CN116085290A - Sliding window thermal imaging-based fan thermal fault detection method and system - Google Patents
Sliding window thermal imaging-based fan thermal fault detection method and system 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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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
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- Y02E10/72—Wind turbines with rotation axis in wind direction
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Abstract
The invention provides a fan thermal fault detection method and a system based on sliding window thermal imaging, which are characterized in that an original thermal infrared image frame of a fan is obtained, and an original thermal infrared image frame of each component of the fan is extracted, and the fan rotating speed is regulated in real time in the process of obtaining the original thermal infrared image of the fan, so that when an abnormal detection model is trained, the normal fan rotating speed is corresponding to the original thermal infrared image, then the original thermal infrared image is used as training data to train a fan component robust segmentation model, the trained fan component robust segmentation model is obtained, the temperature change curve of each component of the fan is obtained based on each thermal infrared image frame to be detected and a binary mask image data set corresponding to the thermal infrared image frame to be detected, so that real-time temperature detection of each component of the fan is realized, manual temperature measurement is not needed, meanwhile, the first derivative curve and the second derivative curve corresponding to each temperature change curve are obtained, the sliding time window mode is adopted to train the fan component robust segmentation model, and the abnormal running time score is also judged according to the abnormal running time score of each fan, and the abnormal running fault is judged in real time.
Description
Technical Field
The present disclosure relates to the field of fault detection technologies, and in particular, to a method and a system for detecting an operation fault of a fan.
Background
.. the high-speed direct-driven fan is an efficient, energy-saving and environment-friendly fan and is widely applied to the industries of medicine, electric power, chemical industry, garbage disposal and the like. The most important detection direction in the fan operation fault detection method is temperature. The temperature variation significantly affects the performance of the high speed direct drive fan. For example, abnormal changes in temperature may cause fan failure, rotor stall, rotor friction, etc. Therefore, the temperature monitoring and timely detecting of abnormal changes of the temperature and giving early warning are particularly important for the high-speed direct-drive fan.
In the temperature data acquisition stage, the traditional fan operation fault detection method generally uses a thermocouple to directly measure the surface temperature of the high-speed direct-drive fan, and the method can obtain an accurate temperature value, but the thermocouple equipment is expensive, and only can obtain the temperature of a certain point.
In the detection stage of detecting abnormality according to temperature data, two methods are generally adopted in the conventional fan operation fault detection method.
1) And the temperature is checked manually in real time and compared with a temperature threshold value, so that whether the high-speed direct-drive fan is abnormal or not is judged. The method is relatively original, a large amount of manpower is required for 24 hours of monitoring, staff is usually required to be trained, and when the number of high-speed direct-drive fans is large, huge workload is required, the detection efficiency is low, and the detection cost is high.
2) Abnormality detection methods based on machine learning, but such methods generally detect only numerical abnormalities and cannot detect temperature change trends in real time.
Disclosure of Invention
In order to solve the problems that the traditional fan operation fault detection method cannot achieve high efficiency at the same time, is low in detection cost and can achieve real-time detection on the trend of temperature change, the application relates to a fan operation fault detection method and system.
In one aspect, the application provides a method for detecting a fan thermal fault based on sliding window thermal imaging.
A fan thermal fault detection method based on sliding window thermal imaging comprises the following steps:
acquiring an original thermal infrared video of a fan; in the process of acquiring an original thermal infrared image of the fan, the rotating speed of the fan is adjusted in real time;
extracting an original thermal infrared image frame in an original thermal infrared video, carrying out component labeling on the original thermal infrared image frame, generating a plurality of component labels and labeled original thermal infrared images, and training a fan component robust segmentation model by taking the component labels and the labeled original thermal infrared images as training data to obtain a trained fan component robust segmentation model;
inputting each thermal infrared image frame to be detected in the thermal infrared video to be detected into a trained fan component robust segmentation model to obtain a binary mask image dataset of each thermal infrared image frame to be detected output by the trained fan component robust segmentation model; the binary mask map dataset includes a binary mask map for each component of the blower;
acquiring a temperature change curve of each part of the fan based on each thermal infrared image frame to be detected and a binary mask image data set corresponding to the thermal infrared image frame to be detected;
carrying out smoothing and normalization on each temperature change curve to obtain a first derivative curve and a second derivative curve corresponding to each temperature change curve;
judging whether the fan operation fault occurs according to the temperature change curves of all the components and the first derivative curve and the second derivative curve corresponding to each temperature change curve.
In another aspect, the present application provides a fan operation failure detection system.
A fan operation fault detection system comprises a thermal infrared imager and an upper computer;
the thermal infrared imager is used for acquiring infrared images and data of the fan;
the upper computer fits the infrared image and the data into a real-time temperature curve, and analyzes the possibility of abnormal conditions of the fan by utilizing a sliding window.
The invention provides a fan thermal fault detection method based on sliding window thermal imaging, which is characterized in that an original thermal infrared image frame of a fan is obtained, and an original thermal infrared image frame in the original thermal infrared image frame is extracted, and the fan rotating speed is regulated in real time in the process of obtaining the original thermal infrared image of the fan, so that when an abnormal detection model is trained, the normal fan rotating speed corresponds to the original thermal infrared image, then the original thermal infrared image is used as training data to train a fan component robust segmentation model, a trained fan component robust segmentation model is obtained, the temperature change curve of each component of the fan is obtained based on each thermal infrared image frame to be detected and a binary mask image data set corresponding to the thermal infrared image frame to be detected, real-time temperature detection of each component of the fan is realized, manual temperature measurement is not needed, meanwhile, the first derivative curve and the second derivative curve corresponding to each temperature change curve are subjected to smoothing treatment and normalization treatment, the first-order derivative curve and the second derivative curve corresponding to each temperature change curve are obtained in a sliding time window mode, the abnormal running score in each time window is calculated, and whether the abnormal running score of the fan in each time window is detected according to the abnormal running score is also realized, and the real-time fault trend is detected.
Drawings
Fig. 1 is a flowchart of a method for detecting a fan thermal fault based on sliding window thermal imaging according to an embodiment of the present application.
Fig. 2 is a sliding window diagram of a fan thermal fault detection method based on sliding window thermal imaging according to an embodiment of the present application.
Fig. 3 is a connection diagram of a fan operation fault detection system according to an embodiment of the present application.
Reference numerals:
100-thermal infrared imager; 200-upper computer.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The application provides a fan thermal fault detection method based on sliding window thermal imaging.
As shown in fig. 1, in an embodiment of the present application, a method for detecting a thermal fault of a fan based on sliding window thermal imaging includes:
s100, acquiring an original thermal infrared video of a fan; in the process of acquiring an original thermal infrared image of the fan, the rotating speed of the fan is adjusted in real time.
S200, extracting an original thermal infrared image frame in an original thermal infrared video, carrying out component labeling on the original thermal infrared image frame, generating a plurality of component labels and labeled original thermal infrared images, and training a fan component robust segmentation model by taking the component labels and the labeled original thermal infrared images as training data to obtain a trained fan component robust segmentation model.
S300, inputting each thermal infrared image frame to be detected in the thermal infrared video to be detected into a trained fan component robust segmentation model, and obtaining a binary mask image dataset of each thermal infrared image frame to be detected output by the trained fan component robust segmentation model; the binary mask map dataset includes a binary mask map for each component of the blower.
S400, acquiring temperature change curves of all parts of the fan based on each thermal infrared image frame to be detected and a binary mask image data set corresponding to the thermal infrared image frame to be detected.
S500, carrying out smoothing and normalization on each temperature change curve to obtain a first derivative curve and a second derivative curve corresponding to each temperature change curve.
S600, judging whether a fan operation fault occurs according to the temperature change curves of all the components and the first derivative curve and the second derivative curve corresponding to each temperature change curve.
Specifically, an original thermal infrared image frame of a fan is obtained, the original thermal infrared image frame in the original thermal infrared image is extracted, the original thermal infrared image is used as training data to train a fan part robust segmentation model, a trained fan part robust segmentation model is obtained, temperature change curves of all parts of the fan are obtained based on each thermal infrared image frame to be detected and a binary mask image dataset corresponding to the thermal infrared image frame to be detected, real-time temperature of all parts of the fan is checked in real time, smoothing and normalization processing is carried out on each temperature change curve to obtain a first derivative curve and a second derivative curve corresponding to each temperature change curve, abnormal scores in each time window are calculated for each temperature change curve in a sliding time window mode, and whether fan operation faults occur is judged according to the abnormal scores in each time window.
The original thermal infrared video can be a plurality of fan parts, so as to ensure the richness of data
The utility model relates to a fan thermal fault detection method based on sliding window thermal imaging, because the original thermal infrared image of fan is obtained, adjust fan rotational speed in real time, so when training unusual detection model, with every normal fan rotational speed corresponding every original thermal infrared image, obtain the temperature change curve of each part of fan based on every heat infrared image frame that awaits measuring and the binary mask image dataset that corresponds with this heat infrared image frame that awaits measuring, just so realized the real-time temperature of each part of real-time inspection fan, do not need artifical temperature measurement, for the temperature change curve of each part, calculate the unusual fraction that is different from unusual detection model in each time window by adopting the mode of sliding time window, judge whether fan operation trouble appears according to the unusual fraction in each time window, also realized carrying out real-time fault detection to fan temperature change trend.
In an embodiment of the present application, S100 includes:
s110, controlling a thermal infrared imager to acquire an original thermal infrared video of a fan; the thermal infrared imager is flush with the physical center of the fan in height when the original thermal infrared video of the fan is acquired.
Specifically, the thermal infrared video data is photographed using a thermal infrared imager. The height is flush with the center of the fan during measurement, and under the condition that the horizontal distance is kept unchanged, the shooting angle of the thermal imager is changed within a certain angle range of the front surface and the back surface of the fan so as to acquire multi-view rich data. The length of the shot video is 10-40 minutes, and different temperature changes are simulated by adjusting the rotating speed of the fan.
In the data acquisition of the embodiment, the video data is shot by using a thermal infrared imager DM10 of the university department, the temperature measurement range of infrared measurement is-20C to +150 ℃, the temperature measurement precision is +/-2 ℃, a single frame of infrared resolution is an image of 1024×768 pixels, the video frame rate is 20 frames per second, the video code rate is 8192kb/s, the coding format is.H264, the video content is a gray infrared image which is not subjected to pseudo color processing, and the final storage format is.Mp4. The height is flush with the center of the fan during measurement, the horizontal distance is 0.6 meter and 1 meter, the horizontal distance is positioned in the range of 45-135 degrees of the square of the fan, the change amount of each rotation is 15 degrees, and the length of the shot video is 10-40 minutes.
The application relates to a thermal infrared imager measuring fan thermal infrared image method, which mainly detects the abnormality of the temperature change trend, so that the subsequent direct use of pixel value change represents the temperature change, the collected infrared gray level image is directly used, the pixel value is in the range of 0-255, and the pixel value at the highest temperature of a volute is about 197 when the rotating speed is 6000 r/min; at the rotation speed of 10000r/min, the pixel value at the highest temperature of the volute is about 219; at the rotating speed of 14000r/min, the pixel value at the highest temperature of the volute is about 226; at a rotation speed of 18000r/min, the pixel value at the highest temperature of the volute is about 234. The higher the fan power, the higher the highest temperature each part can reach.
In an embodiment of the present application, S200 includes:
s210, extracting an original thermal infrared image frame from the original thermal infrared video.
S220, performing component labeling on the original thermal infrared image frame by using Labelme software to obtain an original thermal infrared image after generating a plurality of component labels and labeling.
S230, adding the plurality of component labels and the marked original thermal infrared images into a training set as a training sample.
And S240, returning to the step of extracting one original thermal infrared image frame from the original thermal infrared video until all the original thermal infrared image frames are marked by the components.
Specifically, frames are firstly taken from different thermal infrared videos during construction to obtain an original image I orig Then use Labelme software for I orig Labeling to obtain corresponding label I gt Expanding the data after obtaining all the labels, and obtaining an image I orig And tag I gt Will be added as a training sample to the training set.
It is noted that each part of the fan exists in the same graph, and a plurality of labels exist in the same graph, regardless of whether the abnormality detection model is trained or actually detected by using the abnormality detection model.
The application relates to an anomaly detection model training method, firstly, frames are taken from different infrared videos during construction, and multi-azimuth original images I with different angles and different distances are obtained orig Then use Labelme software for I orig Labeling to obtain corresponding label I gt The fan is divided into six effective structures including a volute, a sensor, a motor, a fan cover, an air outlet pipe and a cable according to the total structure of the fan. And then all the labels and data are expanded, wherein the expansion mainly adopts the modes of overturning, translating, changing brightness and the like, and the data expansion can lead a smaller data set to be richer.
In an embodiment of the present application, S200 includes:
s250, training the fan part robust segmentation model by using a training set, and obtaining the trained fan part robust segmentation model after the total loss function converges.
Specifically, the segmentation model adopts a PointRend network based on Alexander, in order to cope with the interference that the infrared data acquired in an industrial scene contains noise and is compressed by video coding and the like, a noise layer is added in the training process to strengthen, the mainly added noise comprises Gaussian noise, pretzel noise and pink noise, wherein the pink noise is common and special noise in the industrial scene, then compression attack of JPEG is also added, the data in the industrial scene is usually transmitted in a data stream mode, and the obtained data is usually compressed in various ways, namely JPEG compression, in order to accelerate the transmission speed and lighten the storage pressure. The noise floor addition formula may be expressed as:
where r represents the random number between [0,1 ] generated and α1 to α4 represent the adjustable parameters for each attack. Lneoise noise layer, gaussnoise Gaussian noise, pinknise pink noise, s & pnoise pretzel noise, JPEG compression attack layer.
The attack layer only comprises common attack modes, a plurality of unpredictable attacks exist in the data, and for the unusual attacks, the generalization is increased by adding the SE module, the core idea of the SE module is to enable the network to learn the feature weight according to loss, so that the effective feature map has high weight, and the feature map with invalid or small effect has small weight, thus a better result can be achieved by a model, the SE module belongs to the prior art, and a specific formula is not repeated.
The method for training the segmentation network model comprises the steps of training the segmentation network model by utilizing a data set, and obtaining a robust segmentation model of the fan component after the training is completed after the total loss function is converged. And inputting the fan picture to be segmented into a segmentation model to obtain a binary mask picture Mpart of each part. And the process that the abnormality detection model is changed from training to using is further realized, in the process, the robust segmentation model is learned according to sample data contained in training, and the thermal infrared image acquired in the abnormality detection model is segmented and used by utilizing the robust segmentation model after training, so that a binary mask map Mpart of each part of the fan is obtained.
In an embodiment of the present application, S400 includes:
s410, selecting one thermal infrared image frame to be detected, and acquiring a time node corresponding to the thermal infrared image frame to be detected.
S420, selecting one component in the thermal infrared image frame to be detected.
S430, acquiring an image part with the component in the thermal infrared image frame to be detected, and taking the image part with the component in the thermal infrared image frame to be detected as a part to be processed.
S440, obtaining the pixel value of each pixel point in the to-be-processed part, and solving the pixel mean value of the to-be-processed part according to the pixel value of each pixel point in the to-be-processed part.
S450, taking the pixel mean value of the part to be processed as the temperature value of the part under the time node corresponding to the thermal infrared image frame to be detected.
And S460, returning to the selection of one component in the thermal infrared image frame to be detected until the temperature value of each component under the time node corresponding to the thermal infrared image frame to be detected is obtained.
And S470, returning to the selected thermal infrared image frame to be detected, acquiring a time node corresponding to the thermal infrared image frame to be detected until all the thermal infrared image frames to be detected are traversed, and drawing a temperature change curve of each component of the fan according to the temperature value of each component in each thermal infrared image frame to be detected under the time node corresponding to the thermal infrared image frame to be detected.
Specifically, in the abnormal process of detecting the temperature change trend, the pixel value change is directly used for representing the temperature change, and the acquired infrared gray level image has the pixel value in the range of 0-255. Taking the volute as an example, the volute is marked as yellow, the label value is the pixel value is 113, the pixel value is not equal to the whole pixel value of 113 and is set to 0, and the whole pixel value is equal to the whole pixel value of 113 and is set to 1, so that a mask binary image of the volute part about the visual angle can be obtained. A binary mask map of a plurality of parts can be obtained by an infrared fan video, the foreground part is 1, the background part is 0, and still taking the volute part as an example, each frame in the video is multiplied by the volute mask point to obtain an infrared map of an independent volute part. The formula Vi +.M represents the multiplication of the infrared image and the mask point, and the multiplication of the point is the multiplication of the value of the corresponding pixel position, and the corresponding volute position value in the mask is 0, so that the volute position in the infrared image after the point multiplication keeps unchanged and the other positions become 0. A component of a video may obtain a time-temperature profile, which may be expressed as:
wherein V is i Representing the i-th frame in the video, +..
The calculation method of the infrared gray level diagram representative temperature is that all frames in a video are processed identically, then non-0 part value in each frame is averaged to represent the temperature of the volute in each frame, and the temperature curves of the volutes are obtained after the connection. With the same processing for each location, a video can be obtained for multiple temperature profiles, volute, motor, etc.
In an embodiment of the present application, S500 includes:
s510, preprocessing each temperature change curve.
S520, generating a first derivative curve and a second derivative curve corresponding to each temperature change curve according to each preprocessed temperature change curve.
Specifically, the fault detection of the temperature mainly focuses on the overall change trend of the temperature, and the temperature change trend is judged by adopting a derivative method later, so that the vibration in a small range can have a great influence on the result, and therefore, the vibration needs to be removed. The smoothing adopts a local weighted regression (Lowess) method, which mainly takes a point x as a center, intercepts a section of data with fixed length forwards and backwards, and carries out weighted linear regression on the section of data by a weight function w, so that the central value of the regression line is the corresponding value of the fitted curve. All data on the curve can obtain a weighted regression line, and the connecting line of the central value of each regression line is the Lowess curve of the data. The calculation formula is as follows:
w represents a weighting function, R represents linear regression, xi-frac, i+frac is centered on a point xi, a piece of data with the length of 2 x frac is cut back and forth,
is the center value of the regression line.
The normalization has the effect of inducing the statistical distribution of unified samples, and can map the numerical value to be between 0 and 1, so that the average value of the input signals of all samples is close to 0 or is small compared with the mean square error of the input signals of all samples, and the influence of some singular points on the calculation process can be avoided. The normalization process can be expressed as:
wherein Y represents the temperature profile after smoothing. min (), max () represent operations of maximizing and minimizing all data, respectively.
Since the temperature profile is essentially a set of discrete data, the derivation is performed by means of adjacent data differences. The first-order and second-order derivative formulas can be expressed as:
where si represents the first derivative value of curve Y at the i-th frame and li represents the second derivative value of curve Y at the i-th frame.
The temperature curve pretreatment method is to ensure the smoothness of the temperature curve by using a local weighted regression (Lowess) method, and to carry out weighted linear regression on a certain segment of data by using a weight function, wherein the central value of the regression line is the corresponding value of the curve after fitting. The temperature curve pretreatment method also utilizes a normalization function to induce the statistical distribution of unified samples, and can map the numerical value to between 0 and 1, so that the average value of the input signals of all samples is close to 0 or is smaller than the mean square error of the input signals of all samples, and the influence of some singular points on the calculation process can be avoided. The temperature curve is essentially a set of discrete data, so the first and second derivatives are obtained by means of adjacent data differences.
In an embodiment of the present application, S600 includes:
s610, judging whether the temperature of the fan has obvious fluctuation according to the temperature change curves of all the components and the first derivative curve and the second derivative curve corresponding to each temperature change curve.
S620, if the temperature of the fan obviously fluctuates, determining that the fan has operation faults.
Specifically, when the operation parameters of the fan are fixed, the temperature always shows an ascending trend before gradually tending to be stable in the process from starting up to stable normal operation, if in the operation process, the temperature change generates more obvious fluctuation, especially when the temperature is obviously reduced, the fan can be considered to generate abnormal behaviors, such as unexpected shutdown or insufficient power and other problems.
The method for judging the abnormal behavior of the fan comprises the steps of utilizing the first derivative and the second derivative of a temperature curve to specifically reflect whether the temperature curve rises or falls at a certain moment and whether the rising or falling speed is high or low. This provides an intuitive image for monitoring whether the fan's behavior is abnormal. The fan with abnormal behavior can be locked quickly.
In an embodiment of the present application, after S600, it includes:
s700, analyzing the change condition of the temperature at each moment according to the first derivative curve, and analyzing the change speed condition of the temperature at the moment with the change according to the second derivative curve.
S710, judging whether the temperature of the fan has obvious fluctuation according to the change condition of the temperature at each moment and the change speed condition of the temperature at the moment when the change exists.
Specifically, the first derivative and the second derivative of the temperature curve make data support for specifically analyzing the probability of the fan failing. In analyzing the temperature profile, the obtained anomaly score is made real-time and effective. If the window where the temperature curve is located is too large and contains too many normal parts, the abnormal fraction is pulled down to make the abnormal fraction unobvious, and the window is too small and is easily affected by local oscillation, so that three scales of small, medium and large are further divided. The same weight is adopted, the smaller the window is, the smaller the weight is, the amplification of local normal vibration is avoided, the specific weight value is distributed by firstly enabling the sum of the specific weight values to be 1, and when one window is taken by each scale window, the length ratio is 1/7:2/7: and 4/7, rounding the obtained product to obtain a corresponding weight. In the calculation process, firstly, the proportion of the first derivative in the total number of segments occupied by zero in each segment of data is considered, and the more the temperature drop part is, the larger the proportion value is, the more the temperature abnormality drop is likely. Then consider the sum of all absolute values of the first derivatives in the segment that are less than 0, the more pronounced the downward trend of the temperature, the greater its value, the more likely it is that the temperature is abnormally dropped. And finally, considering the sum of absolute values of second derivatives at the positions where the first derivatives in the segments are smaller than 0, wherein the second derivatives represent the change rate of the first derivatives, and the fact that the temperature drop speed in the original temperature data is accelerated or slowed down can be seen, wherein the larger the value is, the more obvious the temperature drop is represented, and the distance can be pulled from the fractional value generated by fluctuation in a small temperature range. The greater the resulting anomaly score, the higher the likelihood of an anomaly occurring within that period is considered.
The present method is a qualitative analysis, and no specific calculation is set.
The specific method for analyzing the fault probability of the fan is to divide a temperature curve, analyze the temperature curve with different scales and weights, specifically analyze the sum of absolute values of all first derivatives smaller than 0, and the more obvious the temperature decline trend is, the larger the value is, and the more likely the temperature abnormality decline is. Finally consider the sum of the absolute values of the second derivatives at positions in the segment where the first derivative is less than 0, the second derivatives representing the rate of change of the first derivatives. And further, analysis of abnormal fan behaviors is accurately predicted.
As shown in fig. 2, in an embodiment of the present application, after S700, the method includes:
s800, the original thermal infrared image frames with the length of every 2WL frames are drawn into a time window, and the step length is set as the WL frames.
S810, dividing each time window into g time units with different lengths so as to acquire and fuse curve derivative information of different scales.
S820, different weights are distributed to different time units, and the anomaly score of each time unit is calculated according to the anomaly score calculation formula.
Specifically, when the temperature curve is analyzed, the anomaly score in each window is calculated by adopting a sliding window mode. It should be noted that the temperature profile is updated continuously with the number of frames, so a sliding window is used in the present application, for example: under a certain frame number, the temperature curve is divided into an A part, a B part and a C part, and a part of Z part appears along with the increase of the frame number, so that the Z part replaces the original A part, the original A part replaces the original B part, the original B part replaces the original C part, and the original C part slides out of the screen view, which is only a brief description, and the segmentation proportion of the A part, the B part and the C part in actual operation can be different.
The calculation of the anomaly score emphasizes the downward trend in the temperature change curve.
Since the small window is susceptible to oscillations within the normal range, a larger window is selected but easily contains the normal part, and the step size is set to half of the window in order to further determine the area of the problem. Calculation of anomaly score within each window:
wherein g represents the number of groups divided into different lengths in the window, wj represents the weight of the j-th group, p represents the number of segments contained in each group,
represents the number of frames belonging to the j-th group for which the first derivative is less than 0 in the k-th segment, +.>
The total number of frames of the kth segment of the jth group,
representing a first derivative value of less than 0 in the kth segment of group j,/>
Score represents the outlier value of the ith window, representing the second derivative value of the jth group in the kth segment where the first derivative is less than zero.
Table 1-anomaly score table.
Table 1 is an anomaly score table of a fan operation fault detection method according to an embodiment of the present application.
As shown in fig. 2 and table 1, each cell is 3000 frames. For the 3,4,5,6,7,8 portions, if the step size is the same as the window, the 3 and 4 windows yield a smaller anomaly score 4.16,5 and the 6 windows yield a larger anomaly score 284.98,7 and 8 yield a smaller anomaly score 0.58. If only the score judgment is seen, the problem accumulation is possibly caused by the whole of the 5 and 6 large windows, and the local large fluctuation is also possibly caused by the fact that a certain part has a large problem. Taking half of the window as the step size, the smaller scores 1.92,6 and 176.62 are obtained by 4 and 5 and the larger score 176.62, which indicates that the window 6 has larger fluctuation, and the abnormal position and the abnormal degree are further determined.
The abnormal score calculation method uses a calculation formula of abnormal scores in a sliding window, wherein the formula takes values smaller than zero in a first derivative and a second derivative of a temperature curve as parameters, and abnormal working state judgment basis is adopted when the temperature of the fan is reduced. A video can be taken out of the temperature curve of each part independently according to the temperature curve of each part, for example, the temperature curve of a volute is calculated, the running condition of the volute in the period of time can be determined, so that the part information is known in advance, and the abnormal information of the part is obtained according to the calculation so as to obtain whether the part is in a normal state or not.
The application provides a fan operation fault detection system.
As shown in fig. 3, in one embodiment of the present application, a fan operation failure detection system includes a thermal infrared imager 100 and a host computer 200.
Thermal infrared imager 100 is used to acquire fan infrared images and data.
The upper computer 200 is in communication connection with the thermal infrared imager 100, and is used for executing a fan operation fault detection method.
Specifically, the thermal infrared imager 100 acquires thermal infrared image videos of all parts of the fan, transmits the videos to the upper computer 200, and the upper computer 200 analyzes the videos and converts the videos into image frames, fits a real-time temperature curve through an anomaly detection model, and analyzes the possibility of the fan anomaly by utilizing a sliding window.
The thermal infrared imager 100 and the upper computer 200 are in communication connection, so that thermal infrared image video of each part of the fan and fan operation fault detection are completed.
The technical features of the above embodiments may be combined arbitrarily, and the steps of the method are not limited to the execution sequence, so that all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description of the present specification.
The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.
Claims (10)
1. A fan thermal fault detection method based on sliding window thermal imaging is characterized by comprising the following steps:
acquiring an original thermal infrared video of a fan; in the process of acquiring an original thermal infrared video of the fan, the rotating speed of the fan is adjusted in real time;
extracting an original thermal infrared image frame in an original thermal infrared video, carrying out component labeling on the original thermal infrared image frame, generating a plurality of component labels and labeled original thermal infrared images, and training a fan component robust segmentation model by taking the component labels and the labeled original thermal infrared images as training data to obtain a trained fan component robust segmentation model;
inputting each thermal infrared image frame to be detected in the thermal infrared video to be detected into a trained fan component robust segmentation model to obtain a binary mask image dataset of each thermal infrared image frame to be detected output by the trained fan component robust segmentation model; the binary mask map dataset includes a binary mask map for each component of the blower;
acquiring a temperature change curve of each part of the fan based on each thermal infrared image frame to be detected and a binary mask image data set corresponding to the thermal infrared image frame to be detected;
carrying out smoothing and normalization on each temperature change curve to obtain a first derivative curve and a second derivative curve corresponding to each temperature change curve;
judging whether the fan operation fault occurs according to the temperature change curves of all the components and the first derivative curve and the second derivative curve corresponding to each temperature change curve.
2. The method of claim 1, wherein the acquiring the raw thermal infrared video of the fan comprises:
controlling a thermal infrared imager to acquire an original thermal infrared video of a fan; the thermal infrared imager is flush with the physical center of the fan in height when the original thermal infrared video of the fan is acquired.
3. The fan operation fault detection method according to claim 2, wherein the extracting an original thermal infrared image frame in an original thermal infrared video, performing component labeling on the original thermal infrared image frame, generating a plurality of component labels and labeled original thermal infrared images, training a fan component robust segmentation model by using the plurality of component labels and labeled original thermal infrared images as training data, and obtaining a trained fan component robust segmentation model, includes:
extracting an original thermal infrared image frame from the original thermal infrared video;
performing component labeling on the original thermal infrared image frame by using Labelme software to obtain an original thermal infrared image after generating a plurality of component labels and labeling;
adding a plurality of component labels and the marked original thermal infrared images into a training set as a training sample;
and returning to the step of extracting one original thermal infrared image frame from the original thermal infrared video until all the original thermal infrared image frames are marked by the components.
4. The fan operation fault detection method according to claim 3, wherein the extracting an original thermal infrared image frame in an original thermal infrared video, performing component labeling on the original thermal infrared image frame, generating a plurality of component labels and labeled original thermal infrared images, training a fan component robust segmentation model by using the plurality of component labels and labeled original thermal infrared images as training data, and obtaining a trained fan component robust segmentation model, and further comprising:
and training the fan part robust segmentation model by using the training set, and obtaining the trained fan part robust segmentation model after the total loss function converges.
5. The method for detecting fan operation failure according to claim 4, wherein the acquiring the temperature change curves of the respective components of the fan based on each thermal infrared image frame to be detected and the binary mask map data set corresponding to the thermal infrared image frame to be detected includes:
selecting one thermal infrared image frame to be detected, and acquiring a time node corresponding to the thermal infrared image frame to be detected;
selecting one component in the thermal infrared image frame to be detected;
acquiring an image part with the component in the thermal infrared image frame to be detected, and taking the image part with the component in the thermal infrared image frame to be detected as a part to be processed;
acquiring a pixel value of each pixel point in the part to be processed, and solving a pixel mean value of the part to be processed according to the pixel value of each pixel point in the part to be processed;
taking the pixel mean value of the part to be processed as the temperature value of the part under the time node corresponding to the thermal infrared image frame to be detected;
returning to the selection of one component in the thermal infrared image frame to be detected until a temperature value of each component under a time node corresponding to the thermal infrared image frame to be detected is obtained;
and returning to the selected thermal infrared image frame to be detected, acquiring a time node corresponding to the thermal infrared image frame to be detected until all the thermal infrared image frames to be detected are traversed, and drawing a temperature change curve of each component of the fan according to the temperature value of each component in each thermal infrared image frame to be detected under the time node corresponding to the thermal infrared image frame to be detected.
6. The method of claim 5, wherein smoothing and normalizing each temperature change curve to obtain a first derivative curve and a second derivative curve corresponding to each temperature change curve, comprising
Preprocessing each temperature change curve;
and generating a first derivative curve and a second derivative curve corresponding to each temperature change curve according to each preprocessed temperature change curve.
7. The method of claim 6, wherein determining whether a fan operation failure has occurred based on the temperature change curves of the respective components and the first derivative curve and the second derivative curve corresponding to each temperature change curve, comprises,
judging whether the temperature of the fan has obvious fluctuation or not according to the temperature change curves of all the components and the first derivative curve and the second derivative curve corresponding to each temperature change curve;
if the temperature of the fan obviously fluctuates, the fan is determined to have operation faults.
8. The method of claim 7, wherein after the step of determining whether the temperature of the fan has significantly fluctuated based on the temperature profiles of the respective components and the first derivative profile and the second derivative profile corresponding to each of the temperature profiles, the method comprises,
analyzing the change condition of the temperature at each moment according to the first derivative curve, and analyzing the change speed condition of the temperature at the moment with the change according to the second derivative curve;
judging whether the temperature of the fan has obvious fluctuation or not according to the change condition of the temperature at each moment and the change speed condition of the temperature at the moment when the change exists.
9. The method of claim 8, further comprising, after said determining a fan operation failure,
drawing the original thermal infrared image frames with the length of each 2WL frame into a time window, and setting the step length as the WL frame;
dividing each time window into g time units with different lengths so as to acquire and fuse curve derivative information of different scales;
different weights are distributed to different time units, and the anomaly score of each time unit is calculated according to an anomaly score calculation formula.
10. A fan operation failure detection system, comprising:
the thermal infrared imager is used for acquiring infrared images and data of the fan;
the upper computer is in communication connection with the thermal infrared imager and is used for executing the fan operation fault detection method according to any one of claims 1-9.
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| CN116823872B (en) * | 2023-08-25 | 2024-01-26 | 尚特杰电力科技有限公司 | Fan inspection method and system based on target tracking and image segmentation |
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