CN106289363A - A kind of interference environment sensor fault judge mark method - Google Patents
A kind of interference environment sensor fault judge mark method Download PDFInfo
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Abstract
A kind of interference environment sensor fault judge mark method: set up sensor database, the data of sensor acquisition first contrast with respective value in initial data base, the data being between maximum sampling value and minimum sample value enter next link and analyze judgement further, the data automatic fitration of over range, to reduce data processing amount；The sensor mark that valid data quantity in unit interval is less than setting value is failure of removal sensor.Fault flag includes: to the valid data quantity in the unit interval less than the sensor of minimum quantity setting value, be labeled as failure of removal sensor；To the valid data quantity in the unit interval more than minimum quantity setting value, but it is not reaching to the sensor of normal number value, is labeled as the fault sensor of partial failure；Valid data quantity in unit interval, more than the sensor of normal number value, is labeled as qualified sensor.And disclose a kind of interference environment sensor fault labeling method and partial failure sensor effective percentage computational methods.
Description
Technical field
The present invention relates to sensor technical field, be specifically related to the breakdown judge side of a kind of interference environment lower sensor
Method, interference environment lower sensor fault flag method and partial failure sensor effective percentage computational methods.
Background technology
Work long hours at sensor, be easier to break down, properly functioning in order to ensure equipment or system, it is necessary to
Often carry out sensor checking verification, find sensor fault in time, in order to take the adequate measures such as replacing, correction.But
It is that sensor fault often has certain disguise and fraudulence, is sometimes difficult to find.
Sensor fault judge mark method, in addition to conventional employing human at periodic intervals calibrates, on May 6th, 2015, by
State gongwu academy chemical materials institute Cao Zhi is big, Liang Xiaohui et al. disclosed " system that sensor fault judges and
Method " open (bulletin) number CN104596564A, disclose the system and method that a kind of sensor fault judges, according to necessarily adopting
The measured value of sample frequency collection sensor；According to described measured value, calculate the meansigma methods of measured value in storehouse respectively, to meansigma methods
Arrange according to size of data, and calculate the maximum difference of meansigma methods；If maximum difference is higher than certain value, then judge sensor event
Barrier；If certain measured value of certain sensor is output as full value, also judge sensor fault.Cigarette on March 23rd, 2016 Henan
Industry Co., Ltd Xu's twilight disclosed " a kind of sensor fault automatic judging method and system " open (bulletin) number
CN105425775A, disclosed a kind of sensor fault automatic judging method, comprise the following steps: set up a sensor fault and sentence
Disconnected data base, when described data base includes the sequence number corresponding to each sensor, minimax range value scope, normal work
Average range and normal variation amplitude；Obtain the detection data in multiple sensor settings times in real time；According to obtaining
Detection data in multiple sensor settings times come the minimax range value scope corresponding with described data base and normal work
Average range and normal variation amplitude when making compare, to judge that the plurality of sensor is the most faulty and the class of fault
Not.
Technique scheme, to using environment to have under certain limitation, especially interference environment, sensor is by on-the-spot each
Planting interference effect, data distortion phenomenon ratio is more serious, due in normal data, containing substantial amounts of interference data.Tighter in interference
In the case of Chong, it is difficult to normally judge sensor fault.
Summary of the invention
In fields such as automation control system, artificial environment system, robot operating system, environment friendly systems, if system
In sensor occur that performance degeneration, fault or inefficacy find, the most in time by follow-up monitoring, control, fault diagnosis
Have a strong impact on etc. bringing, make the reliability of system reduce, even produce maloperation, false alarm, cause immeasurable loss.
Owing to a lot of equipment are all configured with the sensing under the device of soft initiator, converter etc. high interference, interference environment
Device is in electromagnetic field environment complicated and changeable, and the data acquisition of sensor, to carry, process, store be all at high-interference environment
In carry out, interference signal many and chaotic, can say almost without any rule.If using technical side disclosed in CN105425775A
Case, is necessarily mixed into substantial amounts of interference data, according to this in " obtaining the Monitoring Data in multiple sensor settings times in real time "
The technical scheme fault judgment method (description 005 section) to sensor, " when the detection data of any sensor any time have
When exceeding the minimax range value scope of correspondence, it is judged that described sensor is the absolute failure type need to overhauled or change ", several
All of sensor all can be judged to fault sensor." system and method that sensor fault judges " open (bulletin) number
The technical scheme of CN104596564A is to use the sensor of more than two, calculates the measurement data in intervals respectively
Meansigma methods, be made whether to judge normally to its meansigma methods size and difference each other, can also under interference environment
Use, but owing to interference data and normal data have been involved in the calculating of meansigma methods, therefore be likely to cause misjudgment, pass
The precision of sensor breakdown judge is the highest.
It addition, the sensor that part is broken down or the sensor broken down once in a while, one can only be made now
Analyze qualitatively, the most do not find a kind of can be with quantitative response sensor fault amount or the method for effective dose.It is true that
Under high-interference environment, sensor is interfered and makes a mistake once in a while is absolute, the interference data produced because of interference, also
Can a kind of fault at last, we are concerned about whether sensor creates fault, not equal to we are more concerned with fault rate
Size in other words conj.or perhaps efficient size, effective percentage is a kind of dimensionless, and all it doesn't matter with the shape size of sensor, with
The function of sensor, purposes, significance level are relevant.
To this end, inventor team is through substantial amounts of research, it is proposed that a kind of simple and convenient and interference environment that precision is higher
Sensor fault judge mark method: first screening and filtering falls underproof data, and the quantity of statistics valid data, during according to unit
Interior effective quantity is how many, analyzes quantitatively and judges that sensor is the most faulty, is marked various faults, basis at this
On can calculate the efficient size of sensor.
Interference environment sensor fault determination methods, it is characterised in that: a. sets up sensor database, setting sensor number
According to maximum virtual value and minimum virtual value；B. after screening and filtering, the valid data that storage sensors gathers；C. significant figure is added up
According to quantity, according to the quantity of valid data of storage in the unit interval, it is judged that whether sensor there occurs fault.Namely
Say: pre-build a sensor database, set the bound of each sensing data, the data of sensor acquisition, logical
Cross data mapping comparison or other convenient technical process screen, cross and filter to remove number of non-compliances evidence, then add up qualified data
Quantity, according to the valid data quantity of storage in the unit interval, whether comprehensive descision sensor there occurs fault.
Interference environment sensor fault labeling method, on the basis of all the sensors has connected, follows the steps below:
Use the Sybases such as Access, SQL Server, Oracle the most in advance, or sensor integration data set up by other softwares
Storehouse, sensor, in basic database, has the maximum virtual value of more than one correspondence and minimum virtual value, according to user's need
Want, it is also possible to increase by more than 1 layer, the upper limit or the alarm value of lower limit；B. by using the self-editing dedicated programs such as VB, V++
Or other general Data Analysis Services systems, in sensing data and the basic database that will gather, respective sensor is
Big virtual value and minimum virtual value contrast；C. the sensing data of over range is invalid data, and automatic screening filters, to subtract
Few data processing amount；D. the sensing data being between maximum virtual value and minimum virtual value is valid data, stores；
E. the valid data quantity of sensor is added up；F. the valid data quantity in the unit interval is less than the biography of minimum quantity setting value
Sensor, is labeled as failure of removal sensor；G. to the valid data quantity in the unit interval more than minimum quantity setting value, but do not have
There is the sensor reaching normal number value, be labeled as the fault sensor of the partial failures such as droop, from valid data quantity
Ratio with normal number value, it is also possible to the most intuitively arrive the departure degree of sensor fault；H. having in the unit interval
Effect data bulk, more than the sensor of normal number value, is labeled as qualified sensor, and i. further analyzes, by efficient database
Comprehensively analyze with time dimension, it appeared that drifting fault or droop fault.
In addition to the above method, it is possible to use the special-purpose softwares such as configuration software carry out data process, configuration software is utilized
Process function in middle historical data base, carries out data screening filtration；Some special with data processing function can also be used
Data collecting plate card, gather transmission time, as required to gather data automatically filter.Like this, above-mentioned
The step of sensor fault labeling method is reduced to: a. previously according to sensor in basic database corresponding maximum effectively
Value and minimum virtual value, set the filtercondition of sensing data；B. the sensing data of over range is invalid data, automatically
Screening and filtering, to reduce data processing amount；C. the sensing data being between maximum virtual value and minimum virtual value is effective
Data, deliver to data base, store；D. the valid data quantity of sensor is added up；E. to the valid data in the unit interval
Quantity, less than the sensor of minimum quantity setting value, is labeled as failure of removal sensor；F. to the valid data in the unit interval
Quantity is more than minimum quantity setting value, but is not reaching to the sensor of normal number value, is labeled as the partial failures such as droop
Fault sensor, from the ratio of valid data quantity Yu normal number value, it is also possible to the most intuitively arrive sensor fault
Departure degree；G. the valid data quantity in the unit interval is more than the sensor of normal number value, is labeled as qualified sensor.
For partial failure sensor, it is necessary first to judge the failure degree i.e. effective percentage of sensor, in order to intuitively
The duty solving sensor and the size of the probability broken down, consequently facilitating decide whether to change in time or tie up
Repair.The sensor that effective percentage is the least, may be interference once in a while, can disregard and be continuing with；And effective percentage is bigger
Sensor, it is likely that be to occur in that deviation, needs to keep a close eye on, and changes or maintenance suitable time；And for those
The sensor that effective percentage is the biggest, then should change as early as possible.Partial failure sensor effective percentage computational methods comprise the following steps: a.
Setting up sensor integration data base, sensor, in basic database, has a corresponding maximum virtual value and minimum virtual value；
B. the sensor total data quantity of statistics gatherer；C. the sensing data gathered and the maximum of respective sensor in basic database
Virtual value and minimum virtual value contrast；D. the sensing data of over range is invalid data, and automatic screening filters；E. it is in
Sensing data between maximum virtual value and minimum virtual value is valid data, stores；F. the effective of sensor is added up
Data bulk；G. the ratio of the sensor total data quantity of the valid data quantity in the unit of account time and collection.
The fault judgment method of above-mentioned interference environment lower sensor, interference environment lower sensor fault flag method, with
And in partial failure sensor effective percentage computational methods, in order to reduce data processing amount, sensor preprocessed data can be set up
Storehouse, the maximum sampling value of setting sensor data and minimum sample value, before sensing data processes, to the sensing collected
Device primary data carries out pretreatment, removes the data of apparent error.The maximum sampling value of sensing data, the equipment that refers to is static
In state, normal course of operation, during misoperation (i.e. equipment runs abnormal), the data of sensor acquisition are likely to be breached
Maximum；The minimum sample value of sensing data, the equipment that refers in resting state, normal course of operation, misoperation (i.e.
Equipment runs abnormal) during, the minima that the data of sensor acquisition are likely to be breached.Sensor number through pretreatment
According to, filter the part data that sensor is substantially abnormal, i.e. left and be between maximum sampling value and minimum sample value
Data, had both included the valid data that equipment is properly functioning, also included the valid data of equipment irregular operating, also included that equipment is transported
Row is normal but invalid data during sensor failure.Then it is further carried out at the analysis described in claim 1,2,3
Reason.
More than judge that sensor fault method, mark sensor fault method, the effective percentage of partial failure sensor calculate
Method, has the simple and convenient quick advantage such as the most accurately and reliably, is suitable under various environmental conditions using, is particularly suitable under interference environment
Use.
Detailed description of the invention
Detailed description of the invention one: in the field of Environment Protections such as sewage disposal system, the kind of sensor and quantity all compare many,
Include sewage and the flow of liquid medicine, temperature, pH value etc..The data of sensor are transported to industry control electricity by data collecting card
Brain, pre-builds a sensor database in the configuration software in computer, set the bound of each sensing data, passes
The data that sensor gathers, map comparison by data and screen, cross and filter to remove number of non-compliances evidence, write by qualified valid data
Enter in data base, then add up the quantity of qualified data.According to the valid data quantity of storage in data base in the unit interval, and set
Fixed desired value compares, it is judged that whether sensor there occurs fault.
Detailed description of the invention two: in the artificial environmental areas such as central air-conditioning, the kind of sensor and quantity are also a lot, bag
Include the heat source side on water pipe and the flow of air-conditioning side, temperature, pressure, room conditioning air blow and return temperature, air quantity, blast, cleaning
Degree, CO2 concentration etc..Interference environment sensor fault labeling method, in central air conditioner system installation, and all the sensors
Connected on the basis of, follow the steps below: a. uses oracle database in advance, sensor, in basic database, has
The maximum virtual value of more than one correspondence and minimum virtual value；B. by using exclusive data self-editing for VB to analyze and process system
System, by the sensing data of collection with in basic database respective sensor maximum virtual value and minimum virtual value carry out right
Ratio；C. the sensing data of over range is invalid data, and automatic screening filters, to reduce data processing amount；D. it is in maximum to have
Sensing data between valid value and minimum virtual value is valid data, stores；E. the valid data number of sensor is added up
Amount；F. to the valid data quantity in the unit interval less than the sensor of minimum quantity setting value, it is labeled as failure of removal sensing
Device；G. to the valid data quantity in the unit interval more than minimum quantity setting value, but it is not reaching to the sensing of normal number value
Device, is labeled as the fault sensor of the partial failures such as droop, from the ratio of valid data quantity Yu normal number value, also may be used
The most intuitively to arrive the departure degree of sensor fault；H. the valid data quantity in the unit interval is more than normal number value
Sensor, for qualified sensor, i. further analyzes, and efficient database and time dimension is comprehensively analyzed, permissible
Find drifting fault or droop fault.
Detailed description of the invention three: in the artificial intelligence fields such as robot, the kind of sensor and quantity are also a lot, including
Tactile sensor, vision sensor, proximity scnsor and hearing transducer etc..Below to say as a example by feeling
Bright, proximity scnsor includes optical sensor, baroceptor, ultrasonic sensor, current vortex sensor etc., main purpose
Shi Shi robot is moving or is knowing in operating process the degree of closeness of target (obstacle) thing, and mobile robot can realize keeping away
Barrier, operation robot can avoid the impact that object is caused by paw owing to closing speed is too fast.Many sensings in robot
In device, proximity scnsor belongs to an important class sensor, and Sensor section lost efficacy and may can also use reluctantly, but
It is if failure degree is too serious, then may cause than more serious consequence.Sensor effective percentage computational methods include following step
Rapid: a. sets up proximity scnsor basic database, each sensor in basic database, be both provided with one corresponding
Big virtual value and minimum virtual value；B. the sensor total data quantity of statistics gatherer；C. the sensing data gathered and basis number
Contrast according to the maximum virtual value of respective sensor in storehouse and minimum virtual value；D. the sensing data of over range is invalid number
According to, automatic screening filters；E. the sensing data being between maximum virtual value and minimum virtual value is valid data, deposits
Storage；F. the valid data quantity of sensor is added up；G. the valid data quantity in the unit of account time is total with the sensor of collection
The ratio of data bulk, this ratio is exactly the effective percentage of this sensor.If carried out data prediction, it is also possible to calculate single
The ratio of the sensor total data quantity through pretreatment of the valid data quantity in bit time and collection, this ratio is exactly
The effective percentage of this sensor.According to the purposes of robot, different judgment criteria can be set, as effective percentage at 90%-100% is
Operational excellence；Effective percentage is normal operation at 70%-90%；Effective percentage, needs to arrange as early as possible for can run reluctantly at 50%-70%
Maintenance；Effective percentage runs for limiting at 30%-50%, needs just can be continuing with after assessment；Effective percentage is not below 30%
Allowing to run, the effective percentage explanation sensor of less than 30% has occurred that the more serious fault of ratio, at this time runs and the most all may be used
Can collide etc. dangerous.
Detailed description of the invention four: at fault judgment method, the interference ring of the interference environment lower sensor described in this specification
In border lower sensor fault flag method and partial failure sensor effective percentage computational methods, the kind of sensor and quantity
All compare many, include flow, temperature, pH value, room conditioning air blow and return temperature, air quantity, blast, cleanliness factor, CO2 concentration, touch
Sense sensor, vision sensor, proximity scnsor and hearing transducer etc..The data of sensor are defeated by data collecting card
Delivering to the processors such as industrial PC, the configuration software in the processor such as computer etc. processes in software and pre-builds a sensor number
According to storehouse, set effective upper lower limit value of each sensing data, the data of sensor acquisition, map comparison by data and carry out
Screening, crosses and filters to remove number of non-compliances evidence, by qualified valid data write into Databasce, then adds up the quantity of qualified data.Root
According to the valid data quantity of storage in data base in the unit interval, compare with the desired value set, it is judged that whether sensor
There occurs fault.In order to reduce data processing amount, on the basis of the sensor breakdown judge, labelling, calculating, set up sensing
Device preprocessed data storehouse, the maximum sampling value of setting sensor data and minimum sample value, before breakdown judge, to collect
Sensor primary data carries out pretreatment, removes the data of apparent error.The maximum sampling value of sensing data, refers at equipment
In resting state, and the properly functioning running with misoperation, the maximum that sensor is likely to be breached；Sensing data
Minimum sample value, refer in equipment resting state, and in the running of properly functioning and misoperation, sensor may
The minima reached.
The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention, for the skill of this area
For art personnel, all within the spirit and principles in the present invention, any modification, equivalent substitution and improvement etc. made, all should comprise
Within protection scope of the present invention.
Claims (4)
1. interference environment sensor fault determination methods, it is characterised in that: a. sets up sensor database, setting sensor data
Maximum virtual value and minimum virtual value；B. after screening and filtering, the valid data that storage sensors gathers；C. valid data are added up
Quantity, according to the quantity of valid data, it is judged that whether sensor there occurs fault.
2. interference environment sensor fault labeling method, it is characterised in that comprise the following steps: a. sets up sensor integration data
Storehouse, sensor, in basic database, has a corresponding maximum virtual value and minimum virtual value；B. the sensing data gathered
Contrast with maximum virtual value and the minimum virtual value of respective sensor in basic database；C. the sensing data of over range
For invalid data, automatic screening filters, to reduce data processing amount；D. the biography between maximum virtual value and minimum virtual value it is in
Sensor data are valid data, store；E. the valid data quantity of sensor is added up；F. to the significant figure in the unit interval
Data bulk, less than the sensor of minimum quantity setting value, is labeled as failure of removal sensor；G. to the significant figure in the unit interval
Data bulk is more than minimum quantity setting value, but is not reaching to the sensor of normal number value, and the fault being labeled as partial failure passes
Sensor；H. the valid data quantity in the unit interval is more than the sensor of normal number value, is labeled as qualified sensor.
3. partial failure sensor effective percentage computational methods, it is characterised in that comprise the following steps: a. sets up sensor integration number
According to storehouse, sensor, in basic database, has a corresponding maximum virtual value and minimum virtual value；B. the sensing of statistics gatherer
Device total data quantity；C. the sensing data gathered is effective with maximum virtual value and the minimum of respective sensor in basic database
Value contrasts；D. the sensing data of over range is invalid data, and automatic screening filters；E. maximum virtual value and minimum it are in
Sensing data between virtual value is valid data, stores；F. the valid data quantity of sensor is added up；G. list is calculated
The ratio of the sensor total data quantity of the valid data quantity in bit time and collection.
4. according to the method described in claim 1 or 2 or 3, it is characterised in that: set up sensor preprocessed data storehouse, set and pass
The maximum sampling value of sensor data and minimum sample value, before sensing data processes, to the sensor initial number collected
According to carrying out pretreatment, remove the data of apparent error.
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| CN110319957A (en) * | 2019-06-25 | 2019-10-11 | 哈尔滨工程大学 | The irregular exceptional value method for diagnosing faults of Ship Structure stress monitoring system sensor |
| CN110531741A (en) * | 2019-09-11 | 2019-12-03 | 珠海格力电器股份有限公司 | Sensor fault recognition methods and storage medium |
| CN110688376A (en) * | 2019-09-27 | 2020-01-14 | 中冶赛迪重庆信息技术有限公司 | Temperature data cleaning method, system and equipment |
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| CN110906508A (en) * | 2019-12-09 | 2020-03-24 | 珠海格力电器股份有限公司 | Fault detection method and system for air conditioner sensor |
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| CN113607270A (en) * | 2021-07-07 | 2021-11-05 | 中广核工程有限公司 | Fault diagnosis method for steam turbine tile vibration sensor |
| CN110275170B (en) * | 2018-03-15 | 2021-12-03 | 郑州宇通客车股份有限公司 | Radar detection control method for vehicle and vehicle |
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