CN113313365A - Degradation early warning method and device for primary air fan - Google Patents
Degradation early warning method and device for primary air fan Download PDFInfo
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Abstract
The invention discloses a deterioration early warning method and equipment for a primary air fan, wherein the method comprises the following steps: acquiring historical operating data of the primary air fan during normal operation; training a preset neural network model based on the historical operating data to obtain a degradation early warning model of the primary air fan; the method is used for monitoring the state of the primary air fan in real time, improving the safety of the primary air fan and reducing the labor cost.
Description
Technical Field
The application relates to the field of wind energy utilization, in particular to a deterioration early warning method and equipment of a primary air fan.
Background
The primary fan is one of important auxiliary equipment for ensuring the normal operation of a thermal power plant, and the operating state of the primary fan directly influences the economy and safety of power generation.
The basic idea of the algorithm mode for carrying out preventive maintenance by artificial intelligence at present is to take the running time of equipment after the previous maintenance as input quantity, take the probability of equipment (machine) failure as output quantity, and correct the actual output quantity according to the change of the input quantity by utilizing deep learning. In the actual equipment maintenance, the equivalent operation hours of the equipment are taken as input quantity, the equivalent operation hours are corrected according to the actual operation time of the equipment, calculated statistical data, an experience curve and the like, and whether the equipment is maintained or not is determined by taking the corrected equivalent operation hours as a main basis. For example, the Equivalent Operating Hour (EOH) of the combustion engine can be decomposed into accumulated actual operating hours, equivalent hours of start-stop times, equivalent hours of trip, equivalent hours of load shedding, equivalent hours of quick load change and the like. And an expert base is established according to expert knowledge, the state monitoring of the equipment is realized by establishing related rules or reasoning, or the abnormal symptoms are continuously tracked and the state of the equipment is monitored based on fault early warning, so that the state maintenance of the power generation equipment is realized.
Although the mode adopts the artificial intelligence algorithm based on statistics, the thinking mode of the traditional manufacturing industry is still used on the whole, and the finally calculated value is still the mode of how long the equipment runs, should be overhauled and returns to the regular maintenance. Therefore, the method and the prior application result cannot be used for judging and guiding equipment (machines) to carry out state maintenance in a targeted manner. Although industrial equipment is equipment of the same type, the operating conditions of the industrial equipment are often very different, and in addition, the overhaul and maintenance levels of maintainers are different, and the health degree of the equipment after the previous overhaul also has difference. Therefore, artificial intelligence needs to search a new technical route according to actual conditions, so that the state of equipment can be maintained in the industrial field.
Therefore, a deterioration early warning method for a primary air fan is provided to solve the technical problems that the operation state of the primary air fan cannot be monitored in real time and labor cost is high in the prior art, and technical problems to be solved by technical personnel in the field are urgently needed.
Disclosure of Invention
The invention provides a deterioration early warning method of a primary air fan, which is used for solving the technical problems that the running state of the primary air fan cannot be monitored in real time and the labor cost is high in the prior art, and comprises the following steps:
acquiring historical operating data of the primary air fan during normal operation;
training a preset neural network model based on the historical operating data to obtain a degradation early warning model of the primary air fan;
and monitoring the real-time operation data of the primary air fan based on the degradation early warning model, and sending early warning information to a user when the real-time operation data has a degradation trend.
Preferably, training a preset neural network model based on the historical operating data to obtain the degradation early warning model of the primary air fan, specifically:
performing feature extraction processing on the historical operating data, and obtaining data features corresponding to the historical operating data;
and training the preset neural network model based on the data characteristics to obtain the degradation early warning model.
Preferably, the characteristic extraction processing is performed on the historical operating data, and the data characteristic corresponding to the historical operating data is obtained, specifically:
performing data cleaning on the historical operating data;
carrying out normalization processing on the washed historical operation data, and carrying out dimensionality reduction processing on the normalized historical operation data through principal component analysis;
and acquiring the data characteristics through a correlation coefficient method and historical operating data after dimension reduction processing.
Preferably, the preset neural network model is a self-coding neural network model, the activation functions of the input layer and the output layer of the self-coding neural network model select sigmoid activation functions, and the activation function of the intermediate layer selects tanh activation functions.
Preferably, the historical operating data comprises primary air fan inlet pressure data, primary air fan outlet air quantity data and primary air fan bearing vibration data.
Preferably, the preset neural network model is trained based on the data features to obtain the degradation early warning model, specifically:
and performing model training on the data characteristics by a machine learning method or a deep learning method, and acquiring the degradation early warning model.
Preferably, the real-time operation data of the primary air fan is monitored based on the degradation early warning model, and early warning information is sent to a user when the real-time operation data has a degradation trend, specifically:
inputting the real-time operation data into the degradation early warning model, and comparing an output value of the degradation early warning model with a preset value;
and if the difference between the output value and the preset value exceeds a preset range, determining that the primary air fan has a degradation trend, and sending early warning information to a user.
Preferably, the method further comprises:
and storing the real-time operation data as new historical operation data in a database, and updating the degradation early warning model in real time.
Preferably, before obtaining the historical operating data of the primary air fan during normal operation, the method further includes:
and carrying out validity check on the historical operation data.
Correspondingly, the invention also provides a deterioration early warning device of the primary air fan, which comprises:
the acquisition module is used for acquiring historical operating data of the primary air fan during normal operation;
the training module is used for training a preset neural network model based on the historical operating data so as to obtain a degradation early warning model of the primary air fan;
and the monitoring module is used for monitoring the real-time operation data of the primary air fan based on the degradation early warning model and sending early warning information to a user when the real-time operation data has a degradation trend.
Compared with the prior art, the invention has the following beneficial effects:
the invention discloses a deterioration early warning method and equipment for a primary air fan, wherein the method comprises the following steps: acquiring historical operating data of the primary air fan during normal operation; training a preset neural network model based on the historical operating data to obtain a degradation early warning model of the primary air fan; the method is used for monitoring the state of the primary air fan in real time, improving the safety of the primary air fan and reducing the labor cost.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, 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 application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 shows a schematic flow chart of a degradation early warning method for a primary air fan according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a degradation early warning device of a primary air fan according to an embodiment of the present invention;
fig. 3 shows an effect schematic diagram of a degradation early warning method of a primary air fan according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, 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 application.
As described in the background art, although the prior art adopts the artificial intelligence algorithm based on statistics, the thinking mode of the traditional manufacturing industry is still used, and the finally calculated value is still the mode of how long the equipment of the type runs, should be overhauled and returns to the regular maintenance. Therefore, the method and the prior application result cannot be used for judging and guiding equipment (machines) to carry out state maintenance in a targeted manner.
In order to solve the above problems, an embodiment of the present application provides a degradation early warning method and device for a primary air fan, where the method includes: acquiring historical operating data of the primary air fan during normal operation; training a preset neural network model based on the historical operating data to obtain a degradation early warning model of the primary air fan; the method is used for monitoring the state of the primary air fan in real time, improving the safety of the primary air fan and reducing the labor cost.
Fig. 1 shows a schematic flow chart of a degradation early warning method for a primary air fan according to an embodiment of the present invention, where the method includes:
and S101, acquiring historical operating data of the primary air fan during normal operation.
Specifically, in the scheme, an early warning model is established by collecting historical data to realize automatic early warning detection of the primary air fan, the running state of the primary air fan can be effectively monitored by the established early warning model, the historical running data of the primary air fan in normal running is collected in the scheme, and in addition, the historical running data is divided into a training set, a verification set and a test set in the scheme for the convenience of establishing a subsequent model.
In order to subsequently establish an early warning model of the primary air fan, in a preferred embodiment of the scheme, the historical operating data comprises primary air fan inlet pressure data, primary air fan outlet air volume data and primary air fan bearing vibration data.
It should be noted that the historical operating data includes, but is not limited to, the above data, and those skilled in the art may flexibly select different data parameters that may represent the operating state of the primary air fan according to the actual equipment or the operating environment.
In order to accurately obtain the historical operating data, in a preferred embodiment of the present disclosure, before obtaining the historical operating data of the primary air fan during normal operation, the method further includes:
and carrying out validity check on the historical operation data.
Specifically, after historical operating data of the primary air fan is received, validity check is carried out on the historical operating data, invalid data is filtered out, and the historical operating data is guaranteed to be valid data.
And S102, training a preset neural network model based on the historical operating data to obtain a degradation early warning model of the primary air fan.
Specifically, a preset neural network model is trained according to the acquired historical operating data, so that a degradation early warning model of the primary air fan is obtained.
In order to obtain a degradation early warning model of the primary air fan, in a preferred embodiment of the application, the preset neural network model is specifically a self-coding neural network model, a sigmoid activation function is selected from activation functions of an input layer and an output layer of the self-coding neural network model, and a tanh activation function is selected from an activation function of a middle layer.
Specifically, the preset neural network model in the scheme is a self-coding neural network model, the activation functions of the input layer and the output layer of the self-coding neural network model select sigmoid activation functions, and the activation function of the middle layer selects tanh activation functions.
It should be noted that the self-coding neural network model is only one preferred embodiment of the present disclosure, and those skilled in the art may select different neural network models for training according to actual needs, and the difference of the neural network models does not affect the protection scope of the present application.
In order to accurately obtain a primary air fan degradation early warning model, training a preset neural network model based on the historical operating data to obtain the primary air fan degradation early warning model, specifically:
performing feature extraction processing on the historical operating data, and obtaining data features corresponding to the historical operating data;
and training the preset neural network model based on the data characteristics to obtain the degradation early warning model.
Specifically, since many objects to be studied for machine learning problems may be classified variables, characters, or even images, we need to perform feature extraction processing on the historical operating data to obtain data features corresponding to the historical operating data, and after obtaining the data features, train the preset neural network model according to the data features to obtain the degradation early warning model of the primary air fan.
In order to obtain the data features corresponding to the historical operating data, in a preferred embodiment of the present application, feature extraction processing is performed on the historical operating data, and the data features corresponding to the historical operating data are obtained, specifically:
performing data cleaning on the historical operating data;
carrying out normalization processing on the washed historical operation data, and carrying out dimensionality reduction processing on the normalized historical operation data through principal component analysis;
and acquiring the data characteristics through a correlation coefficient method and historical operating data after dimension reduction processing.
Specifically, the historical operating data is firstly subjected to data cleaning, when data collection is carried out manually, missing or mistaken collection is avoided, and null values (NAN) and blank spaces may appear in the data. Before data analysis and processing, data cleaning is a necessary process and is the most important link in the whole data analysis process.
And (3) carrying out normalization processing on the washed historical operation data, wherein the normalization has the specific function of inducing the statistical distribution of the unified samples, and the normalization is the meaning of identity, unity and unification. Whether for modeling or calculation, firstly, the basic measurement unit is the same, the neural network is trained and predicted according to the statistical probability of the samples in the event, the value of the sigmoid function is between 0 and 1, and the output of the last node of the network is the same, so the output of the samples is often normalized. In addition, singular sample data often exists in data, and the network training time is increased due to the existence of the singular sample data, and the network can not be converged possibly. In order to avoid this and the convenience of subsequent data processing, and to accelerate the network learning speed, the input signals may be normalized so that the average value of the input signals of all samples is close to 0 or small compared to the mean square error.
After normalization processing is finished, dimension reduction processing is carried out on the historical operating data after normalization processing through principal component analysis, linear irrelevant historical operating data are selected, and then the relevance between the historical operating data and a training result is evaluated through a correlation coefficient method, so that data characteristics corresponding to the historical operating data are obtained.
In order to accurately obtain the degradation early warning model, in a preferred embodiment of the present disclosure, the preset neural network model is trained based on the data features to obtain the degradation early warning model, which specifically includes:
and performing model training on the data characteristics by a machine learning method or a deep learning method, and acquiring the degradation early warning model.
Specifically, when a preset neural network model is trained, the data features may be subjected to model training by a machine learning method, so as to obtain the degradation early warning model, or the data features may be subjected to model training by a deep learning method, so as to obtain the degradation early warning model.
S103, monitoring the real-time operation data of the primary air fan based on the degradation early warning model, and sending early warning information to a user when the real-time operation data has a degradation trend.
Specifically, after the degradation early warning model is obtained, the real-time data of the primary air fan is input into the degradation early warning model, so that whether the real-time operation data of the primary air fan has a degradation trend or not is monitored constantly, and if the degradation trend occurs, early warning information is sent to a user through a display screen or other terminals.
In order to accurately judge whether the real-time operation data has a degradation trend, in a preferred embodiment of the present disclosure, the real-time operation data of the primary air fan is monitored based on the degradation early warning model, and early warning information is sent to a user when the real-time operation data has a degradation trend, specifically:
inputting the real-time operation data into the degradation early warning model, and comparing an output value of the degradation early warning model with a preset value;
and if the difference between the output value and the preset value exceeds a preset range, determining that the primary air fan has a degradation trend, and sending early warning information to a user.
Specifically, the real-time operation data is input into the degradation early warning model, an output value of the degradation early warning model is compared with a preset value, if a difference value between the output value and the preset value exceeds a preset range, it is determined that the primary air fan has a degradation trend, early warning information is sent to a user, a preset duration time can be set, the early warning information is sent to the user only when the degradation trend lasts for a time period exceeding the preset duration time, and the situation that the early warning information is also caused by transient data abnormity or fluctuation and unnecessary work is brought to the user is avoided.
In order to monitor the degradation trend of the primary air fan in real time, in a preferred embodiment of the present application, the method further includes:
and storing the real-time operation data as new historical operation data in a database, and updating the degradation early warning model in real time.
Specifically, the real-time operation data is stored in a database as new historical operation data, so that the degradation early warning model is updated in real time, and the accuracy and the application range of the degradation early warning model are improved.
As shown in fig. 3, an effect schematic diagram of a degradation early warning method for a primary air fan according to an embodiment of the present invention is provided, where a degradation early warning model for the primary air fan is configured in an analysis platform, so as to implement real-time state evaluation and degradation trend detection of a primary air fan bearing device, provide a basis for a power station operator to grasp a health state of the primary air fan, and can effectively and timely troubleshoot possible problems of a primary air fan bearing within a certain time, and as shown in fig. 3, a slow rate indicates a change trend of efficiency thereof, and when a degradation trend thereof is severe (i.e., efficiency is significantly reduced), a system may issue an anomaly that may occur in 30 days or an anomaly that occurs in 10 days according to a related configuration to prompt the operator to pay attention to an operation condition of the primary air fan in time.
By applying the technical scheme, the invention discloses a degradation early warning method of a primary air fan, which comprises the following steps: acquiring historical operating data of the primary air fan during normal operation; training a preset neural network model based on the historical operating data to obtain a degradation early warning model of the primary air fan; the method is used for monitoring the state of the primary air fan in real time, improving the safety of the primary air fan and reducing the labor cost.
In order to achieve the above technical object, an embodiment of the present application further provides a degradation early warning device for a primary air fan, as shown in fig. 2, the device includes:
an obtaining module 201, configured to obtain historical operating data of the primary air fan during normal operation;
the training module 202 is configured to train a preset neural network model based on the historical operating data to obtain a degradation early warning model of the primary air fan;
and the monitoring module 203 is used for monitoring the real-time operation data of the primary air fan based on the degradation early warning model and sending early warning information to a user when the real-time operation data has a degradation trend.
Through the above description of the embodiments, those skilled in the art will clearly understand that the present invention may be implemented by hardware, or by software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present invention can be embodied in the form of a software product, where the software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.), and includes instructions for enabling a degradation early warning method of a primary air fan of a computer (which can be a personal computer, a server, or a degradation early warning method of a network primary air fan, etc.) to execute the methods described in the embodiments of the present invention.
Those skilled in the art will appreciate that the figures are merely schematic representations of one preferred implementation scenario and that the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
Those skilled in the art will appreciate that the modules in the apparatus may be distributed in the apparatus according to the description of the implementation scenario, or may be located in one or more apparatuses different from the present implementation scenario with corresponding changes. The modules of the implementation scenario may be combined into one module, or may be further split into a plurality of sub-modules.
The above-mentioned invention numbers are merely for description and do not represent the merits of the implementation scenarios.
The above disclosure is only a few specific implementation scenarios of the present invention, however, the present invention is not limited thereto, and any variations that can be made by those skilled in the art are intended to fall within the scope of the present invention.
Claims (10)
1. A deterioration early warning method for a primary air fan is characterized by comprising the following steps:
acquiring historical operating data of the primary air fan during normal operation;
training a preset neural network model based on the historical operating data to obtain a degradation early warning model of the primary air fan;
and monitoring the real-time operation data of the primary air fan based on the degradation early warning model, and sending early warning information to a user when the real-time operation data has a degradation trend.
2. The degradation early warning method according to claim 1, wherein a preset neural network model is trained based on the historical operating data to obtain the degradation early warning model of the primary air fan, and specifically:
performing feature extraction processing on the historical operating data, and obtaining data features corresponding to the historical operating data;
and training the preset neural network model based on the data characteristics to obtain the degradation early warning model.
3. The degradation early-warning method according to claim 1, wherein the historical operating data is subjected to feature extraction processing, and data features corresponding to the historical operating data are obtained, and specifically the method comprises the following steps:
performing data cleaning on the historical operating data;
carrying out normalization processing on the washed historical operation data, and carrying out dimensionality reduction processing on the normalized historical operation data through principal component analysis;
and acquiring the data characteristics through a correlation coefficient method and historical operating data after dimension reduction processing.
4. The degradation early warning method of claim 2, wherein the preset neural network model is specifically a self-coding neural network model, the activation functions of the input and output layers of the self-coding neural network model select sigmoid activation functions, and the activation function of the middle layer selects tanh activation functions.
5. The degradation warning method of claim 1, wherein the historical operating data comprises primary air inlet pressure data, primary air outlet volume data, and primary air bearing vibration data.
6. The degradation early-warning method according to claim 2, wherein the preset neural network model is trained based on the data features to obtain the degradation early-warning model, and specifically:
and performing model training on the data characteristics by a machine learning method or a deep learning method, and acquiring the degradation early warning model.
7. The degradation early-warning method according to claim 1, wherein real-time operation data of the primary air fan is monitored based on the degradation early-warning model, and early-warning information is sent to a user when the real-time operation data has a degradation trend, specifically:
inputting the real-time operation data into the degradation early warning model, and comparing an output value of the degradation early warning model with a preset value;
and if the difference between the output value and the preset value exceeds a preset range, determining that the primary air fan has a degradation trend, and sending early warning information to a user.
8. The degradation warning method of claim 1, further comprising:
and storing the real-time operation data as new historical operation data in a database, and updating the degradation early warning model in real time.
9. The degradation early warning method of claim 1, before obtaining historical operating data of the primary air fan during normal operation, further comprising:
and carrying out validity check on the historical operation data.
10. A deterioration early warning apparatus of a primary air fan, the apparatus comprising:
the acquisition module is used for acquiring historical operating data of the primary air fan during normal operation;
the training module is used for training a preset neural network model based on the historical operating data so as to obtain a degradation early warning model of the primary air fan;
and the monitoring module is used for monitoring the real-time operation data of the primary air fan based on the degradation early warning model and sending early warning information to a user when the real-time operation data has a degradation trend.
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