vehicle performance characteristics, state of vehicle, weather and other probabilistic parameters that affect the real position of vehicle such as position error from the Global Positioning System (GPS). It is calculated individually for each vehicle using real-time data and probability simulation. It considers the heading direction of vehicle and thus it is an anisotropic design. Monte Carlo simulations are conducted to estimate the operational safety bound size with a specified probability of failure. Results indicate that uncertainty is crucial for the operational safety bound’s size. Sensitivity study shows that UAV speed has the largest effect on the operational safety bound size. Analysis of impact of failure probability shows that operational safety bound size increases with the decrease in allowable failure probability, but the bound size based on different operational safety bound concept increases at different rate.
Probabilistic Aging Pipe Strength Estimation Using Multimodality Information Fusion
Authors: Chen Jie , Liu Yongming
Published: February 2021

Accurate pipe material strength estimation is critical for the integrity and risk assessment of aging pipeline infrastructure systems. To predict the strength without interrupting the serviceability of the pipeline, inference methods are used through the relationship between the bulk yield tensile strength and surface material properties from nondestructive testing, such as chemical composition, microstructure images, and hardness testing. In order to make the best of information provided by multimodality surface measurements, Bayesian model averaging (BMA) method is used in this paper to integrate the information from various types of surface measurements for a more accurate bulk strength estimation. The models being considered are constructed by randomly combining the multimodality surface measurements and each case of linear combinations is included. The models considered are assessed by assigning different weights based on the posterior model probability. Markov Chain Monte Carlo sampling provides an effective way for numerically computing the marginal likelihoods, which are essential for obtaining the posterior model probabilities. To avoid the risk of overfitting, BMA is implemented to account for model uncertainty. The predictive performance of single model and BMA are compared by logarithmic scoring rule. The data collected from industry are used for demonstration and model predictive performance assessment. It is shown that the Bayesian model averaging approach can provide more reliable results in predicting the strength of the aging pipelines.
Kinematic Frequencies of Rotating Equipment Identified with Sparse Coding and Dictionary Learning
Authors: Martin-del-Campo Sergio , Sandin Fredrik , Schnabel Stephan
Published: February 2021

The detection of faults and operational abnormalities in rotating machine elements like rolling element bearings and gears requires information about kinematic properties, such as ball-pass and gear mesh frequencies. Typically, condition-monitoring experts obtain such information from the manufacturers for diagnostics purposes. However, the reliability of such information can be compromised during installation and maintenance, for example, if components are replaced and do not match the documented specifications. Thus, methods enabling verification and online extraction of such kinematic properties are needed to improve diagnostic reliability. Unsupervised machine learning methods, like sparse coding with dictionary learning, enable automatic modeling and characterization of repeating signal structures in the time domain, which are naturally generated by rotating equipment. Sparse coding with dictionary learning represents a vibration signal as a linear superposition of noise and atomic waveforms. The activation rate of the atomic waveforms typically possesses a cyclic nature in rotating environments, similar to how bearing kinematic frequencies correlate with faults in a rolling element bearing. However, there is no explicit relationship between the activation rates of the atoms and the bearing kinematic frequencies. This motivates this investigation of the possibility to extract bearing kinematic frequencies from sparse representations. Former work describes the use of dictionary learning for the detection of anomalies in rolling element bearings. In this paper, we describe how a similar unsupervised machine learning method can be used to extract kinematic frequencies of bearings and gears, for example for anomaly detection purposes and comparisons with an expected signature. We study the activation rates and changes of atoms learned from vibration signals in two case studies. The first case is based on data from a well-known controlled experiment with faults seeded in the bearings. The second case is based on a public dataset recorded from the high-speed shaft of a wind turbine with a bearing failure. Furthermore, we compare the activation rates and weights of the atoms to the bearing kinematic frequencies and harmonics. Sparse coding with dictionary learning offers a possibility for self-learning of the kinematic frequencies of a bearing, which can be useful for the further improvement of automated anomaly detection methods in condition monitoring.
Ensemble Linear Regression and Paris’ Law Based Methods for Structure Fatigue Crack Length Estimation and Prediction Using Ultrasonic Wave Data
Authors: Zuo Ming J.
Published: February 2021

This paper presents the methods developed for the 2019 PHM Conference Data Challenge. The PHM Data Challenge aims to estimate the fatigue crack length of a type of aluminum structure using ultrasonic signals at the current loading cycle, and to predict the crack length at multiple future loading cycles (multiple-step ahead prediction) as accurate as possible. For the crack length estimation, four crack sensitive features are extracted from ultrasonic signals, namely, the first peak value, root mean squared value, log of kurtosis and correlation coefficient. These features and their 2nd order interactions are used as inputs for an ensemble linear regression model which is built to map the features to crack length. The Best Subset Selection method is employed to select optimal features. For the crack length prediction, variations of the Paris’ law are derived to describe the mutual relationships between crack length and the number of loading cycles. The specimen material parameters and stress range of the Paris’ law are learned using the Genetic Algorithm, and updated with the next-step predicted crack length. The crack length corresponding to a future loading cycle number under constant amplitude cyclic loading and under variable amplitude cyclic loading is predicted respectively. The presented overall methodology achieves a score of 16.14 with the score calculation rule provided by the Data Challenge organization team, and ranks 3rd among all teams.
Novel Waveforms, Models, Algorithms for Cable Health Monitoring
Authors: Wang Xuan
Published: February 2021

Cables in power generation and delivery are under high thermal stress cycles. Such high temperature can lead to cable insulation degradation, which will reduce the projected lifetime. Existing methods mainly focus on cable fault detection or insulation degradation mechanism. There is no existing tools for diagnosing the insulation degradation level and predicting the remaining useful life of the cable. The goal of my Ph.D. research is to develop reflectometry and data based approaches to monitor the health status of cables. The research will be conducted in three steps: (1) development of reflectometry based method to monitor the cable insulation degradation; (2) feature extraction and cable insulation degradation dynamic modeling based on the accelerated aging test data; (3) development of risksenstive particle filtering based fault diagnosis and prognosis algorithms for cable degradation; and (4) verification and validation the proposed solution with new experiment data and comparison with existing approaches.
Validation and Refinement of a Steering Friction Increase Detection Algorithm Using Test Drive Data
Authors: Mohtat Arash , Garner Graeme , Lin Wen-Chiao , Mehrabi Naser
Published: February 2021

The Electric Power Steering (EPS) System provides steering assist in conventional vehicle driving and is the main actuator for vehicle lateral control in active safety features. While the driver can sometimes compensate for reduced or loss of steering assist caused by EPS mechanical or electrical degradations, it may become very difficult to steer for larger vehicles. Furthermore, active safety functions cannot control the vehicle effectively for lateral motions without a healthy EPS system. Hence, comprehensive EPS system fault monitoring is essential for the next generation of vehicles. Previous works have utilized computer simulation and hardware-in-the-loop experiments to develop fault diagnosis and prognosis algorithms for electrical and mechanical failures in EPS systems. Using test drive data collected, this paper validates and refines a previously developed algorithm designed for detecting increases in EPS system internal mechanical friction. The data include 215 minutes of natural driving with different speeds and steering maneuvers. Noise factors such as tire type and level of friction introduced are also considered. The previous algorithm is refined to enhance performance addressing issues of time delays and parameter uncertainty specific to the previous model-based algorithm. Specifically, a Kalman filter-based joint state-parameter estimator that uses a simplified vehicle dynamic model is developed to provide a direct estimate of steering friction increase. Data collected from test drives indicate that the refined algorithm can robustly indicate a friction increase before an average human driver notices a difference in steering feel.
On Modeling of Vibration and Crack Growth in a Rotor for Prognostics
Authors: Abbasi Amirhassan , Nazari Foad , Nataraj C.
Published: February 2021

Prognostics and health management (PHM) include comprehensive engineering approaches that evaluate the real-time health condition of an asset and predict its future states under the actual operating conditions. This predictive ability would result in efficient maintenance approaches such as Condition Based Maintenance (CBM) that can set maintenance strategies optimally and reduce the life cycle costs. Diagnostics and Prognostics are two major concepts in PHM. Detection, Isolation and Identification of faults are done by diagnostics while prognostics deals with estimation of future states. Mechanical fatigue phenomenon that causes crack initiation and propagation is considered as a common reason for failure in mechanical parts. Hence, diagnostics and prognostics of the crack initiation and propagation have been the subject of many research papers recently.

The current paper presents a diagnostics and prognostics method capable of detecting the crack initiation and propagation in a rotor under cyclic loading. At the first step, the coupled equations of rotor motion and crack growth are obtained. An extended model of Paris–Erdogan equation is used for crack growth modeling. The coupled equations are solved numerically. A set of features are extracted from the dynamic response of the rotor for a range of crack lengths. A dataset is compiled including features of response, operating frequency, crack length and number of cycles remained until reaching the critical crack length. With the objective of generalization of the results, the dataset is used for creating a model using an Artifical Neural Network (ANN). In the trained ANN the inputs are the operating speed and the outputs are the crack length and the remaining useful life (RUL) that address the diagnostics and prognostics objectives, respectively.
Digital twin approach for intelligent operation planning and health management of mechanical systems
Authors: Karve Pranav , Guo Yulin , Kapusuzoglu Berkcan , Mahadevan Sankaran , Haile Mulugeta
Published: February 2021

The digital twin paradigm aims to fuse information obtained from sensor data, physics models, and operational data for a mechanical component in use to make well-informed decisions regarding health management and operations of the component. In this work, we discuss a methodology for digital-twin-based operation planning in mechanical systems to enable: a) cost-effective maintenance scheduling, and b) resilient operations of the system. As properties of mechanical systems, as well as their operational parameters, loads and environment are stochastic in nature, our methodology includes probabilistic damage diagnosis, probabilistic damage prognosis, and system optimization under uncertainty. As an illustrative example, we consider the problem of fatigue crack growth in a metal component. We discuss a probabilistic, ultrasonic-guided-wave-based crack diagnosis framework that can handle both aleatory and epistemic uncertainties in the diagnosis process. We build a high-fidelity, finite element model to simulate the piezoelectric effect and ultrasonic guided wave propagation. We use test data obtained by conducting diagnostic experiments on the physical twin to calibrate the error in the diagnosis model. We perform Bayesian diagnosis of crack growth using the corrected diagnosis model, considering data corrupted by measurement noise, and fuse the information from multiple sensors. We build a finite-element-based high-fidelity model for crack growth under uniaxial cyclic loading, and calibrate a phenomenological (low-fidelity) fatigue crack growth model using the high-fidelity model output as well as data from fatigue loading experiments performed on the physical twin. We use the resulting multi-fidelity model in a probabilistic crack growth prognosis framework; thus achieving both accuracy and computational efficiency. Lastly, we utilize the damage diagnosis framework along with the damage prognosis model to optimize system operations under diagnostic and prognostic uncertainty. We perform simulation as well as laboratory experiments that show how the digital twin of the component of interest can be used for intelligent health management and operation planning for mechanical systems.
Modeling exponential decay in maximum capacitance across specified flight patterns in small aircraft
Authors: Rigas Pete Rigas , Kulkarni Chetan
Published: February 2021

With increased autonomy being an integral part of un- manned aerial system (UAS), during flight a vehicle needs to have an accurate estimation of its state of health and capabilities to perform and achieve mission success with utmost safety. Batteries are of key importance in electric-propulsion aircraft and are its most pertinent re- source. It is important to know the state of charge of the battery not only because the health state is directly re- lated to the flight profiles flown by the vehicle, but also because the state of charge of the vehicle and its opera- tional condition must be estimated after each flight.

In this work a methodology is presented to generate pre- dictions for flight plans that experience anomalies, or un- expected system failure in due to a parasitic load in a specified stage of the flight that must return to its starting point of origin. We begin by describing the procedures by which a sequence of steps will be carried out to expo- nentially weigh the impact of different stages of a flight towards thermal strain on the capacitance Cmax of the battery during each flight.
Towards Fault-Tolerant Strategy in Satellite Attitude Control Systems: A Review
Authors: HENNA Hicham , TOUBAKH Houari , Kafi Mohamed Redouane , SAYED-MOUCHAWEH Moamar
Published: February 2021

Spacecrafts are known to be very complex engineering systems where many technological devices enter in interaction to guarantee the overall mission objectives. Regardless of those sophisticated manufacturing systems, faults/failures are inevitable during the satellite lifetime. This paper aims at discussing the state-of-the-art approaches proposed to guarantee the satellites’ attitude control system (ACS) performances when its components suffer from faults. The goal is to highlight their limits to address the specific challenges related to ACS. To localize and identify the potential faults, fault detection and diagnosis (FDD) methodologies are used in an earlier stage before the adaptation of the controller. The so-called fault-tolerant control (FTC) schemes have proven their capabilities during the last decade; nevertheless, those schemes still face some challenges that reduce their optimal performance in the aerospace industry. The contribution of this paper is to highlight the motivations and challenges of FTC/FDD methodologies when used to design spacecraft attitude controllers in the case of actuators and sensors anomalous behavior and to propose hints to address them.
Similarity-based Feature Extraction from Vibration Data for Prognostics
Authors: Huang Wei , Farahat Ahmed , Gupta Chetan
Published: February 2021

Many techniques for prognostics depend on estimating then forecasting health indicators that reflect the overall health or performance of an asset. For vibration data, health indicators are typically calculated by combining various vibration measurements along with derived features extracted from time, frequency or time-frequency domain analysis. However, selecting or handcrafting good features is a labor-intensive task. On the other hand, deep learning models might be able to learn health indicators automatically from vibration data but require large amount of training data, which are typically hard to obtain from real assets. In this paper, we propose an innovative similarity-based feature extraction method for vibration data which can then be used to learn health indicators and estimate remaining useful life of equipment. The method learns a set of representative templates of frequency spectra for both normal and failure states, and then calculates similarity-based features between new vibration data and the set of learned templates. These features are used to estimate health indicators which are then extrapolated to estimate the future health condition of the asset and its remaining useful life. The proposed method has been tested on the PRONOSTIA bearing dataset provided by FEMTO-ST Institute and achieved a higher accuracy in estimating the remaining useful life of bearings compared to other studies. The results demonstrate the effectiveness of the proposed method for assets with limited training data.
New Capacitive Thermal Age Tag for Predicting Remaining Thermal Life of Multiple Products
Authors: Watkins Kenneth , Steelman Stephanie
Published: February 2021

This paper proposes use of a new capacitive thermal age sensor that inherently integrates time and temperature without batteries or electronic memory to predict the remaining thermal life of a wide range of monitored products. The sensor is a tiny capacitor comprising a polymeric dielectric between two conductive plates. Capacitance of the sensor increases during thermal aging due to shrinkage of the polymer. Additives such as catalysts adjust the activation energy (Ea) of capacitance change with thermal age.A thermal age tag, incorporating two capacitive sensors of different activation energy, can be used to determine the effective temperature (Teff) of a complex thermal environment at wide range of product degradation activation energies. Correlation of the thermal age of the tag at the monitored product’s degradation activation energy to product thermal aging data provides estimated remaining thermal life of the product. The thermal age tag requires no batteries or electronic memory required in data-logging approaches resulting in reduced size, weight and cost. These passive tags are potentially maintenance free for the life of the product.This paper describes the development of a universal thermal age (UTA) tag incorporating capacitive thermal age sensors and preliminary co-aging trials with a variety of selected polymeric products to demonstrate feasibility of this approach.
Recursos educativos abiertos de apoyo en la realización del TFM
Authors:   Uned, López González Aitor , Gil Jaurena Inés ,   Uned
Published: February 2021

Los recursos educativos abiertos (REA) –‌open educational resources (OER) en inglés– son materiales didácticos, de aprendizaje e investigación que son de dominio público o con licencia abierta para su uso, adaptación y redistribución, y pueden resultar un verdadero apoyo en los procesos de elaboración del trabajo fin de máster. En este texto se recopilan diferentes recursos –‌algunos de ellos elaborados por la UNED– y recomendaciones sobre su uso, seleccionados desde nuestra experiencia como tutora y autor de un trabajo fin de máster. El capítulo se estructura teniendo en cuenta las diferentes etapas en el camino de elaboración de un trabajo fin de máster: consejos antes de empezar para elegir el soporte y las herramientas informáticas; en los primeros pasos del trabajo fin de máster, con la recomendación de recursos en los que apoyarse para conocer la estructura de este tipo de trabajos académicos, elegir la temática, planificar el proceso de trabajo fin de máster e identificar competencias que se han de reforzar, especialmente en el ámbito metodológico; durante el desarrollo del trabajo fin de máster, con sugerencias de recursos para obtener bibliografía, desarrollar el marco teórico o poner en práctica la metodología de investigación o intervención; y recomendaciones de recursos abiertos que pueden ayudar a la preparación y ejecución de la defensa oral del trabajo fin de máster. Para finalizar, se presentan algunas ideas para la divulgación del trabajo realizado en el trabajo fin de máster, valorando diversas opciones como la publicación de un artículo o la devolución de la información a las personas o colectivos que hayan participado en la investigación o proyecto de intervención.
¿Puede mi TFM interesar al mundo? Primeros pasos para difundir resultados
Authors:   Uned, Arteaga Martinez Blanca , Ruiz Corbella Marta ,   Uned, López Gómez Ernesto ,   Uned, Galán González Arturo ,   Uned
Published: February 2021

El presente capítulo sintetiza una propuesta de etapas que, sin ser estáticas ni lineales, orientan a potenciales jóvenes investigadores en el diseño de una estrategia eficaz para difundir los resultados del trabajo fin de máster entre diferentes colectivos (comunidad académica, profesional y la propia sociedad). El sentido de este texto es reclamar la atención de los estudiantes sobre el relevante papel que tiene en la ciencia una eficaz difusión de resultado, de modo que se facilite el acceso a nuestros hallazgos a través de distintos medios. ¿Cómo? Utilizando diferentes canales y escenarios: congresos, seminarios, revistas científicas, revistas de divulgación, prensa, vídeo, radio, blogs, redes sociales, etc. ¿Para qué? Para que pueda ser consultado, revisado, contrastado, debatido, utilizado y, si es factible, aplicarlo en la práctica. De un modo particular, este capítulo se centra en una propuesta para acometer la adaptación del trabajo fin de máster a la publicación de un artículo científico, por ser uno de los productos de investigación con mayor proyección en el ámbito académico. Para ello, se plantea una serie decisiones y tareas clave, que pueden favorecer el éxito del proceso. De manera complementaria, se sugiere la transferencia de estos resultados por medio de otros canales de difusión más abiertos y flexibles, con el objeto de acercar la ciencia a la ciudadanía.