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Donghua Zhou - One of the best experts on this subject based on the ideXlab platform.

  • A Generalized Result for Degradation Model-Based Reliability Estimation
    IEEE Transactions on Automation Science and Engineering, 2014
    Co-Authors: Donghua Zhou
    Abstract:

    Reliability estimation based on Degradation Model is a feasible and low-cost alternative used to estimate reliability for highly reliable systems when the failure-time data are rare. Based on reliability estimation by Degradation Modeling, preventive maintenance work orders need to be timely triggered to minimize unscheduled downtime. In Trans. Autom. Sci. Eng., vol. 9, no. 1, pp. 209-212, Jan. 2012, Sun et al., an approach to dynamically extract maintenance threshold is presented for maintenance scheduling, in which the reliability threshold for maintenance is determined by maximizing the expected availability and the reliability estimation is achieved by a modified two-stage Degradation Modeling approach. Although this approach is novel and useful, its reliability estimation is an asymptotic solution in long time scale. In this paper, we generalize the above result by considering a general Degradation path Model and provide the exact and explicit formulation for reliability estimation. Additionally, a maximum-likelihood estimation method for parameters in the presented Model is proposed based on the historical Degradation observations. Finally, an example is provided for illustration.

  • a wiener process based Degradation Model with a recursive filter algorithm for remaining useful life estimation
    Mechanical Systems and Signal Processing, 2013
    Co-Authors: Xiaosheng Si, Wenbin Wang, Changhua Hu, Maoyin Chen, Donghua Zhou
    Abstract:

    Abstract Remaining useful life estimation (RUL) is an essential part in prognostics and health management. This paper addresses the problem of estimating the RUL from the observed Degradation data. A Wiener-process-based Degradation Model with a recursive filter algorithm is developed to achieve the aim. A novel contribution made in this paper is the use of both a recursive filter to update the drift coefficient in the Wiener process and the expectation maximization (EM) algorithm to update all other parameters. Both updating are done at the time that a new piece of Degradation data becomes available. This makes the Model depend on the observed Degradation data history, which the conventional Wiener-process-based Models did not consider. Another contribution is to take into account the distribution in the drift coefficient when updating, rather than using a point estimate as an approximation. An exact RUL distribution considering the distribution of the drift coefficient is obtained based on the concept of the first hitting time. A practical case study for gyros in an inertial navigation system is provided to substantiate the superiority of the proposed Model compared with competing Models reported in the literature. The results show that our developed Model can provide better RUL estimation accuracy.

Michael Pecht - One of the best experts on this subject based on the ideXlab platform.

  • prognostics uncertainty reduction by fusing on line monitoring data based on a state space based Degradation Model
    Mechanical Systems and Signal Processing, 2014
    Co-Authors: Wenbin Wang, Michael Pecht
    Abstract:

    Abstract The objective of this study is to develop a state-space-based Degradation Model and associated computational techniques to reduce failure prognostics uncertainty by fusing on-line monitoring data. A key problem in failure prognostics for an individual system under actual operating conditions is uncertainty management. In this study, the various uncertainty sources in failure prognostics are analyzed, and an appropriate uncertainty quantifying and managing mechanism is proposed, accounting for both the item-to-item variability and the Degradation process variability. The method is demonstrated on a crack growth data set, and the results show that the proposed prognostics method has the ability to provide a failure time prediction with less uncertainty by fusing sensor data, which are beneficial for risk assessment and optimal maintenance decision-making.

  • prognostics of lithium ion batteries based on relevance vectors and a conditional three parameter capacity Degradation Model
    Journal of Power Sources, 2013
    Co-Authors: D Wang, Qiang Miao, Michael Pecht
    Abstract:

    Abstract Lithium-ion batteries are widely used as power sources in commercial products, such as laptops, electric vehicles (EVs) and unmanned aerial vehicles (UAVs). In order to ensure a continuous power supply, the functionality and reliability of lithium-ion batteries have received considerable attention. In this paper, a battery capacity prognostic method is developed to estimate the remaining useful life of lithium-ion batteries. This capacity prognostic method consists of a relevance vector machine and a conditional three-parameter capacity Degradation Model. The relevance vector machine is used to derive the relevance vectors that can be used to find the representative training vectors containing the cycles of the relevance vectors and the predictive values at the cycles of the relevance vectors. The conditional three-parameter capacity Degradation Model is developed to fit the predictive values at the cycles of the relevance vectors. Extrapolation of the conditional three-parameter capacity Degradation Model to a failure threshold is used to estimate the remaining useful life of lithium-ion batteries. Three instance studies were conducted to validate the developed method. The results show that the developed method is able to predict the future health condition of lithium-ion batteries.

Baosen Zhang - One of the best experts on this subject based on the ideXlab platform.

  • A Convex Cycle-based Degradation Model for Battery Energy Storage Planning and Operation
    2018 Annual American Control Conference (ACC), 2018
    Co-Authors: Bolun Xu, Baosen Zhang
    Abstract:

    A vital aspect in energy storage planning and operation is to accurately Model its operational cost, which mainly comes from the battery cell Degradation. Battery Degradation can be viewed as a complex material fatigue process that is based on stress cycles. Rainflow algorithm is a popular way for cycle identification in material fatigue process, and has been extensively used in battery Degradation assessment. However, the rainflow algorithm does not have a closed form, which is the major difficulty to include it in optimization. In this paper, we prove the rainflow cycle-based cost is convex. Convexity enables the proposed Degradation Model to be incorporated in different battery optimization problems with a guarantee of the solution quality. We provide a subgradient algorithm to solve the problem. A case study on PJM regulation market demonstrates the effectiveness of the proposed Degradation Model in maximizing the battery operating profits as well as extending its lifetime.

  • ACC - A Convex Cycle-based Degradation Model for Battery Energy Storage Planning and Operation
    2018 Annual American Control Conference (ACC), 2018
    Co-Authors: Bolun Xu, Baosen Zhang
    Abstract:

    A vital aspect in energy storage planning and operation is to accurately Model its operational cost, which mainly comes from the battery cell Degradation. Battery Degradation can be viewed as a complex material fatigue process that is based on stress cycles. Rainflow algorithm is a popular way for cycle identification in material fatigue process, and has been extensively used in battery Degradation assessment. However, the rainflow algorithm does not have a closed form, which is the major difficulty to include it in optimization. In this paper, we prove the rainflow cycle-based cost is convex. Convexity enables the proposed Degradation Model to be incorporated in different battery optimization problems with a guarantee of the solution quality. We provide a subgradient algorithm to solve the problem. A case study on PJM regulation market demonstrates the effectiveness of the proposed Degradation Model in maximizing the battery operating profits as well as extending its lifetime.

  • A Convex Cycle-based Degradation Model for Battery Energy Storage Planning and Operation
    Proceedings of the American Control Conference, 2018
    Co-Authors: Yuanyuan Shi, Yushi Tan, Bolun Xu, Baosen Zhang
    Abstract:

    A vital aspect in energy storage planning and operation is to accurately Model its operational cost, which mainly comes from the battery cell Degradation. Battery Degradation can be viewed as a complex material fatigue process that based on stress cycles. Rainflow algorithm is a popular way for cycle identification in material fatigue process, and has been extensively used in battery Degradation assessment. However, the rainflow algorithm does not have a closed form, which makes the major difficulty to include it in optimization. In this paper, we prove the rainflow cycle-based cost is convex. Convexity enables the proposed Degradation Model to be incorporated in different battery optimization problems and guarantees the solution quality. We provide a subgradient algorithm to solve the problem. A case study on PJM regulation market demonstrates the effectiveness of the proposed Degradation Model in maximizing the battery operating profits as well as extending its lifetime.

Wenbin Wang - One of the best experts on this subject based on the ideXlab platform.

  • prognostics uncertainty reduction by fusing on line monitoring data based on a state space based Degradation Model
    Mechanical Systems and Signal Processing, 2014
    Co-Authors: Wenbin Wang, Michael Pecht
    Abstract:

    Abstract The objective of this study is to develop a state-space-based Degradation Model and associated computational techniques to reduce failure prognostics uncertainty by fusing on-line monitoring data. A key problem in failure prognostics for an individual system under actual operating conditions is uncertainty management. In this study, the various uncertainty sources in failure prognostics are analyzed, and an appropriate uncertainty quantifying and managing mechanism is proposed, accounting for both the item-to-item variability and the Degradation process variability. The method is demonstrated on a crack growth data set, and the results show that the proposed prognostics method has the ability to provide a failure time prediction with less uncertainty by fusing sensor data, which are beneficial for risk assessment and optimal maintenance decision-making.

  • a wiener process based Degradation Model with a recursive filter algorithm for remaining useful life estimation
    Mechanical Systems and Signal Processing, 2013
    Co-Authors: Xiaosheng Si, Wenbin Wang, Changhua Hu, Maoyin Chen, Donghua Zhou
    Abstract:

    Abstract Remaining useful life estimation (RUL) is an essential part in prognostics and health management. This paper addresses the problem of estimating the RUL from the observed Degradation data. A Wiener-process-based Degradation Model with a recursive filter algorithm is developed to achieve the aim. A novel contribution made in this paper is the use of both a recursive filter to update the drift coefficient in the Wiener process and the expectation maximization (EM) algorithm to update all other parameters. Both updating are done at the time that a new piece of Degradation data becomes available. This makes the Model depend on the observed Degradation data history, which the conventional Wiener-process-based Models did not consider. Another contribution is to take into account the distribution in the drift coefficient when updating, rather than using a point estimate as an approximation. An exact RUL distribution considering the distribution of the drift coefficient is obtained based on the concept of the first hitting time. A practical case study for gyros in an inertial navigation system is provided to substantiate the superiority of the proposed Model compared with competing Models reported in the literature. The results show that our developed Model can provide better RUL estimation accuracy.

Xiaosheng Si - One of the best experts on this subject based on the ideXlab platform.

  • a wiener process based Degradation Model with a recursive filter algorithm for remaining useful life estimation
    Mechanical Systems and Signal Processing, 2013
    Co-Authors: Xiaosheng Si, Wenbin Wang, Changhua Hu, Maoyin Chen, Donghua Zhou
    Abstract:

    Abstract Remaining useful life estimation (RUL) is an essential part in prognostics and health management. This paper addresses the problem of estimating the RUL from the observed Degradation data. A Wiener-process-based Degradation Model with a recursive filter algorithm is developed to achieve the aim. A novel contribution made in this paper is the use of both a recursive filter to update the drift coefficient in the Wiener process and the expectation maximization (EM) algorithm to update all other parameters. Both updating are done at the time that a new piece of Degradation data becomes available. This makes the Model depend on the observed Degradation data history, which the conventional Wiener-process-based Models did not consider. Another contribution is to take into account the distribution in the drift coefficient when updating, rather than using a point estimate as an approximation. An exact RUL distribution considering the distribution of the drift coefficient is obtained based on the concept of the first hitting time. A practical case study for gyros in an inertial navigation system is provided to substantiate the superiority of the proposed Model compared with competing Models reported in the literature. The results show that our developed Model can provide better RUL estimation accuracy.