CN114637645A - Calibration method for sensor measurement data - Google Patents
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- CN114637645A CN114637645A CN202210173810.1A CN202210173810A CN114637645A CN 114637645 A CN114637645 A CN 114637645A CN 202210173810 A CN202210173810 A CN 202210173810A CN 114637645 A CN114637645 A CN 114637645A
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- G06F11/00—Error detection; Error correction; Monitoring
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Abstract
The invention discloses a method for verifying measured data of a sensor, which comprises the following steps: collecting the measurement data of different types of sensors in real time; modeling the measurement data of different types of sensors; establishing a sensor fault model based on fault characteristics of the sensor; selecting a corresponding intelligent verification algorithm according to different types of sensor measurement data; and establishing an alarm rule base and a data identification base, and giving an abnormal alarm and a sensor fault alarm to the measured data of the sensor. The invention can find the abnormity of the sensor measurement data in time, is used for sensor fault diagnosis and data accuracy of production control decision, does not need to add a new measuring point of the sensor, has low cost and reduces the operation and maintenance workload.
Description
Technical Field
The invention relates to the field of safety detection of industrial production, in particular to a calibration method of sensor measurement data.
Background
Various sensors of electric, temperature, pressure, vibration and the like in an industrial field basically work in severe environments of high temperature, high humidity, dust, noise and the like, and an industrial control system needs to acquire data of various sensors deployed in different scenes and different devices in a production field and is used for monitoring and deciding a production process. Sensor/smart meter failures due to harsh field environments occur at times. If the wrong measurement data is light, the statistical measurement is wrong, the performance of the industrial production control system is reduced, and if the wrong measurement data is wrong, the production operation decision is wrong, and the production is interrupted or even a safety accident is caused. When the traditional system is used for solving the problem, a threshold detection method is adopted, and abnormal acquisition is reported when acquired data exceeds a set threshold range interval. However, the method is not suitable for industrial systems with complex working conditions and variable load moments; there are alternative sensors that are more reliable, expensive and fail-safe.
In the prior art, a method for selecting hardware redundancy is also provided, namely a plurality of sensors are arranged near a measuring point of a certain part of certain equipment to measure the same parameter, but the method needs to set calculation rules of the plurality of sensors in advance, and the equipment is complex and increases the cost.
Disclosure of Invention
In view of the above technical problems, embodiments of the present invention provide a method for verifying sensor measurement data, which can detect sensor measurement data anomalies and ensure data accuracy for production control decisions.
The embodiment of the invention provides a calibration method for sensor measurement data, which is used for collecting the measurement data of different types of sensors in real time; modeling the measurement data of different types of sensors; establishing a sensor fault model based on fault characteristics of the sensor; selecting a corresponding intelligent verification algorithm according to different types of sensor measurement data; and establishing an alarm rule base and a data identification base, and giving an abnormal alarm and a sensor fault alarm to the measured data of the sensor.
Optionally, the step of modeling the measurement data of the different types of sensors specifically includes: for different types of sensors, sensor modeling is carried out from three dimensions of a measuring object, a working type and an output signal.
Optionally, the method further comprises: the sensor is modeled through three dimensions, and the range boundary of the sensor for acquiring signals is determined.
Optionally, the sensor fault module includes at least one of a functional failure fault type, a constant deviation fault type, a drift deviation fault type, a precision degradation fault type, an over-range saturation fault type, and a spike fault type.
Optionally, the method further comprises: and establishing a sensor data checking algorithm library, wherein the checking algorithm library comprises intelligent checking algorithms of various signal processing mechanisms.
Optionally, the check algorithm library at least includes one of a state estimation method, a parameter estimation method, an equivalent space method, a correlation function method, a wavelet analysis method, a principal component analysis method, a multiple linear regression method, and a partial least squares method.
Optionally, the data identification library tags different ones of the measured data.
Optionally, the step of establishing a sensor fault model based on the fault characteristics of the sensor further includes: identifying whether the sensor data has different types of fault characteristics through a sensor fault model, and marking the fault type of the fault data; and selecting a corresponding intelligent verification algorithm for verifying the data marked by the fault type.
Optionally, the step of selecting a corresponding intelligent verification algorithm for verifying the data marked by the fault type includes: performing prediction correction on the abnormal data, and outputting the corrected data; and labeling the corrected data according to a data identification rule, and simultaneously recording the corrected data before and after correction.
According to the technical scheme provided by the embodiment of the invention, the measurement data of different types of sensors are collected in real time, the measurement data is modeled, the sensor fault model is established based on the fault characteristics of the sensors, the corresponding verification algorithm is selected according to the measurement data, the alarm rule base and the data identification base are established, and the alarm is given to the abnormity of the measurement data.
Drawings
Fig. 1 is a schematic flow chart illustrating a method for verifying sensor measurement data according to an embodiment of the present invention.
FIG. 2 is a mathematical logic diagram of a sensor fault model in an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a method for verifying measured data of a sensor, please refer to fig. 1, which comprises the following steps:
and step S10, collecting the measurement data of different types of sensors in real time.
In step S20, metrology data for different types of sensors is modeled.
In one embodiment of the present invention, the step of modeling the measurement data of the different types of sensors specifically includes:
for different types of sensors, sensor modeling is carried out from three dimensions of a measuring object, a working type and an output signal. The sensor is modeled through three dimensions, and the range boundary of the sensor for acquiring signals is determined.
Wherein, the measuring objects comprise displacement, force, speed, temperature, flow, gas composition, voltage, current and the like; the working types comprise resistance type, capacitance type, inductance type, voltage type, Hall type, photoelectric type, grating type, thermocouple type and the like; the output signals comprise analog signal types (further subdivided into 4-20 mA types, 0-5V voltage types and the like), pulse signal types, digital code types, switch signal types and the like; determining the range boundary of the sensor for acquiring signals through three-dimensional modeling; the present invention supports the addition of new sensor models that are not in the sensor model library.
For example: after the three-dimensional modeling of the transformer for collecting voltage in the industrial field, the information of three dimensions is as follows:
the model of the sensor is: a transformer;
the measurement object is as follows: a secondary voltage;
the working type is as follows: a capacitance type;
outputting a signal: 0 to 5V.
According to the characteristics of the sensor model, corresponding acquisition rules are built in the system. The capacitor voltage transformer is characterized in that: faults such as ferromagnetic resonance and the like caused by saturation of an iron core of the electromagnetic voltage transformer can be prevented; the output capacity is small, the factors influencing the error are more (such as temperature, frequency and the like), and the precision is poorer than that of the electromagnetic voltage transformer; the transmission signal is used for transmitting a secondary voltage of 100V into a voltage of 0-5V.
Based on this feature, the data acquisition boundary is determined as: data acquisition range: 0-5V (abnormal or fault if the out-of-range is exceeded).
In step S30, a sensor fault model is established based on the fault characteristics of the sensor.
In one embodiment of the present invention, the step of establishing a sensor fault model based on the fault characteristics of the sensor further includes:
identifying whether the sensor data has different types of fault characteristics through a sensor fault model, and marking the fault type of the fault data;
and selecting a corresponding intelligent verification algorithm for verifying the data marked by the fault type.
The invention establishes a sensor fault model based on the fault characteristics of common faults of a sensor, wherein the sensor fault model comprises the following steps: a functional failure fault model, a constant deviation fault model, a drift deviation fault model, a precision descent fault model, an over-range saturation fault model, a spike-type fault (instantaneous sampling failure) model, etc.
Constant deviation fault: the fault is a constant deviation fault if the difference between the measured value and the true value of the sensor is a constant;
drift bias fault: are a type of fault in which the difference between the measured value and the true value of the sensor changes with time.
Failure to function fault (complete fault): when the sensor suddenly fails during measurement, and the measured value is always a constant, the fault is a functional failure fault.
Accuracy degradation failure: the measurement capability of the sensor becomes poor and the accuracy becomes low. When the accuracy level is lowered, the measured average value is not changed, but the measured variance is changed.
And (3) over-range fault: when data exceeding the measuring range of the sensor occurs during measurement, the sensor is saturated and the like, and the data is displayed as the situation of a maximum value or a minimum value in the measuring range.
Instantaneous sampling failure: when the measuring signal suddenly changes due to impact (such as lightning overvoltage, load sudden change and the like) and the like during measurement, the sensor fails to transmit in time or fails to transmit, so that instantaneous sampling failure faults are caused.
Through the establishment of a sensor fault model, different types of sensor faults can be identified, different mutual inductor maintenance strategies and different data verification and correction strategies are selected based on the fault types, and the data accuracy for production control decision is guaranteed; the invention supports the addition of new sensor fault models that are not in the fault model library.
Each of the above fault models may be described by mathematical logic rules, such as: constant deviation: x' + dy, dy representing a constant difference from the true value, possibly representing a positive or negative offset in a particular scenario; drift deviation: x' + dy (t), dy (t) is the difference between the measured value and the true value, and changes with the increase of time; and (4) functional failure: x ═ f, f is a fixed value that exceeds a threshold value; and (3) precision reduction: the variance value of the continuous sampling values is larger than the average value and keeps fluctuating within a certain range, but the variance value exceeds the fluctuation range.
And step S40, selecting a corresponding intelligent verification algorithm according to the measurement data of the sensors of different types.
And establishing a sensor data verification algorithm library according to the signal processing and intelligent verification algorithm resources, wherein the verification algorithm library comprises intelligent verification algorithms of various signal processing mechanisms. Specifically, the check algorithm library includes a state estimation method, a parameter estimation method, an equivalent space method, a correlation function method, a wavelet analysis method, a principal component analysis method, a multiple linear regression method, a partial least square method, and the like.
The state estimation method comprises the steps of reconstructing the process state of an object, comparing the process state with measurable variables to obtain residual errors, and detecting faults from the residual error sequence by constructing a proper model and utilizing statistical verification.
The parameter estimation method detects and separates faults according to model parameters and corresponding physical parameter changes.
The equivalent space method utilizes the linear correlation in the system dynamic equation, and the residual error sequence is formed by the difference between the measured value and the linear combination value of the rest measured values. And deducing the fault state according to the difference between the abnormal value and the normal value of the system output.
The wavelet analysis method is to analyze the non-stationary time sequence, separate the trend term, the period term and the random term, analyze and predict respectively, and synthesize to obtain the predicted value of the original time sequence.
The partial least square method is used for compressing data and extracting information of a system with serious correlation, and projecting training data from a high-dimensional space to a low-dimensional space to reduce the number of input variables of a model.
The algorithm can be used for fault feature identification, for example, a wavelet analysis method is suitable for diagnosing instantaneous sampling failure faults; the partial least squares method is more efficient for sensors that output multiple sets of correlated data. The state estimation method is high in calculation speed and suitable for most of initial fault identification scenes. In a specific embodiment, it may be considered that the fault result information is comprehensively displayed after all fault models are traversed, and an algorithm may also be selectively configured.
Step S50, an alarm rule base and a data identification base are established, and abnormal alarm and sensor fault alarm are given to the measured data of the sensor.
The alarm rule base provides abnormal alarm and sensor fault alarm of sensor measurement data. The data identification library marks different data in the measurement data. The invention supports the addition of alarm rules and data identifications which are not in the rule base and the identification base.
The method for verifying the sensor measurement data is applied to an intelligent verification system, the intelligent verification system comprises a sensor model base, a sensor fault model base, an intelligent verification algorithm base, an alarm rule base and a data identification base, and a basic module is configured according to the condition of a sensor which is actually accessed.
The method comprises the steps of collecting measurement data of different types of sensors in real time, modeling the measurement data of the different types of sensors, establishing a sensor model base, determining a characteristic threshold value of the sensor model base according to a data collection boundary determined by the sensor model base, preliminarily judging whether the measurement data are normal or not, labeling abnormal data, and entering the next diagnosis process regardless of whether the data are normal or not. For example, the electromagnetic voltage transformer has a transmission signal range of 0-5V, and can be set with a characteristic threshold value: the lower limit is 0V and the upper limit is 5V. After the operation is carried out for a period of time, the numerical state of the device is known to fluctuate by not more than 0.5V at about 4V, and the device can be further set to be 3.5V-4.5V.
Identifying whether it has different types of fault signatures via a library of sensor fault models: carrying out abnormal rule diagnosis on abnormal data in the previous process, and diagnosing the fault type by combining the characteristics of a fault model library; and (4) carrying out fault type diagnosis on the normal data in the previous step according to the fault flow related to the fault model library, if the fault occurs, marking the fault type, and if the fault occurs, marking the data to be normal.
The data marked with the fault type enters an intelligent verification link and an alarm and data identification link, and the specific steps are as follows:
a) and selecting a corresponding intelligent verification algorithm by combining the fault types, predicting and correcting the abnormal data, and outputting the abnormal data as corrected data.
b) And generating sensor abnormity and fault alarm information by combining the set alarm rules.
c) And labeling the corrected data according to a data identification rule, recording the corrected data before and after correction, and calling the required data by a subsequent module according to the functional requirement.
The method can be deployed at an edge end, can also be deployed at a system end, and can be integrated in an edge acquisition device or platform software based on modular deployment.
According to the invention, by arranging the calibration method of the sensor measurement data applied to the industrial scene, the problem of abnormal acquisition of the mutual inductor can be solved, the acquisition problem of the mutual inductor can be positioned in time, the abnormal mutual inductor can be early warned in time, the abnormal or wrong data can be intelligently calibrated and corrected, and the data accuracy for production control decision can be ensured.
The method can find the abnormality of the measured data of the sensor in time, is used for the data accuracy of production control decision, does not need to add a new measuring point of the sensor, has low cost, is suitable for various industrial scenes, is used for fault diagnosis of the sensor, can quickly find the fault of the sensor and reduces the workload of operation and maintenance.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. A method for verifying sensor measurement data, the method comprising:
collecting the measurement data of different types of sensors in real time;
modeling the measurement data of different types of sensors;
establishing a sensor fault model based on fault characteristics of the sensor;
selecting a corresponding intelligent verification algorithm according to different types of sensor measurement data;
and establishing an alarm rule base and a data identification base, and giving an abnormal alarm and a sensor fault alarm to the measured data of the sensor.
2. A method for verifying measured data of a sensor according to claim 1, wherein said step of modeling measured data of different types of sensors comprises:
for different types of sensors, sensor modeling is carried out from three dimensions of a measuring object, a working type and an output signal.
3. A method of verifying sensor metrology data as claimed in claim 2, further comprising:
the sensor is modeled through three dimensions, and the range boundary of the sensor for acquiring signals is determined.
4. A method for verifying sensor metrology data as claimed in claim 1 wherein said sensor fault module comprises at least one of a functional failure fault type, a constant offset fault type, a drift offset fault type, a loss of precision fault type, an over-range saturation fault type, and a spike fault type.
5. A method of verifying sensor measurement data as claimed in claim 1, further comprising:
and establishing a sensor data checking algorithm library, wherein the checking algorithm library comprises intelligent checking algorithms of various signal processing mechanisms.
6. The method of claim 5, wherein the calibration algorithm library comprises at least one of a state estimation method, a parameter estimation method, an equivalent space method, a correlation function method, a wavelet analysis method, a principal component analysis method, a multiple linear regression method, and a partial least squares method.
7. The method of claim 4, wherein the data identifier library marks different measured data.
8. A method of validating sensor measurement data as claimed in claim 1, wherein the step of modeling the sensor fault based on the fault signature of the sensor further comprises:
identifying whether the sensor data has different types of fault characteristics through a sensor fault model, and marking the fault type of the fault data;
and selecting a corresponding intelligent checking algorithm for checking the data marked by the fault type.
9. The method for verifying measured data of a sensor according to claim 8, wherein the step of selecting the corresponding intelligent verification algorithm for verifying the data with the fault type flag comprises:
carrying out prediction correction on the abnormal data and outputting the corrected data;
and labeling the corrected data according to a data identification rule, and simultaneously recording the data before and after correction.
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