Other affiliations: Vanderbilt University
Bio: Indranil Roychoudhury is an academic researcher from Ames Research Center. The author has contributed to research in topics: Prognostics & Fault detection and isolation. The author has an hindex of 18, co-authored 71 publications receiving 1088 citations. Previous affiliations of Indranil Roychoudhury include Vanderbilt University.
TL;DR: This paper originally resorts to an ensemble of ESNs, for enhancing the performances of individual ESNs and providing also an estimation of the uncertainty affecting the RUL prediction, which outperforms a static ensemble and a standard MVE approach for uncertainty estimation in tests performed on a synthetic and two industrial datasets.
Abstract: The use of Echo State Networks (ESNs) for the prediction of the Remaining Useful Life (RUL) of industrial components, i.e. the time left before the equipment will stop fulfilling its functions, is attractive because of their capability of handling the system dynamic behavior, the measurement noise, and the stochasticity of the degradation process. In particular, in this paper we originally resort to an ensemble of ESNs, for enhancing the performances of individual ESNs and providing also an estimation of the uncertainty affecting the RUL prediction. The main methodological novelties in our use of ESNs for RUL prediction are: i) the use of the individual ESN memory capacity within the dynamic procedure for aggregating of the ESNs outcomes; ii) the use of an additional ESN for estimating the RUL uncertainty, within the Mean Variance Estimation (MVE) approach. With these novelties, the developed approach outperforms a static ensemble and a standard MVE approach for uncertainty estimation in tests performed on a synthetic and two industrial datasets.
01 May 2010
TL;DR: Hybrid Transcend is a comprehensive model-based diagnosis scheme that uses the hybrid bond graph modeling language to systematically develop computational models and algorithms for hybrid state estimation, robust fault detection, and efficient fault isolation.
Abstract: The application of model-based diagnosis schemes to real systems introduces many significant challenges, such as building accurate system models for heterogeneous systems with complex behaviors, dealing with noisy measurements and disturbances, and producing valuable results in a timely manner with limited information and computational resources. The Advanced Diagnostics and Prognostics Testbed (ADAPT), which was deployed at the NASA Ames Research Center, is a representative spacecraft electrical power distribution system that embodies a number of these challenges. ADAPT contains a large number of interconnected components, and a set of circuit breakers and relays that enable a number of distinct power distribution configurations. The system includes electrical dc and ac loads, mechanical subsystems (such as motors), and fluid systems (such as pumps). The system components are susceptible to different types of faults, i.e., unexpected changes in parameter values, discrete faults in switching elements, and sensor faults. This paper presents Hybrid Transcend, which is a comprehensive model-based diagnosis scheme to address these challenges. The scheme uses the hybrid bond graph modeling language to systematically develop computational models and algorithms for hybrid state estimation, robust fault detection, and efficient fault isolation. The computational methods are implemented as a suite of software tools that enable diagnostic analysis and testing through simulation, diagnosability studies, and deployment on the experimental testbed. Simulation and experimental results demonstrate the effectiveness of the methodology.
13 Oct 2010
TL;DR: This paper presents an approach that combines the analytic modelbased and featuredriven diagnosis approaches, the analytic approach is used to reduce the set of possible faults and then features are chosen to best distinguish among the remaining faults.
Abstract: Model-based diagnosis typically uses analytical redundancy to compare predictions from a model against observations from the system being diagnosed. However this approach does not work very well when it is not feasible to create analytic relations describing all the observed data, e.g., for vibration data which is usually sampled at very high rates and requires very detailed finite element models to describe its behavior. In such cases, features (in time and frequency domains) that contain diagnostic information are extracted from the data. Since this is a computationally intensive process, it is not efficient to extract all the features all the time. In this paper we present an approach that combines the analytic model-based and feature-driven diagnosis approaches. The analytic approach is used to reduce the set of possible faults and then features are chosen to best distinguish among the remaining faults. We describe an implementation of this approach on the Flyable Electro-mechanical Actuator (FLEA) test bed.
TL;DR: Both diagnostic and prognostic run-to-failure experiments, conducted in laboratory and flight conditions for several types of faults, demonstrated robust fault diagnosis on the selected set of component and sensor faults and high-accuracy predictions of failure time in prognostic scenarios.
Abstract: systemthatdiagnoseselectromechanicalactuatorfaultsandemploysprognosticalgorithmstotrackfaultprogression and predict the actuator’s remaining useful life. The diagnostic algorithm is implemented using a combined modelbased and data-driven reasoner. The prognostic algorithm, implemented using Gaussian process regression, estimates the remaining life of the faulted component. The paperalso covers the selection of fault modes for coverage and methods developed for fault injection. Validation experiments were conducted in both laboratory and flight conditions using a flyable electromechanical actuator test stand. The stand allows test actuators to be subjected to realistic environmental and operating conditions while providing the capability to safely inject and monitor propagation of various fault modes. The paper covers both diagnostic and prognostic run-to-failure experiments, conducted in laboratory and flight conditions for several types of faults. The experiments demonstrated robust fault diagnosis on the selected set of component and sensor faults and high-accuracy predictions of failure time in prognostic scenarios.
TL;DR: This paper presents two algorithms for designing the local diagnosers and analyzes their time and space complexity, and demonstrates the effectiveness of the approach by applying it to the Advanced Water Recovery System developed at the NASA Johnson Space Center.
Abstract: Wear and tear from sustained operations cause systems to degrade and develop faults. Online fault diagnosis schemes are necessary to ensure safe operation and avoid catastrophic situations, but centralized diagnosis approaches have large memory and communication requirements, scale poorly, and create single points of failure. To overcome these problems, we propose an online, distributed, model-based diagnosis scheme for isolating abrupt faults in large continuous systems. This paper presents two algorithms for designing the local diagnosers and analyzes their time and space complexity. The first algorithm assumes the subsystem structure is known and constructs a local diagnoser for each subsystem. The second algorithm creates the partition structure and local diagnosers simultaneously. We demonstrate the effectiveness of our approach by applying it to the Advanced Water Recovery System developed at the NASA Johnson Space Center.
09 Mar 2012
TL;DR: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems as mentioned in this paper, and they have been widely used in computer vision applications.
Abstract: Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; email@example.com. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.
01 Dec 1979
TL;DR: The U.S. commercial space launch industry has changed considerably since the enactment of the Commercial Space Launch Amendments Act of 2004 as discussed by the authors, which prohibited FAA from regulating crew and spaceflight participant safety before 2012, a moratorium that was later extended but will now expire on September 30, 2015.
Abstract: The U.S. commercial space launch industry has changed considerably since the enactment of the Commercial Space Launch Amendments Act of 2004. FAA is required to license or permit commercial space launches, but to allow the space tourism industry to develop, the act prohibited FAA from regulating crew and spaceflight participant safety before 2012—a moratorium that was later extended but will now expire on September 30, 2015. Since October 2014, there have been three mishaps involving FAA licensed or permitted launches.
01 Oct 2015
TL;DR: In this article, the Extreme Learning Machine (ELM) was used to train a classifier for learning to solve problems in the real world, and the results showed that the classifier achieved good performance.
Abstract: 본 논문에서는 인공 신경망의 일종인 Extreme Learning Machine의 학습 알고리즘을 기반으로 하여 노이즈에 강한 특성을 보이는 퍼지 집합 이론을 이용한 새로운 패턴 분류기를 제안 한다. 기존 인공 신경망에 비해 학습속도가 매우 빠르며, 모델의 일반화 성능이 우수하다고 알려진 Extreme Learning Machine의 학습 알고리즘을 퍼지 패턴 분류기에 적용하여 퍼지 패턴 분류기의 학습 속도와 패턴 분류 일반화 성능을 개선 한다. 제안된 퍼지 패턴 분류기의 학습 속도와 일반화 성능을 평가하기 위하여, 다양한 머신 러닝 데이터 집합을 사용한다.
TL;DR: In this paper, the authors reviewed and evaluated contemporary forecasting techniques for photovoltaics into power grids, and concluded that ensembles of artificial neural networks are best for forecasting short-term PV power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty.
Abstract: Integration of photovoltaics into power grids is difficult as solar energy is highly dependent on climate and geography; often fluctuating erratically. This causes penetrations and voltage surges, system instability, inefficient utilities planning and financial loss. Forecast models can help; however, time stamp, forecast horizon, input correlation analysis, data pre and post-processing, weather classification, network optimization, uncertainty quantification and performance evaluations need consideration. Thus, contemporary forecasting techniques are reviewed and evaluated. Input correlational analyses reveal that solar irradiance is most correlated with Photovoltaic output, and so, weather classification and cloud motion study are crucial. Moreover, the best data cleansing processes: normalization and wavelet transforms, and augmentation using generative adversarial network are recommended for network training and forecasting. Furthermore, optimization of inputs and network parameters, using genetic algorithm and particle swarm optimization, is emphasized. Next, established performance evaluation metrics MAE, RMSE and MAPE are discussed, with suggestions for including economic utility metrics. Subsequently, modelling approaches are critiqued, objectively compared and categorized into physical, statistical, artificial intelligence, ensemble and hybrid approaches. It is determined that ensembles of artificial neural networks are best for forecasting short term photovoltaic power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty. Additionally, convolutional neural network is found to excel in eliciting a model's deep underlying non-linear input-output relationships. The conclusions drawn impart fresh insights in photovoltaic power forecast initiatives, especially in the use of hybrid artificial neural networks and evolutionary algorithms.