CN112524077A - Method, device and system for detecting fan fault - Google Patents
Method, device and system for detecting fan fault Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F04—POSITIVE - DISPLACEMENT MACHINES FOR LIQUIDS; PUMPS FOR LIQUIDS OR ELASTIC FLUIDS
- F04D—NON-POSITIVE-DISPLACEMENT PUMPS
- F04D27/00—Control, e.g. regulation, of pumps, pumping installations or pumping systems specially adapted for elastic fluids
- F04D27/001—Testing thereof; Determination or simulation of flow characteristics; Stall or surge detection, e.g. condition monitoring
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Abstract
The application discloses a method, a device and a system for detecting fan faults. Wherein, the method comprises the following steps: sensing a plurality of sensing data of the fan, wherein the plurality of sensing data are sensed by sensors mounted on different components of the fan; and processing a plurality of sensing data based on the convolutional neural network model, and predicting to obtain the working state of the fan. The method and the device solve the technical problem that in the prior art, the running solution of the safety of the fan is mostly used for detecting and alarming after the fault occurs, or the fault of the fan is predicted in a manual diagnosis mode, so that the accuracy of the fault prediction is poor.
Description
Technical Field
The application relates to the field of fan fault prediction, in particular to a method, a device and a system for detecting fan faults.
Background
Early fault diagnosis of safe operation of the fan, namely early warning of the fan before the actual occurrence of the fault, is a difficult point in the fan operation and maintenance technology and is also a blank. Generally, the fan is complicated, the number of the detected analog quantities is large, and the fault types are large. The huge characteristics of data volume make traditional modeling and statistical modeling methods based on physical principles unable to effectively early warn faults in advance.
The existing fan safe operation solution is mostly based on fault detection. Both a Supervisory Control And Data Acquisition (SCADA) system, a blade health monitoring system, oil metal particle monitoring And the like can only give an alarm after a fault occurs. The monthly report provided by the drive chain vibration monitoring system contains a certain degree of prediction on faults, and the occurrence of mechanical faults is avoided. However, since the drive train vibration monitoring system does not include an intelligent diagnostic system that is automated by a computer, the prediction of faults needs to be done by a diagnostic engineer, and the quality and accuracy of the fault prediction provided by the drive train vibration monitoring system is greatly dependent on the level of the diagnostic engineer. Because of the relatively rare number of diagnostic engineers and the unstable level, the reliability of the failure prediction provided by the drive train vibration monitoring system cannot be guaranteed.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the application provides a method, a device and a system for detecting fan faults, and at least solves the technical problem that in the prior art, the accuracy of fault prediction is poor due to the fact that a safe operation solution of a fan is mostly used for detecting and alarming after the fault occurs or the fault of the fan is predicted in a manual diagnosis mode.
According to an aspect of an embodiment of the present application, there is provided a method for detecting a fan fault, including: sensing a plurality of sensing data of the fan, wherein the plurality of sensing data are sensed by sensors mounted on different components of the fan; and processing a plurality of sensing data based on the convolutional neural network model, and predicting to obtain the working state of the fan.
Optionally, processing the plurality of sensing data based on the convolutional neural network model, and predicting the working state of the wind turbine, including: acquiring a plurality of sensing data, wherein the sensing data records event information of a corresponding part occurring at a preset time; extracting event information contained in each sensing data; judging whether the event information contained in each sensing data is matched with the corresponding target information, and if so, determining that the working state of the fan is a normal state; and if the event information contained in any one sensing data does not match with the corresponding target information, determining that the working state is a fault state.
Optionally, the convolutional neural network model comprises at least: the device comprises an input layer, a convolutional layer, a pooling layer and a full-link layer, wherein a plurality of sensing data are acquired through the input layer.
Optionally, extracting event information contained in each sensing data includes: scanning corresponding sensing data through each convolution kernel in the convolution layer to obtain a characteristic layer of a corresponding component; performing redundancy removal processing on the feature map layer of the corresponding component through the pooling layer; and converting the characteristic image layers subjected to redundancy removal processing through at least one full connection layer to obtain event information of the component.
Optionally, before processing the plurality of sensing data based on the convolutional neural network model to predict the working condition of the wind turbine, the method further includes: acquiring a training sample set, wherein the training sample set comprises historical working data of various types of fans; marking fan fault information of each historical working data in a training sample set, wherein the fan fault information comprises: fault type and fault signature; and inputting the marked training sample set into an initialized convolutional neural network for training to obtain a convolutional neural network model.
Optionally, after the plurality of sensing data are processed based on the convolutional neural network model and the working state of the wind turbine is predicted, the method further includes: counting the number of different working states predicted in a preset time, wherein the working states at least comprise: a normal state and a fault state; and acquiring the probability value of the fan in the normal state and/or the probability value of the fan in the fault state based on the statistical result.
According to another aspect of the embodiments of the present application, there is also provided a device for detecting a fan fault, including: the fan comprises a sensing module, a control module and a control module, wherein the sensing module is used for sensing and obtaining a plurality of sensing data of the fan, and the plurality of sensing data are sensed and obtained through sensors arranged on different parts of the fan; and the prediction module is used for processing the plurality of sensing data based on the convolutional neural network model and predicting the working state of the fan.
According to another aspect of the embodiments of the present application, there is also provided a system for detecting a fan fault, including: the sensor is arranged on different parts of the fan and used for sensing and obtaining a plurality of sensing data of the fan; and the processor is in communication connection with the sensor and is used for processing the multiple sensing data based on the convolutional neural network model and predicting the working state of the fan.
According to still another aspect of the embodiments of the present application, there is also provided a storage medium, where the storage medium includes a stored program, and the program, when running, controls a device where the storage medium is located to perform the above method for detecting a fan fault.
According to still another aspect of the embodiments of the present application, there is also provided a processor, where the processor is configured to run a program, where the program executes the above method for detecting a fan failure.
In the embodiment of the application, a plurality of sensing data of the fan are obtained by adopting sensing, wherein the plurality of sensing data are obtained by sensing through sensors arranged on different parts of the fan; the method comprises the steps of processing a plurality of sensing data based on a convolutional neural network model, predicting the working state of the fan, acquiring the plurality of sensing data of the fan by using a sensor, inputting the plurality of sensing data into a trained neural network model for prediction, and obtaining the normal working probability and the fault probability of the fan, thereby realizing the technical effect of improving the fault prediction accuracy of the fan, and further solving the technical problem that the fault prediction accuracy is poor due to the fact that the fan safety operation solution in the prior art is mostly used for detecting and alarming after the fault occurs or the fault of the fan is predicted by adopting a manual diagnosis mode.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flow chart of a method of detecting a fan failure according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a method for predicting a wind turbine fault using a convolutional neural network model according to an embodiment of the present application;
FIG. 3 is a block diagram of an apparatus for detecting a failure in a wind turbine according to an embodiment of the present application;
FIG. 4 is a block diagram of a system for detecting a fan failure according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, 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 partial embodiments of the present application, but not all 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.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present application, there is provided an embodiment of a method for detecting a wind turbine failure, it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system, such as a set of computer executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that illustrated herein.
Fig. 1 is a flowchart of a method for detecting a fan failure according to an embodiment of the present application, and as shown in fig. 1, the method includes the following steps:
step S102, sensing to obtain a plurality of sensing data of the fan, wherein the plurality of sensing data are sensed by sensors mounted on different parts of the fan.
According to an alternative embodiment of the present application, the wind turbine is a wind turbine for wind power generation. Step S102 is data collected by 100-200 sensors installed on the blower.
And step S104, processing the multiple sensing data based on the convolutional neural network model, and predicting to obtain the working state of the fan.
According to an alternative embodiment of the present application, the convolutional neural network model in step S104 at least includes: the device comprises an input layer, a convolutional layer, a pooling layer and a full-link layer, wherein a plurality of sensing data are acquired through the input layer. The data for each sensor is input as a whole to one channel of the convolutional neural network model.
Through the steps, the sensor is used for collecting a plurality of sensing data of the fan, the sensing data are input into the trained neural network model for prediction, the normal working probability and the fault probability of the fan are obtained, and therefore the technical effect of improving the fan fault prediction accuracy is achieved.
In some optional embodiments of the present application, step S104 is implemented by: acquiring a plurality of sensing data, wherein the sensing data records event information of a corresponding part occurring at a preset time; extracting event information contained in each sensing data; judging whether the event information contained in each sensing data is matched with the corresponding target information, and if so, determining that the working state of the fan is a normal state; and if the event information contained in any one sensing data does not match with the corresponding target information, determining that the working state is a fault state.
The time information is event information of the fan component acquired by the sensor in real time, the target information is event information of the fan component during normal operation, and if the event information of the fan component acquired by the sensor in real time is matched with the event information of the fan component during normal operation, the fan component is in a normal operation working state; otherwise, the component is in a fault state.
According to an alternative embodiment of the present application, the event information contained in each sensed data is extracted by: scanning corresponding sensing data through each convolution kernel in the convolution layer to obtain a characteristic layer of a corresponding component; performing redundancy removal processing on the feature map layer of the corresponding component through the pooling layer; and converting the characteristic image layers subjected to redundancy removal processing through at least one full connection layer to obtain event information of the component.
According to an optional embodiment of the present application, the feature map layer of the component may be processed through a neural network, so as to obtain event information occurring in the component.
In an optional embodiment of the present application, before performing step S104, a training sample set is further required to be obtained, where the training sample set includes historical operating data of multiple types of fans; marking fan fault information of each historical working data in a training sample set, wherein the fan fault information comprises: fault type and fault signature; and inputting the marked training sample set into an initialized convolutional neural network for training to obtain a convolutional neural network model.
The method is a method for predicting the convolutional neural network model in the step S104, and specifically, a training sample set for predicting the convolutional neural network model is obtained first, where the training sample set includes historical working data of multiple types of fans; then, marking the training sample set, and marking the fan fault information of each historical working data; and inputting the marked training sample set into a convolutional neural network for training to obtain a trained convolutional neural network model. The input of the convolutional neural network model is sensing data of the fan acquired by a sensor, and the output of the convolutional neural network model is the working state of the fan corresponding to the input sensing data, including a normal operation state and a fault state.
According to an alternative embodiment of the present application, after step S104 is executed, the number of different working states predicted within a predetermined time is counted, where the working states at least include: a normal state and a fault state; and acquiring the probability value of the fan in the normal state and/or the probability value of the fan in the fault state based on the statistical result.
Fig. 2 is a schematic diagram of predicting a fan fault by using a convolutional neural network model according to an embodiment of the present application, and as shown in fig. 2, 120 collected events occurring in a plurality of components of a fan are input to a trained convolutional neural network model, and a probability distribution of a fan operating state is output through processing of a volume base layer, a pooling layer and a full connection layer.
The running state distribution of the fan output by the convolutional neural network model can be used for knowing that fan maintenance personnel correspondingly overhaul the fan, for example, a prediction result is displayed in a future preset time period, the probability of the fan failing is higher than a preset threshold value, and at the moment, the related maintenance personnel need to accelerate overhaul and troubleshooting work on the fan, so that the hidden trouble is eliminated before the failure occurs as much as possible.
Fig. 3 is a block diagram of an apparatus for detecting a fan failure according to an embodiment of the present application, and as shown in fig. 3, the apparatus includes:
and the sensing module 30 is used for sensing and obtaining a plurality of sensing data of the fan, wherein the plurality of sensing data are sensed and obtained through sensors installed on different parts of the fan.
According to an alternative embodiment of the present application, the wind turbine is a wind turbine for wind power generation.
And the prediction module 32 is used for processing the plurality of sensing data based on the convolutional neural network model and predicting the working state of the fan.
According to an alternative embodiment of the present application, the prediction module 32 comprises: an acquisition unit configured to acquire a plurality of sensing data, wherein the sensing data records event information that a corresponding component has occurred at a predetermined time; an extraction unit for extracting event information contained in each of the sensed data; the judging unit is used for judging whether the event information contained in each sensing data is matched with the corresponding target information or not, and if so, determining that the working state of the fan is a normal state; and if the event information contained in any one sensing data does not match with the corresponding target information, determining that the working state is a fault state.
According to an optional embodiment of the present application, the extracting unit is further configured to scan corresponding sensing data through each convolution kernel in the convolution layer to obtain a feature layer of the corresponding component; performing redundancy removal processing on the feature map layer of the corresponding component through the pooling layer; and converting the characteristic image layers subjected to redundancy removal processing through at least one full connection layer to obtain event information of the component.
In some embodiments of the present application, the apparatus further comprises: the training module is used for acquiring a training sample set, wherein the training sample set comprises historical working data of various types of fans; marking fan fault information of each historical working data in a training sample set, wherein the fan fault information comprises: fault type and fault signature; and inputting the marked training sample set into an initialized convolutional neural network for training to obtain a convolutional neural network model.
According to an alternative embodiment of the present application, the apparatus further comprises: the statistic module is used for counting the number of different working states predicted in a preset time, wherein the working states at least comprise: a normal state and a fault state; and acquiring the probability value of the fan in the normal state and/or the probability value of the fan in the fault state based on the statistical result.
It should be noted that, reference may be made to the description related to the embodiment shown in fig. 1 for a preferred implementation of the embodiment shown in fig. 3, and details are not described here again.
Fig. 4 is a block diagram of a system for detecting a fan failure according to an embodiment of the present application, and as shown in fig. 4, the system includes:
and the sensor 40 is arranged on different parts of the fan and used for sensing and obtaining a plurality of sensing data of the fan.
According to an alternative embodiment of the present application, the sensors mounted on different fan components are of different kinds. In the present embodiment, 100-200 sensors are adopted to collect the sensing data of the fan component.
And the processor 42 is in communication connection with the sensor 40 and is used for processing a plurality of sensing data based on the convolutional neural network model and predicting the working state of the fan.
The processor 42 may be a server or a cloud server, and the processor 42 is configured to run a trained convolutional neural network model, and predict the sensing data of the fan component acquired by the sensor 40 by using the convolutional neural network model to obtain the running state information of the fan in a future preset time period.
It should be noted that, reference may be made to the description related to the embodiment shown in fig. 1 for a preferred implementation of the embodiment shown in fig. 4, and details are not described here again.
The embodiment of the application also provides a storage medium, wherein the storage medium comprises a stored program, and when the program runs, the equipment where the storage medium is located is controlled to execute the method for detecting the fan fault.
The storage medium stores a program for executing the following functions: sensing a plurality of sensing data of the fan, wherein the plurality of sensing data are sensed by sensors mounted on different components of the fan; and processing a plurality of sensing data based on the convolutional neural network model, and predicting to obtain the working state of the fan.
The embodiment of the application also provides a processor, wherein the processor is used for running the program, and the method for detecting the fan fault is executed when the program runs.
The processor is for processing a program for performing the following functions: sensing a plurality of sensing data of the fan, wherein the plurality of sensing data are sensed by sensors mounted on different components of the fan; and processing a plurality of sensing data based on the convolutional neural network model, and predicting to obtain the working state of the fan.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (10)
1. A method of detecting a fan fault, comprising:
sensing a plurality of sensed data of the fan, wherein the plurality of sensed data are sensed by sensors mounted on different components of the fan;
and processing the sensing data based on a convolutional neural network model, and predicting the working state of the fan.
2. The method of claim 1, wherein processing the plurality of sensed data based on a convolutional neural network model to predict the operating condition of the wind turbine comprises:
acquiring the plurality of sensing data, wherein the sensing data records event information of corresponding parts occurring at preset time;
extracting event information contained in each sensing data;
judging whether event information contained in each sensing data is matched with corresponding target information or not, and if so, determining that the working state of the fan is a normal state;
and if the event information contained in any one sensing data does not match with the corresponding target information, determining that the working state is a fault state.
3. The method of claim 2, wherein the convolutional neural network model comprises at least: an input layer, a convolutional layer, a pooling layer, and a full-link layer, wherein the plurality of sensing data is acquired through the input layer.
4. The method of claim 3, wherein extracting event information contained in each sensed data comprises:
scanning corresponding sensing data through each convolution kernel in the convolution layer to obtain a characteristic layer of a corresponding component;
performing redundancy removal processing on the feature map layer of the corresponding component through the pooling layer;
and converting the characteristic image layers subjected to redundancy removal processing through at least one full connection layer to obtain event information of the component.
5. The method of any one of claims 1 to 4, wherein prior to processing the plurality of sensed data based on a convolutional neural network model to predict the operating condition of the wind turbine, the method further comprises:
acquiring a training sample set, wherein the training sample set comprises historical working data of various types of fans;
marking fan fault information of each historical working data in the training sample set, wherein the fan fault information comprises: fault type and fault signature;
and inputting the marked training sample set into an initialized convolutional neural network for training to obtain the convolutional neural network model.
6. The method of claim 5, wherein after processing the plurality of sensed data based on a convolutional neural network model to predict the operating condition of the wind turbine, the method further comprises:
counting the number of different working states predicted in a preset time, wherein the working states at least comprise: a normal state and a fault state;
and acquiring the probability value of the fan in the normal state and/or the probability value of the fan in the fault state based on the statistical result.
7. A device for detecting fan failure, comprising:
the fan comprises a sensing module, a control module and a control module, wherein the sensing module is used for sensing and obtaining a plurality of sensing data of the fan, and the plurality of sensing data are sensed and obtained through sensors arranged on different parts of the fan;
and the prediction module is used for processing the sensing data based on the convolutional neural network model and predicting the working state of the fan.
8. A system for detecting a fan fault, comprising:
the sensor is arranged on different parts of the fan and used for sensing and obtaining a plurality of sensing data of the fan;
and the processor is in communication connection with the sensor and is used for processing the sensing data based on a convolutional neural network model and predicting the working state of the fan.
9. A storage medium comprising a stored program, wherein the program is operable to control a device on which the storage medium is located to perform the method of detecting a fan failure of any one of claims 1 to 6.
10. A processor, characterized in that the processor is configured to run a program, wherein the program is executed to perform the method for detecting a fan failure according to any one of claims 1 to 6.
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| CN202011429865.1A CN112524077A (en) | 2020-12-09 | 2020-12-09 | Method, device and system for detecting fan fault |
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| CN116335977A (en) * | 2023-03-31 | 2023-06-27 | 苏州瑞波机械有限公司 | Axial flow fan, control method and storage medium |
| CN117514885A (en) * | 2023-11-23 | 2024-02-06 | 德州隆达空调设备集团有限公司 | Fault detection method and device for axial flow fan |
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Application publication date: 20210319 |