Patent Number: 047724455
Section: description

DESCRIPTION OF THE PREFERRED EMBODIMENT Conventionally, control systems utilize sensors 30 (FIG. 2) to measure critical variables in a system controlled by a control system 35. The system being controlled may be in an aircraft or spacecraft, a chemical manufacturing plant, etc. The present invention can be applied to any such system, but will be described below for sensors 30 in a pressurized light water nuclear reactor. The sensors 30 provide sensor signals via lines 40 to an analog/digital converter 45, such as the A/D converter on an Intel 88/40 single board computer which can provide some preprocessing. Even using the best shielding available, the sensor signals received by the analog/digital converter 45 will contain some amount of noise since the sensors typically see process noise in the parameter they are monitoring. An example is steam generation level noise caused by the boiling in the steam generator. In addition, the sensor signals may contain direct current (DC) drift due to: poor initial calibration of the sensors 30, drifting of the sensors 30 during operation or failure of the sensors 30. The present invention identifies DC drift and noise signals present in the sensor signals by analyzing digitized sensor signals output by the analog/digital converter 45 in a processor 50, such as an Intel 86/05. The results of the analysis can be displayed on an auxiliary display 55 or used to control the nuclear reactor control system 35 after conversion to analog signals in a digital/analog converter 60. The first step in measuring direct current drift and noise according to the present invention is to generate component parity vector signals as described above with reference to equations (1) through (17). As illustrated in FIG. 1, the parity vectors define coordinates 100 that may be limited by an envelope 110 which defines the limit of the noise seen by a long sequence of measurements. Averaging the coordinates 100 over time produces a centroid 120 which corresponds to composite direct current drift, since the effects of noise will be averaged out. The centroid 120 can be calculated using standard matrix arithmetic. Given the coordinates of the centroid 120, it is possible to generate sensor direct current drift signals in the direction of the q or (l-1) measurement axes 130, 140 and 150 corresponding to sensors #1, #2 and #3, respectively. Since, as described above, the parity vector has l-1 dimensions when there are l sensors, the maximum number of constituent sensors corresponding to the sensor direct current drift signals is l-1. An example will be given below for l equals three, since the resulting parity vector will have two dimensions and can be easily illustrated. However, the number of sensors which can be handled by the present invention is not limited to three. If only two sensors are available, it may be possible to calculate one or more estimated sensor signals by assuming certain relationships between other measured parameters and the parameter being measured by the two remaining sensors (see the EPRI report on Research Project 1541 referenced above). One method for identifying the constituent sensors is to consider equations (16) and (17). Since the direct current drift signal is simply an average parity vector signal, if an average residual .eta..sub.j is kept for each of the sensors, the sensor producing the smallest average residual .eta..sub.1 will produce the smallest projection p.sub.1 according to equation (17). In other words, the sensor producing the smallest average residual .eta..sub.1 contributes the least to the DC drift signal and is, therfore, not a constituent sensor. The first method of identifiying constituent sensors may be better understood in view of a second method for identifying the contituent sensors of the DC drift signal. The second method is most easily explained by considering the graphical representation of two-dimensional parity. In the case of a two-dimensional composite DC drift signal or vector 155, defined by the centroid 120 and the origin 156, there are at most two constituent sensors which may have corresponding component DC drift signals. The constituent sensors can be identified by the angle between the vector 155 and a given measurement axis. Given the definition of matrix V, in particular equation (9) which defines an upper triangular matrix, the first column has only a single non-zero element V.sub.11 and thus can be easily assigned to the X-axis. Thus, in FIG. 3, the sensor signal provided by sensor number 1 is assigned to the X-axis 130. The matrix elements in the sensor signals from sensors 2 and 3 are thus displayed along axes 140 and 150 forming +120.degree. and -120.degree. angles with the positive X-axis, respectively. The angle .phi. formed between vector 155 and the X-axis 130 identifies the constituent sensors. If the angle .phi. is greater than zero and less than 60.degree., then the parity vector signal of DC drift signal will include a sensor signal from sensor number 1 with a positive deviation from the average and a sensor signal from sensor number 3 with a negative deviation, as illustrated in FIG. 3. Similarly, positive values of angle .phi. between 60.degree. and 120.degree., and 120.degree. and 180.degree. result in constituent sensors 2 and 3, and 1 and 2, respectively, while negative values of .phi. between 0.degree. and -60.degree., -60.degree. and -120.degree., and -120.degree. and -180.degree. have corresponding constituent sensors 1 and 2, 2 and 3, and 1 and 3, respectively, as illustrated in FIG. 3. After the constituent sensors have been identified, sensor DC drift signals can be found for each of the constituent sensors using geometric and trigonometric relationships. The case of three measurements of a scalar parameter will be used. Two examples of the computational methods will be given, but it is should be understood that others are possible. In the first method, it is noted that the measurement axis 150 of sensor #3 is defined by y=.sqroot.3 x (see the third column of matrix in equation (14)), while y=-.sqroot.3 x defines the measurement axis 140 of sensor #2. In four out of the six regions defined in FIG. 3, sensor #1 is a constituent sensor and the sensor #1 DC drift can easily be found by finding the X-intercept of a line 160 (FIG. 1) parallel to the measurement axis of the other constituent sensor (#2 or #3). The other sensor (#2 or #3) DC drift has a Y-coordinate equal to the Y-coordinate of the composite DC drift and its X-coordinate can be found by solving the appropriate equation above defining the measurement axis for that sensor. Thus, the component DC drift signals 210 and 220 in FIG. 1 can be easily found. In the case of a composite DC drift signal in a region in which the constituent sensors are 2 and 3, calculation of the component DC drift signals is slightly more difficult, but the same principles are involved. See for example, the composite DC drift vector 155' in FIG. 3 in a region having constituent sensors #2 and #3. A second method of calculating sensor DC drift signals utilizes the residual signals corresponding to each of the sensors. As noted above, the projection along each of the measurement axes is defined by equation (17). However, .sqroot.l/(l-1) is simply a scale factor which puts calculations using the residuals into the same scale as those using matrix V, defined in equation (14). It is possible to perform the calculations directly on residuals .eta..sub.j and multiply by the scale factor .sqroot.l/(l-1) at a later time or perform scaling using some other factor. With reference to FIG. 3, the coordinates of the vector 155' representing composite DC drift and the sensor #2 and sensor #3 DC drift signals represented by vectors 220' and 230' can be calculated from the projections p.sub.j along the measurement axes, as defined by equation (17). By definition, a line 170 between the tip of the DC drift vector 10' and the tip p.sub.3 of the projection along the sensor #3 measurement axis 150 forms an angle of 30.degree. with the measurement axis 130, since the smallest angle between all of the measurement axes if 60.degree.. Therefore, X=-p.sub.3 /sin 30.degree.. The X-coordinate X.sub.DC equals the projection p.sub.1 along the sensor #1 measurement axis 130 and the Y-coordinate Y.sub.DC of the composite DC vector 10' is defined by equation (24). EQU Y.sub.DC =(p.sub.1 -X) tan 30.degree. (24) The sensor #2 and #3 DC drift signals represented by vectors 220' and 230' can be calculated from the composite DC drift vector 155' utilizing the parallelogram having sides formed by vectors 220' and 230' and having a major diagonal formed by vector 155', together with the triangle defined by the tips of vectors 155', 220' and p.sub.3. The distance D between p.sub.3 and the tip of vector 10' can be found by vector subtraction or as D=p.sub.3 cos .theta., where .theta.=120.degree.-arctan Y.sub.DC /X.sub.DC. Thus, the sensor #2 DC drift signal DC.sub.2 =-D/sin 60.degree. and the sensor #3 DC drift signal DC.sub.3 =p.sub.3 -DC.sub.2 cos 60.degree.. When one of the consituent sensors of the composite DC drift signal is sensor #1, the sensor DC drift signals are easily calculated. For example, the sensor #1 and #3 DC drift signals DC.sub.3 and DC.sub.1 represented by vectors 210 and 220 (FIG. 1), respectively, are found as DC.sub.3 =-Y.sub.DC /sin 60.degree. and DC.sub.1 =X.sub.DC +DC.sub.3 cos 60.degree.. Increased sensitivity to sudden changes in DC drift can be provided according to the present invention by weighting the average which produces the centroid 120. Such weighting is provided by equation 25, where X.sub.i,j is the weighted running average for sensor direction i of the parity vector in the jth sample of the sensor signals, (1-W) is a weighting coefficient in which W is a weighting function and X.sub.i,j is the sensor #i parity vector signal for in the jth sample. The weighting function W may, for example, be 0.01 or 0.02, representing an average over the last 100 to 50 samples, respectively. EQU X.sub.i,j =(1-W)X.sub.i,j-1 +W.multidot.X.sub.i,j (25) Equation (25) represents first order lag, but other equations may be used to provide a desired response, representing, e.g., second order lag, for recent samples of the sensor signals, as is known the art. Other variations are also possible. For example, the parity vector can be averaged in two dimensions and periodically (e.g., every 50 or 100 samples) the sensor components can be calculated, as described above. As noted above, noise on lines 40 (FIG. 2) cause fluctuations in the parity vector corresponding to the sample of the sensor signals. Thus, in a recent sample of the sensor signals, a parity vector 310 (FIG. 5) may be produced having coordinates which are different from the coordinates of the composite DC drift vector 155. Using standard vector arithmetic, a composite instaneous noise signal can be calculated and represented by vector 320. The composite instaneous noise signal has sensor instaneous noise signals corresponding to constituent sensors. A magnified drawing of the region on display screen 20 bounded by positive values of sensor #1 and negative values of sensor #3 is illustrated in FIG. 6. Component instaneous noise signals corresponding to constituent sensors and represented by vectors 340 and 350 can be found by transposing the axes 130, 140 and 150 to the centroid 120 to produce a coordinate system for noise signals defined by axes 130', 140' and 150', corresponding to sensors #1, #2 and #3, respectively. After the constituent sensors of the noise signal 230 are identified, the sensor instantaneous noise signals represented by vector 340 and 350 can be found using the same methods used to find the sensor DC drift signals. The average amount of noise in each sensor signal can be found using a weighted RMS average of the RMS instantaneous noise signals. Thus, a sensor noise signal N.sup.2.sub.k,j can be calculated using standard matrix arithmetic and equation (26), where N.sup.2.sub.k,j is the sensor noise signal for sensor number k in the jth sample of the sensor signals, W.sub.n is a weighting function for noise signals which may or may not be the same as the weighting function W for DC drift signals, X.sub.j is the parity vector signal for the jth sample of the sensor signals, X.sub.j is the direct current drift vector, and k is a unit vector along the axis for sensor number k. EQU N.sup.2.sub.k,j =(1-W.sub.n)N.sup.2.sub.k,j-1 +W.sub.n [(X.sub.j -X.sub.j).multidot.k].sup.2 (26) The lines parallelling each of the axes 130, 140 and 150 in FIGS. 1 and 2 indicate the error bounds of each of the sensors. In the above example, it has been assumed that the error bound for each of these sensor signals is the same and has a value of b, as used in equations (4), (5a), (5b) and (9). However, it is possible for the error bounds of the sensors to differ and this can be taken into account in the parity-space algorithm. The many features and advantages of the present invention are apparent from the detailed specification, and thus it is intended by the appended claims to cover all such features and advantages of the system which fall within the true spirit and scope of the invention. Further, since numerous modifications and changes will readily occur to those skilled in the art, it is not desired to limit the invention to the exact construction and operation illustrated and described, accordingly, all suitable modifications and equivalents may be resorted to falling within the scope and spirit of the invention.