A sensor selection method for fault diagnostics

Reeves, J.; Remenyte‐Prescott, Rasa; Andrews, John

doi:10.1201/9781315210469-446

Cited by 3 publications

(5 citation statements)

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“…It is proposed that the performance metric is the average of the three terms. However, as discussed by Reeves et al [1], there may be some applications where it is desirable to favour one term of the performance metric over the others. Therefore, the performance metric can be used to determine a number of highly ranked combinations of sensors, and then use the values of the individual terms for the final selection of the most suitable combination of sensors for a specific application.…”

Section: Discussionmentioning

confidence: 99%

“…In summary, this paper proposes an extension to the sensor selection method proposed by Reeves et al [1] in order to consider the effect of the time of failure occurrence in a mission on the performance of the system. The resultant sensor performance metric favours sensors that detect and diagnose failures as soon as possible after failure occurrence, sensors that detect failures that can cause system failure, and favours sensors that can detect components with a higher probability of failure occurrence.…”

Section: Discussionmentioning

confidence: 99%

“…The detection term in [1] of the performance metric considers the percentage of component failures that can be detected by the sensors. In order to detect the component failures, the sensor reading must deviate from the sensor reading that is produced in normal operating behaviour.…”

Section: Detection Termmentioning

confidence: 99%

“…Reeves et al [1] propose a performance metric to be used for sensor selection, and uses a Bayesian Belief Network for a simple system in order to verify that the selected sensors can detect faults and diagnose failures correctly. The approach proposed in this paper extends this work through the introduction of time-dependence aspect in the metric, an alternative system modelling technique and a two-level GA method, in order to determine the most suitable sensor suite for the fuel system of the Airbus A380-800.…”

Section: Introductionmentioning

confidence: 99%

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A sensor selection method using a performance metric for phased missions of aircraft fuel systems

Reeves

Remenyte‐Prescott

Andrews

et al. 2018

Reliability Engineering & System Safety

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Component failures in complex systems, such as aircraft fuel systems, can have catastrophic effects on system performance. There are a large number of components in these systems, each with a number of different failure modes, some of which can cause system failure. In order to detect and diagnose these component failures, sensors that monitor system performance need to be included. However, the number of sensors installed is typically limited by sensor cost and weight. An approach for selecting sensors could be taken considering sensor usefulness for fault diagnostics. In this paper, the sensor performance metric proposed by Reeves et al. [1] is extended to consider a phased mission operation, with component failures occurring at various points in the mission. The performance metric favours sensors that can detect the most failures, the failures that affect the system for longest and the failures that cause system failure. In addition, the performance metric considers the ability of sensors to distinguish between component failures, i.e. to diagnose which components have caused the faults observed by these sensors. The proposed approach is illustrated on the Airbus A380-800 fuel system, where the best combination is found using the performance metric within a Genetic Algorithm method.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Detection Termmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A sensor selection method using a performance metric for phased missions of aircraft fuel systems

Reeves

Remenyte‐Prescott

Andrews

et al. 2018

Reliability Engineering & System Safety

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“…Criticality is a term that was introduced by Reeves [ 27 ]. This method considers the effect of the failures that can be detected by the sensors on the system.…”

Section: An Ordered Fuzzy Clustering Approach To Sensor Selectionmentioning

confidence: 99%

Sensor Selection Framework for Designing Fault Diagnostics System

Kulkarni

Terpenny

Prabhu

2021

Sensors

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In a world of rapidly changing technologies, reliance on complex engineered systems has become substantial. Interactions associated with such systems as well as associated manufacturing processes also continue to evolve and grow in complexity. Consider how the complexity of manufacturing processes makes engineered systems vulnerable to cascading and escalating failures; truly a highly complex and evolving system of systems. Maintaining quality and reliability requires considerations during product development, manufacturing processes, and more. Monitoring the health of the complex system while in operation/use is imperative. These considerations have compelled designers to explore fault-mechanism models and to develop corresponding countermeasures. Increasingly, there has been a reliance on embedded sensors to aid in prognosticating failures, to reduce downtime, during manufacture and system operation. However, the accuracy of estimating the remaining useful life of the system is highly dependent on the quality of the data obtained. This can be enhanced by increasing the number of sensors used, according to information theory. However, adding sensors increases total costs with the cost of the sensors and the costs associated with information-gathering procedures. Determining the optimal number of sensors, associated operating and data acquisition costs, and sensor-configuration are nontrivial. It is also imperative to avoid redundant information due to the presence of additional sensors and the efficient display of information to the decision-maker. Therefore, it is necessary to select a subset of sensors that not only reduce the cost but are also informative. While progress has been made in the sensor selection process, it is limited to either the type of the sensor, number of sensors or both. Such approaches do not address specifications of the required sensors which are integral to the sensor selection process. This paper addresses these shortcomings through a new method, OFCCaTS, to avoid the increased cost associated with health monitoring and to improve its accuracy. The proposed method utilizes a scalable multi-objective framework for sensor selection to maximize fault detection rate while minimizing the total cost of sensors. A wind turbine gearbox is considered to demonstrate the efficacy of the proposed framework.

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