CN113934191A - Fault diagnosis system for water chilling unit - Google Patents
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- 238000003745 diagnosis Methods 0.000 title claims abstract description 59
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- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0221—Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
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Abstract
The invention discloses a fault diagnosis system of a water chilling unit, which comprises a data acquisition module, a data processing module and a fault detection module; the data acquisition module acquires a plurality of specific parameters, including dependent variable parameters and independent variable parameters; the data processing module is provided with a plurality of thermodynamic models and receives each specific parameter; solving the coefficient to be determined by each specific parameter and thermodynamic model in a period of time to generate a plurality of prediction models and dynamic threshold formulas; when the diagnosis stage is started, the fault detection module receives each specific parameter acquired in real time and is configured to bring each variable parameter into a corresponding prediction model and a dynamic threshold value formula to obtain each dependent variable prediction value and each dynamic threshold value; calculating the residual error between each dependent variable parameter in real time and the corresponding dependent variable predicted value; and when the residual error is greater than the corresponding dynamic threshold value, judging that the fault occurs. According to the invention, the prediction model is formed by the operation data, so that the difficulty in obtaining the fault model data is reduced, and the fault diagnosis range and accuracy are increased.
Description
Technical Field
The invention relates to the technical field of air conditioning, in particular to a fault diagnosis system of a water chilling unit.
Background
In public buildings, such as large stores, office buildings, hospitals, etc., Heating, Ventilation and Air-conditioning (HVAC) systems are mostly adopted, HVAC and its automatic control systems are increasingly complex, and the kinds and number of devices are increasing. The water chilling unit is a main component of the HVAC system, and the actual operation state of the HVAC system is complex and variable and often shows strong nonlinearity, coupling performance and partial load performance.
In order to maintain the heat and humidity balance in the air-conditioning room and ensure the heat comfort of users, the number of measuring and monitoring devices of the water chiller system is increased obviously, the complexity of an automatic control strategy is deepened obviously, and the probability of system or part failure is increased obviously.
With the increase of the operation age, the oscillation operation under the long-term partial load is easy to cause equipment aging, scaling, filth blockage and control failure, so that most of the water chilling units are in abnormal operation states. If adverse factors and the influence thereof cannot be eliminated in time, the water chilling unit system is bound to deviate from the normal operation condition, and a series of adverse consequences are brought, such as: the system has the advantages of rapidly reducing the running performance, increasing the energy consumption waste and the carbon emission, influencing the indoor heat-humidity balance and reducing the thermal comfort, even directly damaging equipment under certain conditions, causing the thorough failure and the incapability of the system, causing larger economic loss or influencing the normal working life and reducing the user experience.
The fault diagnosis system of the existing water chilling unit obtains fault data by carrying out a simulation fault experiment and stores the fault data into the water chilling unit; and when the data condition of the simulation experiment occurs in the operation process of the machine, alarming, observing the fault part according to the alarm code, searching the fault reason, and maintaining. In the method, the simulation fault experiment has certain destructiveness and dangerousness and is poor in practicability; in addition, the fault experiment has uncertainty, so that universal and accurate fault data are not easy to obtain, and the probability of misjudgment is increased by taking the fault data as a fault judgment standard; furthermore, the items of the fault experiment are also limited, limiting the range of coverage.
Disclosure of Invention
In order to solve the problems of difficulty in obtaining fault data, small diagnosis range and poor accuracy of the water chilling unit in the prior art, the invention provides the water chilling unit fault diagnosis system, the operation data forms a prediction model of a thermodynamic model, whether a fault occurs is judged according to the data monitored in real time, the difficulty in obtaining the fault data is reduced, the fault diagnosis range and accuracy are increased, the fault diagnosis practicability is improved, the fault is timely eliminated, the economic loss is reduced, and the user experience is improved.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a fault diagnosis system of a water chilling unit, which comprises a data acquisition module, a data processing module and a fault detection module;
the data acquisition module is arranged on the water chilling unit and used for acquiring a plurality of specific parameters in a data acquisition stage; the specific parameters comprise dependent variable parameters and independent variable parameters;
the data processing module is provided with a plurality of thermodynamic models, receives each specific parameter and preprocesses each specific parameter; solving undetermined coefficients of the thermodynamic model by using each specific parameter and the corresponding thermodynamic model in a period of time to generate a plurality of prediction models and dynamic threshold formulas;
when the diagnosis stage is started, the fault detection module receives each preprocessed specific parameter collected in real time and is configured to bring each independent variable parameter into the corresponding prediction model to obtain each dependent variable prediction value; calculating each dynamic threshold value according to each specific parameter and the corresponding dynamic threshold value formula; calculating a residual error between each dependent variable parameter in real time and the corresponding dependent variable predicted value; when the residual error is larger than the corresponding dynamic threshold value, judging that a fault occurs; otherwise, the operation is normal.
In an embodiment, the pre-processing of the specific parameter comprises a normalization or a de-noising process.
In one embodiment, the undetermined coefficients of the thermodynamic model are solved using a least squares method.
In one embodiment, the specific parameters for generating the prediction models are collected in units of seasons or years.
Preferably, each of the specific parameters of one year of trouble-free operation is collected for generating each of the predictive models.
In some embodiments, when a fault occurs, the fault state of the dependent variable parameter is determined according to the predicted value, the dependent variable parameter and the dynamic threshold.
In some embodiments, the fault diagnosis system of the water chilling unit further comprises a fault diagnosis module configured to group the dependent variable parameters according to thermodynamic principles, and define fault categories according to fault states of one or more dependent variable parameters in each group to generate a fault rule base;
and receiving the fault state of each dependent variable parameter, and obtaining the current fault type corresponding to the fault rule base.
In some embodiments, the acquisition, processing, calculation and diagnosis of specific parameters are performed in hours when entering the diagnostic phase.
In some embodiments, the data processing module, the fault detection module and the fault diagnosis module run on a PC side and/or a cloud platform.
Compared with the prior art, the technical scheme of the invention has the following technical effects:
according to the fault diagnosis system of the water chilling unit, a plurality of specific parameters, which are acquired by the data acquisition module and are used for the water chilling unit to normally operate for a period of time, are used as the data basis of the thermodynamic model for fault judgment, so that basic data for fault analysis can be obtained more easily; compared with the method for acquiring the fault data of the existing fault diagnosis method, the method can cover more aspects and a larger range, so that the fault diagnosis range can be correspondingly enlarged, the water chilling unit is protected from the occurrence of the fault with non-component service life in a larger range, the fault diagnosis efficiency is improved, and the economic loss is reduced; in addition, the running data of the machine is used for establishing the basic data of the fault model, and the machine has pertinence and practicability, so that fault diagnosis is more accurate, the probability of misjudgment is reduced, and user experience is improved.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart illustrating the operation of a fault diagnosis system for a chiller according to the present 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.
It is to be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are used in the description of the invention to indicate orientations and positional relationships based on the orientations and positional relationships shown in the drawings, and are used merely for convenience in describing the invention and for simplicity in description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed in a particular orientation, and be operated, and thus should not be construed as limiting the invention.
In the description of the present invention, it should be noted that the terms "mounted," "connected," and "connected" are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected unless otherwise explicitly stated or limited. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art. In the foregoing description of embodiments, the particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
The terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present invention, "a plurality" means two or more unless otherwise specified.
In a first embodiment, referring to fig. 1, the fault diagnosis system for a chiller according to the present embodiment includes a data acquisition module, a data processing module, and a fault detection module.
The data acquisition module is a data acquisition device arranged at each part of the water chilling unit, is mainly a data acquisition sensor, is used for acquiring a plurality of specific parameters of each part or each part of the water chilling unit in real time and is used for forming a fault model or fault diagnosis. The specific parameters include dependent variable parameters and independent variable parameters.
The data processing module receives each specific parameter acquired by the data acquisition module and preprocesses each specific parameter. The processing method preferably carries out standardization and/or normalization and/or denoising processing to remove burrs and interference of each specific parameter, so that the data has better practicability.
A plurality of thermodynamic models are configured in the data processing module, each thermodynamic model comprises a dependent variable parameter and a plurality of independent variable parameters, and each thermodynamic model is provided with a pending coefficient in front of each variable parameter.
Defining a period of time for the machine to enter normal operation as a data acquisition stage; preprocessing and storing each specific parameter acquired in the stage; solving each undetermined coefficient of the thermodynamic model through multiple groups of specific parameters of the stored data acquisition stage corresponding to each thermodynamic model to obtain a prediction model corresponding to each thermodynamic model; and a dynamic threshold value formula of each dependent variable parameter is obtained by using a plurality of groups of dependent variable parameters corresponding to each thermodynamic model acquired in the data acquisition stage.
Preferably, each prediction model is solved by a least squares method.
Defining a life cycle after finishing the data acquisition stage as a diagnosis stage, and when the fault diagnosis system of the water chilling unit enters the diagnosis stage, receiving each specific parameter acquired in real time by the data processing module through the fault detection module; and applying the independent variable parameters of the specific parameters to the prediction models to obtain the predicted values of the dependent variable parameters. The real-time acquisition value of each dependent variable parameter can be acquired by the data acquisition module in real time, and can also be calculated by the specific parameter acquired by the data acquisition module. And the dependent variable parameter value directly obtained from the specific parameter acquired in real time is the measured value of the dependent variable parameter.
And obtaining the dynamic threshold of the dependent variable parameter through a dynamic threshold formula according to each specific parameter acquired in real time.
The fault detection module is configured to calculate an absolute value of a difference between the dependent variable parameter measurement value and the dependent variable parameter prediction value, namely a residual error; and comparing the residual with a dynamic threshold. When the residual error is larger than the dynamic threshold value, judging that the dependent variable parameters acquired in real time have faults; otherwise, the operation is normal.
In the embodiment, a plurality of specific parameters which normally operate within a life cycle for a period of time are acquired, processed and stored, and are applied to each thermodynamic model set in the data processing module, so that undetermined coefficients of the thermodynamic models are obtained, and a corresponding prediction model and a dynamic threshold formula are obtained. And collecting each specific parameter in real time in the diagnosis stage of the life cycle, substituting each variable parameter into a prediction model to solve a dependent variable parameter prediction value and a dynamic threshold value, performing residual error between the dependent variable parameter prediction value and a dependent variable measurement value, comparing the residual error with the dynamic threshold value, and judging whether the measurement of the dependent variable parameter has a fault according to a comparison result. The data of the fault diagnosis method comes from the running water chilling unit, and the data is easier to obtain; and the data is collected by the water chilling unit and is applied to the water chilling unit, so that the pertinence is stronger, and the fault diagnosis is more accurate.
In addition, compared with the method for acquiring the fault data of the conventional fault diagnosis method, the fault diagnosis system of the water chilling unit can cover more aspects and a wider range, so that the fault diagnosis range can be correspondingly enlarged, the water chilling unit is protected from the fault of non-component service life in a wider range, the fault diagnosis efficiency is improved, and the economic loss is reduced.
Preferably, the data acquisition phase is performed in units of quarters or years.
Further preferably, the fault diagnosis system of the water chilling unit takes a year of entering normal operation as a data acquisition stage, so that the influence on each acquired specific parameter caused by seasons and temperature is reduced.
Preferably, the real-time acquisition of each specific parameter of the diagnostic phase is performed in hours.
Further preferably, the fault diagnosis system of the water chilling unit collects each specific parameter once every hour in the fault diagnosis stage.
In one example, a thermodynamic model of one of the water chilling units is established through four parameters, including independent variable parameters of a unit load rate (delta), a chilled water inlet temperature (Tin) and a condenser outlet temperature (Tcout); and Y is the supercooling degree and is a dependent variable parameter.
A polynomial regression thermodynamic model of the system is built based on these four variables. The method comprises the following specific steps:
and solving the model by using the specific parameters acquired in the data acquisition stage and a least square method to obtain the value of the b-family coefficient to be determined, namely obtaining the prediction model of one of the water chiller systems. And solving the predicted value of the supercooling degree by using the specific parameters acquired in the diagnosis stage and the prediction model.
And solving the dynamic threshold value by using the specific parameters of the diagnosis stage and a dynamic threshold value formula. Because the residual error is the difference between the measured value or the calculated value of the specific parameter and the predicted value, the error of the residual error is derived from the calculation error of the specific parameter and the modeling error of the model, and the two errors are influenced by the running condition of the water chilling unit, the dynamic threshold value for judging whether the fault occurs also changes along with the change of the running condition of the water chilling unit. The dynamic threshold value can be calculated under a certain confidence level, and the calculation formula is as follows:
in the formula, Th0，iIs the threshold value for the ith particular parameter,
is an estimate of the i-th parameter-specific residual, riIs the residual of the ith particular parameter, ta/2，n-pIs the t distribution value of (n-p) degrees of freedom at the (1-a) confidence level, n is the regression model data sum, p is the number of regression model coefficients, Y isiIs the (i) th specific parameter which,
is the regression variance, x, of the ith particular parameter0Is the input vector of the running state of the unit,
is x0Transposed vector of (2), XregIs a model-building matrix of a regression model,
is XregThe transposed matrix of (2).
Directly obtaining or calculating a supercooling degree measured value by using specific parameters acquired in a diagnosis stage; calculating a residual error between the supercooling degree predicted value and the supercooling degree measured value; and comparing the residual error with the dynamic threshold value, and judging whether the supercooling degree is normal or not.
Due to the existence of the measurement error of the sensor and the model error, even under the condition of no fault, the calculated value of the characteristic parameter and the predicted value of the model have residual errors, the residual errors of the characteristic parameter are compared with a reference value, and if the residual error value exceeds the reference value, the abnormality of the characteristic parameter is judged, and the reference value is called as a fault threshold value. The fault threshold is calculated by specific parameters acquired in real time in a diagnosis stage, so that the fault threshold is called a dynamic threshold; the fault threshold may also be obtained in a summarized manner, i.e., analyzed and summarized based on historical operating data of the refrigeration unit system to determine the fault threshold. The default value for the fault threshold of the chiller is typically derived from expert knowledge.
The selection of the fault threshold value is important for detecting the faults of the unit, a large number of faults cannot be detected in time in the fault detection process due to the excessively high threshold value, and a large number of false alarms can be caused due to the excessively low threshold value.
Example two
Referring to fig. 1, the fault diagnosis system of the water chilling unit of the present embodiment further includes a fault diagnosis module and an output module; the fault diagnosis module is configured with various dependent variable parameters grouped according to thermodynamic characteristics and system experience, and defines fault types according to fault states of one or more dependent variable parameters in each group to generate a fault rule base.
In this embodiment, when the fault detection module detects a fault of one or more dependent variable parameters, a fault state is defined according to the positive or negative of the difference between the predicted value of the dependent variable parameter and the measured value of the dependent variable parameter.
When some dependent variable parameter or some dependent variable parameters have faults in the fault diagnosis stage, the fault diagnosis module receives the fault state parameters of the dependent variable parameters, corresponds to the fault rule base to obtain fault types, and outputs the fault types through the output module, so that faults of the water chilling unit are timely found and accurately positioned, and the water chilling unit is convenient to maintain.
The embodiment is convenient for timely discovery and maintenance of the faults of the water chilling unit, prolongs the service life of the water chilling unit, reduces unnecessary economic loss and improves user experience.
Preferably, when the fault detection module judges that one or more dependent variable parameters have faults, the fault condition is counted, and if faults occur continuously for multiple times, the fault state is sent to the fault diagnosis module for fault diagnosis and output.
The accuracy of the fault is further improved, the fault false alarm is prevented, and the user experience is improved.
In one example, the following table is part of a fault rule base established based on thermodynamic characteristics of the chiller and system experience to explain and illustrate the principles of the present invention.
If the detection finds that the sensors related to a plurality of dependent variable parameters have faults, the possible fault types can be deduced by referring to the rule base model.
For example, when the rise of the exhaust pressure sensor value, the rise of the exhaust temperature sensor value and the rise of the compressor current value are detected, the fault type of the blockage of the condenser can be judged, the fault can be positioned, and the fault finding and maintenance are convenient.
EXAMPLE III
The data processing module, the fault detection module and the fault diagnosis module of the water chilling unit fault diagnosis system of the embodiment operate on a PC (personal computer) end and/or a cloud platform.
The system runs at a computer end to monitor the site in real time, can carry out fault diagnosis more accurately and timely, carries out corresponding maintenance aiming at fault types, and has rapid response and high efficiency.
When the system runs on the cloud platform, peripheral personnel can know the running condition of the water chilling unit in a shorter time, and once a fault is found, maintenance personnel are informed to check, confirm and maintain the system in time. The running state of the water chilling unit can be monitored in a wider range, resources and manpower are saved, and cost is reduced.
In the foregoing description of embodiments, the particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A chiller fault diagnostic system, comprising:
the data acquisition module is arranged on the water chilling unit and is used for acquiring a plurality of specific parameters of a data acquisition stage; the specific parameters comprise dependent variable parameters and independent variable parameters;
the data processing module is provided with a plurality of thermodynamic models, receives each specific parameter and preprocesses each specific parameter; solving undetermined coefficients of the thermodynamic model by using each specific parameter and the corresponding thermodynamic model in a period of time to generate a plurality of prediction models and dynamic threshold formulas;
the fault detection module receives each preprocessed specific parameter collected in real time when entering a diagnosis stage, and is configured to bring each independent variable parameter into the corresponding prediction model to obtain each dependent variable prediction value; calculating each dynamic threshold value according to each specific parameter and the corresponding dynamic threshold value formula; calculating a residual error between each dependent variable parameter in real time and the corresponding dependent variable predicted value; when the residual error is larger than the corresponding dynamic threshold value, judging that a fault occurs; otherwise, the operation is normal.
2. The water chiller fault diagnostic system of claim 1, wherein the pre-processing of the particular parameter comprises normalization and or de-noising.
3. The water chiller fault diagnostic system of claim 1,
and solving the undetermined coefficient of the thermodynamic model by using a least square method.
4. The chiller fault diagnostic system of claim 1, wherein the specific parameters used to generate the predictive models are collected quarterly or yearly.
5. The chiller fault diagnostic system of claim 4, wherein each of the specific parameters of a year of trouble-free operation are collected for use in generating each of the predictive models.
6. The water chiller fault diagnostic system of any of claims 1 to 5,
and when a fault occurs, judging the fault state of the corresponding dependent variable parameter according to the predicted value, the corresponding dependent variable parameter and the dynamic threshold value.
7. The water chiller fault diagnostic system of claim 6,
the fault diagnosis module is configured to group the dependent variable parameters according to the thermodynamic principle, and define fault types according to the fault states of one or more dependent variable parameters in each group to generate a fault rule base;
and receiving the fault state of each dependent variable parameter, and obtaining the current fault type corresponding to the fault rule base.
8. The chiller fault diagnosis system of claim 7, wherein upon entering the diagnosis phase, the collection, processing, calculation and diagnosis of specific parameters are performed on an hourly basis.
9. The water chilling unit fault diagnosis system according to claim 7, wherein the data processing module, the fault detection module, and the fault diagnosis module operate on a PC side and/or a cloud platform.
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| CN116822317A (en) * | 2022-03-17 | 2023-09-29 | 华能上海石洞口发电有限责任公司 | Condenser fault diagnosis method and system based on GA-BP neural network |
| CN114353854A (en) * | 2022-03-21 | 2022-04-15 | 蘑菇物联技术（深圳）有限公司 | Method, apparatus, and medium for online locating of anomaly sensors |
| CN114353854B (en) * | 2022-03-21 | 2022-05-24 | 蘑菇物联技术（深圳）有限公司 | Method, apparatus, and medium for online locating of anomaly sensors |
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