CN102538859A - Method for monitoring and processing various sensors - Google Patents
Method for monitoring and processing various sensors Download PDFInfo
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Abstract
The invention belongs to the technical field of industrial monitoring and particularly relates to a method for monitoring and processing various sensors. The method utilizes relativity of signals measured by the various sensors in various sensor systems and adopts multipath measurement signals to achieve failure diagnosis, warning and failure isolation of the sensors, and does not need a single sensor work state monitoring system, thereby simplifying system structure and improving reliability of a self-checking system.
Description
Technical field
The invention belongs to the industry monitoring technical field, be specifically related to a kind of monitoring process method of multiclass sensor.
Background technology
In the industry monitoring field; Usually need to adopt multisensor syste that the different measuring object that same measuring object perhaps has correlativity is carried out synchronous monitoring; To realize message complementary sense and fusion, reach purposes such as the range of expanding the application of sensor field, widening measurement, the precision that improves measurement and robustness.But increasing of number of sensors not only increased the complexity of measuring system, also increased the probability of malfunction of system.Therefore, above factor has proposed requirements at the higher level to the trouble diagnosibility of multisensor syste itself, to guarantee the reliability of process monitoring and parameter measurement, ensures the safe operation of industrial processes and scientific experiment etc.
In order to ensure the accuracy that measuring system information is obtained, need sensing system is monitored in real time, be in normal operating conditions to guarantee it.Usually can adopt extra detecting unit that the duty of sensor is monitored, but this mode has not only increased system complexity and system constructing cost, also introduced new unreliable factor simultaneously, like the duty of detecting unit.
Summary of the invention
The technical matters that quasi-solution of the present invention is determined is: the deficiency that overcomes existing method; A kind of monitoring process method of multiclass sensor is provided; This method can realize fault diagnosis, warning and the fault isolation of multiclass sensor system according to the correlativity of different sensors measuring-signal in the multiclass sensor.
For realizing above-mentioned purpose, technical solution of the present invention is:
A kind of monitoring process method of multiclass sensor, this method may further comprise the steps:
Step 1; Initialization; Confirm that the output signal in the known multisensor syste has the self check state and the number of the sensor of correlativity, for comprising the multisensor syste that t has the sensor of correlativity output, promptly exporting the number of sensors that signal has correlativity is t; When t greater than 3, then jump to step 2; Work as t=3, then jump to step 3; Work as t=2, then jump to step 4;
Step 2, in sensing system, adopting the sensor number is t and t＞existing m sensor carried out self check, and 0＜m＜t; If through self check, system works is normal, then optional sensor from the set of sensors of operate as normal; Optional sensor in remaining set of sensors of not carrying out self check; Form the set of two sensors fault diagnosis, promptly t=2 jumps to step 4; Do not carry out self check if still have sensor in the sensing system, i.e. m=0, perhaps all sensors is all through self check; Be m=t; Three sensors of picked at random then, t=3 constitutes the fault diagnosis set; The i.e. fault diagnosis set of three sensors formation is carried out fault diagnosis and isolation, order execution in step three;
Step 3; For the above-mentioned fault diagnosis set that constitutes by sensors A, sensor B, three sensors of sensor C, same time period, three measuring-signals that it obtains respectively with correlativity; Said three measuring-signals are obtained the related coefficient of the measuring-signal of any two sensors through correlation computations in twos; With its with compare according to the operating mode pre-set threshold, further, the duty of each sensor is judged according to self check decision-making truth table;
All be in normal operating conditions if diagnostic result is sensors A, sensor B, sensor C, then jump to step 7 in proper working order;
If diagnostic result is some the breaking down among sensors A, sensor B, the sensor C, then jump to the step 5 of fault isolation;
If diagnostic result is at least two sensor faults among sensors A, sensor B, the sensor C, then jump to the step 6 of serious warning;
Step 4; For comprising two fault diagnosis set with sensor of correlativity; Suppose that the fault diagnosis set comprises sensors A, sensor B, the same time period, measuring-signal Sa, Sb that it obtains respectively have correlativity; The related coefficient that calculate sensors A and sensor B same with step 3 compares itself and pre-set threshold;
If diagnose out sensors A and sensor B all to be in normal operating conditions, jump to the step 7 of operate as normal;
If diagnose out among sensors A and the sensor B to have at least one to break down, divide following situation: if known one of them working sensor is normal, then another sensor breaks down, and can realize localization of fault, jumps to the step 5 of fault isolation;
If the malfunction of sensors A and sensor B is all unknown, then can't position fault sensor, jump to the step 6 of serious warning;
Step 5, with the sensor measurement signal isolation processing that breaks down, the measuring-signal that only adopts the operate as normal sensor to obtain obtains the reliability of information to guarantee measuring system, the general alarm of order execution in step six;
Step 6 if carry out fault isolation, is carried out general alarm, and shows the sensor sequence number that breaks down, order execution in step seven; If be unrealized fault isolation, and have at least one to break down in the fault diagnosis set of two sensor formations, then program provides serious warning message and stops to carry out, and prompting must be carried out the integral body maintenance to sensing system immediately; If be unrealized fault isolation, and have at least two to break down in the fault diagnosis set of three sensors formations of picked at random, then program provides serious warning message and stops to carry out, and prompting must be carried out the integral body maintenance to sensing system immediately;
Step 7, in proper working order, jump to step 1.
Effect of the present invention is that the present invention utilizes in the multisensor syste; Correlation between signals that each sensor is surveyed; Adopt the multichannel measurement signal to realize fault diagnosis, warning and the fault isolation of sensor; Need not independent working sensor condition monitoring system, simplified system architecture, increased the reliability of self-checking system.
Description of drawings
The picture that this description of drawings provided is used for auxiliary to further understanding of the present invention, constitutes the application's a part, does not constitute to improper qualification of the present invention, in the accompanying drawings:
Fig. 1 is the process flow diagram that resolves of multisensor fault diagnosis of the present invention and isolated algorithm.
Embodiment
To combine accompanying drawing and practical implementation method to specify the present invention below, be used for explaining the present invention in schematic enforcement of the present invention and explanation, but not as to qualification of the present invention.
Embodiment 1:
The invention discloses a kind of monitoring process method of multiclass sensor, may further comprise the steps:
Step 1; Initialization; Confirm that the output signal in the known multisensor syste has the self check state and the number of the sensor of correlativity, for comprising the multisensor syste that t has the sensor of correlativity output, promptly exporting the number of sensors that signal has correlativity is t; When t greater than 3, then jump to step 2; Work as t=3, then jump to step 3; If t=2 then jumps to step 4.
Step 2 when to adopt the sensor number in the sensing system be t and t＞3, judges whether that at first existing m sensor carried out self check; And 0＜m＜t, if through self check, system works is normal; Then from the set of sensors of operate as normal, choose a sensor wantonly; Optional sensor is formed the two sensors fault diagnosis system, jump procedure four in remaining set of sensors of not carrying out self check;
Do not carry out self check if still have sensor in the sensing system, i.e. m=0, perhaps all sensors is all through self check, i.e. m=t, then three sensors of picked at random constitute the fault diagnosis set and carry out fault diagnosis and isolation, in proper order execution in step three.
Step 3; For by three sensors, i.e. the fault diagnosis set that constitutes of sensors A, sensor B, sensor C supposes that system comprises sensors A, sensor B and sensor C; The same time period, has correlativity between measuring-signal Sa, Sb and the Sc that it obtains respectively.
Calculate the correlation coefficient r AB (0) of sensors A and sensor B, rAB (0) and pre-set threshold r Tab (0) are compared.
Calculate the correlation coefficient r AC (0) of sensors A and sensor C, rAC (0) and pre-set threshold r Tac (0) are compared.
Calculate the correlation coefficient r BC (0) of sensor B and sensor C, r BC (0) and pre-set threshold r Tbc (0) are compared.After comparing, for the fault diagnosis set that is made up of sensors A, sensor B, sensor C, the decision-making truth table of all possible self-detection result can be classified table 1 as.In case from measuring-signal, obtain rAB (0), rAC (0), the value of r BC (0), the duty of each sensor can be inferred according to table 1.Wherein, "---" represent that this situation does not exist.
Table 1 self check decision-making truth table
If diagnostic result is (1) in the table 1, can think that sensors A, sensor B and sensor C all are in normal operating conditions, then jump to step 7 in proper working order;
If diagnostic result is one of (4), (6), (7) in the table 1, can judge among sensors A, sensor B and the sensor C which and break down, then jump to the step 5 of fault isolation;
If diagnostic result is (8) in the table 1, can judge at least two sensor faults among sensors A, sensor B and the sensor C, then jump to the step 6 of serious warning.
Step 4 for comprising two fault diagnosis set with sensor of correlativity, supposes that the fault diagnosis set comprises sensors A and sensor B, and the same time period, measuring-signal Sa and Sb that it obtains respectively have correlativity.Calculate the correlation coefficient r AB (0) of sensors A and sensor B, r AB (0) and pre-set threshold r Tab (0) are compared, because related coefficient is big more, two measuring-signals are approaching more.Therefore, the value of r Tab (0) is the lower limit of sensors A and sensor B correlativity.
Because in the course of work; The possibility that sensors A and sensor B synchronization break down is less; And there is not inevitable correlativity in the measuring-signal that obtains when the two breaks down; Therefore if r AB (0) greater than r Tab (0), can think that sensors A and sensor B all are in normal operating conditions, jump to the step 7 of operate as normal.
If r AB (0) is less than or equal to r Tab (0), then sensors A and sensor B wherein have at least one to break down, and divide following three kinds of situation:
If known sensor A is in proper working order, then sensor B breaks down, and jumps to the step 5 of fault isolation.
If known sensor B is in proper working order, then sensors A breaks down, and jumps to the step 5 of fault isolation.
If sensors A and sensor B all do not carry out self check as yet, then can't position fault sensor, jump to the step 6 of serious warning.
Step 5, with the measuring-signal isolation processing of the sensor that breaks down, the measuring-signal that only adopts the operate as normal sensor to obtain obtains the reliability of information to guarantee measuring system, and order is carried out the step 6 of general alarm.
Step 6 if carry out fault isolation, is carried out general alarm, and shows the sensor sequence number that breaks down, order execution in step seven; If be unrealized fault isolation, and have at least one to break down in the fault diagnosis set of two sensor formations, then program provides serious warning message and stops to carry out, and prompting must be carried out the integral body maintenance to sensing system immediately; If be unrealized fault isolation, and have at least two to break down in the fault diagnosis set of three sensors formations of picked at random, then program provides serious warning message and stops to carry out, and prompting must be carried out the integral body maintenance to sensing system immediately.
Step 7, in proper working order, jump to step 1;
The theoretical foundation that related coefficient is calculated in above-mentioned fault diagnosis and the partition method is:
Suppose x (n) | n=0,1 ... N-1} with y (n) | n=0,1 ... the sampling value of the measuring-signal that N-1} representes to obtain on any two sensors, n is a sample length.The standardization cross correlation function of these two samplings is represented as follows:
R (k)=Σ N=0N-1 [(x (n)-x‾ ) · (y (n+k)-y‾ )] Σ N=0N-1 [x (n)-x‾ ] 2· Σ N=0N-1 [y (n)-y‾ ] 2---(1)]] x and y are the mean value of two samplings here.Among the present invention, the related coefficient between two sampled values of r (0) expression, scope from-1 to 1.If r (0)=1, then two measuring-signals are identical.Related coefficient is big more, and two measuring-signals are approaching more.
More than the technical scheme that the embodiment of the invention provided has been carried out detailed introduction; Used concrete example among this paper the principle and the embodiment of the embodiment of the invention are set forth, the explanation of above embodiment only is applicable to the principle that helps to understand the embodiment of the invention; Simultaneously, for one of ordinary skill in the art, according to the embodiment of the invention, the part that on embodiment and range of application, all can change, in sum, this description should not be construed as limitation of the present invention.
Claims (6)
1. the monitoring process method of a multiclass sensor, this method may further comprise the steps:
Step 1; Initialization; Confirm that the output signal in the known multisensor syste has the self check state and the number of the sensor of correlativity, for comprising the multisensor syste that t has the sensor of correlativity output, promptly exporting the number of sensors that signal has correlativity is t; When t greater than 3, then jump to step 2; Work as t=3, then jump to step 3; Work as t=2, then jump to step 4;
Step 2, in sensing system, adopting the sensor number is t and t＞3, when promptly diagnosis object is the fault diagnosis set of the individual sensor formation of t (t＞3); Judge whether that at first existing m sensor carried out self check, and 0＜m＜t, if through self check; System works is normal; Optional sensor from the set of sensors of operate as normal then, optional sensor in remaining set of sensors of not carrying out self check obtains the fault diagnosis set that two sensors constitutes; Be t=2, jump to step 4; Do not carry out self check (m=0) or all sensors all through self check (m=t) if still there is sensor in the sensing system; Three sensors of picked at random then, t=3 obtains the fault diagnosis set; The i.e. fault diagnosis set of three sensors formation is carried out fault diagnosis and isolation, order execution in step three;
Step 3 is for by three sensors (A, B; C) the fault diagnosis set that constitutes, same time period, the measuring-signal (Sa that it obtains respectively with correlativity; Sb Sc), obtains the related coefficient between the measuring-signal of any two sensors to said three measuring-signals through correlation computations in twos; With its with compare according to the operating mode pre-set threshold, further, the duty of each sensor is judged according to self check decision-making truth table; If diagnostic result is that (A, B C) all are in normal operating conditions to sensor, then jump to step 7 in proper working order; If diagnostic result is that (some the breaking down in C) then jumps to the step 5 of fault isolation to sensor for A, B; If diagnostic result be sensor (A, B, C) at least two sensor faults, then jump to the step 6 of serious warning;
Step 4 for comprising two fault diagnosis set with sensor of correlativity, supposes that the fault diagnosis set comprises sensor (A; B); The same time period, (Sa Sb) has correlativity to its measuring-signal that obtains respectively; The related coefficient that calculate sensor (A) and sensor (B) same with step 3, with its with compare according to the operating mode pre-set threshold; (A B) all is in normal operating conditions, jumps to the step 7 of operate as normal if diagnose out sensor; If (A has at least one to break down in B), divides following situation: if known one of them working sensor is normal, then another sensor breaks down, and can realize localization of fault, jumps to the step 5 of fault isolation to diagnose out sensor; If the malfunction of sensor (A) and sensor (B) is all unknown, then can't position fault sensor, jump to the step 6 of serious warning;
Step 5, with the sensor measurement signal isolation processing that breaks down, the measuring-signal that only adopts the operate as normal sensor to obtain obtains the reliability of information to guarantee measuring system, the general alarm of order execution in step six;
Step 6 if carry out fault isolation, is carried out general alarm, and shows the sensor sequence number that breaks down, order execution in step seven; If be unrealized fault isolation, and have at least one to break down in the fault diagnosis set of two sensor formations, then program provides serious warning message and stops to carry out, and prompting must be carried out the integral body maintenance to sensing system immediately; If be unrealized fault isolation, and have at least two to break down in the fault diagnosis set of three sensors formations of picked at random, then program provides serious warning message and stops to carry out, and prompting must be carried out the integral body maintenance to sensing system immediately;
Step 7, in proper working order, jump to step 1.
2. the monitoring process method of multiclass sensor according to claim 1, it is characterized in that: have two sensors in the said multisensor syste at least, there is correlativity in its measuring-signal that obtains.
3. the monitoring process method of multiclass sensor according to claim 1; It is characterized in that: when certain sensor broke down in the said multisensor syste, it obtained measuring-signal and other sensors and obtains correlativity between measuring-signal and significantly descend or completely lose.
4. the monitoring process method of multiclass sensor according to claim 1 is characterized in that:
With the fault diagnosis set that exists in the said multisensor syste, resolve into set of sensors or three set of sensors that the measurement of correlation signal is exported of comprising two measurement of correlation signal outputs.
5. the monitoring process method of multiclass sensor according to claim 1 is characterized in that:
General alarm refers to when certain sensor breaks down, but if can realize the still safe operation of fault isolation and system, and show the sequence number of fault sensor.
6. the monitoring process method of multiclass sensor according to claim 1 is characterized in that:
The serious warning refer to when certain sensor breaks down, and fails to realize fault isolation and show the sequence number of fault sensor.
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Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107544464A (en) * | 2017-09-11 | 2018-01-05 | 上海斐讯数据通信技术有限公司 | A kind of detection method and system of industrial failure |
| CN107576346A (en) * | 2017-08-31 | 2018-01-12 | 广东美的制冷设备有限公司 | Detection method, device and the computer-readable recording medium of sensor |
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| WO2018126339A1 (en) * | 2017-01-03 | 2018-07-12 | Intel Corporation | Sensor management and reliability |
| CN108292464A (en) * | 2015-11-25 | 2018-07-17 | 鞍点有限责任两合公司 | The method and condition monitoring unit of the threshold value of condition monitoring unit for determining fire monitoring and/or the control centre that puts out a fire and the system with condition monitoring unit |
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| WO2021146927A1 (en) * | 2020-01-21 | 2021-07-29 | Siemens Schweiz Ag | Method and apparatus for sensor fault detection |
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2045950A (en) * | 1979-03-23 | 1980-11-05 | Sun Electric Corp | Testing arrangement for a rotary-movement electric signal transducer system |
| DE4436658B4 (en) * | 1993-10-13 | 2004-02-12 | Hitachi, Ltd. | Device and method for troubleshooting |
| CN101266273A (en) * | 2008-05-12 | 2008-09-17 | 徐立军 | Multi- sensor system fault self-diagnosis method |
| CN101382439A (en) * | 2008-10-27 | 2009-03-11 | 哈尔滨工业大学 | Multi-parameter self-confirming sensor and state self-confirming method thereof |
| CN102695943A (en) * | 2010-10-27 | 2012-09-26 | 帕威泰英有限公司 | Universal sensor self-diagnosis device and diagnosis method therefor |
-
2011
- 2011-05-19 CN CN2011101309589A patent/CN102538859A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB2045950A (en) * | 1979-03-23 | 1980-11-05 | Sun Electric Corp | Testing arrangement for a rotary-movement electric signal transducer system |
| DE4436658B4 (en) * | 1993-10-13 | 2004-02-12 | Hitachi, Ltd. | Device and method for troubleshooting |
| CN101266273A (en) * | 2008-05-12 | 2008-09-17 | 徐立军 | Multi- sensor system fault self-diagnosis method |
| CN101382439A (en) * | 2008-10-27 | 2009-03-11 | 哈尔滨工业大学 | Multi-parameter self-confirming sensor and state self-confirming method thereof |
| CN102695943A (en) * | 2010-10-27 | 2012-09-26 | 帕威泰英有限公司 | Universal sensor self-diagnosis device and diagnosis method therefor |
Cited By (45)
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|---|---|---|---|---|
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| US10885771B2 (en) | 2015-11-25 | 2021-01-05 | Minimax Gmbh & Co. Kg | Method for determining thresholds of a state monitoring unit for a fire detection and/or extinguishing control center, state monitoring unit, and system comprising same |
| WO2018126339A1 (en) * | 2017-01-03 | 2018-07-12 | Intel Corporation | Sensor management and reliability |
| US11662222B2 (en) | 2017-01-03 | 2023-05-30 | Intel Corporation | Sensor management and reliability |
| US11402235B2 (en) | 2017-01-03 | 2022-08-02 | Intel Corporation | Sensor management and reliability |
| CN111094936B (en) * | 2017-08-18 | 2022-08-02 | 山东诺方电子科技有限公司 | Anti-wind-interference atmosphere pollutant monitoring device |
| CN111094936A (en) * | 2017-08-18 | 2020-05-01 | 山东诺方电子科技有限公司 | Anti-wind-interference atmosphere pollutant monitoring device |
| CN107576346A (en) * | 2017-08-31 | 2018-01-12 | 广东美的制冷设备有限公司 | Detection method, device and the computer-readable recording medium of sensor |
| CN107544464A (en) * | 2017-09-11 | 2018-01-05 | 上海斐讯数据通信技术有限公司 | A kind of detection method and system of industrial failure |
| CN107991987A (en) * | 2018-01-04 | 2018-05-04 | 上海高诚智能科技有限公司 | A kind of flood-control wall remote supervision system |
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| WO2020020255A1 (en) * | 2018-02-01 | 2020-01-30 | 司书春 | Taxi roof light with multi-core sensor capable of resisting wind disturbance |
| WO2019149230A1 (en) * | 2018-02-01 | 2019-08-08 | 山东诺方电子科技有限公司 | High-low frequency multi-core sensor system |
| CN111316085A (en) * | 2018-02-01 | 2020-06-19 | 山东诺方电子科技有限公司 | Atmospheric pollutant monitoring equipment with self-cleaning function |
| CN111373238A (en) * | 2018-02-01 | 2020-07-03 | 山东诺方电子科技有限公司 | Vehicle-mounted atmospheric pollutant monitoring equipment |
| GB2583684A (en) * | 2018-02-01 | 2020-11-04 | Ogrands Innovation Inc | Method for rotating use of sub-sensor in multi-core sensor |
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| GB2589283B (en) * | 2018-02-01 | 2021-11-17 | Touchless Innovation Ltd | Method for rotating rest of six-core sensor system in taxi roof light |
| GB2588880B (en) * | 2018-02-01 | 2021-11-17 | Touchless Innovation Ltd | Method for rotating rest of sub-sensor in quad-core sensor system |
| WO2019149232A1 (en) * | 2018-02-01 | 2019-08-08 | 山东诺方电子科技有限公司 | Method for rotating use of sub-sensor in multi-core sensor |
| CN112567241A (en) * | 2018-07-25 | 2021-03-26 | 山东诺方电子科技有限公司 | Environmental sensor collaborative calibration method |
| CN111203862A (en) * | 2020-01-07 | 2020-05-29 | 上海高仙自动化科技发展有限公司 | Data display method and device, electronic equipment and storage medium |
| WO2021146927A1 (en) * | 2020-01-21 | 2021-07-29 | Siemens Schweiz Ag | Method and apparatus for sensor fault detection |
| CN113671373A (en) * | 2021-07-27 | 2021-11-19 | 三门三友科技股份有限公司 | Electrolytic process monitoring system and method in electrolytic cell with self-checking function |
| CN114025251A (en) * | 2021-11-03 | 2022-02-08 | 国家石油天然气管网集团有限公司华南分公司 | Method, device and medium for alarming abnormality of instrument |
| CN114353854A (en) * | 2022-03-21 | 2022-04-15 | 蘑菇物联技术（深圳）有限公司 | Method, apparatus, and medium for online locating of anomaly sensors |
| CN114353854B (en) * | 2022-03-21 | 2022-05-24 | 蘑菇物联技术（深圳）有限公司 | Method, apparatus, and medium for online locating of anomaly sensors |
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