CN112597581B - High-speed train temperature anomaly detection method based on space-time fusion decision - Google Patents
High-speed train temperature anomaly detection method based on space-time fusion decision Download PDFInfo
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Abstract
The invention discloses a high-speed train temperature anomaly detection method based on a space-time fusion decision. The time dimension shaft temperature abnormity detection model and the space dimension shaft temperature abnormity detection model are fused, and the time-space fusion-based detection is carried out on the temperature abnormity condition of the high-speed train vehicle component by combining the D-S evidence theory. The invention combines the temperature anomaly detection models of two dimensions of time and space by using the D-S evidence theory, thereby not only reducing the false alarm rate, but also ensuring larger early warning quantity, and realizing ' making best of the time and space dimensions ' and avoiding the shortest of the time and space dimensions '.
Description
Technical Field
The invention relates to a method for detecting temperature anomaly of a high-speed train, belongs to the field of temperature anomaly prediction in the running process of the high-speed train, and relates to a method for detecting temperature anomaly in the running process of the high-speed train based on deep learning.
Background
The method for predicting the temperature of the high-speed train comprises the steps of transmitting original signals acquired by a plurality of sensors to a computer in the running process of the high-speed train, carrying out a series of signal processing on the original signals to establish a mapping relation with temperature real-time prediction, predicting the temperature of a vehicle component at a later period of time after the current period of time by an intelligent temperature prediction algorithm, and taking the temperature as the basis for judging an abnormal state.
The current vehicle-mounted intelligent operation and maintenance system for the high-speed train mainly monitors the temperature, and utilizes the formulated corresponding logic rules and temperature threshold values to realize vehicle component state identification.
In recent years, abundant ideas and methods are provided for detecting abnormal temperature of a high-speed train by using temperature data, but the defect that early warning of abnormal temperature rise of a vehicle-mounted intelligent operation and maintenance system is difficult to realize still exists. Because the temperature data of the vehicle component of the high-speed train has strong correlation in the space dimension and the time dimension, it is very important to find a vehicle component temperature anomaly detection mechanism capable of reflecting the internal correlation between the space dimension and the time dimension of the temperature data.
Disclosure of Invention
The invention provides a high-speed train component temperature anomaly detection method based on space-time comparison in order to overcome the defects of vehicle component temperature anomaly detection in the prior art.
In order to achieve the above object, the technical solution of the present invention specifically includes the following technical steps:
a high-speed train temperature anomaly detection method based on a space-time fusion decision is characterized by comprising the following steps:
step 1, constructing a high-speed train vehicle component temperature anomaly detection model based on space dimensionality;
step 2, constructing a high-speed train vehicle component temperature abnormity detection model based on a time dimension;
step 3, constructing a high-speed train vehicle component temperature abnormity detection model based on a space-time fusion decision based on the abnormity detection models in the step 1 and the step 2, and judging whether the temperature of the high-speed train vehicle component is abnormal or not; wherein the vehicle component comprises one or more of a gearbox, a bearing, a motor.
Preferably, the constructing of the high-speed train vehicle component temperature anomaly detection model based on the spatial dimension specifically includes:
step A: sampling temperature data in a historical neighborhood region of similar measuring points of the vehicle component at each moment for multiple times by using a sliding window, acquiring 15-dimensional time domain characteristics of a maximum value, a minimum value, a peak-peak value, a mean value, a variance, a mean square value, a mean square amplitude, a square root amplitude, a mean amplitude, a peak value, a waveform, a pulse, a margin and a kurtosis, and establishing a characteristic space of each moment, wherein the similar measuring points refer to the temperature measuring points with the same space position of the vehicle component on the same vehicle;
and B: performing dimensionality reduction on the 15-dimensional time domain feature data by using a Principal Component Analysis (PCA) method to obtain a first principal component of the 15-dimensional time domain feature data, reserving a part of information which can represent the data characteristics most, and removing existing redundant information;
and C: the time domain characteristic information processed by the PCA is subjected to K-means clustering to cluster the first main component of the temperature time domain characteristic into normal and abnormal components, so that diagnosis and positioning of abnormal temperature rise among similar measuring points are realized to obtain a primary abnormal detection result;
step D: and constructing an abnormal temperature rise decision model of the high-speed train vehicle component to carry out secondary judgment on the primary abnormal detection result to obtain a secondary abnormal detection result.
The high-speed train vehicle component abnormal temperature rise decision model constructed in the step D specifically comprises the following steps:
step 1): dividing the temperature environment of the vehicle part into a low temperature area, a medium temperature area and a high temperature area; dividing each temperature zone into three continuous conditions of short time, medium time and long time according to duration; setting three criteria of large variance amplification, small variance and large variance under a short-time continuous condition, setting two criteria of small variance and large variance under a medium-time and long-time continuous condition, and taking the variance value of the first principal component after PCA processing as a measurement index of the degree of outlier;
step 2): determining the weight score of each criterion set in the step 1) based on expert experience, wherein the weight score is in direct proportion to the risk degree, and when the sum of the weight scores of the criteria of any one of the three temperature zones is greater than a set threshold value, judging that the temperature rise state at the moment belongs to fault temperature rise, so that an initial model of abnormal temperature rise of the vehicle part is constructed;
step 3): and (3) optimizing the initial model based on the initial model of the abnormal temperature rise of the vehicle component constructed in the step (2) by utilizing an AHP-entropy method to obtain a decision model of the abnormal temperature rise of the vehicle component of the high-speed train.
Preferably, the constructing of the time-dimension-based high-speed train vehicle component temperature anomaly detection model specifically includes:
step A: acquiring historical history data of the high-speed train, wherein the historical history data comprises temperature data of similar measuring points of vehicle parts of the high-speed train and multiple working condition data in the running process of the high-speed train;
and B: screening out sensitive data which is beneficial to temperature prediction of the vehicle component from the working condition data through a random forest algorithm, and screening out data with weak relevance with temperature change of the vehicle component;
and C: constructing a vehicle component temperature prediction model; forming the sensitive data and the temperature data into an input matrix XtInputting the matrix XtInputting the temperature data into a bidirectional long-time memory neural network, and outputting a temperature predicted value at the next moment; the learning rate in the bidirectional long-short time memory neural network is set to be 0.0005, the number of hidden nodes of the bidirectional long-short time memory neural network is 64, the whole network structure is provided with a two-layer bidirectional long-short time memory neural network structure, the loss proportion of a Dropout layer is set to be 0.5, the batch is set to be 100, the maximum iteration number is set to be 600, and the optimizer selects Adam gradient optimization;
step D: and constructing a high-speed train vehicle component temperature abnormity early warning strategy according to the residual error between the temperature predicted value and the actual monitoring value.
Preferably, the constructing of the high-speed train vehicle component temperature anomaly detection model based on the space-time fusion decision specifically includes:
step A: constructing a frame theta ═ A for identifying abnormal temperature of vehicle components of the high-speed train1,A2,A3}; wherein A is1、A2、A3Respectively representing normal, uncertain and fault states;
and B: constructing a high-speed train component temperature anomaly evidence body and a basic probability distribution function; respectively outputting the temperature anomaly detection model of the high-speed train vehicle component in the space dimension and the temperature anomaly detection model of the high-speed train vehicle component in the time dimension as two independent evidence bodies, and calculating a basic probability distribution function of the two dimension evidence bodies, wherein the calculation formula is as follows:
wherein i is 1, 2, 3, riThe average deviation degree in historical neighborhood intervals with different lengths is the mean value of the variance of the temperature of the same type of measuring points in the spatial dimension, and the mean value of the residual error between the predicted value and the actual value in the time dimension;
and C: fusing evidence; combining the basic probability distribution functions corresponding to the evidence body through a Dempster combination rule to obtain a new basic probability distribution function, and providing a basis for the temperature abnormity diagnosis of the high-speed train vehicle component based on a space-time contrast fusion decision;
step D: decision diagnosis; after obtaining the evidence fusion result, if m (A) existsi) Max { m (a) } and i ≠ 2, and satisfies:
then determine AiIs the final decision result; in the formula of1、ε2To set the threshold, set here to ε1＝0.1，ε20.3; judging the high-speed row according to the final decision resultWhether the vehicle component temperature is abnormal.
Compared with the prior art, the method takes the similarity of the working conditions of the vehicle components with the same type of measuring points and the outlier characteristic of temperature rise of the fault component under the normal condition as the starting point, and uses K-means to cluster and position the abnormal temperature rise measuring points, so that the online abnormal detection of the temperature of the vehicle components without the historical training model is realized; on the basis of a vehicle component abnormal temperature rise detection model, the invention provides an abnormal temperature rise decision model optimized based on an AHP-entropy method to carry out secondary judgment on a vehicle component temperature abnormal detection result, in the aspect of judging accuracy rate aiming at fault temperature data, the first-stage and second-stage abnormal detections provided by the invention can ensure 100% accuracy rate, and aiming at two groups of normal temperature data with suspected fault temperature rise, the decision model provided by the invention greatly eliminates high-frequency misjudgment of the abnormal detection model on the suspected fault temperature rise; in the running process of a high-speed train, the existing vehicle-mounted intelligent operation and maintenance system realizes temperature alarm in the running process by utilizing an absolute temperature threshold and a unit time temperature rise threshold, but when the alarm condition is triggered, the train often has faults or even operation accidents, and time allowance cannot be provided for accident prevention. The invention can effectively predict the predicted temperature value 1min after the current time by utilizing the constructed bidirectional long-time and short-time memory neural network; the invention overcomes the defects that the detection dimension of the temperature abnormity of the current vehicle component is single, and the diagnosis result is not comprehensive and accurate enough. Temperature sensitive information with higher characteristic proportion is purposefully selected through a random forest algorithm, redundant information is avoided being excessive, characteristic space dimensionality is reduced, analysis cost is reduced, and accuracy of an overall model is improved; the invention uses the idea that the temperature change trends of the same type measuring points of a train are synchronous and approximate, and inputs the temperature data of the same type measuring points as model data. Meanwhile, if the temperature data of the target measuring points are also input as a model, the abnormal temperature rise data section can also appear in a prediction result due to the existence of the abnormal temperature rise data section training model, and the searching of the abnormal temperature points is not facilitated, so that the temperature data of the target measuring points are screened out, and the problem that the abnormal temperature rise cannot be accurately identified due to the fact that the predicted temperature does not accord with the normal temperature rise rule is avoided; the model prediction precision of the two-way long-and-short-term memory neural network training is obviously higher than that of the common long-and-short-term memory neural network model, the limitation of the common long-and-short-term memory neural network on processing time sequence data in one way is overcome, the defect is effectively overcome by using the two-way long-and-short-term memory neural network, and the fact that the rule information of the temperature change of the vehicle part is simultaneously mined from the positive time sequence direction and the negative time sequence direction is more beneficial to temperature prediction and reduction; the invention is mainly from the aspect of facial diagnosis, hopes that the difference between the predicted value and the actual value for normal vehicle components fluctuates stably in a smaller range, and for abnormal vehicle components, the difference between the predicted value and the actual value is obvious and has continuity, and abnormal points can be found correctly; the method utilizes the residual error combined with a related criterion strategy as the criterion for early warning of abnormal temperature rise of the component; the invention utilizes the D-S evidence theory to realize the fusion decision of an anomaly detection model (space dimension) based on the AHP-entropy method optimization decision and an anomaly detection model (time dimension) based on the BilSTM prediction; compared with the method that the misjudgment points generated by the independent time or space dimension model diagnosis are eliminated after evidence fusion, the final fault diagnosis result conforms to the corresponding actual state, the fault early warning response can be made early, the reference basis is provided for the driving strategy adjustment, and therefore the occurrence of operation accidents is avoided more effectively.
Description of the drawings:
FIG. 1 is a method for detecting temperature anomaly of a bearing of a high-speed train in a spatial dimension according to the invention;
FIG. 2 is a first principal component distribution plot of a generic bearing feature;
FIG. 3 is a temperature rising characteristic diagram of a bearing of a high-speed train;
FIG. 4 is a high-speed train bearing abnormal temperature rise initial decision model;
FIG. 5 is an optimization decision model based on AHP-entropy method;
FIG. 6 is a hierarchical structure model for bearing abnormal temperature rise decision;
FIG. 7 is a diagram illustrating an example of misjudgment;
FIG. 8 illustrates a method for detecting temperature anomaly of a bearing of a time-dimension high-speed train according to the present invention;
FIG. 9 is a diagram showing the position distribution of the bearing temperature measurement points;
FIG. 10 is a diagram illustrating an order of importance of various types of measurement point information with respect to bearing temperature;
FIG. 11 is a bearing temperature prediction model based on a two-way long-and-short-term memory neural network;
FIG. 12 is a temperature rise state diagram of the train 1 in fault;
FIG. 13 is a temperature rise state diagram of the train 2 in fault;
FIG. 14 is a temperature rise state diagram of a train 3 in fault;
FIG. 15 is a diagram of the predicted absolute error distribution of the bearing temperature of the train 1;
FIG. 16 is a diagram of the predicted absolute error distribution of the bearing temperature of the train 2;
FIG. 17 is a diagram of the predicted absolute error distribution of the bearing temperature of the train 3;
FIG. 18 shows the prediction results of the fault measurement points of the day of the fault of the train 1;
FIG. 19 shows the results of the forecast of the fault stations on the day of the train 2 fault;
FIG. 20 shows the results of the forecast of the fault measurement points on the day of the fault of the train 3;
FIG. 21 is a time-space dimension oriented method for detecting abnormal axle temperature of a high-speed train;
FIG. 22 is a spatiotemporal contrast evidence fusion decision flow diagram;
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments.
Thus, the following detailed description of the embodiments of the invention is not intended to limit the scope of the invention as claimed, but is merely representative of some embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the embodiments of the present invention and the features and technical solutions thereof may be combined with each other without conflict.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
In the description of the present invention, it should be noted that the terms "upper", "lower", and the like refer to orientations or positional relationships based on those shown in the drawings, or orientations or positional relationships that are conventionally arranged when the products of the present invention are used, or orientations or positional relationships that are conventionally understood by those skilled in the art, and such terms are used for convenience of description and simplification of the description, and do not refer to or imply that the devices or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and the like are used merely to distinguish one description from another, and are not to be construed as indicating or implying relative importance.
The invention provides a high-speed train temperature anomaly detection method based on a space-time fusion decision for vehicle parts, and exemplarily, the invention provides a bearing as a detection object in a specific embodiment, however, the detection object targeted by the invention is not limited to the bearing, and the invention is also applicable to other vehicle parts of a high-speed train, such as a gear box and a motor.
Specifically, a construction process of the high-speed train bearing temperature anomaly detection model based on the spatial dimension is shown in the attached drawing 1. As various working conditions of the temperature measuring points at the same positions of the four axles on the same vehicle are similar, the temperature measuring points can be regarded as the same type of measuring points. Based on the consistency of the temperature rising trend of the same type of measuring points and the outlier of the temperature rising trend of the fault measuring points, a clustering diagnosis method is adopted to construct a bearing temperature anomaly detection model without historical data training. The method comprises the steps of utilizing a sliding window to conduct multiple sampling on bearing temperature data in a historical neighborhood region of the same type measuring point at each moment, obtaining 15-dimensional time domain characteristics of a maximum value, a minimum value, a peak-peak value, a mean value, a variance, a mean square value, a mean square amplitude, a square root amplitude, a mean amplitude, a peak value, a waveform, a pulse, a margin and a kurtosis, and establishing a characteristic space of each moment, wherein the same type measuring point refers to temperature measuring points at the same positions of four axles on the same vehicle. Similarly, for the gear box and the motor, the information of the corresponding temperature measuring point can be obtained in the manner. Because a part of redundant information exists in the 15-dimensional time domain feature space, a first principal component is extracted from the 15-dimensional time domain feature by using a Principal Component Analysis (PCA) method, a part of information which can represent the data features most is reserved, and noise and unimportant features in the data are removed. As shown in FIG. 2, the distribution of the first principal components of the similar bearing characteristics shows that the distribution of the first principal components of the temperature characteristics of the similar measuring points is relatively uniform under normal conditions, and the first principal components of the temperature characteristics of the bearing with the fault measuring points have obvious outliers compared with other similar measuring points under abnormal conditions. And then, the time domain characteristic information processed by PCA is subjected to K-means clustering to cluster the first principal component of the temperature time domain characteristic into normal and abnormal categories, if the clustering result has an isolated category, the position of a fault bearing in the same category of measuring points can be judged by separating normal data and abnormal data, and the first-level abnormal detection result of diagnosis and positioning of abnormal temperature rise among the same category of measuring points can be realized by judging whether the isolated category exists.
The complex inducement endows more uncertainty to the temperature state of the bearing, so that the abnormal temperature rise phenomenon of more suspected faults exists in the monitoring data of the bearing in the normal state, and if the abnormal detection result is directly used for identifying the bearing faults, more false alarms are bound to exist, so that the first-stage abnormal detection result needs to be further optimized and corrected.
As shown in fig. 3, based on the phenomenon, there are three general characteristics of abnormal temperature rise of the bearing: (1) the temperature rises suddenly in a short time, and the deviation degree is greatly increased; (2) deviation phenomenon continuously exists within a period of time, and the deviation degree is small; (3) the deviation phenomenon continuously exists in a period of time, and the deviation degree is large. And (3) making an abnormal temperature rise initial decision model by taking the three abnormal temperature rise state characteristics as a basis of criteria, wherein the basic structure of the initial decision model is shown in figure 4. Firstly, dividing the temperature zones into a low temperature zone, a middle temperature zone and a high temperature zone according to different bearing temperature environments, and setting different temperature zone division thresholds for each type of measuring points by considering the difference of working conditions of different measuring points. According to the characteristic of abnormal temperature rise, each temperature zone is divided into three continuous conditions of short time, medium time and long time according to the duration time, and the variance value of the first principal component obtained by PCA processing is used as the measurement index of the degree of outlier. Three criteria of large variance amplification, small variance and large variance are set under the short-time continuous condition, and two criteria of small variance and large variance are set under the medium-time and long-time continuous conditions. Since the same criterion usually represents abnormal conditions of different severity in different temperature zones and continuous conditions, the corresponding discrimination parameters should be different. And then, scoring is adopted to make an expression form of an initial decision model result, respective weight scores are set for each criterion, the scores of the same criterion in different temperature regions are different due to the difference of working conditions, the numerical value is in direct proportion to the danger degree, and when the sum of the weight scores of the criteria in any one of the three temperature regions is greater than a set threshold value, the temperature rise state at the moment is judged to belong to fault temperature rise. Specifically, 5 experts are subjected to questionnaire survey, the degree of risk of 21 temperature rise state indexes is respectively subjected to 0-15 points of scoring and alarm threshold setting, and the rounding integer numerical value of each average value is used as a preliminarily drawn scoring result. The score is in positive correlation with the degree of risk, i.e. the score is higher and more dangerous, and the specific calculation results are shown in the following table:
for the gear box and the motor, the initial model of the abnormal temperature rise of the corresponding parts is obtained by the same method.
And then, as shown in the attached figure 5, optimizing the initial decision model based on an AHP-entropy method to construct a high-speed train bearing abnormal temperature rise decision model. Specifically, a bearing abnormal temperature rise decision hierarchical structure model shown in fig. 6 is constructed according to an initial decision model and a hierarchical principle. And then determining a system layer and index layer judgment matrix based on the order of each layer of the hierarchical structure model, the scaling rule and the comprehensive consideration opinion of experts in the field, and obtaining the system layer weight, the index layer weight and the total weight of the judgment matrix according to an AHP method. Because the judgment matrix determined by the analytic hierarchy process has stronger subjectivity, the weight of the judgment matrix is corrected by an entropy method, certain high-speed train shaft temperature fault data is adopted, the sampling frequency is 1 time/min, each outlier index value of 2629 sampling points in total serves as an entropy method input sample, the entropy value and the entropy weight of each index are calculated by the steps of the entropy method, and finally the subjective weight obtained by the analytic hierarchy process is corrected by an AHP-entropy method combined weight calculation formula, so that the combined weight of each temperature rise state index on a target layer is obtained.
Specifically, a system layer judgment matrix and a weight calculation result are obtained through the levels of each layer of the hierarchical structure model, the scaling rule and the comprehensive consideration opinions of experts in the field, and are shown in table 3, and the maximum characteristic root lambda of the judgment matrix is obtained through calculation of an analytic hierarchy processmax3.014, the identity index CI was 0.007 and the random identity index CR was 0.012 as calculated by the analytic hierarchy process<0.10, passing the consistency test.
The judgment matrix of each index layer and the weight calculation result obtained by the order of each layer of the hierarchical structure model, the scaling rule and the comprehensive consideration opinion of experts in the field are shown in the following table. Similarly, the maximum characteristic root lambda of the index judgment matrix of the low temperature region is calculated according to the analytic hierarchy processmax7.316, 0.053 for consistency index CI and 0.040 for random consistency index CR<0.10; maximum characteristic root lambda of medium temperature zone index judgment matrixmax7.592, oneConsistency index CI is 0.099, and random consistency index CR is 0.075<0.10; maximum characteristic root lambda of index judgment matrix of high temperature zonemax7.715, identity index CI 0.119, and random identity index CR 0.090<0.10. It can be seen that each index layer judgment matrix passes the consistency check.
In order to ensure the objectivity of weight calculation, the temperature fault data of a bearing of a certain type of high-speed train is adopted, the axle temperature monitoring data of each bearing on one vehicle running for one day is defined as a sample, and the sampling frequency is 1 time/min. The test experiment set consists of 934 vehicle data, and comprises 930 bearing normal state vehicles (formed by combining part of vehicles extracted from different trains) and 4 bearing fault state vehicles (wherein 1 vehicle is used for adjusting the entropy value, and the other 3 vehicles are newly increased fault vehicles). Each vehicle has 4 bearings of the same type, i.e. the total number of test samples of each type of bearing is 3736.
And (3) taking each outlier index value of 2629 sampling points as an entropy method input sample, calculating the entropy value and the entropy weight of each index, and adjusting the hierarchy analysis method weight by combining the entropy weight with a combined weight calculation formula to obtain the combined weight of each temperature rise state index on the target layer, which is shown in the following table.
And performing weighted correction on each temperature rise state index score of the initial decision model by combining weights by an AHP-entropy method, rounding the score to an integer number, and finally obtaining the index score of the decision model as shown in the following table.
The spatial dimension high-speed train bearing temperature anomaly detection model is adopted to judge the data of each sample in one day point by point according to time sequence, and once an alarm occurs, the sample is defined as an alarm sample. In order to verify the effect of the AHP-entropy method optimization decision model on reducing the misjudgment rate, a primary anomaly detection result (without decision) and a secondary anomaly detection result (with AHP-entropy method optimization decision) are respectively counted, and the statistical results are shown in the following table.
If the bearing fails and is not detected, the behavior is dangerous, so the accuracy of the model for fault discrimination must be determined, and the calculated fault discrimination accuracy is shown in the following table. The results in the table clearly show that the fault discrimination accuracy for 4 groups of fault data can reach 100% before and after the decision model is added.
And the other condition is a false alarm, namely, the normal state of the bearing is judged as a fault state, as shown in fig. 7, the temperature data of the bearing at the position combined with the theoretical reality is normal data, but the model triggers an alarm, namely, the model is judged as a false alarm. False alarms affect user experience, may also cause economic losses when applied, and should be avoided as much as possible, so that the misjudgment rate of the test model is required, and the statistical result is shown in the following table. As can be clearly seen from the results in the table, in the normal data on the motor side of the large gear box in 155 days, the first-level abnormality detection misjudgment rate is 0.08%, and the second-level abnormality detection misjudgment rate is 0; in the normal data of the motor transmission end, the first-stage anomaly detection misjudgment rate is 1.10%, and the second-stage anomaly detection misjudgment rate is reduced to 0.24%, which shows that the misjudgment rate of the final result can be effectively reduced by carrying out secondary diagnosis on the shaft temperature anomaly detection model result by using the anomaly temperature rise decision model optimized based on the AHP-entropy method.
Therefore, the misjudgment rate of the primary abnormal detection result at the large gear box motor side of the high-speed train bearing temperature abnormal detection model with the space dimension is 0.08%, and the sample is not misjudged by the secondary abnormal detection result. Meanwhile, in view of the early warning time lead, the early warning time for the abnormal temperature rise of the motor side bearing of the pinion box can reach 173min, so that the early warning time is longer.
As mentioned above, the abnormal temperature rise decision model can be constructed for the gearbox and the motor in the same way as described above.
Subsequently, a high-speed train bearing temperature anomaly detection model based on a time dimension needs to be constructed. As shown in fig. 8, the high-speed train bearing temperature anomaly detection model is constructed based on the bidirectional long-and-short time memory neural network and is used for predicting the high-speed train bearing temperature anomaly in real time. Similarly, a time-dimension high-speed train gearbox or motor temperature anomaly detection model can be constructed based on the bidirectional long-time memory neural network. Specifically, the method comprises the following steps:
the information of each bearing temperature measuring point is collected through a vehicle-mounted intelligent operation and maintenance system of the high-speed train, the position distribution of each bearing temperature measuring point is as indicated by an arrow in the attached figure 9, namely the bearing temperature measuring point, and various working condition data information in the running process of the related high-speed train, such as the running speed of the train, the ambient temperature, the bearing load, the voltage, the current, the air spring pressure, the motor force and the like.
After sufficient data information is collected, because the data is redundant and complicated, a random forest algorithm is selected to screen out a part of information with weak correlation with bearing temperature change, an optimal characteristic data set is searched from the known data, and the characteristic space dimensionality is reduced.
Illustratively, the invention provides monitoring data of measuring points of a certain type of high-speed train part as shown in the following table, wherein the monitoring data comprises 7 whole train working condition data, 36 bearing temperature measuring point data and 28 rest types of measuring point data of a bogie.
Taking the construction of a bogie 1 axle pinion box wheel side bearing measurement point prediction model as an example, taking the bearing temperature as a target value of importance analysis, sensitive information selection is performed on various measurement point data except the bearing temperature by using a random forest method, and information importance distribution as shown in fig. 10 is obtained. It can be seen from the figure that the 6 measuring points of the combined pressure of the air springs, the pressure of the air springs of the channel 2, the ambient temperature, the pressure of the air springs of the channel 1 and the air springs of the vehicle and the running speed have significantly higher importance ratio than other measuring points, and account for 93.226 percent in total. The vehicle mass and the pressure of the 3 types of overhead gas springs all occupy higher importance proportion, and the data of the measuring points are directly or indirectly reflected by the train load, so that the fact that the train load belongs to an important factor influencing the temperature change of the bearing is explained. Meanwhile, the ambient temperature has a higher ratio, because the change of the bearing temperature in one day and the change of the ambient temperature have obvious synchronism, the temperature difference between the bearing and the outside of the vehicle is reduced due to the rise of the temperature outside the vehicle, the heat dissipation capacity of the bearing is reduced, and the temperature of the bearing is higher; and when the temperature outside the automobile is reduced, the temperature difference is increased, the heat dissipation capacity of the bearing is increased, and therefore the temperature of the bearing is lower. The running speed is also a big factor influencing the temperature rise of the bearing, and after the train starts to run, the bearing continuously runs, generates heat, accumulates heat and continuously rises in temperature; in the process of train braking, the heat generation amount begins to decrease, the temperature rising speed of the bearing is slowed down, but the temperature still continues to rise; after the train stops, the bearing stops generating heat, and the temperature of the bearing begins to gradually decrease.
In conclusion, the data of 6 measuring points, namely the comprehensive pressure of the air spring, the pressure of the air spring in the channel 2, the ambient temperature, the pressure of the air spring in the channel 1, the vehicle mass and the running speed, are used as input and added into the construction process of the bearing temperature prediction model. Combining the 6 measuring point data with bearing temperature data T1, T2 and T3 of 3 similar measuring points, totaling 9 measuring point data, wherein the sampling frequency is 1 time/min, and the data in a 5min historical neighborhood region at the current moment are included to form an input matrix Xt：
For a gear box and a motor, similarly, the collected historical data is screened by using a random forest algorithm, so that temperature sensitive data is determined, and an input matrix is generated by combining the temperature sensitive data with temperature information of similar measuring points and is used for inputting the temperature sensitive data into a constructed bidirectional long-time and short-time memory neural network.
As shown in fig. 11, 6 types of measurement point data obtained by other related signals except for bearing temperature data through a random forest algorithm are combined with 3 types of measurement point bearing temperature data T1, T2 and T3, and 9 types of measurement point data are summed as input data and input into the bidirectional long-short-time memory neural network, so that a mapping relation between data in a 5min history neighborhood interval at the current moment and a temperature predicted value 1min after the current moment is established, a temperature predicted value at the next moment is output, and a training model is continuously optimized to adjust parameters, so that the best effect is achieved, and online prediction of the bearing temperature is realized.
The bidirectional long-time and short-time memory neural network is provided for training, the selected data set is historical data collected by a vehicle-mounted sensor in the running process of a high-speed train, and the bidirectional long-time and short-time memory neural network has complex working condition parameters and huge data volume and has analysis value. Specifically, the adopted high-speed train data set is divided according to train numbers 1, 2 and 3, continuous operation history data of a certain type of high-speed train on the day of failure and a period of time before the failure occurs are selected to construct the data set, data information mainly comprises the temperature of 28 bearing measuring points on 2-7 motor train bogie gear boxes and traction motors of each train, the train operation speed, the environment temperature, various selected sensitive parameters and time information corresponding to the sensitive parameters, the sampling frequency is 1 time/min, 3 trains generate hot shaft alarm of a pinion box wheel side bearing on the last day in the data set, and the trains are subjected to emergency deceleration and stop processing after the alarm is triggered. Considering that the abnormal temperature rise of the bearing is usually caused by self abrasion or continuous abnormal working conditions, in order to avoid abnormal information of a model in the early stage of a fault from learning, a certain time interval needs to be set between a training set and test set data, the reliability of a model output result is ensured, the test set is divided into a normal type and a fault type, the test set 1 is used for verifying the model prediction precision, and the test set 2 is used for verifying the abnormal diagnosis effect of the model.
Fig. 12 to 14 show the temperature rise state of each train fault, fig. 15 to 17 show comparative analysis of the absolute error distribution diagram of the abnormal bearing temperature prediction of the bidirectional long-and-short term memory neural network proposed by the present invention and other typical methods, and the following table is a statistical table of the percentage of the relative error distribution interval of the prediction result:
the following table is a statistical table of the temperature prediction errors of normal data of the bearing:
wherein, the calculation formula of the average relative error and the average absolute error is as follows:
and then, outputting a predicted value by the constructed bearing temperature prediction model based on the bidirectional long-term and short-term memory neural network, taking a residual error between the predicted value and an actual monitoring value of the bearing temperature of the high-speed train as a representation index, and combining a formulated continuous condition based on a statistical process control exception criterion as a discrimination basis, wherein if one of the discrimination basis is met, the bearing temperature actual value and a theoretical value are continuously deviated for a long time, and then carrying out exception temperature rise early warning.
Specifically, the mean μ and the variance σ are calculated from the residuals, and the calculation formula is as follows:
in the formula, tiThe actual value of the bearing temperature at the time of the history i,
a bearing temperature predicted value representing a history i time; taking mu +/-3 sigma as an upper and lower limit threshold for distinguishing abnormal distribution of residual errors, and considering the difference of working conditions, calculating different thresholds for different bearing measuring points respectively, wherein the residual error overrun threshold of each bearing measuring point is shown in the following table:
starting from the actual phenomenon characterized by the residual variation, the following continuous conditional discriminant criteria are established according to Statistical Process Control (SPC):
(1) residual errors continuously exceed the limit for 3 min;
(2) residual errors continuously exceed the limit for 10 min;
(3) the residual error is continuously 6min and monotonically increases or decreases;
(4) the judgment criteria (1) in the train running are accumulated for more than 5 times in the same day.
The criterion (1) is only used as an internal recording index and not used as a model output result. When the residual statistical analysis result meets one of the criteria (2), (3) and (4), the long-time continuous deviation of the actual value and the theoretical value of the bearing temperature is shown, and then abnormal temperature rise early warning is carried out.
Example verification shows that the prediction results of each fault point on the day of the fault are shown in fig. 18 to 20, alarm prompts generally appear at each fault point at the actual alarm time in advance of the train alarm log, and the results in the following table show that the train 1 has continuous overrun and alarm prompts in advance of 302 minutes; the train 3 is advanced 91 minutes to generate continuous overrun condition and alarm prompt; the train 2 only gives an alarm prompt in advance for 16 minutes because the fault occurs about 30 minutes after the train starts to run on the same day, which shows that the proposed time dimension model can realize early warning of abnormal temperature rise of the bearing, and the specific early warning time information is shown in the following table:
it can be further found from the above table that, in the misjudgment points of the train 1, the motor side of the pinion box of the 6 th vehicle 1 shaft also has a great prediction accuracy decrease phenomenon at the approximate moment when the actual fault occurs, and considering that the measurement point and the actually faulty pinion box wheel side belong to adjacent measurement points on the transmission chain, the abnormal temperature increase phenomenon of synchronization can be caused by the influence of the abnormal impact of the fault measurement point, so that the prediction accuracy decreases, and false alarm is triggered. The 1-axle pinion-box wheel side of the 1-car 7 th vehicle also presented a small amount of false positives due to the effect of longitudinal shock transmitted by the actual point of failure.
In the misjudgment points of the train 2, the motor side of the pinion gear box of the 2-section train 2 also has a phenomenon of descending prediction precision to a greater extent at the approximate moment of the actual fault, and also has a phenomenon of synchronous abnormal temperature rise due to the influence of the motor side of the pinion gear box adjacent to the actual fault detection point, so that the prediction precision is descended, and a false alarm is triggered. The 4-shaft of 2-section vehicles with 2 cars and the 2-shaft pinion box wheel side of 3-section and 4-section vehicles also have different numbers of false alarms, and are also caused by longitudinal impact transmitted by actual fault measuring points in train operation.
The invention uses a plurality of groups of experimental data for verification, the experimental result is effective, the prediction accuracy is excellent, and the early warning time is greatly advanced. Integrating the collected bearing temperature signals and other channel data which can represent the temperature signals correspondingly, screening bearing temperature sensitive information for the channel data except the bearing temperature by adopting a random forest algorithm, eliminating some redundant data, and finally obtaining a 9-channel data input matrix XtThe relevance between deep-level feature searching data is extracted by the input bidirectional long-time and short-time memory neural network, the bearing temperature value at the next moment is finally output, and then continuous conditions are used as judgment of early warning of abnormal temperature of the bearing by combining residual errors between predicted values and actual monitoring values of the bearing temperatureOn the other hand, a high-speed train bearing abnormal temperature early warning strategy is constructed, and misjudgment of a diagnosis result caused by short-time large errors brought by various randomness in the bearing temperature monitoring and predicting process is avoided. The invention utilizes the random forest algorithm to screen the characteristics, combines the bearing temperature abnormity monitoring model constructed by the bidirectional long-time memory neural network, can effectively excavate deep-level relation in data set, is provided for the first time, and has innovativeness, practicability and economy.
For a gear box and a motor, a temperature anomaly detection model constructed based on a long-time memory neural network is constructed in the same way.
Finally, in order to take the advantages of the high-speed train bearing temperature anomaly detection model with the time dimension and the high-speed train bearing temperature anomaly detection model with the space dimension into consideration and better adapt to the intelligent operation and maintenance environment of the high-speed train at the present stage, the invention combines the axle temperature anomaly detection models with the time dimension and the space dimension by utilizing the D-S evidence theory, and provides the high-speed train axle temperature anomaly detection method based on the space-time fusion decision, so that the advantages of the two models can be fully exerted, and the defects of the two models can be made up. The specific flow is shown in fig. 21.
Specifically, as shown in fig. 22, the method for detecting abnormal axle temperature of a high-speed train based on a space-time fusion decision provided by the invention specifically comprises the following steps:
1. and constructing a fault identification frame. According to the actual requirement of abnormality detection, constructing a high-speed train abnormal bearing temperature identification frame theta ═ { A }1,A2,A3Represents three states of normal, uncertain and faulty, respectively.
2. And constructing a proof body and a basic probability distribution function. Based on the constructed space dimension and time dimension anomaly detection models, the respective outputs of the anomaly detection model (space dimension) based on AHP-entropy method optimization decision and the anomaly detection model (time dimension) based on BilSTM prediction are used as two independent evidence bodies, and the basic probability distribution function of the two dimension evidence bodies is calculated by using the following formula.
Wherein i is 1, 2, 3, riThe average deviation degree in the history neighborhood intervals with different lengths is the variance mean value of the temperature measuring points of the same bearing under a space model, and the residual mean value of the predicted value and the actual value under a time model. Wherein r is1The interval of (1) is the first 20 minutes of the history neighborhood of 30 minutes, the deviation mean value of the interval is regarded as a stable numerical value of the bearing temperature in a longer period of time, and the stable numerical value is used as a reference for evaluating whether the shaft temperature is in a normal state; r is2The interval of (1) is the last 10 minutes of the history neighborhood of 30 minutes, and the deviation mean value of the interval is used for representing the fluctuation condition of the bearing temperature rise state in the latest period of time; r is3And reflecting the instant state of the bearing temperature for the bearing temperature deviation degree at the current moment. When i is 1, diIs equivalent to r1Absolute value of (d); when i ≠ 1, diThen represent r2And r3Relative to r1The absolute value of the increment of (c). When the bearing temperature is always in a normal state, the deviation mean values of the three intervals are relatively close, so that r2And r3Relative to r1The change amplitude of (2) is small; when the bearing begins to rise at abnormal temperature, the deviation state of the abnormal temperature rise relative to the normal temperature rise is gradually obvious, and r is2And r3Will also gradually increase in magnitude. Thus, corresponds to normal (A) in the recognition frame Θ1) Uncertain (A)2) And fault (A)3) Three states, d1、d2、d3And respectively taking the ratio of the total sum of the two as the probability that the bearing belongs to the corresponding state at the current moment, so as to construct a basic probability distribution function.
3. And fusing the evidence. And combining the basic probability distribution functions corresponding to the evidence bodies through a Dempster combination rule to obtain a new basic probability distribution function, and providing a basis for the high-speed train axle temperature abnormity diagnosis based on space-time comparison fusion decision.
4. And (5) decision diagnosis. After the evidence fusion result is obtained, the result needs to be decided, so as to obtain a final diagnosis conclusion. If m (A) is presenti) Max { m (a) } and i ≠ 2, and satisfies:
then determine AiIs the final decision result. In the formula of1、ε2To set the threshold, set here to ε1＝0.1，ε2＝0.3。
The experimental data source is continuous operation monitoring data of a certain type of three-row high-speed train of a time dimension model on the same day of failure alarm and a certain number of days before the alarm, the three rows of trains are all 8 marshalling, various bearing temperature measuring points on traction motors and gear boxes of 2-7 motor trains of each train and other modeling related working condition parameters are extracted, and the sampling frequency of various input parameters is 1 time/min.
The method comprises the steps of achieving space-time fusion decision based on the identification frame theta with the structure as the basis, on the basis of three-train running monitoring data, using historical neighborhood deviation index average values with different lengths output by a space and time dimension anomaly detection model to construct evidence bodies, calculating basic probability distribution functions of the two dimension evidence bodies, achieving evidence body fusion through a Dempster combination rule, obtaining a new basic probability distribution function, and finally applying an evidence fusion result to a decision diagnosis rule to obtain a final conclusion.
As the accumulated alarm times of the alarm measuring points are possibly more, only basic probability distribution functions and decision results of different evidence bodies when the fault measuring points diagnosed by the three models of time dimension, space dimension and space-time fusion decision trigger the axle temperature alarm for the first time are listed.
The following table respectively shows basic probability distribution functions of the shaft temperature measuring points for triggering time and space dimension model alarm for the first time, and the information in the table shows that misjudgment of large scale exists in the abnormal detection result of the time dimension model, and the specific reason is explained in the foregoing of the invention, and is not repeated here, but the early warning capability is the advantage thereof; the abnormal detection result of the space dimension model shows that the error judgment points exist, but the number of the error judgment points is much smaller than that of the time dimension model, so that the secondary decision making by using the abnormal temperature rise decision making model under the data set has the obvious effect of reducing the error judgment rate, but the space dimension model can only realize instant alarm or hot axis early warning of extremely short time lead in the aspect of alarm.
The following table shows the basic probability distribution function of the alarm measuring point of the first-time triggered space-time fusion decision model and the comparison of the diagnosis results of the three-dimensional models. By means of the D-S evidence fusion theory and decision rules, misjudgment points existing in the previous single-dimensional model preliminary diagnosis are screened out, the number of alarm times output by final diagnosis is reduced, the diagnosis result obtained by fusion decision is consistent with the actual state, and false alarms appearing on the data set by the time and space dimension model are successfully eliminated. Meanwhile, when the abnormal temperature rise phenomenon of an actual hot shaft fault measuring point is faced by the space-time fusion decision model, the early warning of the fault can be realized to a certain extent, although the early warning lead is not as good as that of the time dimension model, the hot shaft fault can be informed to be about to occur in advance under the condition that the misjudgment rate is reduced to a large extent, and the 'good-out and short-out' of the time and space dimension model is realized.
For a gear box and a motor, similarly, the established anomaly detection model result based on the time dimension and the space dimension is used for constructing an evidence body, basic probability distribution functions of the two dimensional evidence bodies are calculated, then evidence body fusion is realized by a Dempster combination rule, a new basic probability distribution function is obtained, and finally an evidence fusion result is applied to a decision diagnosis rule to obtain a final conclusion.
The invention takes a high-speed train bearing as an example, the feasibility of the method is proved by comparing and analyzing the actual temperature monitoring values of a plurality of groups of high-speed trains, from the safety perspective, the invention can accurately find the abnormal temperature rise position of the temperature for the abnormal temperature rise phenomenon of the vehicle components, realize the early warning of the abnormal temperature with larger lead, provide more sufficient time for fault treatment, prompt related operators to take corresponding measures, reduce unnecessary loss and improve the running safety of the high-speed trains.
The above embodiments are only used for illustrating the invention and not for limiting the technical solutions described in the invention, and although the present invention has been described in detail in the present specification with reference to the above embodiments, the present invention is not limited to the above embodiments, and therefore, any modification or equivalent replacement of the present invention is made; all such modifications and variations are intended to be included herein within the scope of this disclosure and the appended claims.
Claims (3)
1. A high-speed train temperature anomaly detection method based on a space-time fusion decision is characterized by comprising the following steps:
step 1, constructing a high-speed train vehicle component temperature anomaly detection model based on space dimensionality;
step 2, constructing a high-speed train vehicle component temperature abnormity detection model based on a time dimension;
step 3, constructing a high-speed train vehicle component temperature abnormity detection model based on a space-time fusion decision based on the abnormity detection models in the step 1 and the step 2, and judging whether the temperature of the high-speed train vehicle component is abnormal or not; wherein the vehicle component comprises one or more of a gearbox, a bearing, a motor;
wherein, the step 1 specifically comprises:
step A: sampling temperature data in a historical neighborhood region of similar measuring points of the vehicle component at each moment for multiple times by using a sliding window, acquiring 15-dimensional time domain characteristics of a maximum value, a minimum value, a peak-peak value, a mean value, a variance, a mean square value, a mean square amplitude, a square root amplitude, a mean amplitude, a peak value, a waveform, a pulse, a margin and a kurtosis, and establishing a characteristic space of each moment, wherein the similar measuring points refer to the temperature measuring points with the same space position of the vehicle component on the same vehicle;
and B: using a Principal Component Analysis (PCA) to perform dimensionality reduction on the 15-dimensional time domain feature data to obtain a first principal component of the 15-dimensional time domain feature data, reserving a part of information which can represent the data characteristics most, and removing existing redundant information;
and C: the time domain characteristic information processed by the PCA is subjected to K-means clustering to cluster the first main component of the temperature time domain characteristic into normal and abnormal components, so that diagnosis and positioning of abnormal temperature rise among similar measuring points are realized to obtain a primary abnormal detection result;
step D: constructing an abnormal temperature rise decision model of the high-speed train vehicle components to carry out secondary judgment on the primary abnormal detection result to obtain a secondary abnormal detection result,
the step 3 specifically comprises the following steps:
step A: constructing a frame theta ═ A for identifying abnormal temperature of vehicle components of the high-speed train1,A2,A3}; wherein A is1、A2、A3Respectively representing normal, uncertain and fault states;
and B: constructing a high-speed train component temperature anomaly evidence body and a basic probability distribution function; respectively outputting the temperature anomaly detection model of the high-speed train vehicle component in the space dimension and the temperature anomaly detection model of the high-speed train vehicle component in the time dimension as two independent evidence bodies, and calculating a basic probability distribution function of the two dimension evidence bodies, wherein the calculation formula is as follows:
wherein i is 1, 2, 3, riThe average deviation degree in historical neighborhood intervals with different lengths is the mean value of the variance of the temperature of the same type of measuring points in the spatial dimension, and the mean value of the residual error between the predicted value and the actual value in the time dimension;
and C: fusing evidence; combining the basic probability distribution functions corresponding to the evidence body through a Dempster combination rule to obtain a new basic probability distribution function, and providing a basis for the temperature abnormity diagnosis of the high-speed train vehicle component based on a space-time contrast fusion decision;
step D: decision diagnosis; after obtaining the evidence fusion result, if m (A) existsi) Max { m (a) } and i ≠ 2, and satisfies:
then determine AiIs the final decision result; in the formula of1、ε2To set the threshold, set here to ε1＝0.1，ε20.3; and judging whether the temperature of the high-speed train component is abnormal or not according to the final decision result.
2. The method for detecting the abnormal temperature of the high-speed train based on the space-time fusion decision as claimed in claim 1, wherein the abnormal temperature rise decision model of the vehicle components of the high-speed train constructed in the step D specifically comprises:
step 1): dividing the temperature environment of the vehicle part into a low temperature area, a medium temperature area and a high temperature area; dividing each temperature zone into three continuous conditions of short time, medium time and long time according to duration; setting three criteria of large variance amplification, small variance and large variance under a short-time continuous condition, setting two criteria of small variance and large variance under a medium-time and long-time continuous condition, and taking the variance value of the first principal component after PCA processing as a measurement index of the degree of outlier;
step 2): determining the weight score of each criterion set in the step 1) based on expert experience, wherein the weight score is in direct proportion to the risk degree, and when the sum of the weight scores of the criteria of any one of the three temperature zones is greater than a set threshold value, judging that the temperature rise state at the moment belongs to fault temperature rise, so that an initial model of abnormal temperature rise of the vehicle part is constructed;
step 3): and (3) optimizing the initial model based on the initial model of the abnormal temperature rise of the vehicle component constructed in the step (2) by utilizing an AHP-entropy method to obtain a decision model of the abnormal temperature rise of the vehicle component of the high-speed train.
3. The method for detecting the temperature anomaly of the high-speed train based on the space-time fusion decision as claimed in claim 1, wherein the step 2 specifically comprises:
step A: acquiring historical history data of the high-speed train, wherein the historical history data comprises temperature data of similar measuring points of vehicle parts of the high-speed train and multiple working condition data in the running process of the high-speed train;
and B: screening out sensitive data which is beneficial to temperature prediction of the vehicle component from the working condition data through a random forest algorithm, and screening out data with weak relevance with temperature change of the vehicle component;
and C: constructing a vehicle component temperature prediction model; forming the sensitive data and the temperature data into an input matrix XtInputting the matrix XtInputting the temperature data into a bidirectional long-time memory neural network, and outputting a temperature predicted value at the next moment; wherein, the learning rate in the bidirectional long and short term memory neural network is set to be 0.0005, the hidden node number of the bidirectional long and short term memory neural network is 64, and the whole network structure is provided with twoThe neural network structure is memorized in a two-way long-time mode, the loss proportion of a Dropout layer is set to be 0.5, the batch setting is 100, the maximum iteration time is set to be 600, and the optimizer selects Adam gradient optimization;
step D: and constructing a high-speed train vehicle component temperature abnormity early warning strategy according to the residual error between the temperature predicted value and the actual monitoring value.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108920426A (en) * | 2018-07-04 | 2018-11-30 | 西北工业大学 | A kind of method for diagnosing faults based on power equal operator and DS evidence theory |
| DE102018221684A1 (en) * | 2018-12-13 | 2020-06-18 | Robert Bosch Gmbh | Device and method for anomaly detection and for training a model for anomaly detection |
| CN111829693A (en) * | 2020-07-30 | 2020-10-27 | 成都运达科技股份有限公司 | Bearing temperature detection method and system based on LSTM model of time and space dimensions |
| CN111931806A (en) * | 2020-06-23 | 2020-11-13 | 广州杰赛科技股份有限公司 | Equipment fault diagnosis method and device for multi-sensor data fusion |
-
2020
- 2020-11-16 CN CN202011281777.1A patent/CN112597581B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108920426A (en) * | 2018-07-04 | 2018-11-30 | 西北工业大学 | A kind of method for diagnosing faults based on power equal operator and DS evidence theory |
| DE102018221684A1 (en) * | 2018-12-13 | 2020-06-18 | Robert Bosch Gmbh | Device and method for anomaly detection and for training a model for anomaly detection |
| CN111931806A (en) * | 2020-06-23 | 2020-11-13 | 广州杰赛科技股份有限公司 | Equipment fault diagnosis method and device for multi-sensor data fusion |
| CN111829693A (en) * | 2020-07-30 | 2020-10-27 | 成都运达科技股份有限公司 | Bearing temperature detection method and system based on LSTM model of time and space dimensions |
Non-Patent Citations (2)
| Title |
|---|
| 基于AHP-熵值法优化决策的轨道车辆轴承异常温升检测模型;吴宇等;《铁道科学与工程学报》;20201115;第2909-2919页 * |
| 基于随机森林的长短期记忆网络气温预测;陶晔等;《计算机工程与设计》;20190316;第737-743页 * |
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