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30
Contribution of belief functions to Hidden Markov Models
- IEEE Workshop on Machine Learning and Signal Processing
, 2009
"... of belief functions to hidden markov ..."
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Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life
- Reliability Engineering & System Safety
, 2012
"... The traditional data-driven prognostic approach is to construct multiple candidate algorithms using a training data set, evaluate their respective performance using a testing data set, and select the one with the best performance while discarding all the others. This approach has three shortcomings: ..."
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The traditional data-driven prognostic approach is to construct multiple candidate algorithms using a training data set, evaluate their respective performance using a testing data set, and select the one with the best performance while discarding all the others. This approach has three shortcomings: (i) the selected standalone algorithm may not be robust, i.e., it may be less accurate when the real data acquired after the deployment differs from the testing data; (ii) it wastes the resources for constructing the algorithms that are discarded in the deployment; (iii) it requires the testing data in addition to the training data, which increases the overall expenses for the algorithm selection. To overcome these drawbacks, this paper proposes an ensemble data-driven prognostic approach which combines multiple member algorithms with a weighted-sum formulation. Three weighting schemes, namely, the accuracy-based weighting, diversity-based weighting and optimization-based weighting, are proposed to determine the weights of member algorithms for data-driven prognostics. The k-fold cross validation (CV) is employed to estimate the prediction error required by the weighting schemes. Two case studies were employed to demonstrate the effectiveness of the proposed prognostic approach. The results suggest that the ensemble approach with any weighting scheme gives more accurate RUL predictions compared to any sole algorithm and that the optimization-based weighting scheme gives the best overall performance among the three weighting schemes.†
Connexionist-Systems-Based Long Term Prediction Approaches for Prognostics
, 2012
"... Abstract—Prognostics and Health Management aims at estimating the remaining useful life of a system (RUL), i.e. the remaining time before a failure occurs. It benefits thereby from an increasing interest: prognostic estimates (and related decisionmaking processes) enable increasing availability and ..."
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Abstract—Prognostics and Health Management aims at estimating the remaining useful life of a system (RUL), i.e. the remaining time before a failure occurs. It benefits thereby from an increasing interest: prognostic estimates (and related decisionmaking processes) enable increasing availability and safety of industrial equipment while reducing costs. However, prognostics is generally based on a prediction step which, in the context of data-driven approaches as considered in this paper, can be hard to achieve because future outcomes are in essence difficult to estimate. Also, a prognostic system must perform sufficient long term estimates, whereas many works focus on short term predictions. Following that, the aim of this paper is to formalize and discuss the connexionist-systems-based approaches to ensure multi-step ahead predictions for prognostics. Five approaches are pointed out: the Iterative, Direct, DirRec, Parallel, and MISMO approaches. Conclusions of the paper are based, on one side, on a literature review; and on the other side, on simulations among 111 time series prediction problems, and among a real engine fault prognostics application. These experiments are performed using the exTS (evolving extended Takagi-Sugeno system). As for comparison purpose, three types of performances measures are used: prediction accuracy, complexity (computational time), and implementation requirements. Results show that all three criteria are never optimized at the same time (same experiment), and best practices for prognostics application are finally pointed out. Index Terms—Prognostics and health management, multistep ahead predictions, connexionist system, evolving extended Takagi-Sugeno system. CBM exTS pdf RLS
E2GK-pro: An Evidential Evolving Multimodeling Approach for Systems Behavior Prediction
, 2011
"... Nonlinear dynamic systems identification and nonlinear dynamic behavior prediction are important tasks in several areas of industrial applications. Multiple works proposed multimodel-based approaches to model nonlinear systems. Multimodeling permits to blend different model types together to form hy ..."
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Nonlinear dynamic systems identification and nonlinear dynamic behavior prediction are important tasks in several areas of industrial applications. Multiple works proposed multimodel-based approaches to model nonlinear systems. Multimodeling permits to blend different model types together to form hybrid models. It advocates the use of existing, well known model types within the same model structure. Recently, a multimodeling strategy based on belief functions theory was developed based on a fuzzy rule based system. We propose a different approach of this latter taking advantage of new efficient evidential clustering algorithms for the determination of the local models and the assessment of the global model. In particular, the algorithm called E2GK-pro relies on an online procedure based on the Evidential Evolving Gustafsson-Kessel (E2GK) algorithm that ensures an evolving partitioning of the data into clusters corresponding to operating regions of the global system. Thus the estimation of the local models is dynamically performed by upgrading and modifying their parameters while the data arrive. Each local model is weighted by a belief mass provided by E2GK, and the global model (multimodel) is a combination of all the local models. 1.
Resiliencedriven system design of complex engineered systems
- Journal of Mechanical Design
, 2011
"... Most engineered systems are designed with a passive and fixed design capacity and, therefore, may become unreliable in the presence of adverse events. Currently, most engineered systems are designed with system redundancies to ensure required system reliability under adverse events. However, a high ..."
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Most engineered systems are designed with a passive and fixed design capacity and, therefore, may become unreliable in the presence of adverse events. Currently, most engineered systems are designed with system redundancies to ensure required system reliability under adverse events. However, a high level of system redundancy increases a system's life-cycle cost (LCC). Recently, proactive maintenance decisions have been enabled through the development of prognostics and health management (PHM) methods that detect, diagnose, and predict the effects of adverse events. Capitalizing on PHM technology at an early design stage can transform passively reliable (or vulnerable) systems into adaptively reliable (or resilient) systems while considerably reducing their LCC. In this paper, we propose a resilience-driven system design (RDSD) framework with the goal of designing complex engineered systems with resilience characteristics. This design framework is composed of three hierarchical tasks: (i) the resilience allocation problem (RAP) as a top-level design problem to define a resilience measure as a function of reliability and PHM efficiency in an engineering context, (ii) the system reliability-based design optimization (RBDO) as the first bottom-level design problem for the detailed design of components, and (iii) the system PHM design as the second bottom-level design problem for the detailed design of PHM units. The proposed RDSD framework is demonstrated using a simplified aircraft control actuator design problem resulting in a highly resilient actuator with optimized reliability, PHM efficiency and redundancy for the given parameter settings.
Evaluation of Health Assessment Techniques for Rotating Machinery
- Master’s Thesis, Division of Research and Advanced Studies of the University of Cincinnati
, 2009
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Classification with imperfect labels for fault prediction
- In Proceedings of the first international
, 2011
"... Classification techniques have been widely used in fault predic-tion for industrial systems. However, an inherent issue with this approach is label imperfections in training data, since the line of demarcation between classes is determined based on field expert experience and maintenance capability. ..."
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Classification techniques have been widely used in fault predic-tion for industrial systems. However, an inherent issue with this approach is label imperfections in training data, since the line of demarcation between classes is determined based on field expert experience and maintenance capability. To address this issue we propose a noisy-label model in which the labeling noise function is derived from a point of view motivated by reliability analysis. We also present a novel label bootstrapping method that can better reflect the true uncertainty of the labeling process than the standard approach for addressing label imperfections. The proposed tech-nique gives encouraging results on two industrial fault detection data sets.
Investigating Computational Geometry for Failure Prognostics in Presence of Imprecise Health Indicator: Results and Comparisons
"... Prognostics and Health Management (PHM) is a multidisci-plinary field aiming at maintaining physical systems in their optimal functioning conditions. The system under study is assumed to be monitored by sensors from which are obtained measurements reflecting the system’s health state. A health index ..."
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Prognostics and Health Management (PHM) is a multidisci-plinary field aiming at maintaining physical systems in their optimal functioning conditions. The system under study is assumed to be monitored by sensors from which are obtained measurements reflecting the system’s health state. A health index (HI) is estimated to feed a data-driven PHM solution developed to predict the remaining useful life (RUL). In this paper, the values taken by an HI are assumed imprecise (IHI). An IHI is interpreted as a planar figure called polygon and a case-based reasoning (CBR) approach is adapted to estimate the RUL. This adaptation makes use of computational geom-etry tools in order to estimate the nearest cases to a given testing instance. The proposed algorithm called RULCLIP-PER is assessed and compared on datasets generated by the NASA’s turbofan simulator (C-MAPSS) including the four turbofan testing datasets and the two testing datasets of the PHM’08 data challenge. These datasets represent 1360 test-ing instances and cover different realistic and difficult cases considering operating conditions and fault modes with un-known characteristics. The problem of feature selection, health index estimation, RUL fusion and ensembles are also tackled. The proposed algorithm is shown to be efficient with few pa-rameter tuning on all datasets. 1.
Towards a Framework for Evaluating and Comparing Diagnosis Algorithms
"... Abstract: Diagnostic inference involves the detection of anomalous system behavior and the identification of its cause, possibly down to a failed unit or to a parameter of a failed unit. Traditional approaches to solving this problem include expert/rule-based, model-based, and data-driven methods. E ..."
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Abstract: Diagnostic inference involves the detection of anomalous system behavior and the identification of its cause, possibly down to a failed unit or to a parameter of a failed unit. Traditional approaches to solving this problem include expert/rule-based, model-based, and data-driven methods. Each approach (and various techniques within each approach) use different representations of the knowledge required to perform the diagnosis. The sensor data is expected to be combined with these internal representations to produce the diagnosis result. In spite of the availability of various diagnosis technologies, there have been only minimal efforts to develop a standardized software framework to run, evaluate, and compare different diagnosis technologies on the same system. This paper presents a framework that defines a standardized representation of the system knowledge, the sensor data, and the form of the diagnosis results – and provides a run-time architecture that can execute diagnosis algorithms, send sensor data to the algorithms at appropriate time steps from a variety of sources (including the actual physical system), and collect resulting diagnoses. We also define a set of metrics that can be used to evaluate and compare the performance of the algorithms, and provide software to calculate the metrics. 1.
Role of Prognostics in Support of Integrated Risk-based Engineering in Nuclear Power Plant Safety
"... There is a growing trend in applying a prognostics and health management approach to engineering systems in general and space and aviation systems in particular. This paper reviews the role of prognostics and health management approach in support of integrated risk-based applications to nuclear powe ..."
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There is a growing trend in applying a prognostics and health management approach to engineering systems in general and space and aviation systems in particular. This paper reviews the role of prognostics and health management approach in support of integrated risk-based applications to nuclear power plants, like risk-based inservice inspection, technical specification optimization, maintenance optimization, etc. The review involves a survey of the state-of-art technologies in prognostics and health management and an exploration of its role in support of integrated risk-based engineering and how the technology can be adopted to realize enhanced safety and operational performance. An integrated risk-based engineering framework for nuclear power plants has been proposed, where probabilistic risk assessment plays the role of identification, prioritization and optimization of systems, structures, and components, while deterministic assessment is performed using a prognostics and health management approach. Keeping in view the requirements of structural reliability assessment, the paper also proposes essential features of a ‘Mechanics-of-Failure ’ approach in support of integrated risk-based engineering. The performance criteria used in prognostics and health management has been adopted to meet requirements of risk-based applications. 1.
