초록 ▼

Provided are a method of detecting and isolating fault in sensors and a method of accommodating fault in sensors using the same. In the method of detecting and isolating fault in sensors, a one reduced-order parity vector is obtained by excluding the output of one sensor selected from n sensors, a t

Provided are a method of detecting and isolating fault in sensors and a method of accommodating fault in sensors using the same. In the method of detecting and isolating fault in sensors, a one reduced-order parity vector is obtained by excluding the output of one sensor selected from n sensors, a two reduced-order parity vector is obtained by excluding output of two sensors selected from the n sensors, and when there are a plurality of parity vectors obtained at plural points of time, one reduced-order parity vectors are averaged to obtain an averaged one reduced-order parity vector and likewise, two reduced-order parity vectors are averaged to obtain an averaged two reduced-order parity vector. Therefore, a decrease in fault detection and isolation (FDI) performance can be hindered, and even when double faults occur, sensors to be excluded can be selected. Thus, a system including sensors has high reliability and high accuracy.

대표청구항 ▼

1. A method of accommodating fault in sensors, the method comprising: detecting fault in sensors which are each independently geometrically configured;when it is determined there are faulty sensors, isolating faulty sensors so that the location of the faulty sensors is identified; anddetermining sen

1. A method of accommodating fault in sensors, the method comprising: detecting fault in sensors which are each independently geometrically configured;when it is determined there are faulty sensors, isolating faulty sensors so that the location of the faulty sensors is identified; anddetermining sensors to be excluded among the faulty sensors by calculating a first covariance matrix comprising a plurality of faulty sensor outputs, a second covariance matrix excluding one faulty sensor output, and a third covariance matrix excluding all of the faulty sensor outputs, with respect to {circumflex over (x)}=[{circumflex over (x)}x {circumflex over (x)}y {circumflex over (x)}z]T that is a triad solution of the measurement equation for n sensors m=Hx+f+ε,ε˜N(0n,σIn);and comparing traces of the first, second, and third covariance matrices. 2. The method of claim 1, wherein the detecting fault comprises: obtaining n−1 first reduced-order parity vectors by sequentially excluding one sensor output among n sensors which are each independently geometrically configured;obtaining nC2 cases of second reduced-order parity vectors by sequentially excluding two sensor outputs among the n sensors;averaging the first reduced-order parity vectors obtained at plural points of time and the second reduced-order parity vectors obtained at plural points of time to obtain an averaged first reduced-order parity vector and an averaged second reduced-order parity vector, respectively;obtaining a first fault detection function by multiplying the averaged first reduced-order parity vector by transpose of the averaged first reduced-order parity vector;obtaining a second fault detection function by multiplying the averaged second reduced-order parity vector by transpose of the averaged second reduced-order parity vector, anddetermining that at least one sensor selected from the n sensors has fault if the maximum value of the obtained first fault detection function of the n sensors is equal to or greater than a first threshold value which has been set in advance. 3. The method of claim 2, wherein the isolating fault further comprising: when it is determined that at least one sensor selected from the n sensors has fault, when the minimum value of the first fault detection function of the n sensors is smaller than a second threshold value which has been set in advance, determining that a single fault occur and when the minimum value of the first fault detection functions of the n sensors is equal to or greater than the second threshold value, determining that double faults occur;when it is determined that a single fault occurs, determining that a sensor that corresponds to a parameter of the averaged the first reduced-order parity vector that makes the first fault detection function to have the minimum value is a faulty sensor; andwhen it is determined that double faults occur, determining that a pair of sensors that correspond to a pair of parameters of the averaged second reduced-order parity vector which make the obtained nC2 cases of second fault detection function to have the minimum value are faulty sensors. 4. The method of claim 1, wherein, in the determining sensors to be excluded, when i-th sensor and j-th sensor are supposed to have fault and the fault vector fi of the i-th sensor and the fault vector fj of the j-th sensor satisfy three inequalities as follows, the i-th sensor and the j-th sensor are not excluded. i)⁢⁢fi2⁢(HT⁢H)-1⁢hi22+fj2⁢(HT⁢H)-1⁢hj22+2⁢fi⁢fj(HT⁢H)-1⁢hi22(HT⁢H)-1⁢hi22≥σ2vi22⁢⁢iii)⁢⁢fj≧ζ1  i)fj2≧ζ1  i)fj2<ζ2  ii)|fj|<|fi|  iii).