Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/135766
Title: Optimize the Signal Quality of the Composite Health  Index via Data Fusion for Degradation Modeling&x2005; and Prognostic Analysis
Authors: Kaibo Liu;Abdallah Chehade;Changyue Song
Year: 2017
Publisher: IEEE
Abstract: Due to the rapid development of sensing and computing technologies, multiple sensors have been widely used in a system to simultaneously monitor the health status of an operating unit. Such a data-rich environment creates an unprecedented opportunity to better understand the degradation behavior of the system and make accurate inferences about the remaining lifetime. Since data collected from multiple sensors are often correlated and each sensor data contains only partial information about the degraded unit, data fusion methodologies that integrate the data from multiple sensors provide an essential tool for degradation modeling and prognostics. To achieve this goal, a fundamental question needs to be answered first is how to measure the signal quality of a degradation signal. If such a question can be addressed, then the data fusion approach can be simplified as a mission-specific task: to construct a composite health index with the goal of optimizing its signal quality. In this paper, a new signal-to-noise ratio (SNR) metric that is tailored to the needs of degradation signals is proposed. Then, based on the new quality metric, we develop a data-level fusion model to construct a health index via fusion of multiple degradation-based sensor data. Our goal is that the developed health index provides a much better characterization of the health condition of the unit and thus leads to a better prediction of the remaining lifetime. A case study that involves the degradation dataset of aircraft gas turbine engines is conducted to numerically evaluate the performance of the developed health index regarding prognostics and further compare the result with existing literature.
URI: http://localhost/handle/Hannan/135766
volume: 14
issue: 3
More Information: 1504,
1514
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7165684.pdf2.12 MBAdobe PDF
Title: Optimize the Signal Quality of the Composite Health  Index via Data Fusion for Degradation Modeling&x2005; and Prognostic Analysis
Authors: Kaibo Liu;Abdallah Chehade;Changyue Song
Year: 2017
Publisher: IEEE
Abstract: Due to the rapid development of sensing and computing technologies, multiple sensors have been widely used in a system to simultaneously monitor the health status of an operating unit. Such a data-rich environment creates an unprecedented opportunity to better understand the degradation behavior of the system and make accurate inferences about the remaining lifetime. Since data collected from multiple sensors are often correlated and each sensor data contains only partial information about the degraded unit, data fusion methodologies that integrate the data from multiple sensors provide an essential tool for degradation modeling and prognostics. To achieve this goal, a fundamental question needs to be answered first is how to measure the signal quality of a degradation signal. If such a question can be addressed, then the data fusion approach can be simplified as a mission-specific task: to construct a composite health index with the goal of optimizing its signal quality. In this paper, a new signal-to-noise ratio (SNR) metric that is tailored to the needs of degradation signals is proposed. Then, based on the new quality metric, we develop a data-level fusion model to construct a health index via fusion of multiple degradation-based sensor data. Our goal is that the developed health index provides a much better characterization of the health condition of the unit and thus leads to a better prediction of the remaining lifetime. A case study that involves the degradation dataset of aircraft gas turbine engines is conducted to numerically evaluate the performance of the developed health index regarding prognostics and further compare the result with existing literature.
URI: http://localhost/handle/Hannan/135766
volume: 14
issue: 3
More Information: 1504,
1514
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7165684.pdf2.12 MBAdobe PDF
Title: Optimize the Signal Quality of the Composite Health  Index via Data Fusion for Degradation Modeling&x2005; and Prognostic Analysis
Authors: Kaibo Liu;Abdallah Chehade;Changyue Song
Year: 2017
Publisher: IEEE
Abstract: Due to the rapid development of sensing and computing technologies, multiple sensors have been widely used in a system to simultaneously monitor the health status of an operating unit. Such a data-rich environment creates an unprecedented opportunity to better understand the degradation behavior of the system and make accurate inferences about the remaining lifetime. Since data collected from multiple sensors are often correlated and each sensor data contains only partial information about the degraded unit, data fusion methodologies that integrate the data from multiple sensors provide an essential tool for degradation modeling and prognostics. To achieve this goal, a fundamental question needs to be answered first is how to measure the signal quality of a degradation signal. If such a question can be addressed, then the data fusion approach can be simplified as a mission-specific task: to construct a composite health index with the goal of optimizing its signal quality. In this paper, a new signal-to-noise ratio (SNR) metric that is tailored to the needs of degradation signals is proposed. Then, based on the new quality metric, we develop a data-level fusion model to construct a health index via fusion of multiple degradation-based sensor data. Our goal is that the developed health index provides a much better characterization of the health condition of the unit and thus leads to a better prediction of the remaining lifetime. A case study that involves the degradation dataset of aircraft gas turbine engines is conducted to numerically evaluate the performance of the developed health index regarding prognostics and further compare the result with existing literature.
URI: http://localhost/handle/Hannan/135766
volume: 14
issue: 3
More Information: 1504,
1514
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7165684.pdf2.12 MBAdobe PDF