bluuebunny/crossref_metadata_2025 huggingface dataset:
Revitalisasi Peran Srategis Sekretaris Daerah Dalam Mengakselerasi Reformasi Birokrasi
Authors: Ilyas Ilyas
Published: February 2021

Penelitian ini mengkaji revitalisasi peran strategis Sekretaris Daerah dalam akselerasi reformasi birokrasi, dengan metode penelitian deskriptif kualitatif, dan desain penelitian evaluatif, analitik, eksploratif, eksplanatif dan komparative, serta teknik analisis kualitatif. Hasilnya, secara umum dan keseluruhan, ada 8 (delapan) dimensi pokok dan utama satu kesatuan kelompok peran strategis seorang Sekretaris Daerah dalam birokrasi pemerintahan yaitu: (1) Pemimpin (Leader) birokrasi dan ASN. (2) Manajer (Manager) birokrasi dan ASN, (3) Konseptor, konsultan, desainer, formulator kebijakan daerah, organisasi birokrasi dan ASN, (4) Administrator birokrasi, ASN dan pelayanan public, (5) Koordinator, komunikator, fasilitator, mediator, motivator, dinamisator, energisator organisasi birokrasi, ASN dan pelayanan public, (6) Supervisor, educator, trainer, learner, advocator, promotor dan empower organisasi birokrasi, ASN dan pelayanan public, (7) Implementor dan eksekutor kebijakan organisasi birokrasi, ASN dan pelayanan public, (8) Controller, monitor dan evaluator organisasi birokrasi, ASN dan pelayanan publik. Model revitalisasi peran strategis Sekretaris Daerah dalam akselerasi reformasi birokrasi adalah revitalisasi peran Sekda sebagai aktor Reinventer Government, Leader Manager birokrasi, Entrepreuner birokrasi, aktor New Public Management (NPM), aktor Strategi Banishing Bureucracy reformasi birokrasi, dan aktor reformasi birokrasi berbasis kearifan budaya lokal. Disarankan untuk mempertimbangkan penerapan model revitalisasi peran strategis Sekretaris Daerah dalam akselerasi reformasi birokrasi tersebut.

This research explore revitalization a strategic role of Local Secretary owned Local Government to accelerate bureaucracy reformation, by use a qualitative descriptive as method, and an evaluative, analytic, explorative, explanative and comparative as investigation design, and a qualitative as analyses. Results, by all dan generally, there are eight main dimensions as unity at strategic role played by Local Government Secretary within government bureaucracy include: (1) Leader, (2) Manager, (3) Conseptor, consultant, designer and formulator for local policy and officer or employment, (4) Administrator for public service and employment, (5) Coordinator, communikator, facilitator, mediator, motivator, dynamisator, energizer, (6) Supervisor, educator, trainer, learner, advocator, promotor and empower for bureaucracy, public service and employment, (7) Implementor and executor, (8) Controller, monitor and evaluator. Revitalization model of strategic role of Local Government Secretrary to accelerate bureaucracy reformation are revitalization as Reinventer Government actor, Leader Manager, Entrepreuner, New Public Management (NPM) actor, Banishing Bureucracy Strategy actor, and actor bureaucracy reformation based local wisdom. Recommended to actualize those revitalization model as mentioned above in order to accelerate any strategic role of Local Government Secretary into bureaucracy reformation it.
Communication Coroutines For Parallel Program Using DW26010 Many Core Processor
Authors: Singh Ajit
Published: February 2021

Communication between parallel programs is an indispensable part of parallel computing. SW26010 is a heterogeneous many-core processor used to build the Sunway Taihu Light supercomputer, which is well suited for parallel computing. There is the designing and implementing a coroutine scheduling system on the SW26010 processor to improve its concurrency, it is very important and necessary to achieve communication between coroutines for the coroutine scheduling system in advance. Therefore, this paper proposes a communication system for data and information exchange between coroutines on SW26010 processor, which contains the following parts. The designing and implementation a producer-consumer mode channel communication based on ring buffer, and it designs synchronization mechanism for condition of multi-producer and multi-consumer based on the different atomic operation on MPE (management processing element) and CPE (computing processing element) of SW26010. There is also the designing of a wake-up mechanism between the producer and the consumer, which reduces the waiting of the program for communication. The testing and analysis of the performance of channel in different numbers of producers and consumers, draw the conclusion that when the number of producers and consumers increases, the channel performance will decrease.
Integrated Hand Cleaner and Dryer Product Design using QFD as a Solution for New Normal Era
Authors:   laboratory Assistant in Industrial Modelling and Simulation Laboratory, Faculty of Industrial Engineering, Universitas Islam Indonesia., Amalia Putri , Wahyu Setyawan ,   Head of the Electronics Laboratory, Majoring in Electrical Engineering at the Islamic University of Indonesia, Yahya Diyas Aledya ,   Chemical Engineering, Faculty of Industrial Technology, Islamic University of Indonesia, Adrianto Ari ,   Studying Chemical Engineering at the Islamic University of Indonesia
Published: February 2021

At this time the coronavirus pandemic has resulted in new adaptations in daily life through new normal rules that have many rules against public activities such as using masks, keeping distance, and maintaining cleanliness by washing hands using water or hand sanitizer after interacting with objects or people in public areas. This resulted in increased use and demand for hand sanitizers and public facilities for handwashing. This study aims to obtain the design of cleaning tools and hand dryers that suit the wishes of the community. The method used in this application of Quality Function Development (QFD) by using matrix House of Quality (HOQ) to obtain the criteria of cleaning tools and hand dryers according to the wishes of the community. The results of this study obtained the design of automatic and integrated cleaning tools and hand dryers in one tool that uses a hand sanitizer made of natural antiseptic so that it does not require water and soap and with a price range of Rp.700.000.
Hybrid Battery Model for Prognostics in Small-size Electric UAVs
Authors: Sierra Paez Gina Katherine , Orchard Marcos , Kulkarni Chetan , Goebel Kai
Published: February 2021

Electric Unmanned Aerial Vehicles (UAVs) experience problems and risks associated with battery aging and abuse effects. Therefore, a Battery Health Management (BHM) system is necessary to make the battery a safe, reliable, and cost-efficient solution. BHM systems are essential to ensure that the mission goal(s) can be achieved and to aid in online decision-making activities such as fault mitigation and mission replanning. To accomplish these tasks, we have adopted a model-based prognostics architecture for battery-powered UAVs where a battery model is used as the basis of two sequential task, (i) the State of Charge (SOC) estimation, and (ii) the End of Discharge (EOD) prediction. Small-size aircraft usually have weight, size and cost constraints. Therefore, there is a need to accurately (i) estimate the SOC, and (ii) predict the EOD time of Li-Po batteries in small-size UAVs that can operate in constrained environments. This work proposes a modification to an electrochemistry-based battery model that allows reducing computational resources without losing accuracy in prognostic results. The resulting hybrid battery model is validated and applied to prognostic of the EOD time in discharge cycles of a Li-Po battery of a small size quadcopter that performs delivery missions. Prediction results using the proposed hybrid battery model are shown to be very accurate while its estimation and prediction processing times are significantly lower than processing times using the electrochemistry-based battery model.
Review of PHM Data Competitions from 2008 to 2017
Authors: Jia Xiaodong , Huang Bin , Feng Jianshe , Cai Haoshu , Lee Jay
Published: February 2021

Recently, the data driven approaches are winning popularity in Prognostics and Health Management (PHM) community due to its great scalability, reconfigurability and the reduced development cost. As the data-driven approaches flourished, the data competitions hosted by the PHM society over the last ten years contribute a valuable repository of public resources for benchmarks and improvements.  To better define the directions for future development, this paper reviews the cutting-edge PHM methodologies and analytics based on the data competitions over the last decade. In this review, the goal of PHM and the major research tasks are stated and depicted, then the methodologies and analytics for the PHM practices are summarized in terms of failure detection, diagnosis, assessment and prediction, and the applications of PHM in various industrial sectors are highlighted as well. The data competitions in the last ten years are utilized as examples and case studies to support the ideas presented in this paper. Based on all the discussions and reviews, the current challenges and future opportunities are pointed out, and a conclusion remark is given at the end of the paper to summarize the current achievements and to foresee the future trends.
Theoretically Rigorous Approach to Failure Prognosis
Authors: Acuña David , Orchard Marcos
Published: February 2021

For more than twenty years, we have witnessed a continuous and significant growth in the scope and quality of research in Prognostics and Health Management (PHM). Prognostic algorithms and risk assessment metrics naturally play a critical role in this regard, since they provide the necessary information to take preventive measures and avoid catastrophic system failures. Unfortunately, the problem of failure prognostics has been treated many times from a heuristic, and mostly intuitive, standpoint. Indeed, the PHM community has often validated contributions to the state-of-the-art solely based on the performance experienced under specific run-to-failure experiments, and accepted lack of mathematical rigor in the formulation of the prediction problem itself. In this paper, we revisit the fundamentals of the prognostic problem, providing constructive criticism to inconsistencies found in approaches that have been adopted by many researchers within the PHM community. In addition, we propose a rigorous mathematical framework for failure prognostics, introducing failure probability measures for both discrete- and continuous-time dynamical systems that truly formalize the prognostic problem. We further discuss the philosophical implications of these novel notions in the context of a paradigm change, using as an illustrative example the problem of Lithium-Ion battery condition monitoring.
Identification of Diagnostic-related Features Applicable to EEG Signal Analysis
Authors: Beganovic Nejra , Kevric Jasmin , Jokic Dejan
Published: February 2021

The regulation of functions such as respiratory or heart rate in human body as well as the control of motor movements are under the control of nervous system. As these actions and correlated tasks are directly influenced by the brain, the brain monitoring gives the possibility to differentiate the tasks, enabling at the same time the prediction of further actions. In this contribution, publicly available electroencephalography (EEG) datasets are analyzed with respect to the detection of epileptic seizure occurrence and BCI-related actions (here: cued motor imagery). For these purposes, timefrequency- based feature extraction alongside different classification methods is used. To perform the classification, Artificial Neural Network (ANN) and Support Vector Machine (SVM) are utilized and compared with previously obtained results. The feasibility of particular features for the detection of epileptic seizures and BCI-related tasks is discussed. Four different feature vectors per analyzed problem are identified. Acceptable accuracy of classification using ANN- and SVMbased classifiers is achieved using identified feature vectors.
Just-in-time Point Prediction Using a Computationally-efficient Lebesgue-sampling-based Prognostic Method
Authors: Reyes Camilo , Jaramillo Francisco , Zhang Bin , Kulkarni Chetan , Orchard Marcos
Published: February 2021

Battery energy systems are becoming increasingly popular in a variety of systems, such as electric vehicles. Accurate estimation of the total discharge of a battery is a key element for energy management. Problems such as path planning for drones or road choices in electric vehicles would benefit greatly knowing beforehand the end of discharge time. These tasks are generally performed online and require continuously quick estimations. We propose a novel prognostic method based on a combination of classic Riemann sampling (RS) and Lebesgue sampling (LS) applied to a discharge model of a battery. The method utilizes an early and inaccurate prediction using a RS-based method combined with a particle-filter based prognostic. Once a fault condition has been detected, subsequent Just-in-Time Point (JITP) estimations are updated using a novel LS-based method. The JITP prediction are triggered when the Kullback-Leibler divergence between the probability density functions (PDF) of the long-term-based prediction and the last filtered state reaches a threshold. The CPU time needed to execute a procedure is used as a measure of the computational resources. Results show that this combined approach is several orders of magnitude faster than the classical prognosis scheme. The combination of these two methods provides a robust JITP prognosis with less computational resources, a key factor to consider in real-time applications in embedded systems.
Feasibility Study of a PHM System for Electro-hydraulic Servo-actuators for Primary Flight Controls
Authors: Autin Sylvain , Socheleau Jérôme , Dellacasa Andrea , De Martin Andrea , Jacazio Giovanni , Vachtsevanos George
Published: February 2021

Electro-Hydraulic Servo-Actuators (EHSA) are by far the mostly used type of actuators in aircraft primary flight control systems. Though electrical actuation is been considered since long as a possible replacement of hydraulic actuation for aircraft systems, EHSAs are still the technology of choice in the primary flight control systems of new commercial aircrafts. Considering that 10 or more EHSAs are typically used in an aircraft flight control system, the development of an effective PHM system for this equipment could provide large benefits and be of great interest for the OEMs and for the air fleet operators.

This paper presents the results of a feasibility study making up the first part of an ongoing research activity focused on the development of a PHM system for EHSAs used in fly-by-wire primary flight control systems and takes as a use case the primary flight control actuator of a wide body commercial aircraft. One of the key features of the research is the implementation of a PHM system without the addition of new sensors, taking advantage of the available signals. This offers the possibility of implementation of the PHM system on the existing platforms and not only as a proposition for new aircrafts designed with a complement of additional sensors. The enabling technologies for this PHM system borrow from the area of Bayesian estimation theory and specifically particle filtering and the information acquired from EHSA in-flight and during pre-flight check is processed by appropriate algorithms in order to obtain relevant features, detect the degradation and estimate the Remaining Useful Life (RUL). The results are evaluated through appropriate metrics in order to assess the performance and effectiveness of the implemented PHM.

This paper describes the methodology of the feasibility study, which shows how the novel PHM technologies proposed for a PHM system for the EHSAs of primary flight control actuators can allow the migration from unscheduled / on-condition maintenance to condition based maintenance targeting the perceived objectives of the OEM and of the aircraft operator.
Physics-based Learning for Aircraft Dynamics Simulation
Authors: Yu Yang , Yao Houpu , Liu Yongming
Published: February 2021

The accurate prediction of flight trajectories is crucial for the real-time prognostics of air transportation system. However, the computation costs of predictions can be expensive or even prohibitive especially for a large number of aircrafts in the air traffic system. This study proposes the concept of physics-based learning, a hybrid approach based on data-driven learning and physical models, as a computationally efficient method for the simulation of aircraft dynamics. The physics-based learning integrates the underlying physics of dynamical systems into learning models such as neural networks to reduce the training and simulation costs. The application of physics-based learning for simulating aircraft dynamics is demonstrated using a recently introduced physics-aware network known as the deep residual recurrent neural network (DR-RNN) on a Boeing 747-100 aircraft. The aircraft dynamics are described using a six degrees-of-freedom aircraft model. The DR-RNN is first trained using the simulated responses of the aircraft and then the trained network is used to predict the response of aircraft under arbitrary control inputs and disturbances. The results show that the DR-RNN can accurately predict aircraft responses and has excellent extrapolation capabilities. Moreover, the DR-RNN exhibits superior computation efficiency compared with a classical numerical method, the fourth-order Runge-kutta method, highlighting its suitability in serving as surrogating models for aircraft dynamical systems.
Framework for Unifying Model-based and Data-driven Fault Diagnosis
Authors: Khorasgani Hamed , Farahat Ahmed , Ristovski Kosta , Gupta Chetan , Biswas Gautam
Published: February 2021

Model-based diagnosis methods rely on a model that defines nominal behavior of a dynamic system to detect abnormal behaviors and isolate faults. On the other hand, data-driven diagnosis algorithms detect and isolate system faults by operating exclusively on system measurements and using very little knowledge about the system. Recently, several researchers have combined model-based diagnosis techniques with datadriven approaches to propose hybrid1solutions for fault diagnosis. Many researchers have proposed methods to integrate specific approaches. In this paper, we demonstrate that data-driven and model-based diagnosis methods follow a similar procedure and can be represented by a general unifying framework. This unifying framework for fault detection and isolation can be used to integrate different methodologies developed by two communities. As a case study, we use the proposed framework to build a crossover solution for fault diagnosis in a wind turbine benchmark. In this case study, we show that it is possible to achieve a better diagnosis performance by using a hybrid method that follows the proposed framework.
Fault Isolation of an Electro-mechanical Linear Actuator
Authors: Kemp Matt , Martin Eric J.
Published: February 2021

We apply model-based fault-isolation to an electromechanical linear actuator, and demonstrate its use on an unmanned underwater vehicle mass-shifter. Models incorporating the physics of the motor and of the load, and the effect of the servo-controller, are derived for nominal operations, overload faults, and coupling loss faults. A simple parameter identification method based on close-form solutions during startup and at steady-state is used, and is shown to produce good agreement with measurements. Fault-isolation is done by representing the system as a time-dependent mixture of its models, and selecting the model with the smallest error residual. We tested this in three situations – an actual overload fault, an actual coupling fault, and a false-alarm – and found that the correct model was successfully isolated in each case.
Examining Workcell Kinematic Chains to Identify Sources of Positioning Degradation
Authors: Klinger Alexander , Weiss Brian A.
Published: February 2021

Automated industrial workcells are becoming increasingly complex and varied due to greater accessibility of advanced robotic and sensing technologies. Degradation monitoring and diagnostics must advance to reduce the impact of increased system complexity on troubleshooting faults and failures and to optimize system operations. A new methodology is being developed for the design and implementation of monitoring kinematic chains commonly found in robot workcells. This method will enable the identification of degraded components which contribute to relative positioning accuracy error between moving objects, tools, devices, and other components. The proposed methodology is being developed and tested on a six degree of freedom industrial robot arm workcell use case developed at the National Institute of Standards and Technology (NIST). Industrial robot users and integrators can use this method to examine the kinematic chains within their workcells and design a key position monitoring implementation. With the added key position monitoring, degradations can be identified at a designed resolution allowing for enhanced maintenance planning and production control. The methodology will be extended to other manufacturing workcells in the future.
Electronic Circuit PHM with No Data
Authors: Khemani Varun
Published: February 2021

Operational data from the target system is widely considered a pre-requisite for implementation of PHM, as it used as training data. Often this data is not available to PHM practitioners because health monitoring capabilities may not be installed in legacy systems unless there is a guarantee that a PHM framework will be viable/profitable. This research presents an approach in which PHM can be implemented without any operational data and is generic enough to be applied to any electronic circuit provided a simulation model of the system with acceptable fidelity can be developed. The research also introduces the Space-Filling Design, which can be used to generate the training data in a systematic, statistically valid framework, and is especially valuable for complex circuit with a large number of components. This design provides sufficient coverage of the parametric design space to be representative of the unavailable operational data, as well as incorporating the effects of parameter interaction on the simulated response of the system. Most PHM studies in literature ignore the effect of the degradation of interacting components. We show, how such an assumption can lead to incorrect fault diagnosis/RUL estimation and propose methods to screen for two-way and higher order interactions. Finally, we use various deep learning approaches in conjunction with physics-of-failure models for individual components to diagnose circuit faults and estimate circuit RUL. This simulation-based fusion prognostics approach is a holistic framework for all types of electronic circuits.
Operation-Aware Health Management for Environmental Control and Life Support System
Authors: Tang Shijie
Published: February 2021

To enable effective management, planning, and operations for future missions that involve a crewed space habitat, operational support must be migrated from Earth to the habitat. Intelligent System Health Management technologies (ISHM) promise to enable the future space habitats to increase the safety and mission success while minimizing operational risks. Aligning with this objective, my Ph.D. study aims to develop fault diagnosis, prognosis, and automatic contingency management strategies for Environmental Control and Life Support Systems (ECLSS). Innovations of the research include a combination of physics-based and data-driven modeling, low-cost diagnosis and prognosis, probabilistic reasoning, and optimization for decision-making. Data from Photovoltaic (PV) arrays and Water Recycling System (WRS) deployed at NASA Ames Research Center's Sustainability Base will be used for comparison studies with existing methods and technology validation and verification of the proposed system.
A Deep Learning-Based Method for Cutting Parameter Optimization for Band Saw Machine
Authors: Li Pin , Feng Jianshe , Zhu Feng , Davari Hossein , Chen Liang-Yu , Lee Jay
Published: February 2021

In the rough machining stage, band saw machines are widely used to cut various raw materials into the required dimensions. The replacement of blade due to the blade degradation accounts for a large part of the total cost of band saw machine users. Therefore, mitigating the blade degradation by dynamically optimizing the cutting parameters can produce great economic benefits and is also a good exploration for smart manufacturing. To achieve this goal, the Convolutional Neural Network (CNN) model is proposed to map the complicated relationship between the cutting parameters and blade degradation. Then a simulation technique is used to search the optimal cutting parameters based on the blade degradation estimation from the model output, which would help alleviate the blade degradation. The proposed optimization method is validated on the data collected during the real manufacturing process of band saw machines. The comparative results demonstrate that the obtained optimal cutting parameters can effectively extend the service life of the blade for a band saw machine.
Deployment of Prognostics to Optimize Aircraft Maintenance - A Literature Review
Authors: Sprong Jorben Pieter , Jiang Xiaoli , Polinder Henk
Published: February 2021

Historic records show that the cost of operating and supporting an aircraft may exceed the initial purchase price as much as ten times. Maintenance, repair and overhaul activities represent around 10-15% of an airlines’ operational costs. Therefore, optimization of maintenance operations is extremely important for airlines in order to stay competitive. Prognostics, a process to predict remaining useful life of systems and/ or components suffering from aging or degradation, has been recognized as a revolutionary discipline that can improve efficiency of aircraft operations and optimize the aircraft maintenance. In this paper, the origin and development of prognostics is firstly introduced. Thereafter, the state of art of aircraft maintenance is reviewed. Next, the applicability of prognostics to optimize aircraft maintenance is explained, reviewed, and potential challenges and opportunities are explored. Finally, some recommendations for further research are discussed in perspective of deployment of prognostics to optimize aircraft maintenance.
Real time Diagnostics and Prognostics of UAV Lithium-Polymer Batteries
Authors: Eleftheroglou Nick , Zarouchas Dimitrios , Loutas Theodoros , Mansouri Sina Sharif , Georgoulas George , Karvelis Petros , Nikolakopoulos George , Benedictus Rinze
Published: February 2021

This paper examines diagnostics and prognostics of Lithium-Polymer (Li-Po) batteries for unmanned aerial vehicles (UAVs). Several discharge voltage histories obtained during actual indoor flights constitute the training data for a data-driven approach, utilizing the Non-Homogenous Hidden Semi Markov model (NHHSMM). NHHSMM is a suitable candidate as it has a rich mathematical structure, which is capable of describing the discharge process of Li-Po batteries and providing diagnostic and prognostic measures. Diagnostics and prognostics in unseen data are obtained and compared with the actual remaining flight time in order to validate the effectiveness of the selected model.
Operating Condition-Invariant Neural Network-based Prognostics Methods applied on Turbofan Aircraft Engines
Authors: Duarte Pasa Gabriel , Paixão de Medeiros Ivo , Yoneyama Takashi
Published: February 2021

Neural networks in their many flavors have been widely used in prognostics of engineered systems due to their versatility and increasing potential, especially with recent breakthroughs in Deep Learning and specialized architectures. Despite these advances, some problems can still significantly benefit from a solid exploratory analysis and simple task-specific data/target transformations. In this work, popular architectures including Feedforward, Convolutional and LSTM (Long Short-Term Memory) networks are evaluated in a case study of RUL (Remaining Useful Life) prediction for turbofan aircraft engines, using data from publicly available repositories. A robust set of over 20,000 model configurations are tested, evaluating the effects of several hyper-parameters and design choices. The latter includes a maximum prediction horizon, revealing a trade-off between prediction accuracy and timeliness which can have significant impact in real-world applications. An operating condition-specific standardization scheme is also evaluated, in order to minimize the impact of normal changes in operating regimes which obfuscate the fault degradation patterns. A comparison with existing works in literature shows some simple policies for operating condition-invariance have lead to results which outperform the current state-of-the-art methods for some of the data subsets with multiple operating conditions.
Tooth Crack Severity Assessment in the Early Stage of Crack Propagation Using Gearbox Dynamic Model
Authors: Yang Xingkai , Zuo Ming J. , Tian Zhigang (Will)
Published: February 2021

Localized tooth crack in gearboxes may be reflected in impulse components of gearbox vibration signals. Crack induced impulses have been used for crack detection and fault diagnosis. In reported studies, researchers have used statistical indicators of the identified impulses, such as root mean square (RMS) and kurtosis, to track the growth of crack. These reported statistical indicators are only effective when crack levels are high and they are unable to detect tooth crack and assess crack severity in the early stage of crack propagation. In addition, no reported studies have focused on studying how tooth crack level affects crack induced impulses. Specifically, what the dominant segments of crack induced impulses are and which segment is affected more by crack growth within a certain crack level range. This paper uses dynamic modeling to study how crack level affects crack induced impulses. First, impulses are generated with a spur gearbox dynamic model under constant working conditions. Second, an exponentially damped sinusoidal model is utilized to fit the impulses and the Matrix Pencil Method is used for model parameter estimation. Finally, relationships between crack level and impulses are studied based on the obtained model parameters. The results have shown that the segments in the fifth and the sixth frequency bands of impulses are two dominant segments, while other segments have little contribution, for the gearbox system under investigation. Within a certain crack level range, there exists an impulse segment which is more affected by the crack level. In terms of the early stage of crack propagation, the segment in the sixth frequency band of the impulse is more affected by crack growth. On this basis, three new statistical indicators have been developed with the segment in sixth frequency band of the impulse and have shown their effectiveness for tooth crack severity level assessment in the early stage of crack propagation. These results have good potential for detection and severity assessment of early tooth cracks in gearboxes.
Prognostics As-A-Service: A Scalable Cloud Architecture for Prognostics
Authors: Watkins Jason , Teubert Christopher , Ossenfort John
Published: February 2021

Comprehensive aircraft system health-state awareness is crit- ical for maintaining safe, efficient growth in global oper- ations, enabling higher levels of autonomy, and facilitat- ing new forms of aviation. Maintainers, vehicle operators, air traffic controllers, dispatchers, pilots, autonomous sys- tems, and other decision-makers must have reliable real-time knowledge of the vehicle health, the health of its critical com- posite systems, predictions of how health changes with time, and forecasts of how its capabilities change with health degra- dation to preserve safety and efficiency. Providing this infor- mation in a reliable manner in computationally constrained environments and across a wide range of vehicles and sys- tems continues to be a challenge. This challenge can be par- tially resolved through cloud computing, where the execu- tion of prognostic and diagnostic algorithms is performed on a network of remote servers hosted on the internet. NASA is developing a cloud computing service, Prognostics As-A- Service (PaaS), that explores the feasibility and challenges of cloud-enhanced prognostics. Though such a system has broad applicability, this research effort is focused on aviation applications.
Classification Based Diagnosis:
Authors: Matei Ion , De Kleer Johan , Feldman Alexander , Zhenirovskyy Maksym , Rai Rahul
Published: February 2021

Machine learning methods based on classifiers are more robust to system complexity, but they ignore the relations that exist in the data due to the physical laws governing the behavior of the system. In this paper we discuss how (partial) knowledge about the physical system can be integrated in the machine learning process. We focus on classification based diagnosis. We show how the partially known model is integrated in the classification algorithm, and how the new algorithm differs from a machine learning classifier based on neural networks. We demonstrate that by integrating the partial system knowledge, the cross-entropy optimization problem used for learning a classifier can be expressed as a set of regression problems in terms of the parameters of the model representing the unknown behavior, followed by a simpler classifier learning. We showcase our approach when diagnosis faults for a rail switch system.
Towards an Enhanced Data- and Knowledge Management Capability: A Data Life Cycle Model Proposition for Integrated Vehicle Health Management
Authors: Maindze Alexslis , Skaf Zakwan , Jennions Ian
Published: February 2021

The creation, capturing, using and sharing of knowledge is based on data. The rate of data creation, collection, and elicitation through wide range experiments, simulations and measurements is rapidly increasing within Integrated Vehicle Health Management (IVHM). In addition, Knowledge Management (KM), data abstraction, analyses, storage and accessibility challenges persist, resulting in loss of knowledge and increased costs. This growth in the creation of research data, algorithms, technical papers, reports and logs, requires both a strategy and tool to address these challenges. A Data Life Cycle Model (DLCM) ensures the efficient and effective abstraction and management of both data and knowledge outputs. IVHM which depend heavily on high-quality data to perform data-driven, model-based and hybrid computational analysis of asset health. IVHM Centre does not yet have a systematic and coherent approach to its data management. The absence of a DLCM means that valuable knowledge might be lost or is difficult to find. Data visualization is fragmented and done on a project by project basis leading to increased costs. There is insufficient algorithm documentation and communication for easy transition between subsequent researchers and personnel. A systematic review of DLCMs, frameworks, standards and process models pertaining to data- and KM in the context of IVHM, found that there is no DLCM that is consistent with IVHM data and knowledge management requirements. Specifically, there is a need to develop a DLCM based on Open System Architecture for Condition-Based Maintenance framework.
Process Control Decision Inference, Monitoring, and Execution
Authors: Matania Robert , Foret Jean-Marie , Camarillo Vicente , Walker Mark
Published: February 2021

Approaches for inferring process control system decision trees from plant data have been heavily researched and demonstrated, but the utility of applying such decision tree inferences for autonomously monitoring, guiding, and executing control philosophies has been lacking. The authors have implemented an architecture leveraging model-based reasoning and graphical programming that investigates the utility of using control decision tree inferencing for validating, monitoring, and executing control strategies for both simple and complex process control problems. The techniques are potentially useful for assessing process control health, as well as extracting process control knowledge that may exist in daily operations but not recognized or well understood by analysts and management. Plant operators and managers can readily employ such insights for improving, augmenting, and extending process control system behavior, but can also potentially employ the refined and validated decision trees as a supervisory control layer on top of their existing control systems.
Semi-supervised Constrained Hidden Markov Model Using Multiple Sensors for Remaining Useful Life Prediction and Optimal Predictive Maintenance
Authors: Zhao Xinyu , Kang Yunyi , Yan Hao , Ju Feng
Published: February 2021

Remaining Useful Life (RUL) estimation is critical in many engineering systems where proper predictive maintenance is needed to increase a unit's effectiveness and reduce time and cost of repairing. Typically for such systems, multiple sensors are normally used to monitor performance, which create difficulties for system state identification. In this paper, we develop a semi-supervised left-to-right constrained Hidden Markov Model (HMM) model, which is effective in estimating the RUL, while capturing the jumps among states in condition dynamics. In addition, based on the HMM model learned from multiple sensors, we build a Partial Observable Markov Decision Process (POMDP) to demonstrate how such RUL estimation can be effectively used for optimal preventative maintenance decision making. We apply this technique to the NASA Engine degradation data and demonstrate the effectiveness of the proposed method.
Simulation Engine for the Characterization of Capacity Degradation Processes in Lithium-ion Batteries Undergoing Heterogeneous Operating Conditions
Authors: Perez Aramis , Rozas Heraldo , Jaramillo Francisco , Quintero Vanessa , Orchard Marcos
Published: February 2021

Characterizing the degradation of batteries is still a matter of ongoing research due to the diverse operating conditions at which they are submitted. For example, different current discharge rates  and asymmetrical charge/discharge cycles are critical operating conditions that affect the both the performance and the lifespan. This article extends and improves a previously published methodology to estimate the degradation process of Li-ion batteries by using the Kalman Filter to estimate one of the parameters of the model. Furthermore, the Kalman Filter is then combined with a Similarity-Based-Modeling framework, which integrates information of the State-of-Charge and different discharge currents in each operating cycle to estimate the degradation process. The results are obtained using information provided by the manufacturer and also with measured data. Moreover, this model is applied to a random usage proﬁle of an Electric-Vehicle to characterize of the degradation process of the batteries under more realistic usage conditions.
Diagnostic Framework for Wind Turbine Gearboxes Using Machine Learning
Authors: Koukoura Sofia , Carroll James , McDonald Alasdair
Published: February 2021

Operation and maintenance costs of wind turbines are highlydriven by gearbox failures, especially offshore were the logisticsof replacements are more demanding. It is therefore verycritical to foresee incipient gearbox faults before they becomecatastrophic failures. Wind turbine gearbox condition monitoringis usually performed using vibration signals comingfrom accelerometers installed on the gearbox surface. Thecurrent monitoring practice is a rule-based approach, wherealarms are activated based on thresholds. However, too muchmanual analysis may be required for some failure modes andthis can become quite challenging as the installed wind capacitygrows. Also, since false alarms have to be avoided,these thresholds are set quite high, resulting in late stage diagnosisof components. Given the fact there is a large amountof historic operating data with confirmed gearbox failure incidents,this paper proposes a framework that uses a machinelearning approach. Vibration signals are used from the gearboxsensors and processed in the frequency domain. Featuresare extracted from the processed signals based on the fault locationsand failure modes, using domain knowledge. Thesefeatures are used as inputs in a layer of pattern recognitionmodels that can determine a potential component fault locationand failure mode. The proposed framework is illustratedusing failure examples from operating offshore wind turbines.
Permanent Magnet Synchronous Motor Winding Fault Simulation and Diagnosis
Authors: Liu Enhui , Niu Guangxing , Tang Shijie , Zhang Bin , Williams Jesse , Martin Rodney , Moore Craig
Published: February 2021

This paper presents the theory, simulation, diagnosis and prognosis evaluation of an anomaly detector for Permanent Magnet Synchronous Motor (PMSM) stator winding insulation faults. Physics-of-failure mechanisms are used to develop the PMSM model and its insulation fault model. Then, the diagnostic features are identified using Hilbert transforms based on artificial data acquired from the simulation results of different degree of the stator winding insulation faults. Next, the diagnosis and prognosis routine pass the diagnostic features to the Extended Kalman Filter (EKF) based on Bayesian estimation theory. Finally, the real-time diagnosis and prognosis of an anomaly detector for PMSM stator winding insulation faults are performed using Simulink. Simulation results are presented to demonstrate the effectiveness of the proposed method.
A Methodology for the Experimental Validation of an Aircraft ECS Digital Twin Targeting System Level Diagnostics
Authors: Chowdhury Shafayat Hasan , Ali Fakhre , Jennions Ian K.
Published: February 2021

The Environmental Control System (ECS) of an aircraft is responsible for regulating and conditioning the airflow into the cockpit, cabin and avionics bay. The ECS is composed of several complex sub-systems and components that are reported as key unscheduled maintenance drivers for legacy aircraft by aircraft operators. Furthermore, the incorporated temperature and flow control valves in these sub-systems have the capability to mask potential faults at the component level, making the diagnostic process very challenging. To overcome this challenge, the aviation industry is currently proactively exploring the predictive maintenance approach that allows real-time monitoring of the key systems, sub-systems and components. In the context of the ECS, this necessitates the requirement to equip the system with appropriate condition monitoring capabilities. To do this, the performance characteristics of the ECS at sub-system and component level needs to be well understood under a wide-range of aircraft operating scenarios. Existing literature provides component level and system level analyses of the ECS. However, it lacks an experimentally verified and validated ECS sub-system and component level simulation tool (ECS Digital Twin), capable of simulating the thermodynamic performance and component health state parameters under wide-ranging aircraft operational scenarios. The ECS Digital Twin (DT) developed by the Cranfield University IVHM Centre offers the capability to simulate healthy and faulty cases of the Passenger Air Conditioner (PACK). This paper proposes a methodology for full-scale experimental Verification & Validation (V&V) of the developed ECS DT, to enable component level simulation, and enabling accurate diagnostics, of the civil aircraft ECS. The paper reports on progress to date in this project.
Hybrid Approach of Data-Driven and Physics-based Methods for Estimation and Prediction of Fatigue Crack Growth
Authors: Kong Hyeon Bae , Jo Soo-Ho , Jung Joon Ha , Ha Jong M. , Shin Yong Chang , Yoon Heonjun , Sun Kyung Ho , Seo Yun-Ho , Jeon Byung Chul
Published: February 2021

This paper aims to develop a hybrid method to estimate the fatigue crack growth of an aluminum lap joint specimen with and without Lamb wave signals. The proposed method is validated on the two validation specimens (T7 and T8), using the training data sets of six different specimens (T1-T6). Each validation data set includes crack length estimation of few loading cycles with the given Lamb wave signals, followed by crack estimation without the signals. First, the crack length estimation using the signals for T7 and T8 sets was performed by the data-driven based method. A set of features was extracted from the preprocessed signals. Then, a random forest model was used to estimate crack lengths with grid search-based feature selection and hyper-parameter optimization. Next, different approaches were used to estimate the crack length without the signals, since T7 and T8 were tested under different loading conditions. Assuming that the homogeneous constant loading condition leads to a similar fatigue crack growth patterns, an ensemble prognostics approach with simplified particle filter-based weight update was used to predict the crack lengths of T7 specimen. In contrast, Walker’s equation model-based approach was chosen for T8 specimen as it was tested under a different loading condition. Considering the uncertainties of the model parameters, Walker’s equation models were generated by Monte Carlo methods. The average of generated models were used to predict the remaining crack lengths of T8 specimen. The proposed method led to Top 3 in 2019 PHM Conference Data Challenge.
Adapting Approximate Entropy as a Health Indicator of Rotating Machinery for Estimation of Remaining Useful Life
Authors: Walker Cody
Published: February 2021

Despite significant attention to online health monitoring and prognostics of bearings, many common health indicators are not sensitive to early stages of degradation. This research investigates the use of approximate entropy (ApEn), previously developed for fault diagnostics, as a health indicator for prognostics. ApEn quantifies the regularity of a signal; as bearings degrade, the frequency content of vibration signals changes and affects the ApEn as the vibration becomes more chaotic. Early results suggest ApEn supports earlier degradation detection and more predictable progression from fault to failure. This research focuses on optimizing parameters of the ApEn calculation to provide guidance across a variety of bearing types, sizes, and geometries in both steady-state and transient operation.