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A general prognostic tracking algorithm for predictive maintenance

机译：一种预测维护的一般预测跟踪算法

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摘要

Prognostic Health Management (PHM) is a technology that uses objective measurements of condition and failure hazard to adaptively optimize a combination of availability, reliability, and total cost of ownership of a particular asset. Prognostic utility for the signature features are determined by transitional failure experiments. Such experiments provide evidence for the failure alert threshold and of the likely advance warning one can expect by tracking the feature(s) continuously. Kalman filters are used to track changes in features like vibration levels, mode frequencies, or other waveform signature features. This information is then functionally associated with load conditions using fizzy logic and expert human knowledge of the physics and the underlying mechanical systems. Herein is the greatest challenge to engineering. However, it is straightforward to track the progress of relevant features over time using techniques such as Kalman filtering. Using the predicted states, one can then estimate the fliture failure hazard, probability of survival, and remaining useful life in an automated and objective methodology.

机译：预后卫生管理（PHM）是一种技术，它使用客观测量条件和故障危害，以便于自适应优化特定资产的可用性，可靠性和总体拥有成本的组合。用于签名特征的预后实用性由过渡失败实验确定。此类实验为失败警报阈值提供了证据，并且可以通过连续跟踪特征来期望的可能预先警告。卡尔曼滤波器用于跟踪振动级别，模式频率或其他波形签名特征等特征的变化。然后，该信息在功能上与使用脱脂逻辑和物理学的专家知识和潜在的机械系统相关联。这里是对工程的最大挑战。然而，使用Kalman滤波等技术随着时间的推移，跟踪相关特征的进度很简单。使用预测的状态，然后可以估计有用破坏危害，生存概率，以及在自动和客观方法中剩余的使用寿命。

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《IEEE Aerospace Conference》|2001年||共7页
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David C. Swanson;
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5. Predictive control algorithms with guaranteed stability and asymptotic tracking. [D] . Manayathara, Thomas Jolly. 1994

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A general prognostic tracking algorithm for predictive maintenance

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