Sensor Optimization Selection Model Based on Testability Constraint

Yang, Shuming; Qiu, Jing; Liu, Guanjun

doi:10.1016/s1000-9361(11)60386-5

Cited by 20 publications

(9 citation statements)

References 14 publications

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“…Considering the fact that sensors may express different sensitivities to fuel cell performance change, and environment/measurement noise is usually contained in the collected measurements, more sensor measurements may not provide better performance, thus it is necessary to find the optimal sensors providing reliable results with the minimum computation cost. From a literature review, several sensor selection approaches have been applied in different systems [19][20][21][22], but their applications in fuel cell system PHM have not been fully investigated.…”

Section: Introductionmentioning

confidence: 99%

Application of the Sensor Selection Approach in Polymer Electrolyte Membrane Fuel Cell Prognostics and Health Management

2017

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Abstract:In this paper, the sensor selection approach is investigated with the aim of using fewer sensors to provide reliable fuel cell diagnostic and prognostic results. The sensitivity of sensors is firstly calculated with a developed fuel cell model. With sensor sensitivities to different fuel cell failure modes, the available sensors can be ranked. A sensor selection algorithm is used in the analysis, which considers both sensor sensitivity to fuel cell performance and resistance to noise. The performance of the selected sensors in polymer electrolyte membrane (PEM) fuel cell prognostics is also evaluated with an adaptive neuro-fuzzy inference system (ANFIS), and results show that the fuel cell voltage can be predicted with good quality using the selected sensors. Furthermore, a fuel cell test is performed to investigate the effectiveness of selected sensors in fuel cell fault diagnosis. From the results, different fuel cell states can be distinguished with good quality using the selected sensors.

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Section: Introductionmentioning

confidence: 99%

Application of the Sensor Selection Approach in Polymer Electrolyte Membrane Fuel Cell Prognostics and Health Management

2017

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“…The system fault detection reliability can be described by FDR, which is the ratio of the number of faults detected correctly by the sensors to the total number of system faults during the stated time span. The larger the value of FDR, the higher the fault detection reliability, as shown in Equation (7) [22].…”

Section: B Fault-sensor Dependency Matrixmentioning

confidence: 99%

“…The FAP is the probability of a false alarm when a fault is mistakenly detected. By considering the impact of the sensor attributes on detectability and predictability, the FIR and FAP can be formulated using Equation (9) and Equation (10) [22].…”

Section: B Fault-sensor Dependency Matrixmentioning

confidence: 99%

“…requirements for health evaluation, fault diagnostics and prognostics) into consideration [21]. Sensor selection is considered as the foundation of PHM for AEs and it has to coordinate with other parts in the PHM system [22]. Finally, it receives feedback from the performance evaluation module in the PHM system and some adjustments can be made based on real-time information [23].…”

Section: Introductionmentioning

confidence: 99%

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PHM-Oriented Sensor Optimization Selection Based on Multiobjective Model for Aircraft Engines

Wang

2015

IEEE Sensors J.

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Prognostics and health management (PHM) is a widely-used effective discipline consisting of advanced technologies and methods to assess the reliability of a system under its actual application conditions. It can be used to improve the safety and reliability of aircraft engines (AEs). In a PHM system for AEs, a large number of sensors are installed to provide health condition information. A selection of a minimal subset of the most informative and cost-effective sensors is required to monitor the optimum performance. In this paper, a PHM-oriented sensor optimization selection model is developed, which considers both sensor cost and monitoring performance as objective functions, and takes the PHM requirements as the constraints. A numerical example is given to demonstrate the sensor selection approach for an aircraft gas turbine engine. The results demonstrated that the proposed model and algorithm were effective and feasible, and were able to effectively guide the sensor selection for AEs.Index Terms-Prognostics and health management, multiobjective, sensor optimization selection, aircraft engines.1530-437X (c) He is also an IEEE member. His area of research interest mainly focuses on complex systems integrated health management, systems engineering, and risk management.

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“…Therefore, it is vital to optimize sensor selection to reduce the scale of sensors and obtain the same or an even better monitoring effect. The existing sensor selection methods can be grouped into two categories: the model-based method [10,[13][14][15][16] and the optimization method considering testability constraints [17,18]. The former establishes a graph or mathematical model to describe the relationship between the sensors and failure modes, such as the dependency model, heuristic graph, and state diagram.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Evaluation Method of Sensor Selection for PHM Based on Grey Clustering

Guan

Cui

et al. 2020

Sensors

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Sensor selection plays an essential and fundamental role in prognostics and health management technology, and it is closely related to fault diagnosis, life prediction, and health assessment. The existing methods of sensor selection do not have an evaluation standard, which leads to different selection results. It is not helpful for the selection and layout of sensors. This paper proposes a comprehensive evaluation method of sensor selection for prognostics and health management (PHM) based on grey clustering. The described approach divides sensors into three grey classes, and defines and quantifies three grey indexes based on a dependency matrix. After a brief introduction to the whitening weight function, we propose a combination weight considering the objective data and subjective tendency to improve the effectiveness of the selection result. Finally, the clustering result of sensors is obtained by analyzing the clustering coefficient, which is calculated based on the grey clustering theory. The proposed approach is illustrated by an electronic control system, in which the effectiveness of different methods of sensor selection is compared. The result shows that the technique can give a convincing analysis result by evaluating the selection results of different methods, and is also very helpful for adjusting sensors to provide a more precise result. This approach can be utilized in sensor selection and evaluation for prognostics and health management.

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Sensor Optimization Selection Model Based on Testability Constraint

Cited by 20 publications

References 14 publications

Application of the Sensor Selection Approach in Polymer Electrolyte Membrane Fuel Cell Prognostics and Health Management

Application of the Sensor Selection Approach in Polymer Electrolyte Membrane Fuel Cell Prognostics and Health Management

PHM-Oriented Sensor Optimization Selection Based on Multiobjective Model for Aircraft Engines

A Comprehensive Evaluation Method of Sensor Selection for PHM Based on Grey Clustering

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