A review of self-validating sensor technology stocks, choose your preferred view mode
These authors also performed a research about the use of some fuzzy logic rules, comparing the predicted values with the actual measurements to obtain the confidence evaluation [ 36 ].
Data validation and confidence of self-validating multifunctional sensor
As an example, the authors of [ 23 ] proposed the use of the Kalman filter for the validation of the GPS data. Successful results show that the proposed method is feasible, the HRD could be used to quantitatively indicate the health level and it does have a fast response to the performance changes of multifunctional sensors.
The objective weight of each sensitive unit is obtained as follows: Each sensitive unit is also healthy and their measured data are nearly close to the true value. Most of existing automated methods only provide two health states typically, healthy and faulty [ 11 — 13 ], which is essentially a fault diagnosis.
Here, the four stages can be also taken as the health features of a single sensitive unit. It is mainly used in incomplete information.
Besides, the development of healthcare solutions based on the sensors available on the mobile devices increases the requirement of the validation of the data collected by the sensors available on the mobile devices.
The greater the output Gi is, the more important the sensitive unit is: Validated uncertainty VU evaluation: Mechanical Engineering The validity, reliability and the performance of complex systems depend on the reliability of the sensors that are used for monitoring and diagnosis of the systems.
From a quantitative point of view, the problem will become far more difficult and the quantitative health level analysis of multifunctional sensor not only involves the health level of each sensitive unit itself, but relates to their distinct weight distribution [ 16 ].
The HRD of multifunctional self-validating sensor means that the health evaluation is implemented from a global way, so it is related to all the sensitive units.
More sensitive units may cause more potential faults.
Firstly, faulty data detection methods may be either simple test based methods or physical or mathematical model based methods, and they are classified in valid data and invalid or missingness data [ 67 ].
For the use of SEVA technologies, the authors of [ 34 ] also proposed the validated random fuzzy variable VRFV based uncertainty evaluation strategy for the online validated uncertainty VU estimation.
One temperature sensitive unit, one humidity sensitive unit and four gas sensitive units were included in the multifunctional sensor. Due to the presence of many more sensitive units, the health evaluation of multifunctional sensor not only includes each single sensitive unit but the overall sensor itself.
The research content is shown in Figure 2.
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