CN102175266A - Fault diagnosis method for mobile gyroscope inertia subassembly - Google Patents
Fault diagnosis method for mobile gyroscope inertia subassembly Download PDFInfo
- Publication number
- CN102175266A CN102175266A CN 201110040473 CN201110040473A CN102175266A CN 102175266 A CN102175266 A CN 102175266A CN 201110040473 CN201110040473 CN 201110040473 CN 201110040473 A CN201110040473 A CN 201110040473A CN 102175266 A CN102175266 A CN 102175266A
- Authority
- CN
- China
- Prior art keywords
- gyro
- fault
- component
- inertial
- moving body
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 75
- 238000003745 diagnosis Methods 0.000 title claims abstract description 17
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 27
- 230000036541 health Effects 0.000 claims abstract description 23
- 238000012544 monitoring process Methods 0.000 claims abstract description 20
- 238000004364 calculation method Methods 0.000 claims abstract description 13
- 238000012545 processing Methods 0.000 claims abstract description 12
- 238000000528 statistical test Methods 0.000 claims abstract description 6
- 239000013598 vector Substances 0.000 claims description 21
- 230000008569 process Effects 0.000 claims description 18
- 238000012216 screening Methods 0.000 claims description 18
- 238000001514 detection method Methods 0.000 claims description 15
- 230000001186 cumulative effect Effects 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 abstract description 2
- 238000005259 measurement Methods 0.000 description 5
- 230000008859 change Effects 0.000 description 4
- 239000011159 matrix material Substances 0.000 description 4
- 238000003672 processing method Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000013459 approach Methods 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 230000001965 increasing effect Effects 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 230000005802 health problem Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Landscapes
- Gyroscopes (AREA)
Abstract
The invention discloses a fault diagnosis method for a mobile gyroscope inertia subassembly, and specifically relates to a health monitoring technology of the mobile gyroscope inertia subassembly based on equivalence relation and empirical mode decomposition. The fault diagnosis method is used for solving the problem that a conventional equivalence relation method generally only has the ability of separating single sensor faults and a health monitoring method based on signal processing has large amount of calculations. The method provided by the invention comprises the following steps of: step 1, detecting whether the gyroscope inertia subassembly has fault through the equivalence relation method; step 2, when fault is detected, acquiring N data points of an output signal of each gyroscope sensor in the gyroscope inertia subassembly as a fault data input signal of the gyroscope inertia subassembly, performing empirical mode decomposition on the fault data input signal to obtain a first-order IMF component, and taking the first-order IMF component as a fault characteristic signal of the gyroscope inertia subassembly; step 3, processing the first-order IMF component with summation CUSUM of statistical test to judge whether the gyroscope sensor has fault and finish the health monitoring on the gyroscope inertia subassembly.
Description
Technical Field
The invention relates to a fault diagnosis method for a moving body gyroscope inertia assembly, in particular to a health monitoring technology for the moving body gyroscope inertia assembly based on equivalence relation and empirical mode decomposition.
Background
The inertial gyro component is composed of a plurality of gyro sensors, the number of the gyro sensors is generally 2-4 according to different tasks, the gyro sensors are used for measuring the angular velocity of a moving body, particularly a space vehicle and an ocean aircraft relative to an inertial reference system, the gyro sensors are important components for measuring the attitude of the moving body, and the working performance and the health state of the gyro sensors directly influence the attitude measurement of the whole moving body, and the attitude control precision and the reliability of the moving body.
Because the gyro sensor belongs to a high-precision and relatively vulnerable part, the gyro sensor is easy to have health problems of failure, performance reduction and the like after being subjected to complex environment and violent movement of a moving body. At present, when the health condition of an inertial gyro component is monitored, in view of the fact that the traditional equivalence relation method generally only has the capability of separating single sensor faults, two approaches are generally adopted: the method is characterized in that internal test equipment is used for monitoring certain state quantities such as temperature, current, voltage and the like in the gyroscope in real time, and whether the gyroscope has faults or not is judged according to the parameters. On the other hand, the method is only effective under the condition that the attitude sensor system forms the redundancy relation; on the other hand, the working condition of the redundant sensor needs to be considered.
In recent years, modern signal processing methods are increasingly used for extracting sensor health condition characteristic information. For example, if researchers apply wavelet transformation to fault diagnosis and classification of gyro sensors, good effects are obtained. The sensor health monitoring based on the signal processing method has the advantages that health characteristic information can be directly extracted from sensor output, and the limitation of redundancy relation can be overcome. And, in the case of a plurality of sensors simultaneously failing, the detection and isolation of the failure can still be achieved. On the other hand, compared with the method based on hardware redundancy or analytic redundancy, the health monitoring method based on signal processing often requires more operations and processing, and the calculation amount is relatively large.
Disclosure of Invention
The invention aims to solve the problem that the traditional equivalence relation method only has the capacity of separating single sensor faults generally and the calculation amount of a health monitoring method based on signal processing is relatively large, and provides a fault diagnosis method for a moving body gyro inertia assembly.
The invention discloses a fault diagnosis method of a moving body gyro inertia assembly, which comprises the following steps:
the method comprises the following steps that firstly, hardware redundancy of a moving body inertial gyro component is utilized, and whether the inertial gyro component breaks down or not is detected through an equivalence relation method;
the process for detecting whether the inertial gyro component has a fault is as follows:
step 11, constructing an equivalence relation by utilizing hardware redundancy configuration of the moving body inertial gyro component, and calculating an equivalence vector of the equivalence relation;
if the judgment result is yes, the inertial gyro component is considered to be not in fault; and if the judgment result is negative, the inertial gyro component is considered to be in fault.
Secondly, when the inertial gyro component is detected to be in fault, collecting N data points of output signals of each gyro sensor in the inertial gyro component as fault data input signals of the gyro sensor, carrying out empirical mode decomposition on the fault data input signals, and taking an obtained first-order IM F component as a fault characteristic signal of the gyro sensor;
the process of obtaining the first order IMF component is:
setting a fault data input signal as x (t), t 1, 2, N,
step 21, initializing an IMF decomposition process: n is 1 and satisfies the relation rn-1(t) x (t) holds, where rn-1(t) is a trend function after the (n-1) th decomposition;
step 22, initializing the screening process, wherein k is 1 and satisfies the relation hn(k-1)(t)＝rn-1(t) is true, wherein hn(k-1) (t) is a residual function after the (k-1) th screening in the nth empirical mode decomposition;
step 23, obtaining the residual function h after the k-th screening according to the screening programnk(t)；
Obtaining a residual function hnkThe process of (t) is:
231, obtaining a residual function h of the fault data input signal x (t) after k-1 times of screening in nth empirical mode decomposition by utilizing a cubic spline functionn(k-1) (t),
step 232, calculating the residual function hn(k-1) (t) mean value of upper and lower envelope curves at each t
Step 233, obtaining the residual function of the fault data input signal x (t) after the k-time screening in the n-time empirical mode decomposition <math><mrow><msub><mi>h</mi><mi>nk</mi></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>=</mo><msub><mi>h</mi><mrow><mi>n</mi><mrow><mo>(</mo><mi>k</mi><mo>-</mo><mn>1</mn><mo>)</mo></mrow></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>-</mo><msub><mover><mi>m</mi><mo>‾</mo></mover><mrow><mi>n</mi><mrow><mo>(</mo><mi>k</mi><mo>-</mo><mn>1</mn><mo>)</mo></mrow></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>.</mo></mrow></math>
Step 24, judging the residual function h obtained in the step 23 by adopting a standard deviation criterionnk(t) whether the condition of the intrinsic mode function IMF is satisfied, i.e.Whether or not it is less than threshold HSD，0.2≤HSD≤0.3；
If yes, step 25 is executed, if no, k is k +1, and then step 23 is executed,
step 25, extracting a first-order IMF component: c. C1(t)＝h1k(t)。
And step three, performing cumulative summation CUSUM processing of statistical test on the first-order IM F component obtained by each gyro sensor in the step two to judge whether the gyro sensor has a fault, and further separating the gyro sensor with the fault from the inertial gyro component to complete the health monitoring of the inertial gyro component.
Step 31, obtaining a CUSUM calculation result W of a single data point in the first-order IMF component according to the following formulai：
Wi＝Wi-1+|Xi|，
Wherein i is 1, 2, … N, Wi-1Calculate the result for CUSUM for the i-1 th data point, and let W0＝0，XiThe first order IMF value at the ith data point,
step 32, judging the CUSUM calculation result W of the Nth data pointNWhether or not it is greater than a diagnostic threshold epsilonI，
If yes, the gyro sensor has a fault, and if not, the gyro sensor does not have a fault.
The invention has the advantages that:
1) the health monitoring method provided by the invention monitors the fault of the moving body inertial gyroscope component by an equivalence relation method, and simultaneously separates the fault gyroscope by using an empirical mode decomposition method, so that the calculated amount can be effectively reduced compared with a fault diagnosis method which only adopts signal processing.
2) The health monitoring method provided by the invention not only utilizes the hardware redundancy of the gyro component, but also effectively utilizes the self output information of the gyro sensor, thereby enhancing the fault diagnosis capability of the algorithm.
3) The health monitoring method provided by the invention is only used for fault diagnosis on the basis of the output signal of the gyro component, and does not need to utilize the information of other sensors, thereby avoiding introducing other potential fault sources and being beneficial to improving the effectiveness of the fault diagnosis method.
4) The health monitoring method provided by the invention is not limited to single-point fault assumption, can conveniently realize multi-fault diagnosis, and breaks through the limitation that other methods can only carry out single-point fault diagnosis generally.
Drawings
FIG. 1 is a flow chart of a method for diagnosing a fault of a gyro inertial component of a moving body based on equivalence relation and empirical mode decomposition;
FIG. 2 is a flow chart of empirical mode decomposition;
FIG. 3 is a schematic view of an experimental verification apparatus;
FIG. 4 is an equivalent vector when a constant drift increase fault occurs;
FIG. 5 is a first order IMF signal when a constant drift increase fault occurs;
FIG. 6 is an equivalent vector when a noise level increase fault occurs;
FIG. 7 is a first order IMF signal when a noise level increase fault occurs;
FIG. 8 is an equivalent vector when the X-axis gyro and the S-axis gyro simultaneously have sudden change failures;
fig. 9 is a first-order IMF signal when the X-axis gyro and the S-axis gyro simultaneously have sudden change failures.
Detailed Description
The first embodiment is as follows: the present embodiment will be described below with reference to fig. 1 and 2.
In order to effectively utilize a signal processing method to monitor the health of the sensor and reduce the calculation amount of the algorithm to a certain extent, the patent provides a sensor health monitoring method based on equivalence relation and Empirical Mode Decomposition (EMD), which is used for monitoring the health of an inertial gyro component.
The empirical Mode decomposition method was proposed by tsuba of National Aeronautics and astronautics Administration (NASA) in 1998, which uses the variation of the internal time scale of the signal to perform energy and frequency analysis and develop the signal into a finite number of internal solid Mode functions (IMFs). Unlike the conventional method using a fixed morphology window as the bounding basis function, the basis function of the EMD is extracted from the signal, i.e., IMF is used as the basis. Whereas IMF must satisfy the following conditions:
1) in the whole function, the number of the extreme points is equal to or different from the number of the zero crossing points by 1;
2) at any instant, the envelope defined by the local extremum envelope has a local mean of zero. Wherein the first condition is similar to the narrow bandwidth requirement in the conventional gaussian smoothing process. The second condition is a new idea: the global requirement is changed to a local requirement so that the instantaneous frequency does not cause unnecessary jitter due to the presence of an asymmetric waveform. The EMD constructed by the two conditions is considered to be an adaptive method for solving nonlinear and non-stationary signals powerfully, is a great breakthrough to traditional signal analysis methods such as Fourier transform and the like in recent years, and is widely applied.
Considering that the change of the characteristics of the gyro output signal caused by the fault of the gyro sensor, the method decomposes the gyro output signal into signal superposition of IMF components from high frequency to low frequency by an EMD method, and provides a reasonable approach for the characteristic quantity selection in the health monitoring process.
The method for diagnosing the fault of the gyro inertia assembly of the moving body in the embodiment comprises the following steps of:
the method comprises the following steps that firstly, hardware redundancy of a moving body inertial gyro component is utilized, and whether the inertial gyro component breaks down or not is detected through an equivalence relation method;
secondly, when the inertial gyro component is detected to be in fault, collecting N data points of output signals of each gyro sensor in the inertial gyro component as fault data input signals of the gyro sensor, carrying out empirical mode decomposition on the fault data input signals, and taking an obtained first-order IM F component as a fault characteristic signal of the gyro sensor;
and step three, performing cumulative summation CUSUM processing of statistical test on the first-order IM F component obtained by each gyro sensor in the step two to judge whether the gyro sensor has a fault, and further separating the gyro sensor with the fault from the inertial gyro component to complete the health monitoring of the inertial gyro component.
The process of detecting whether the inertial gyro component fails through an equivalence relation method in the first step is as follows:
step 11, constructing an equivalence relation by utilizing hardware redundancy configuration of the moving body inertial gyro component, and calculating an equivalence vector of the equivalence relation;
if the judgment result is yes, the inertial gyro component is considered to be not in fault; and if the judgment result is negative, the inertial gyro component is considered to be in fault.
Fault detection threshold epsilon D8 sigma to 12 sigma, wherein sigma is the standard deviation of gyro noise. Theoretically,. epsilonDThe detection accuracy probability when taking 4 σ is already very close to 1, but to reduce the false alarm rate, the fault detection threshold is increased to 8 σ to 12 σ.
The process of obtaining the first-order IM F component in the second step is as follows:
setting a fault data input signal as x (t), t 1, 2, N,
step 21, initializing an IMF decomposition process: n is 1 and satisfies the relation rn-1(t) x (t) holds, where rn-1(t) is a trend function after the (n-1) th decomposition;
step 22, initializing the screening process, wherein k is 1 and satisfies the relation hn(k-1)(t)＝rn-1(t) is true, wherein hn(k-1)(t) is a residual function after (k-1) screening in the nth empirical mode decomposition;
step 23, obtaining the residual function h after the k-th screening according to the screening programnk(t)；
Step 24, judging the residual function h obtained in the step 23 by adopting a standard deviation criterionnk(t) whether the condition of the intrinsic mode function IMF is satisfied, i.e.
Whether or not it is less than threshold HSD，0.2≤HSD≤0.3；
If yes, step 25 is executed, if no, k is k +1, and then step 23 is executed,
step (ii) of25. Extracting a first order IMF component: c. C1(t)＝h1k(t)。
Obtaining the residual function h in step 23nkThe process of (t) is:
231, obtaining a residual function h of the fault data input signal x (t) after k-1 times of screening in nth empirical mode decomposition by utilizing a cubic spline functionn(k-1)(t) the upper and lower envelopes of (t),
step 232, calculating the residual function hn(k-1)(t) mean value of upper and lower envelope curves at each t
Step 233, obtaining the residual function of the fault data input signal x (t) after the k-time screening in the n-time empirical mode decomposition <math><mrow><msub><mi>h</mi><mi>nk</mi></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>=</mo><msub><mi>h</mi><mrow><mi>n</mi><mrow><mo>(</mo><mi>k</mi><mo>-</mo><mn>1</mn><mo>)</mo></mrow></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>-</mo><msub><mover><mi>m</mi><mo>‾</mo></mover><mrow><mi>n</mi><mrow><mo>(</mo><mi>k</mi><mo>-</mo><mn>1</mn><mo>)</mo></mrow></mrow></msub><mrow><mo>(</mo><mi>t</mi><mo>)</mo></mrow><mo>.</mo></mrow></math>
In the third step, the process of performing summation CUSUM processing of statistical test on the first-order IM F component to judge whether the gyro sensor has a fault is as follows:
step 31, obtaining a CUSUM calculation result W of a single data point in the first-order IMF component according to the following formulai：
Wi＝Wi-1+|Xi|，
Wherein i is 1, 2, … N, Wi-1Calculate the result for CUSUM for the i-1 th data point, and let W0＝0，XiThe first order IMF value at the ith data point,
step 32, judging the CUSUM calculation result W of the Nth data pointNWhether or not it is greater than a diagnostic threshold epsilonI，
If yes, the gyro sensor has a fault, and if not, the gyro sensor does not have a fault.
If yes, the gyro sensor has a fault, and if not, the gyro sensor does not have a fault.
Diagnostic threshold εIObtained according to the following formula:
εIn σ, where σ is the standard deviation of the gyro noise.
The second embodiment is as follows: the present embodiment is different from the first embodiment in that a failure detection threshold value epsilonDIs 10 σ, and the other is the same as in the first embodiment.
The third concrete implementation mode: the present embodiment differs from the first embodiment in that H in step 24SDThe other is the same as in the first embodiment 0.25.
The fourth concrete implementation mode: the present embodiment is described below with reference to fig. 3 to 9, and the present embodiment provides a specific example: the schematic diagram of the test validation device is shown in fig. 3. The monitoring object is a three-upright-mounted one-inclined-mounted gyroscope inertia assembly composed of four single-axis fiber-optic gyroscopes VG951D, the motion of the moving body can be simulated by a three-axis turntable, the inertia gyroscope assembly is mounted on the three-axis turntable, and the measurement output of the inertia gyroscope assembly is the rotation angular velocity. The health monitoring processor adopts LPC2478 as a main controller, and LPC2478 is a microcontroller which is designed by NXP semiconductor company, has extremely high integration level and takes ARM7TDMI-S as an inner core.
Executing the step one: hardware redundancy of the inertial gyro component of the moving body is utilized, and whether the inertial gyro component fails or not is detected by an equivalence relation method.
In the step, the failure of the gyro component is detected by adopting an equivalence relation method. In order to ensure the safety and reliability of the system, the gyro sensor system of the moving body inertial gyro component mostly adopts redundant configuration, so that an equivalence relation can be constructed, and an equivalence vector can be calculated for detecting and separating sensor faults. Assume the measurement equation for the system is:
m＝Hx+w+f (1)
wherein,
is the output signal of the I sensors;
installing a matrix for the sensor;is the n-dimensional measured signal;
respectively measurement noise and additional fault signals.
If a matrix is selectedSatisfies the following conditions:
VH＝0(l-n)×n (2)
VVT＝Il-n (3)
i.e., the column vectors of H form an n-dimensional space and the row vectors of V form an (l-n) -dimensional orthogonal space of this space.
The equivalence vector is defined as:
p＝Vm＝V(w+f) (4)
the equivalent vector p is independent of the measured signal and is a function of the noise w and the fault f only. If the influence of noise is not considered, the equivalent vector p is the component of the fault vector f in the (l-n) -dimensional subspace spanned by V. Therefore, a certain threshold value can be set to detect a failure of the sensor system based on the norm of the equivalent vector p.
Inertial gyro components in a moving body are usually arranged in a three-orthogonal and one-inclined mode, and the effectiveness of the proposed fault diagnosis method is explained aiming at the gyro configuration.
The gyro component measurement matrix of the three-orthogonal one-inclined configuration mode is as follows:
obtaining a projection matrix satisfying the formulas (2) and (3) as
V＝[0.4082 0.4082 0.4082 -0.707] (6)
The equivalent vectors are:
p＝Vm (7)
the fault detection law is as follows:
wherein epsilonDFor the fault detection threshold, it is optionalIs 10 σ, σ is the standard deviation of the gyro noise. And detecting whether the gyro sensor group has a fault or not by an equivalence relation method.
And (5) executing the step two: when the inertial gyro component is detected to have a fault, collecting N data points of an output signal of each gyro sensor in the inertial gyro component as a fault data input signal of the gyro sensor, carrying out empirical mode decomposition on the fault data input signal, and taking an obtained first-order IM F component as a fault characteristic signal of the gyro sensor.
After the gyro assembly is detected to be out of order, collecting N-128 data points near the fault detection time, and carrying out empirical mode decomposition once. Since the first order IMF contains sufficient feature information, the EMD only needs to decompose the 1 st IMF component c1And (t) stopping after the processing, thereby effectively improving the processing speed and reducing the calculated amount.
And step three is executed: performing CUSUM operation on the obtained first-order IMF, and comparing the CUSUM operation with a passing threshold epsilonIAnd (4) diagnosing whether each axis gyro has a fault or not.
The effectiveness of the fault diagnosis method provided by the invention is verified by adopting the centralized typical inertial gyro component faults, the time interval adopted in the simulation is 0.025s, and the standard deviation of gyro noise is 1 multiplied by 10-6rad/s (i.e.. epsilon.)D＝10σ＝1×10-5rad/s，εI＝Nσ＝1.28×10-4rad/s). The three faults are respectively:
1) when t is 6s, the X-axis gyro sensor has constant drift increasing fault, and the mutation amplitude is-1 multiplied by 10-4rad/s, equivalent vector is shown in FIG. 4, and first order IMF signals are shown in FIG. 5. The CUSUM calculation result W of the Nth data point in the first-order IMF component of each axis gyro is calculatedNRespectively as follows: an X axis: 3.354X 10-4rad/s, Y-axis: 9.5566X 10-5rad/s, Z-axis: 9.5738X 10-5rad/S, S-axis: 8.7282X 10-5rad/s. Wherein, W of X axisNGreater than a diagnostic threshold epsilonI(1.28×10-4rad/s) and thus, the X-axis gyro generation can be determinedAnd (4) failure.
2) When t is 11s, the Z-axis gyro malfunctions, resulting in an increase in noise variance of 1 × 10-9rad/s, equivalent vector is shown in FIG. 6, and first order IMF signals are shown in FIG. 7. The CUSUM calculation result W of the Nth data point in the first-order IMF component of each axis gyro is calculatedNRespectively as follows: an X axis: 8.8193X 10-5rad/s, Y-axis: 9.8038X 10-5rad/s, Z-axis: 1.7X 10-3rad/S, S-axis: 9.2548X 10-5rad/s. Wherein, W of Z axisNGreater than a diagnostic threshold epsilonI(1.28×10-4rad/s) and thus, it can be determined that the Z-axis gyro is malfunctioning.
3) When t is 15S, the X-axis gyro and the S-axis gyro simultaneously have sudden change faults, and the fault sizes are both 6 multiplied by 10-5rad/s, equivalent vector is shown in FIG. 8, and first order IMF signals are shown in FIG. 9, respectively. The CUSUM calculation result W of the Nth data point in the first-order IMF component of each axis gyro is calculatedNRespectively as follows: an X axis: 2.1543X 10-4rad/s, Y-axis: 9.1255X 10-5rad/s, Z-axis: 8.149X 10-5rad/S, S-axis: 1.7414X 10-4rad/S, wherein W is in the X and S axesNAre all greater than a diagnostic threshold epsilonI(1.28×10-4rad/S) from which it can be determined that the X-axis gyro and the S-axis gyro are malfunctioning.
The method can effectively diagnose the faults under the condition that a plurality of faults occur simultaneously without information provided by other sensors.
Claims (9)
1. A fault diagnosis method for a gyro inertia assembly of a moving body is characterized by comprising the following steps:
the method comprises the following steps that firstly, hardware redundancy of a moving body inertial gyro component is utilized, and whether the inertial gyro component breaks down or not is detected through an equivalence relation method;
secondly, when the inertial gyro component is detected to be in fault, collecting N data points of output signals of each gyro sensor in the inertial gyro component as fault data input signals of the gyro sensor, carrying out empirical mode decomposition on the fault data input signals, and taking an obtained first-order IM F component as a fault characteristic signal of the gyro sensor;
and step three, performing cumulative summation CUSUM processing of statistical test on the first-order IM F component obtained by each gyro sensor in the step two to judge whether the gyro sensor has a fault, and further separating the gyro sensor with the fault from the inertial gyro component to complete the health monitoring of the inertial gyro component.
2. The method for diagnosing the failure of the moving body gyro inertia assembly as claimed in claim 1, wherein the process of detecting whether the inertia gyro assembly fails or not by the equivalence relation method in the first step is as follows:
step 11, constructing an equivalence relation by utilizing hardware redundancy configuration of the moving body inertial gyro component, and calculating an equivalence vector of the equivalence relation;
step 12, judging whether the norm of the equivalent vector is smaller than a fault detection threshold value,
if the judgment result is yes, the inertial gyro component is considered to be not in fault; and if the judgment result is negative, the inertial gyro component is considered to be in fault.
3. The method of claim 2, wherein the threshold value of failure detection ε is a threshold value of failure detectionD8 sigma to 12 sigma, wherein sigma is the standard deviation of gyro noise.
4. The method of claim 2, wherein the threshold value of failure detection ε is a threshold value of failure detectionDIs 10 σ, where σ is the standard deviation of the gyro noise.
5. The method for diagnosing the failure of the gyro inertia assembly of the moving body according to claim 1, wherein the process of obtaining the first-order IM F component in the second step is:
setting a fault data input signal as x (t), t 1, 2, N,
step 21, initializing an IMF decomposition process: n is 1 and satisfies the relation rn-1(t) x (t) holds, where rn-1(t) is a trend function after the (n-1) th decomposition;
step 22, initializing the screening process, wherein k is 1 and satisfies the relation hn(k-1)(t)＝rn-1(t) is true, wherein hn(k-1)(t) is a residual function after (k-1) screening in the nth empirical mode decomposition;
step 23, obtaining the residual function h after the k-th screening according to the screening programnk(t)；
Step 24, judging the residual function h obtained in the step 23 by adopting a standard deviation criterionnk(t) whether the condition of the intrinsic mode function IMF is satisfied, i.e.
Whether or not it is less than threshold HSD，0.2≤HSD≤0.3；
If yes, step 25 is executed, if no, k is k +1, and then step 23 is executed,
step 25, extracting a first-order IMF component: c. C1(t)＝h1k(t)。
6. The method for diagnosing faults of a gyroscopic inertial assembly of a moving body according to claim 5, wherein the residual function h obtained in step 23 isnkThe process of (t) is:
231, obtaining a residual function h of the fault data input signal x (t) after k-1 times of screening in nth empirical mode decomposition by utilizing a cubic spline functionn(k-1)(t) the upper and lower envelopes of (t),
step 232, calculating the residual function hn(k-1)(t) mean value of upper and lower envelope curves at each t
Step 233, obtaining the fault data input signal x (t) and performing an nth empirical mode decompositionResidual function after k-th screening
7. The method for diagnosing faults of a gyro inertia assembly of a moving body as claimed in claim 5, wherein step 24, HSD＝0.25。
8. The method for diagnosing faults of a gyro inertia assembly of a moving body as claimed in claim 1, wherein the step three of performing the sum CUSUM process of statistical test on the first-order IM F component to determine whether the gyro sensor has faults comprises the steps of:
step 31, obtaining a CUSUM calculation result Wi of a single data point in the first-order IMF component according to the following formula:
Wi＝Wi-1+|Xi|，
wherein i is 1, 2, … N, Wi-1Calculate the result for CUSUM for the i-1 th data point, and let W0＝0，XiThe first order IMF value at the ith data point,
step 32, judging the CUSUM calculation result W of the Nth data pointNWhether or not it is greater than a diagnostic threshold epsilonI，
If yes, the gyro sensor has a fault; and if not, the gyro sensor has no fault.
9. The method of claim 8, wherein the diagnostic threshold ε is defined as the threshold valueIObtained according to the following formula:
εIn σ, where σ is the standard deviation of the gyro noise.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201110040473A CN102175266B (en) | 2011-02-18 | 2011-02-18 | Fault diagnosis method for mobile gyroscope inertia subassembly |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201110040473A CN102175266B (en) | 2011-02-18 | 2011-02-18 | Fault diagnosis method for mobile gyroscope inertia subassembly |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102175266A true CN102175266A (en) | 2011-09-07 |
| CN102175266B CN102175266B (en) | 2012-09-19 |
Family
ID=44518476
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201110040473A Expired - Fee Related CN102175266B (en) | 2011-02-18 | 2011-02-18 | Fault diagnosis method for mobile gyroscope inertia subassembly |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102175266B (en) |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102645230A (en) * | 2012-04-11 | 2012-08-22 | 中航捷锐（北京）光电技术有限公司 | Fault detection method and device of optical fiber gyroscope |
| CN102735265A (en) * | 2012-06-18 | 2012-10-17 | 北京控制工程研究所 | Method for star sensor periodic fault detection based on gyro drift estimate value |
| CN103034232A (en) * | 2012-11-30 | 2013-04-10 | 北京控制工程研究所 | Automatic failure handling and protection method of deep space probe global navigation chart (GNC) system base on layered structure |
| CN103234553A (en) * | 2013-03-29 | 2013-08-07 | 北京控制工程研究所 | Fault diagnosis method for gyro measurement system |
| CN103697881A (en) * | 2013-12-27 | 2014-04-02 | 北京航天时代光电科技有限公司 | High-reliability redundant four-shaft optical fiber gyroscope inertia measurement device |
| CN104019831A (en) * | 2014-06-20 | 2014-09-03 | 哈尔滨工业大学 | Gyroscope fault diagnosis method based on EMD (Empirical Mode Decomposition) and entropy weight |
| CN104573248A (en) * | 2015-01-16 | 2015-04-29 | 东南大学 | EMD based fiber-optic gyroscope temperature drift multi-scale extreme learning machine training method |
| CN105043416A (en) * | 2015-07-14 | 2015-11-11 | 哈尔滨工业大学 | On-track fault diagnosis method of hemispherical resonance gyroscope combination |
| CN106643810A (en) * | 2017-02-15 | 2017-05-10 | 上海航天控制技术研究所 | Diagnosis method of measured data of gyroscope combination |
| CN106767898A (en) * | 2016-11-17 | 2017-05-31 | 中国人民解放军国防科学技术大学 | A kind of method for detecting measuring system of satellite attitude small fault |
| CN106840202A (en) * | 2017-01-11 | 2017-06-13 | 东南大学 | A kind of gyroscopic vibration signal extraction and compensation method |
| CN107356264A (en) * | 2017-07-07 | 2017-11-17 | 上海航天控制技术研究所 | A kind of isomery Gyro mutually examines method |
| CN111509443A (en) * | 2020-04-30 | 2020-08-07 | 南京盟瑞自动化有限公司 | Canned type high pressure connector convenient to quick installation |
| CN114690066A (en) * | 2022-06-01 | 2022-07-01 | 深圳市明珞锋科技有限责任公司 | Power supply abnormal output alarm calculation module |
| CN115059690A (en) * | 2022-07-15 | 2022-09-16 | 中国人民解放军战略支援部队航天工程大学 | Three orthogonal three freedom translation magnetic bearings of one kind of slope mounting |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6760664B1 (en) * | 2001-06-25 | 2004-07-06 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | Autonomous navigation system based on GPS and magnetometer data |
| WO2007031409A1 (en) * | 2005-09-12 | 2007-03-22 | Siemens Vdo Automotive Ag | Method and system for monitoring a sensor arrangement |
| CN101369897A (en) * | 2008-07-31 | 2009-02-18 | 成都市华为赛门铁克科技有限公司 | Method and equipment for detecting network attack |
| CN100491916C (en) * | 2007-12-26 | 2009-05-27 | 北京控制工程研究所 | Method for detecting, recovering and controlling for independence trouble during orbital transfer course |
| CN101459561A (en) * | 2009-01-09 | 2009-06-17 | 北京邮电大学 | Apparatus and method for detecting SIP message flooding attack based on CUSUM algorithm |
-
2011
- 2011-02-18 CN CN201110040473A patent/CN102175266B/en not_active Expired - Fee Related
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6760664B1 (en) * | 2001-06-25 | 2004-07-06 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | Autonomous navigation system based on GPS and magnetometer data |
| WO2007031409A1 (en) * | 2005-09-12 | 2007-03-22 | Siemens Vdo Automotive Ag | Method and system for monitoring a sensor arrangement |
| CN100491916C (en) * | 2007-12-26 | 2009-05-27 | 北京控制工程研究所 | Method for detecting, recovering and controlling for independence trouble during orbital transfer course |
| CN101369897A (en) * | 2008-07-31 | 2009-02-18 | 成都市华为赛门铁克科技有限公司 | Method and equipment for detecting network attack |
| CN101459561A (en) * | 2009-01-09 | 2009-06-17 | 北京邮电大学 | Apparatus and method for detecting SIP message flooding attack based on CUSUM algorithm |
Non-Patent Citations (3)
| Title |
|---|
| 《中国惯性技术学报》 20080831 马静等 基于故障树分析的光纤陀螺用探测器组件可靠性分析 全文 1-5 第16卷, 第4期 * |
| 《宇航学报》 20060930 贾鹏等 基于奇异值分解的冗余惯导系统故障诊断 全文 1-5 第27卷, 第5期 * |
| 《系统仿真学报》 20060831 贾鹏等 基于冗余惯性组件故障诊断方法的比较研究 全文 1-5 第18卷, * |
Cited By (27)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102645230B (en) * | 2012-04-11 | 2015-06-24 | 中航捷锐（北京）光电技术有限公司 | Fault detection method and device of optical fiber gyroscope |
| CN102645230A (en) * | 2012-04-11 | 2012-08-22 | 中航捷锐（北京）光电技术有限公司 | Fault detection method and device of optical fiber gyroscope |
| CN102735265A (en) * | 2012-06-18 | 2012-10-17 | 北京控制工程研究所 | Method for star sensor periodic fault detection based on gyro drift estimate value |
| CN102735265B (en) * | 2012-06-18 | 2014-12-17 | 北京控制工程研究所 | Method for star sensor periodic fault detection based on gyro drift estimate value |
| CN103034232A (en) * | 2012-11-30 | 2013-04-10 | 北京控制工程研究所 | Automatic failure handling and protection method of deep space probe global navigation chart (GNC) system base on layered structure |
| CN103034232B (en) * | 2012-11-30 | 2015-04-22 | 北京控制工程研究所 | Automatic failure handling and protection method of deep space probe global navigation chart (GNC) system base on layered structure |
| CN103234553A (en) * | 2013-03-29 | 2013-08-07 | 北京控制工程研究所 | Fault diagnosis method for gyro measurement system |
| CN103234553B (en) * | 2013-03-29 | 2015-08-19 | 北京控制工程研究所 | A kind of method for diagnosing faults of gyro measurement system |
| CN103697881B (en) * | 2013-12-27 | 2016-09-21 | 北京航天时代光电科技有限公司 | A kind of highly reliable redundancy-type four axle inertial measurement unit of optical fiber gyroscope |
| CN103697881A (en) * | 2013-12-27 | 2014-04-02 | 北京航天时代光电科技有限公司 | High-reliability redundant four-shaft optical fiber gyroscope inertia measurement device |
| CN104019831A (en) * | 2014-06-20 | 2014-09-03 | 哈尔滨工业大学 | Gyroscope fault diagnosis method based on EMD (Empirical Mode Decomposition) and entropy weight |
| CN104019831B (en) * | 2014-06-20 | 2017-01-04 | 哈尔滨工业大学 | Gyroscope method for diagnosing faults based on EMD and entropy weight |
| CN104573248A (en) * | 2015-01-16 | 2015-04-29 | 东南大学 | EMD based fiber-optic gyroscope temperature drift multi-scale extreme learning machine training method |
| CN105043416B (en) * | 2015-07-14 | 2017-12-22 | 哈尔滨工业大学 | A kind of hemispherical reso nance gyroscope combines on-orbit fault diagnostic method |
| CN105043416A (en) * | 2015-07-14 | 2015-11-11 | 哈尔滨工业大学 | On-track fault diagnosis method of hemispherical resonance gyroscope combination |
| CN106767898A (en) * | 2016-11-17 | 2017-05-31 | 中国人民解放军国防科学技术大学 | A kind of method for detecting measuring system of satellite attitude small fault |
| CN106767898B (en) * | 2016-11-17 | 2019-04-09 | 中国人民解放军国防科学技术大学 | A method of detection measuring system of satellite attitude small fault |
| CN106840202B (en) * | 2017-01-11 | 2020-02-18 | 东南大学 | Gyro vibration signal extraction and compensation method |
| CN106840202A (en) * | 2017-01-11 | 2017-06-13 | 东南大学 | A kind of gyroscopic vibration signal extraction and compensation method |
| CN106643810B (en) * | 2017-02-15 | 2019-03-26 | 上海航天控制技术研究所 | A kind of diagnostic method of pair of Gyro measurement data |
| CN106643810A (en) * | 2017-02-15 | 2017-05-10 | 上海航天控制技术研究所 | Diagnosis method of measured data of gyroscope combination |
| CN107356264A (en) * | 2017-07-07 | 2017-11-17 | 上海航天控制技术研究所 | A kind of isomery Gyro mutually examines method |
| CN107356264B (en) * | 2017-07-07 | 2020-05-26 | 上海航天控制技术研究所 | Combined diagnosis method for heterogeneous gyros |
| CN111509443A (en) * | 2020-04-30 | 2020-08-07 | 南京盟瑞自动化有限公司 | Canned type high pressure connector convenient to quick installation |
| CN114690066A (en) * | 2022-06-01 | 2022-07-01 | 深圳市明珞锋科技有限责任公司 | Power supply abnormal output alarm calculation module |
| CN115059690A (en) * | 2022-07-15 | 2022-09-16 | 中国人民解放军战略支援部队航天工程大学 | Three orthogonal three freedom translation magnetic bearings of one kind of slope mounting |
| CN115059690B (en) * | 2022-07-15 | 2023-12-22 | 中国人民解放军战略支援部队航天工程大学 | Oblique three-orthogonal three-degree-of-freedom translational magnetic bearing |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102175266B (en) | 2012-09-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102175266B (en) | Fault diagnosis method for mobile gyroscope inertia subassembly | |
| CN102176159B (en) | Satellite attitude control system failure diagnosis device and method based on state observer and equivalent space | |
| KR101189697B1 (en) | Fault detector and detecting method for attitude control system of spacecaft | |
| US9650152B2 (en) | Flight envelope protection system for unmanned aerial vehicles | |
| CN103676941B (en) | Satellite control system method for diagnosing faults based on kinematics and dynamics modeling | |
| Choi et al. | Software-based realtime recovery from sensor attacks on robotic vehicles | |
| CN103234553B (en) | A kind of method for diagnosing faults of gyro measurement system | |
| CN113311803B (en) | On-orbit spacecraft flywheel fault detection method based on kernel principal component analysis | |
| Youn et al. | Accelerometer fault-tolerant model-aided state estimation for high-altitude long-endurance UAV | |
| Van Eykeren et al. | Sensor fault detection and isolation using adaptive extended Kalman filter | |
| D'Amato et al. | Fault tolerant low cost IMUS for UAVs | |
| Wan et al. | Real-time fault-tolerant moving horizon air data estimation for the reconfigure benchmark | |
| Zhang et al. | Fault-tolerant attitude stabilization for spacecraft with low-frequency actuator updates: An integral-type event-triggered approach | |
| CN114923503B (en) | On-orbit spacecraft gyroscope and star sensor fault diagnosis method based on principal component analysis | |
| Han et al. | Quadratic-Kalman-filter-based sensor fault detection approach for unmanned aerial vehicles | |
| Xiao et al. | Prognosis of electric scooter with intermittent faults: Dual degradation processes approach | |
| Chen et al. | On-line and non-invasive anomaly detection system for unmanned aerial vehicle | |
| CN102661751A (en) | Satellite gyroscope group fault detection, separation and estimation method based on equivalence relation and wavelet transform numerical differentiation | |
| CN117405109B (en) | Three-set inertial navigation system attitude voting method based on quaternion spherical linear weighting | |
| Wan et al. | Robust air data sensor fault diagnosis with enhanced fault sensitivity using moving horizon estimation | |
| Chen et al. | A variable projection-based algorithm for fault detection and diagnosis | |
| CN103885451B (en) | Novel anti-interference attitude control circuit resolving system | |
| CN115218927B (en) | Unmanned aerial vehicle IMU sensor fault detection method based on secondary Kalman filtering | |
| Kim et al. | Failure diagnosis of skew-configured aircraft inertial sensors using wavelet decomposition | |
| Wang et al. | UAV actuator fault detection using maximal information coefficient and 1-D convolutional neural network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20120919 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |