CN117554109A - Intelligent monitoring method and system for fault data information of heat exchanger - Google Patents
Intelligent monitoring method and system for fault data information of heat exchanger Download PDFInfo
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Abstract
The invention relates to the technical field of heat exchanger fault detection, in particular to an intelligent heat exchanger fault data information monitoring method and system. Firstly, acquiring fluid speed data of a fluid pipeline of a heat exchanger and fluid temperature data at different positions; carrying out local fluctuation analysis on the fluid temperature data to obtain the mutation degree of the fluid temperature data; determining the influence degree value of the fluid speed data on the fluid temperature data according to the corresponding relation between the fluid temperature data and the fluid speed data; determining the possibility of abnormality of the fluid temperature data according to the difference between the fluid temperature data at different positions, the fluctuation degree of the fluid speed data and the influence degree value of the fluid speed data on the fluid temperature data; and judging whether the heat exchanger fails or not by combining the mutation degree and the abnormality probability. The invention improves the accuracy of fault monitoring.
Description
Technical Field
The invention relates to the technical field of heat exchanger fault detection, in particular to an intelligent heat exchanger fault data information monitoring method and system.
Background
A heat exchanger is a device for transferring heat between two or more fluids. Its main purpose is to achieve heating or cooling of the fluid by heat transfer. Heat exchangers are widely used in various industrial processes, manufacturing and construction fields for energy transfer, such as heating and cooling, in many industries and production processes. The abnormal monitoring of the heat exchanger can ensure that the heat exchanger operates in an optimized mode, so that the energy consumption is reduced, and the operation cost is reduced; and the anomaly monitoring can help discover and identify the performance degradation or abnormal conditions of the heat exchanger as early as possible. By finding problems in time, corrective action can be taken, thereby improving the efficiency and performance of the system.
At present, a common method for monitoring the abnormality of the heat exchanger is to install a temperature sensor and a flow rate sensor at the inlet and outlet of a heat exchanger pipeline to monitor relevant data such as efficiency of the heat exchanger, but in the monitoring process, because the input collected fluid speed and the actual speed have certain difference, certain fluctuation can be generated in the fluid temperature data which is monitored and output, and in the process of detecting abnormal data corresponding to original temperature data, the temperature data can also change to a certain extent due to fault data, so that when the abnormal data in the original data is detected, the data representing the fault is misjudged to be normal data fluctuation, and the accuracy of fault detection is reduced.
Disclosure of Invention
In order to solve the technical problem that the accuracy is low when the heat exchanger is subjected to fault monitoring because the monitored data possibly have certain differences with actual data in the process of fault monitoring of the heat exchanger, the invention aims to provide an intelligent monitoring method and system for heat exchanger fault data information, and the adopted technical scheme is as follows:
in a first aspect, an embodiment of the present invention provides a method for intelligently monitoring fault data information of a heat exchanger, including the following steps:
acquiring fluid velocity data of a fluid pipeline of the heat exchanger and fluid temperature data at different positions;
carrying out local fluctuation analysis on the fluid temperature data to obtain the mutation degree of the fluid temperature data;
determining the influence degree value of the fluid speed data on the fluid temperature data according to the corresponding relation between the fluid temperature data and the fluid speed data;
determining the possibility of abnormality of the fluid temperature data according to the difference between the fluid temperature data at different positions, the fluctuation degree of the fluid speed data and the influence degree value of the fluid speed data on the fluid temperature data;
and judging whether the heat exchanger fails or not by combining the mutation degree and the abnormality probability.
Preferably, the local fluctuation analysis is performed on the fluid temperature data to obtain the mutation degree of the fluid temperature data, which includes:
and constructing a window corresponding to each fluid temperature data by taking each fluid temperature data as a center, and determining the mutation degree of each fluid temperature data according to the numerical value difference between each adjacent fluid temperature data in the window corresponding to each fluid temperature data.
Preferably, the calculation formula of the mutation degree of the fluid temperature data is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The degree of mutation for the ith fluid temperature data; />The difference between the ith fluid temperature data and the fluid temperature data at the last time; />The maximum value of the difference of all adjacent fluid temperature data in the window corresponding to the ith fluid temperature data; />The number of the fluid temperature data in the window corresponding to the ith fluid temperature data; />Is the difference between the (r) th fluid temperature data and the last fluid temperature data except the (i) th fluid temperature data in the window corresponding to the (i) th fluid temperature data.
Preferably, the calculation formula of the influence degree value is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The fluid velocity data at the c-th moment is the influence degree value of the fluid temperature data; />Fluid velocity data at time c; />Fluid temperature data at time c; />Is the average of the ratio of the fluid temperature data to the fluid velocity data at all acquisition times.
Preferably, the determining the abnormality possibility of the fluid temperature data according to the difference between the fluid temperature data, the fluctuation degree of the fluid velocity data, and the influence degree value of the fluid velocity data on the fluid temperature data includes:
taking a temperature sensor corresponding to any fluid temperature data as a target sensor, taking other any sensors as sensors to be selected, and determining a temperature affected value between the target sensor and the sensors to be selected according to the distance between the target sensor and the sensors to be selected;
combining the difference of the fluid temperature data of the target sensor and the fluid temperature data of the sensor to be selected at different times to determine the temperature difference degree of the target sensor and the sensor to be selected;
determining a temperature anomaly value of the fluid temperature data of the target sensor by combining the temperature affected value and the temperature difference degree of the fluid temperature data;
determining a speed anomaly value of the fluid speed data at the same time as the fluid temperature data according to the fluctuation degree of the fluid speed data and the influence degree value of the fluid speed data on the fluid temperature data;
determining the abnormality probability of the fluid temperature data according to the temperature difference degree and the speed abnormality value, wherein the temperature difference degree and the speed abnormality value are in a proportional relation with the abnormality probability.
Preferably, the determining the degree of temperature difference between the target sensor and the candidate sensor by combining the difference between the fluid temperature data of the target sensor and the candidate sensor at different times includes:
the j-th temperature sensor is used as a target sensor, the r-th temperature sensor is used as a to-be-selected sensor, and the calculation formula of the temperature difference degree between the target sensor and the to-be-selected sensor is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>A degree of temperature difference that is fluid temperature data between the jth temperature sensor and the nth temperature sensor; />Fluid temperature data for the j-th temperature sensor; />Fluid temperature data for the r-th temperature sensor; />A difference value of fluid temperature data of the jth temperature sensor and the r temperature sensor at the current moment; />A difference value between fluid temperature data of the jth temperature sensor and the r temperature sensor at the kth time; m is the total duration of all time sampled data.
Preferably, the determining the temperature anomaly value of the fluid temperature data of the target sensor by combining the temperature affected value of the fluid temperature data and the degree of temperature difference comprises:
taking the product of the temperature affected value and the temperature difference degree of the target sensor and the sensor to be selected as a single temperature abnormality of the target sensor and the sensor to be selected;
and taking the sum value of the single temperature anomalies of the target sensor and all the sensors to be selected as the temperature anomaly value of the fluid temperature data of the target sensor.
Preferably, the determining the speed anomaly value of the fluid speed data belonging to the same time as the fluid temperature data according to the fluctuation degree of the fluid speed data and the influence degree value of the fluid speed data on the fluid temperature data includes:
acquiring a difference value between fluid speed data at the current moment and fluid speed data at the previous moment, and recording the difference value as fluctuation degree of the fluid speed data;
taking the influence degree value of the fluid speed data on the fluid temperature data as a numerator, taking the sum value of the fluctuation degree of the fluid speed data and a preset adjusting threshold value as a denominator, and taking the ratio value as the speed abnormal value of the fluid speed data which belongs to the same time as the fluid temperature data; wherein the preset adjustment threshold is a positive number less than 1.
Preferably, the step of determining whether the heat exchanger fails by combining the mutation degree and the abnormality probability includes:
taking a normalized value of the product of the mutation degree and the abnormality probability as a fault degree of the heat exchanger;
when the failure degree of the heat exchanger is larger than a preset failure threshold value, judging that the heat exchanger fails; and when the failure degree of the heat exchanger is smaller than or equal to a preset failure threshold value, judging that the heat exchanger fails.
In a second aspect, an embodiment of the present invention provides an intelligent heat exchanger fault data information monitoring system, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements the above-mentioned intelligent heat exchanger fault data information monitoring method when executing the computer program.
The embodiment of the invention has at least the following beneficial effects:
the invention relates to the technical field of heat exchanger fault detection. Firstly, acquiring fluid speed data and fluid temperature data of a fluid pipeline of a heat exchanger; the method has the advantages that the local fluctuation analysis is carried out on the fluid temperature data to obtain the mutation degree of the fluid temperature data, the local mutation level of the data is reflected by calculating the change of the data in the local part, the error judgment on the subsequent fault data identification caused by the fact that the degree of the change of the data cannot be accurately defined when the temperature data fluctuates is avoided, and the accuracy of data cleaning is improved; determining the influence degree value of the fluid velocity data on the fluid temperature data according to the corresponding relation between the fluid temperature data and the fluid velocity data, and calculating the influence degree of the fluid velocity on the fluid temperature data by combining the data change difference of the fluid temperature data at the same moment based on the general regularity of the fluid temperature data change of the plurality of temperature sensors; determining the possibility of abnormality of the fluid temperature data according to the difference between the fluid temperature data at different positions, the fluctuation degree of the fluid speed data and the influence degree value of the fluid speed data on the fluid temperature data, combining the possibility of abnormality of the fluid temperature data by multiple parties of data, judging the possibility of abnormality caused by the fault at the current moment by combining the calculated influence degree of the fluid speed on the data, avoiding the unreal influence on the change of monitoring data caused by the flow velocity, and improving the authenticity of the abnormality degree reflected by the change of the fluid temperature data under the influence of the flow velocity by only utilizing the inaccuracy of the data abnormality caused by the abnormal change of the single sensor data; and by combining the mutation degree and the abnormality possibility, judging whether the heat exchanger fails or not, and improving the accuracy of fault monitoring.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a method for intelligently monitoring fault data information of a heat exchanger according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects of the intelligent monitoring method and system for heat exchanger fault data information according to the invention in combination with the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The embodiment of the invention provides an intelligent heat exchanger fault data information monitoring method and a specific implementation method of a system. The scenario obtains fluid temperature data and fluid velocity data by a temperature sensor and a fluid flow sensor, respectively. In order to solve the technical problem that in the process of carrying out fault monitoring on the heat exchanger, the monitored data possibly have certain differences with actual data, so that the accuracy is low in the process of carrying out fault monitoring on the heat exchanger. The invention utilizes inaccuracy of data abnormal change caused by single sensor data abnormal change to improve the reality of the abnormal degree reflected by fluid temperature data change under the influence of flow velocity; and by combining the mutation degree and the abnormality possibility, judging whether the heat exchanger fails or not, and improving the accuracy of fault monitoring.
The invention provides a heat exchanger fault data information intelligent monitoring method and a system specific scheme by combining a drawing.
Referring to fig. 1, a flowchart of steps of a method for intelligently monitoring fault data information of a heat exchanger according to an embodiment of the present invention is shown, and the method includes the following steps:
in step S100, fluid velocity data and fluid temperature data at different locations of the fluid conduit of the heat exchanger are acquired.
A temperature sensor is installed in the fluid pipeline of the heat exchanger at a certain distance, and a fluid flow rate sensor is installed at a corresponding position. Data of the sensor within 10 minutes is collected and stored as historical data, wherein the temperature sensor data is used as fluid temperature data of the fluid pipeline, and the fluid flow rate sensor within 10 minutes is collected as fluid speed data of the fluid pipeline. In the embodiment of the invention, the data acquisition interval is acquired every 10 seconds, and in other embodiments, the acquisition interval can be adjusted by an implementer according to actual situations. It should be noted that, in the subsequent analysis, the current heat exchanger is only subjected to fault analysis based on the historical data within 10 minutes, and the excessive historical data may cause excessive calculation amount.
And step 200, carrying out local fluctuation analysis on the fluid temperature data to obtain the mutation degree of the fluid temperature data.
When the temperature sensor is used for collecting fluid temperature data, due to the change of the actual fluid flow speed, certain heat loss exists in the heat conduction process, and the fluid temperature data can generate certain fluctuation; the fluctuations appear as a degree of variance in the continuous time series data; however, as for the failure data, fluctuation of the temperature data also occurs, so that it is necessary to judge the abrupt change level of the data at any time.
Because the change of the fluid temperature data on the time sequence is relatively smooth, inaccurate data in the acquisition process is shown as abrupt change, namely, the more the data change is relatively prominent, the greater the degree of abnormality is, but for any one fluid temperature data, the degree of change of the fluid temperature data in the whole time sequence is analyzed to be easily ignored, so that the invention determines a corresponding window for each fluid temperature data, analyzes the change condition of the fluid temperature data in the corresponding window, and determines the degree of abrupt change of the fluid temperature data. Therefore, a window is constructed by taking the fluid temperature data at any moment as the center, the numerical value difference between each adjacent data point in the window is calculated, and the abrupt change level of the fluid temperature data at any moment is obtained. In the embodiment of the present invention, the length of the window is 7*1, i.e., the length is 7 and the width is 1. When it is difficult to construct a window having a size of 7 centered on the fluid temperature data due to insufficient data amount, the window is not constructed on the fluid temperature data, that is, the subsequent operation is not performed until the data amount is enough to construct a window having a size of 7 centered on the fluid temperature data.
Carrying out local fluctuation analysis on the fluid temperature data to obtain the mutation degree of the fluid temperature data, and specifically: and constructing a window corresponding to each fluid temperature data by taking each fluid temperature data as a center, and determining the mutation degree of each fluid temperature data according to the numerical value difference between each adjacent fluid temperature data in the window corresponding to each fluid temperature data.
The calculation formula of the mutation degree of the fluid temperature data is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The degree of mutation for the ith fluid temperature data; />The difference between the ith fluid temperature data and the fluid temperature data at the last time; />The maximum value of the difference of all adjacent fluid temperature data in the window corresponding to the ith fluid temperature data; />The number of the fluid temperature data in the window corresponding to the ith fluid temperature data; />Is the difference between the (r) th fluid temperature data and the last fluid temperature data except the (i) th fluid temperature data in the window corresponding to the (i) th fluid temperature data.
The ith fluid temperature data is the fluid temperature data at the ith moment.Reflecting->Maximum value of the trend difference>Is due to the maximum value of the difference +.>Is an extreme value, so when->The more tend to +>，The greater the probability of abrupt change of the corresponding fluid temperature data, the corresponding +.>The greater the degree of mutation in the ith fluid temperature data. />Is->Difference from other fluid temperature data in window, when +.>The larger the reflectionThe more prominent the corresponding ith fluid temperature data is, the greater the corresponding mutation probability is, the corresponding +.>The greater the degree of mutation in the ith fluid temperature data. />Reflecting +.>Differences from other fluid temperature data within the window, reflecting +.>The greater the value of the local salience over the time series, the greater the level of mutation corresponding to the ith data.
By calculating the change of the fluid temperature data in the local window so as to reflect the local mutation level of the fluid temperature data, the operation avoids the problem that the degree of the change of the data cannot be accurately defined when the fluid temperature data fluctuates, the subsequent misjudgment on the fault data identification is caused, and the accuracy of data cleaning is improved.
And step S300, determining the influence degree value of the fluid velocity data on the fluid temperature data according to the corresponding relation between the fluid temperature data and the fluid velocity data.
During the acquisition of the actual fluid temperature data, the fluid temperature is changed, i.e. the acquired fluid temperature data itself has a change in time sequence, which change is influenced by the flow rate. And it is known that the greater the fluid flow rate in the heat exchanger tubes, the less residence time of the fluid at the monitoring location, and the less heat exchanged, and the greater the change in temperature of the fluid at the monitoring location.
Thus, the monitored fluid temperature data change values have different abnormal manifestations, and the greater the flow rate, the greater the degree of change of the fluid temperature data itself, so the lower the manifestation of the change values on the degree of abnormality thereof. In combination with the effect of the heat exchanger on the flow rate, the greater the flow rate, the lower the manifestation of the change in fluid temperature data on its degree of anomaly.
Therefore, through the flow velocity of the fluid in the pipeline, the corresponding relation between the fluid temperature data and the fluid velocity data at any moment is acquired, and the influence of the flow velocity on the change of the temperature data is determined.
Namely, according to the corresponding relation between the fluid temperature data and the fluid speed data, determining the influence degree value of the fluid speed data on the fluid temperature data, and specifically: the calculation formula of the influence degree value is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The value of the vibration degree of the influence of the fluid speed data at the c-th moment on the fluid temperature data; />Fluid velocity data at time c; />Fluid temperature data at time c; />Is the average of the ratio of the fluid temperature data to the fluid velocity data at all acquisition times.
Wherein the fluid velocity data at time cThe larger the current fluid temperature is, the larger the change of the current fluid temperature is reflected, so the larger the fluid speed data is, the larger the fluctuation of the generated fluid temperature data is, at the moment, the normal fluctuation of the fluid temperature data does not belong to abnormal information, so the smaller the influence on the abnormal degree is, and the division formula is utilized>To represent. />Reflecting the change in raw data with respect to fluid velocity at the current time. />The difference between the change of the fluid temperature data relative to the fluid speed data at the current moment and the integral change is reflected, the larger the value is, the smaller the change of the fluid temperature data at the current moment is affected by the fluid speed data, so that the greater the abnormal expression degree of the monitored data change is, namely the greater the value of the vibration degree of the fluid speed data on the fluid temperature data is affected.
In the process of acquiring the vibration degree value of the influence of the fluid speed data on the fluid temperature data, the influence of the flow velocity in the heat exchanger pipeline on the temperature data change is considered, the fluid temperature data is acquired, and the influence on the fluid temperature data and the influence on the abnormality degree are also considered. The influence of the fluid speed data on the fluid temperature data is analyzed to obtain the normal variation amplitude of the fluid temperature data, so that the influence of the variation of the fluid temperature data on the degree of abnormality of the fluid temperature data under the influence of the flow velocity is determined, the misjudgment of the normal data variation of the fluid temperature data caused by the flow velocity is avoided, and the accuracy of the abnormality influenced by the data variation is improved.
Step S400, determining the possibility of abnormality of the fluid temperature data according to the difference between the fluid temperature data at different positions, the fluctuation degree of the fluid velocity data, and the influence degree value of the fluid velocity data on the fluid temperature data.
Because the structure of the heat exchanger consists of heat exchange metal and heat exchange pipelines, temperature sensors are uniformly distributed in the fluid pipelines, in theory, under the condition of fixed fluid flow rate, the difference between the fluid temperature data monitored by the sensor at any moment and the fluid temperature data monitored by other sensors should be smaller relative to the difference at all moments; when the flow rate in the heat exchange pipeline changes, the exchange and the transmission of the temperature are a continuous process; the fluid temperature change at the monitoring location is not a change in the fluid immediately upon a change in the flow rate, i.e., the temperature change is responsive to the change in the flow rate. Since different changes in current flow rate have different effects on air flow rate, the effect on temperature changes is different. Typically, as the flow rate increases, the increased velocity fluid is affected by the heat exchange metal from the input end, and then the increase in flow rate is slowly transmitted to the sensor mounting location, and then the monitored temperature change is increased; when the flow rate is reduced, the temperature of the fluid is caused to change more slowly, so that the difference of temperature data acquired by two adjacent sensors is reduced. Meanwhile, the response time is theoretically related to the distance between two adjacent sensors of the pipeline and the fluid flow rate, so that the degree of abnormality of the data at any one moment needs to be redetermined in combination with the analysis so as to determine the possibility of abnormality of the final fluid temperature data.
And acquiring fluid temperature data corresponding to all the sensors, acquiring temperature differences between the current moment and other sensors for the fluid temperature data of the current sensor, traversing all moments of the history data, and judging the possibility of abnormality caused by the change of the temperature data at the current moment.
Determining the possibility of abnormality of the fluid temperature data according to the difference between the fluid temperature data, the fluctuation degree of the fluid speed data and the influence degree value of the fluid speed data on the fluid temperature data, and specifically:
and step one, taking a temperature sensor corresponding to any fluid temperature data as a target sensor, taking other any sensors as to-be-selected sensors, and determining a temperature affected value between the target sensor and the to-be-selected sensors according to the distance between the target sensor and the to-be-selected sensors.
The method for acquiring the affected value of the temperature comprises the following steps: and performing inverse proportion normalization on the distance between the target sensor and the sensor to be selected, and taking the result value after the inverse proportion normalization as the temperature affected value between the target sensor and the sensor to be selected. In the embodiment of the invention, the temperature affected value between the target sensor and the sensor to be selected is realized by taking a natural constant as a base and taking the distance between the target sensor and the sensor to be selected as an exponential function of an index.
And step two, combining the difference of the fluid temperature data of the target sensor and the fluid temperature data of the sensor to be selected at different times to determine the temperature difference degree of the target sensor and the sensor to be selected.
Specific: the j-th temperature sensor is used as a target sensor, the r-th temperature sensor is used as a to-be-selected sensor, and the calculation formula of the temperature difference degree between the corresponding target sensor and the to-be-selected sensor is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>A degree of temperature difference that is fluid temperature data between the jth temperature sensor and the nth temperature sensor; />A difference value of fluid temperature data of the jth temperature sensor and the r temperature sensor at the current moment; />A difference value between fluid temperature data of the jth temperature sensor and the r temperature sensor at the kth time; m is the total duration of all time sampled data.
And step three, determining the abnormal value of the fluid temperature data of the target sensor by combining the affected value of the fluid temperature data and the temperature difference degree.
Firstly, taking the product of the temperature affected value and the temperature difference degree of the target sensor and the sensor to be selected as a single temperature abnormality of the target sensor and the sensor to be selected;
and taking the sum value of the single temperature anomalies of the target sensor and all the sensors to be selected as the temperature anomaly value of the fluid temperature data of the target sensor.
And step four, determining a speed anomaly value of the fluid speed data which belongs to the same time as the fluid temperature data according to the fluctuation degree of the fluid speed data and the influence degree value of the fluid speed data on the fluid temperature data.
The speed anomaly value acquisition method comprises the following steps: acquiring a difference value between fluid speed data at the current moment and fluid speed data at the previous moment, namely, the fluctuation degree of the fluid speed data;
taking the influence degree value of the fluid velocity data on the fluid temperature data as a numerator, taking the sum value of the waveform degree of the fluid velocity data and a preset adjusting threshold value as a denominator, and taking the ratio value as the velocity abnormal value of the fluid velocity data which belongs to the same time as the fluid temperature data. Wherein, the preset adjustment threshold is a positive number smaller than 1, and the preset adjustment threshold is used for avoiding the situation that the denominator is 0. In the embodiment of the present invention, the preset adjustment threshold is set to 0.001, and in other embodiments, the value may be adjusted by an implementer according to the actual situation, for example, the value of the preset adjustment threshold may also be set to 0.1.
Taking the jth temperature sensor as a target sensor and taking the jth temperature sensor as a candidate sensor, the calculation formula of the abnormal possibility of the fluid temperature data of the jth temperature sensor can be directly expressed as:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>An abnormal possibility of fluid temperature data at the present time of the jth temperature sensor; />Is the number of temperature sensors; />The number of temperature sensors other than the jth temperature sensor; />Is a natural constant; />For the jth temperature sensor and except for the jthDistance between the r-th temperature sensor other than the temperature sensor; />Is an affected value for the temperature between the jth temperature sensor and the r temperature sensor other than the jth temperature sensor; m is the total time length of sampling data at all times;fluid temperature data for the j-th temperature sensor; />Fluid temperature data for an nth temperature sensor other than the jth temperature sensor; />A difference value between fluid temperature data of the jth temperature sensor and fluid temperature data of the r temperature sensors except for the jth temperature sensor at the current moment; />A difference value between fluid temperature data of the jth temperature sensor and fluid temperature data of the r temperature sensors other than the jth temperature sensor at the kth time;a single temperature difference between the jth temperature sensor and an r temperature sensor other than the jth temperature sensor; />A degree of temperature difference for fluid temperature data between the jth temperature sensor and an r temperature sensor other than the jth temperature sensor;a temperature anomaly value for fluid temperature data of a jth temperature sensor; />For the j-th temperature sensorA corresponding influence degree value of the fluid speed data at the current moment on the fluid temperature data; />The fluctuation degree of the fluid velocity data at the current moment corresponding to the jth temperature sensor; />A preset adjustment threshold value is set; />Is the abnormal value of the fluid velocity data at the current moment corresponding to the jth temperature sensor.
In the formula of the possibility of abnormality of the fluid temperature data of the temperature sensor, the greater the fluctuation degree of the fluid velocity data at the current time corresponding to the temperature sensor, the greater the change of the fluid temperature data is reflected, but the influence of the fluid temperature data change at the current time on the analysis of the abnormal situation is smaller because the fluid velocity data is too large.And->Reflects the temperature changes of the temperature sensors at different positions at different moments, but theoretically the temperature changes of the temperature sensors at different positions and the temperature changes at different moments are about the same, so that the difference value of the fluid temperature data of the jth temperature sensor and the jth temperature sensor at the current moment is improved>Difference value of fluid temperature data from the jth temperature sensor and the (r) th temperature sensor at the kth time +.>Subtracting to reflect the difference degree of the fluid temperature data corresponding to the temperature sensor, ++>The smaller the temperature sensor is, the smaller the degree of abnormality of the jth temperature sensor at the current moment is reflected, and the lower the possibility of abnormality of the jth temperature sensor caused by the fault is reflected. Temperature-affected value between the jth temperature sensor and the r temperature sensor other than the jth temperature sensor +.>Reflecting the coefficient of influence of the response time variation due to the distance between the temperature sensors on the current temperature sensor, when the distance between the two temperature sensorsThe larger the response time between the calculated data differences between the two different temperature sensors is, the longer the influence of the change of the fluid temperature data generated by the sensor with a longer distance on the change of the fluid temperature data at the same moment of the current temperature sensor is reflected, so that the change of the fluid temperature data is inversely proportional normalized by using an exponential function, the difference of the fluid temperature data counted among the temperature sensors is weighted, all the temperature sensors and all the sampling moments are traversed, and the temperature abnormal value of the fluid temperature data of the j-th temperature sensor is obtained. The smaller the temperature anomaly value is, the smaller the possibility of abnormality caused by fault at the current moment is reflected.
Based on the general regularity of the change of the fluid temperature data of a plurality of temperature sensors, the possibility of abnormality caused by the fault of the current moment is judged by analyzing the data change difference of all other temperature sensor data at the same moment and combining the influence degree of the calculated flow rate on the data, so that the unreal influence of the flow rate on the change of the monitoring data is avoided, the inaccuracy of the abnormal change of the data caused by the abnormal change of the data of only a single sensor is utilized, and the authenticity of the abnormality degree reflected by the change of the fluid temperature data under the influence of the flow rate is improved.
And S500, judging whether the heat exchanger fails or not by combining the mutation degree and the abnormality probability.
After obtaining the abnormality probability, the abnormality probability obtained in step S400 is weighted by the mutation degree of the fluid temperature data, and thereby corrected, so that the monitoring of the abnormality degree of the data of any temperature sensor in the heat exchanger is realized.
The method for acquiring the fault degree of the heat exchanger comprises the following steps: and taking a normalized value of the product of the mutation degree and the abnormality probability as the failure degree of the heat exchanger. The mutation degree and the abnormality probability are in a proportional relation with the failure degree of the heat exchanger, and the greater the abnormality probability is, the greater the failure probability of the corresponding heat exchanger is, and the greater the failure degree of the corresponding heat exchanger is.
After obtaining the failure degree of the heat exchanger, judging whether the heat exchanger has a failure or not, specifically: when the failure degree of the heat exchanger is larger than a preset failure threshold value, judging that the heat exchanger fails, wherein the heat exchanger shows abnormal fluctuation of data due to the failure; and when the failure degree of the heat exchanger is smaller than or equal to a preset failure threshold value, judging that the heat exchanger fails. In the embodiment of the invention, the preset fault threshold value is 0.7, and in other embodiments, the value is adjusted by an implementer according to actual conditions, and when the requirement of the implementer is strict, the value of the preset fault threshold value can be correspondingly reduced.
In summary, the invention relates to the technical field of heat exchanger fault detection. Firstly, acquiring fluid speed data of a fluid pipeline of a heat exchanger and fluid temperature data at different positions; carrying out local fluctuation analysis on the fluid temperature data to obtain the mutation degree of the fluid temperature data; determining the influence degree value of the fluid speed data on the fluid temperature data according to the corresponding relation between the fluid temperature data and the fluid speed data; determining the possibility of abnormality of the fluid temperature data according to the difference between the fluid temperature data at different positions, the fluctuation degree of the fluid speed data and the influence degree value of the fluid speed data on the fluid temperature data; and judging whether the heat exchanger fails or not by combining the mutation degree and the abnormality probability. According to the invention, the local mutation level of the data is reflected by calculating the change of the data in the local part, so that the error judgment on the subsequent fault data identification caused by the fact that the degree of the change of the data cannot be accurately defined when the temperature data fluctuates is avoided, and the accuracy of data cleaning is improved.
The embodiment of the invention also provides an intelligent monitoring system for heat exchanger fault data information, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of the method when executing the computer program. Because the detailed description is given above for the intelligent monitoring method of the fault data information of the heat exchanger, the detailed description is omitted.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (10)
1. The intelligent monitoring method for the fault data information of the heat exchanger is characterized by comprising the following steps of:
acquiring fluid velocity data of a fluid pipeline of the heat exchanger and fluid temperature data at different positions;
carrying out local fluctuation analysis on the fluid temperature data to obtain the mutation degree of the fluid temperature data;
determining the influence degree value of the fluid speed data on the fluid temperature data according to the corresponding relation between the fluid temperature data and the fluid speed data;
determining the possibility of abnormality of the fluid temperature data according to the difference between the fluid temperature data at different positions, the fluctuation degree of the fluid speed data and the influence degree value of the fluid speed data on the fluid temperature data;
and judging whether the heat exchanger fails or not by combining the mutation degree and the abnormality probability.
2. The intelligent monitoring method for heat exchanger fault data information according to claim 1, wherein the step of performing local fluctuation analysis on the fluid temperature data to obtain the mutation degree of the fluid temperature data comprises the following steps:
and constructing a window corresponding to each fluid temperature data by taking each fluid temperature data as a center, and determining the mutation degree of each fluid temperature data according to the numerical value difference between each adjacent fluid temperature data in the window corresponding to each fluid temperature data.
3. The intelligent monitoring method for heat exchanger fault data information according to claim 2, wherein the calculation formula of the mutation degree of the fluid temperature data is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The degree of mutation for the ith fluid temperature data; />The difference between the ith fluid temperature data and the fluid temperature data at the last time; />The maximum value of the difference of all adjacent fluid temperature data in the window corresponding to the ith fluid temperature data; />The number of the fluid temperature data in the window corresponding to the ith fluid temperature data;/>is the difference between the (r) th fluid temperature data and the last fluid temperature data except the (i) th fluid temperature data in the window corresponding to the (i) th fluid temperature data.
4. The intelligent monitoring method for heat exchanger fault data information according to claim 1, wherein the calculation formula of the influence degree value is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>The fluid velocity data at the c-th moment is the influence degree value of the fluid temperature data; />Fluid velocity data at time c; />Fluid temperature data at time c; />Is the average of the ratio of the fluid temperature data to the fluid velocity data at all acquisition times.
5. The intelligent monitoring method for heat exchanger fault data information according to claim 1, wherein the determining the possibility of abnormality of the fluid temperature data according to the difference between the fluid temperature data, the fluctuation degree of the fluid velocity data, and the influence degree value of the fluid velocity data on the fluid temperature data comprises:
taking a temperature sensor corresponding to any fluid temperature data as a target sensor, taking other any sensors as sensors to be selected, and determining a temperature affected value between the target sensor and the sensors to be selected according to the distance between the target sensor and the sensors to be selected;
combining the difference of the fluid temperature data of the target sensor and the fluid temperature data of the sensor to be selected at different times to determine the temperature difference degree of the target sensor and the sensor to be selected;
determining a temperature anomaly value of the fluid temperature data of the target sensor by combining the temperature affected value and the temperature difference degree of the fluid temperature data;
determining a speed anomaly value of the fluid speed data at the same time as the fluid temperature data according to the fluctuation degree of the fluid speed data and the influence degree value of the fluid speed data on the fluid temperature data;
determining the abnormality probability of the fluid temperature data according to the temperature difference degree and the speed abnormality value, wherein the temperature difference degree and the speed abnormality value are in a proportional relation with the abnormality probability.
6. The intelligent monitoring method for heat exchanger fault data information according to claim 5, wherein determining the degree of temperature difference between the target sensor and the candidate sensor by combining the difference of the fluid temperature data of the target sensor and the candidate sensor at different times comprises:
the j-th temperature sensor is used as a target sensor, the r-th temperature sensor is used as a to-be-selected sensor, and the calculation formula of the temperature difference degree between the target sensor and the to-be-selected sensor is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>A degree of temperature difference that is fluid temperature data between the jth temperature sensor and the nth temperature sensor; />Is the j-th temperatureFluid temperature data of the degree sensor; />Fluid temperature data for the r-th temperature sensor; />A difference value of fluid temperature data of the jth temperature sensor and the r temperature sensor at the current moment; />A difference value between fluid temperature data of the jth temperature sensor and the r temperature sensor at the kth time; m is the total duration of all time sampled data.
7. The intelligent monitoring method for heat exchanger fault data information according to claim 5, wherein determining the abnormal value of the fluid temperature data of the target sensor by combining the affected value of the fluid temperature data and the degree of the temperature difference comprises:
taking the product of the temperature affected value and the temperature difference degree of the target sensor and the sensor to be selected as a single temperature abnormality of the target sensor and the sensor to be selected;
and taking the sum value of the single temperature anomalies of the target sensor and all the sensors to be selected as the temperature anomaly value of the fluid temperature data of the target sensor.
8. The intelligent monitoring method for heat exchanger fault data information according to claim 1, wherein determining the abnormal speed value of the fluid speed data at the same time as the fluid temperature data according to the fluctuation degree of the fluid speed data and the influence degree value of the fluid speed data on the fluid temperature data comprises:
acquiring a difference value between fluid speed data at the current moment and fluid speed data at the previous moment, and recording the difference value as fluctuation degree of the fluid speed data;
taking the influence degree value of the fluid speed data on the fluid temperature data as a numerator, taking the sum value of the fluctuation degree of the fluid speed data and a preset adjusting threshold value as a denominator, and taking the ratio value as the speed abnormal value of the fluid speed data which belongs to the same time as the fluid temperature data; wherein the preset adjustment threshold is a positive number less than 1.
9. The intelligent monitoring method for heat exchanger fault data information according to claim 1, wherein the step of combining the mutation degree and the abnormality probability to determine whether the heat exchanger has a fault comprises the steps of:
taking a normalized value of the product of the mutation degree and the abnormality probability as a fault degree of the heat exchanger;
when the failure degree of the heat exchanger is larger than a preset failure threshold value, judging that the heat exchanger fails; and when the failure degree of the heat exchanger is smaller than or equal to a preset failure threshold value, judging that the heat exchanger fails.
10. An intelligent monitoring system for heat exchanger fault data information, comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the steps of the intelligent monitoring method for heat exchanger fault data information according to any one of claims 1-9 when executing the computer program.
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