CN115700363A - Fault diagnosis method and system for rolling bearing of coal mining machine, electronic equipment and storage medium - Google Patents
Fault diagnosis method and system for rolling bearing of coal mining machine, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention provides a fault diagnosis method and system for a rolling bearing of a coal mining machine, electronic equipment and a storage medium, wherein the method comprises the following steps: establishing a digital twin model of the rolling bearing of the target coal mining machine and a corresponding rolling bearing fault diagnosis model based on the rolling bearing of the target coal mining machine; the rolling bearing fault diagnosis model is determined based on a probabilistic neural network; determining a target rolling bearing fault diagnosis model based on a digital twin model and a rolling bearing fault diagnosis model according to a firefly optimization algorithm; and determining a fault result of the rolling bearing of the target coal mining machine based on the fault diagnosis model of the target rolling bearing. The accuracy of predictive fault diagnosis of the rolling bearing of the coal mining machine can be effectively improved, and the running cost of the coal mining machine is reduced.
Description
Technical Field
The invention relates to the technical field of process industry, in particular to a fault diagnosis method and system for a rolling bearing of a coal mining machine, electronic equipment and a storage medium.
Background
Along with the continuous improvement of integration, intellectualization and automation degree, production equipment in the process industrial production line is more and more complex and precise, and the difficulty of equipment maintenance and fault treatment is increased. The method has the advantages that important parameters of the equipment can be accurately mastered, the generation reason of the fault can be analyzed, the potential fault hidden danger of the equipment can be predicted, the field production can be greatly facilitated, the running cost of the equipment can be reduced, and the shutdown time of a production line due to the fault can be reduced.
The working environment of the coal mining machine is complex and severe, the load change is large, and some key parts are easy to overload and have abnormity in normal work. The coal mining machine traction walking chain wheel has large load and uneven load, and the bearing of the coal mining machine traction walking chain wheel is easy to have the problems of abrasion, rolling body fracture and the like. In case of mild injury, it is not easy for the worker to find. Serious damage to the bearings may affect the sprocket shaft, the sprocket and other parts engaged therewith, thereby causing damage to other parts. When the fault is developed to a serious extent and can not work, the fault can be perceived to some extent, and the waste of manpower and financial resources is caused. The fault of the coal mining machine is diagnosed in advance, so that the production efficiency can be effectively improved.
But the most important difficulty in the process industry is the difficulty in modeling, so that the prediction and the optimization are difficult. The method for carrying out predictive fault diagnosis on the rolling bearing of the coal mining machine in the prior art has low accuracy.
Therefore, how to provide a fault diagnosis method and system for a rolling bearing of a coal mining machine, electronic equipment and a storage medium effectively improves the accuracy of predictive fault diagnosis of the rolling bearing of the coal mining machine and reduces the operation cost of the coal mining machine.
Disclosure of Invention
Aiming at the defects in the prior art, the embodiment of the invention provides a fault diagnosis method and system for a rolling bearing of a coal mining machine, electronic equipment and a storage medium.
The invention provides a fault diagnosis method for a rolling bearing of a coal mining machine, which comprises the following steps:
establishing a digital twin model of a rolling bearing of the target coal mining machine and a corresponding rolling bearing fault diagnosis model based on the rolling bearing of the target coal mining machine; the rolling bearing fault diagnosis model is determined based on a probabilistic neural network;
determining a target rolling bearing fault diagnosis model based on a digital twin model and a rolling bearing fault diagnosis model according to a firefly optimization algorithm;
and determining a fault result of the rolling bearing of the target coal mining machine based on the fault diagnosis model of the target rolling bearing.
According to the fault diagnosis method for the rolling bearing of the coal mining machine, provided by the invention, based on the digital twin model and the rolling bearing fault diagnosis model, the target rolling bearing fault diagnosis model is determined according to a firefly optimization algorithm, and the fault diagnosis method specifically comprises the following steps:
determining a fault sample training set according to fault sample data based on a digital twin model;
training a fault diagnosis model of the rolling bearing based on a fault sample training set;
optimizing the smoothing factor of the probabilistic neural network according to a firefly algorithm to determine an optimal smoothing factor;
and determining a fault diagnosis model of the target rolling bearing based on the optimal smoothing factor.
According to the fault diagnosis method for the rolling bearing of the coal mining machine, provided by the invention, based on the optimal smooth factor, a fault diagnosis model of a target rolling bearing is determined, and the fault diagnosis method specifically comprises the following steps:
determining an optimized probabilistic neural network according to the optimal smoothing factor;
testing the optimized probabilistic neural network based on the fault sample test set, and judging whether the optimized probabilistic neural network can accurately carry out fault diagnosis;
if the optimized probabilistic neural network cannot accurately diagnose the fault, repeating the steps of training and testing the fault diagnosis model of the rolling bearing based on the fault sample training set until the optimized probabilistic neural network can accurately diagnose the fault and determine a target probabilistic neural network;
and determining a target rolling bearing fault diagnosis model based on the target probability neural network.
According to the fault diagnosis method for the rolling bearing of the coal mining machine, provided by the invention, the probability neural network smoothing factor is optimized according to a firefly algorithm, and the optimal smoothing factor is determined, and the fault diagnosis method specifically comprises the following steps:
optimizing the probability neural network smoothing factor according to an improved firefly algorithm to determine an optimal smoothing factor;
the improved firefly algorithm is characterized in that inertia weight is added to a standard firefly algorithm;
the position updating formula of the firefly i attracted to move towards the firefly j in the improved firefly algorithm is as follows:
x i (t+1)＝w(t)x i (t)+β(x j (t)-x i (t))+α new (rand-0.5)；
in the above formula, x i (t) and x j (t) are the positions of firefly i and firefly j after the t-th displacement in space, w (t) is the inertial weight, alpha new As step size factor, β is the attraction of firefly j to firefly i, and rand is [0,1 ]]Uniformly distributed random factors are subjected to;
the inertia weight calculation formula is as follows:
in the above formula, w max Is the maximum inertial weight, w min T is the maximum number of iterations for the minimum inertial weight.
According to the fault diagnosis method for the rolling bearing of the coal mining machine, the improved firefly algorithm is characterized in that inertia weight is added in a standard firefly algorithm, and step length factors are improved;
the step factor calculation formula is:
in the above formula, t is the current iteration number, a 0 Is the initial step size factor.
According to the fault diagnosis method for the rolling bearing of the coal mining machine, provided by the invention, the probability neural network smoothing factor is optimized according to an improved firefly algorithm, and the optimal smoothing factor is determined, and the fault diagnosis method specifically comprises the following steps:
initializing basic parameters of the firefly, and taking a probability neural network smoothing factor as a firefly individual;
calculating the fitness value of the firefly individual according to the initial position of the firefly;
comparing the brightness of the firefly based on the fitness value, determining the brightest position as a new position of the firefly, and updating the position of the firefly according to a position updating formula;
and iteratively updating the fitness value and the position of the firefly until a preset optimizing condition is met, outputting the optimal firefly position, and determining an optimal smoothing factor.
According to the fault diagnosis method for the rolling bearing of the coal mining machine, provided by the invention, the preset optimizing condition comprises the following steps:
the iteration times are greater than a preset iteration threshold; or
The result of the objective function is smaller than a preset expected threshold value;
wherein the objective function formula is:
in the above formula, E i For actual output, R i For the desired output, N is the total number of fireflies.
The invention also provides a fault diagnosis system for the rolling bearing of the coal mining machine, which comprises the following components: the device comprises a model building unit, a model determining unit and a fault diagnosis unit;
the model building unit is used for building a digital twin model of the rolling bearing of the target coal mining machine and a corresponding fault diagnosis model of the rolling bearing based on the rolling bearing of the target coal mining machine;
the model determining unit is used for determining a target rolling bearing fault diagnosis model based on the digital twin model and the rolling bearing fault diagnosis model according to a firefly optimization algorithm; the rolling bearing fault diagnosis model is determined based on a probabilistic neural network;
and the fault diagnosis unit is used for determining a fault result of the rolling bearing of the target coal mining machine based on the fault diagnosis model of the target rolling bearing.
The invention also provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the steps of the fault diagnosis method for the rolling bearing of the coal mining machine.
The present invention also provides a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of any of the shearer rolling bearing fault diagnosis methods described above.
According to the fault diagnosis method and system for the rolling bearing of the coal mining machine, the electronic equipment and the storage medium, the digital twin model of the rolling bearing of the coal mining machine in the process industry is constructed, the digital twin technology is combined with the probabilistic neural network to construct the fault diagnosis model of the rolling bearing, the model is optimized by adopting the firefly algorithm, the rapid training of the model is realized, and the problem of low accuracy of a prediction result caused by inaccurate modeling is effectively solved. Through the rolling bearing fault diagnosis model, the rolling bearing can be subjected to predictive fault diagnosis, the accuracy of predictive fault diagnosis of the rolling bearing of the coal mining machine can be effectively improved, and the running cost of the coal mining machine is reduced.
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In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a flow chart of a fault diagnosis method for a rolling bearing of a coal mining machine provided by the invention;
FIG. 2 is a schematic diagram of a digital twin model structure provided by the present invention;
FIG. 3 is a schematic view of a fault diagnosis process of a rolling bearing of a coal mining machine provided by the present invention;
FIG. 4 is a schematic flow chart of a fault diagnosis method for a rolling bearing of a coal mining machine provided by the invention;
FIG. 5 is a graph of the probabilistic neural network prediction effect based on firefly algorithm optimization according to the present invention;
FIG. 6 is a schematic diagram of a probabilistic neural network training error based on firefly algorithm optimization according to the present invention;
FIG. 7 is a diagram of the prediction effect of the probabilistic neural network based on the improved firefly algorithm optimization provided by the present invention;
FIG. 8 is a schematic diagram of a probabilistic neural network training error based on improved firefly algorithm optimization according to the present invention;
FIG. 9 is a schematic structural diagram of a fault diagnosis system for a rolling bearing of a coal mining machine provided by the invention;
fig. 10 is a schematic physical structure diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present 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.
The digital twin technology has virtual-real interaction capacity, can be used for mapping data acquired in real time to a digital twin body in an associated manner, and can be used for predicting and analyzing the behavior of a simulation object, diagnosing faults and early warning through the digital twin body. The method comprises the steps of constructing a digital twin model of a rolling bearing of the coal mining machine in the process industry, and realizing the functions of accurate prediction and control in the production process, full life cycle management of equipment and the like through parallel operation, real-time interaction and iterative optimization of the digital twin model and an equipment entity so as to greatly improve the production quality and efficiency in a production line of the process industry.
Fig. 1 is a flowchart of a fault diagnosis method for a rolling bearing of a coal mining machine, and as shown in fig. 1, the fault diagnosis method for the rolling bearing of the coal mining machine provided by the invention comprises the following steps:
s1, establishing a digital twin model of a rolling bearing of a target coal mining machine and a corresponding rolling bearing fault diagnosis model based on the rolling bearing of the target coal mining machine; the rolling bearing fault diagnosis model is determined based on a probabilistic neural network;
s2, determining a target rolling bearing fault diagnosis model based on the digital twin model and the rolling bearing fault diagnosis model according to a firefly optimization algorithm;
and S3, determining a fault result of the rolling bearing of the target coal mining machine based on the fault diagnosis model of the target rolling bearing.
Specifically, fig. 2 is a structural schematic diagram of the digital twin model provided by the invention, and as shown in fig. 2, in order to ensure the accuracy of modeling the rolling bearing of the target coal mining machine, the actual operation condition of the rolling bearing needs to be analyzed first to establish the digital twin model.
In step S1, a digital twin model of the rolling bearing of the coal mining machine is established using Unity3D based on the rolling bearing of the target coal mining machine. The digital twin model of the coal mining machine rolling bearing comprises a geometric model, a physical model, a behavior model and a rule model.
The geometric model of the coal mining machine rolling bearing comprises parameters such as geometric sizes and shapes of all parts, and position relations between equipment; the physical model is the real-time mapping of the physical state of the rolling bearing, such as strain force, damage and the like, of the rolling bearing; the behavior model comprises the action response of the rolling bearing to internal and external environments and system instructions, so that the digital twin body can simulate actual running conditions in a superrealistic manner to perform fault diagnosis and analysis; the rule model summarizes experience, historical data, real-time data and the running state of the equipment based on expert knowledge, and excavates fault rules of the equipment, so that a fault prediction function is provided.
And establishing a corresponding rolling bearing fault diagnosis model by combining expert knowledge, machine learning and the like on the basis of the digital twin model of the rolling bearing of the target coal mining machine. It is understood that, among other things, the rolling bearing fault diagnosis model is determined based on a Probabilistic Neural Network (PNN) for achieving predictive fault diagnosis of the target shearer rolling bearing.
It should be noted that, the specific construction method and the actual structure of the digital twin model and the corresponding rolling bearing fault diagnosis model in the present invention can be adjusted according to the actual application requirements of the present invention, which is not limited in the present invention.
In step S2, based on the digital twin model and the rolling bearing fault diagnosis model, an initial rolling bearing fault diagnosis model is optimized according to a firefly optimization algorithm, and a target rolling bearing fault diagnosis model is determined.
The digital twin model is real-time mapping of a physical entity in a virtual model, monitors the running state of the rolling bearing entity in real time, and transmits simulation data of the rolling bearing entity to a database in real time to provide sample data for training of the rolling bearing fault diagnosis model.
After the failure diagnosis model is successfully trained, in step S3, based on the target rolling bearing failure diagnosis model, the rolling bearing is subjected to predictive failure diagnosis according to the data information of the target rolling bearing when the target rolling bearing needs to be diagnosed, and a failure result of the target coal mining machine rolling bearing is determined.
It can be understood that, during fault diagnosis, real-time fault prediction may be performed by using current actual data information of the target rolling bearing, or fault detection may be performed by using analog data obtained by the digital twin model, and a specific type of the diagnosis data may be adjusted according to actual requirements, which is not limited in the present invention.
According to the fault diagnosis method for the rolling bearing of the coal mining machine, provided by the invention, the digital twinborn model of the rolling bearing of the coal mining machine in the process industry is constructed, the digital twinborn technology is combined with the probabilistic neural network to construct the fault diagnosis model of the rolling bearing, the model is optimized by adopting a firefly algorithm, the rapid training of the model is realized, and the problem of low accuracy of a prediction result caused by inaccurate modeling is effectively solved. The rolling bearing fault diagnosis model realizes the predictive fault diagnosis of the rolling bearing, can effectively improve the accuracy of the predictive fault diagnosis of the rolling bearing of the coal mining machine, and reduces the operation cost of the coal mining machine.
Optionally, the method for diagnosing the fault of the rolling bearing of the coal mining machine provided by the invention is based on the digital twin model and the rolling bearing fault diagnosis model, and determines the target rolling bearing fault diagnosis model according to the firefly optimization algorithm, and specifically comprises the following steps:
determining a fault sample training set according to fault sample data based on a digital twin model;
training a fault diagnosis model of the rolling bearing based on a fault sample training set;
optimizing the smoothing factor of the probabilistic neural network according to a firefly algorithm to determine an optimal smoothing factor;
and determining a fault diagnosis model of the target rolling bearing based on the optimal smoothing factor.
Specifically, fig. 3 is a schematic diagram of a fault diagnosis process of a rolling bearing of a coal mining machine provided by the present invention, as shown in fig. 3, a physical entity is composed of real production equipment and a sensor, and the sensor collects rolling bearing data in real time and transmits the rolling bearing data to a twin database for storage; the digital twin model is the real-time mapping of a physical entity in a virtual model, monitors the running state of the rolling bearing entity in real time, and transmits the simulation data to a database in real time. The twin database stores historical and real-time data of physical entities of the production facility and data of the digital twin model. The connection among all the parts can realize real-time updating iteration of data, and a digital twin model is optimized to be closer to a physical entity.
And training a fault diagnosis model, and determining the fault diagnosis model of the target rolling bearing. Firstly, based on a digital twin model, a fault sample training set is determined according to fault sample data stored in a twin database.
It can be understood that the possible data volume of real fault sample data is not enough, and the digital twin model can obtain the simulation data to expand the fault sample data, so that the accuracy of the model is improved.
And inputting the fault sample training set into a rolling bearing fault diagnosis model, training the model, optimizing the probabilistic neural network smoothing factor according to a firefly algorithm, and determining the optimal smoothing factor.
It can be understood that the probabilistic neural network is a feed-forward neural network based on bayesian decision, the network training speed is fast, and for complex classification problems, the optimal solution under bayesian criterion can be obtained. Compared with other methods, the probabilistic neural network does not need to be trained for many times, only needs to adjust the smoothing factor, and has more advantages in practice. The invention realizes the improvement of the predictive fault diagnosis accuracy rate by optimizing the smoothing factor through the firefly algorithm. The detailed implementation of the firefly algorithm is not described herein.
And determining a target rolling bearing fault diagnosis model based on the optimal smoothing factor, namely realizing predictive diagnosis of the fault according to the target rolling bearing fault diagnosis model.
According to the fault diagnosis method for the rolling bearing of the coal mining machine, provided by the invention, the digital twin model of the rolling bearing of the coal mining machine in the process industry is constructed, the digital twin technology is combined with the probabilistic neural network to construct the fault diagnosis model of the rolling bearing, the data acquired in real time are mapped to the digital twin model in an associated manner, the target rolling bearing fault diagnosis model is determined through sample data provided by the digital twin model, the behavior of a simulation object is predicted and analyzed, and the fault diagnosis and early warning are carried out, so that the fault diagnosis accuracy is improved. The accuracy of predictive fault diagnosis of the rolling bearing of the coal mining machine is effectively improved and the running cost of the coal mining machine is reduced in a mode of combining a digital twin technology and a probabilistic neural network.
Optionally, according to the coal mining machine rolling bearing fault diagnosis method provided by the present invention, based on the optimal smoothing factor, a target rolling bearing fault diagnosis model is determined, which specifically includes:
determining an optimized probabilistic neural network according to the optimal smoothing factor;
testing the optimized probabilistic neural network based on the fault sample test set, and judging whether the optimized probabilistic neural network can accurately carry out fault diagnosis;
if the optimized probabilistic neural network cannot accurately diagnose the fault, repeating the steps of training and testing the fault diagnosis model of the rolling bearing based on the fault sample training set until the optimized probabilistic neural network can accurately diagnose the fault and determine a target probabilistic neural network;
and determining a target rolling bearing fault diagnosis model based on the target probability neural network.
Specifically, in order to further improve the accuracy of the target rolling bearing fault diagnosis model, fault sample data is subjected to normalization processing, divided into a training set and a test set, and transmitted to the digital twin model. The training set is used for training the model, and the test set is used for testing the training result of the model to meet the requirement.
Fig. 4 is a schematic flow chart of a fault diagnosis method for a rolling bearing of a coal mining machine, as shown in fig. 4, the step of determining a fault diagnosis model for a target rolling bearing based on an optimal smoothing factor specifically includes:
and determining an optimized probabilistic neural network according to the optimal smooth factor determined by the firefly algorithm, testing the optimized probabilistic neural network by using a fault sample test set, and judging whether the optimized probabilistic neural network can accurately diagnose the fault.
And if the optimized probabilistic neural network can accurately diagnose the fault, carrying out the next sample method test until the samples in the fault sample test set are determined to be accurately diagnosed.
If the probability neural network after optimization cannot accurately diagnose the fault, the steps of training and testing the fault diagnosis model of the rolling bearing based on the fault sample training set are repeated, the fault information is relearned, the fault diagnosis model is updated until the probability neural network after optimization can accurately diagnose the fault, and the target probability neural network is determined.
And determining a target rolling bearing fault diagnosis model based on the target probability neural network.
According to the fault diagnosis method for the rolling bearing of the coal mining machine, provided by the invention, the digital twinning model of the rolling bearing of the coal mining machine in the process industry is constructed, the digital twinning technology is combined with the probabilistic neural network to construct the fault diagnosis model of the rolling bearing, the model is optimized by adopting a firefly algorithm, the rapid training of the model is realized, the detection effect of the model is verified by combining a test set, the fault rule can be accurately and effectively learned by the model, and the accuracy of the fault detection of the model is further improved while the problem of low accuracy of the prediction result caused by inaccurate modeling is effectively solved.
Optionally, according to the fault diagnosis method for the rolling bearing of the coal mining machine provided by the invention, the probabilistic neural network smoothing factor is optimized according to a firefly algorithm, and the optimal smoothing factor is determined, which specifically includes:
optimizing the probability neural network smoothing factor according to an improved firefly algorithm to determine an optimal smoothing factor;
the improved firefly algorithm is characterized in that inertia weight is added to a standard firefly algorithm;
the position updating formula of the firefly i attracted to move towards the firefly j in the improved firefly algorithm is as follows:
x i (t+1)＝w(t)x i (t)+β(x j (t)-x i (t))+α new (rand-0.5)；
in the above formula, x i (t) and x j (t) the positions of firefly i and firefly j shifted t times in space, w (t) is the inertial weight, and alpha new As step size factor, β is the attraction of firefly j to firefly i, and rand is [0,1 ]]Uniformly distributed random factors are subjected to;
the inertia weight calculation formula is as follows:
in the above formula, w max Is the maximum inertial weight, w min T is the maximum number of iterations for the minimum inertial weight.
Specifically, because the traditional Firefly Algorithm has the problems of low later convergence speed, poor solving precision, easy falling into local optimization and the like, the invention provides an improved Firefly Algorithm (MFA), which increases the inertia weight in the standard Firefly Algorithm (FA), improves the convergence speed and solving precision of the Algorithm, and improves the accuracy of fault diagnosis.
And optimizing the probability neural network smoothing factor according to the improved firefly algorithm to determine the optimal smoothing factor.
The position updating formula of the firefly i attracted to move towards the firefly j in the improved firefly algorithm is as follows:
x i (t+1)＝w(t)x i (t)+β(x j (t)-x i (t))+α new (rand-0.5)；
in the above formula, x i (t) and x j (t) are the positions of firefly i and firefly j after the t-th displacement in space, w (t) is the inertial weight, alpha new Beta is the attraction of firefly j to firefly i, and rand is [0,1 ]]Uniformly distributed random factors are subjected to;
the inertia weight calculation formula is as follows:
in the above formula, w max Is the maximum inertial weight, w min And T is the minimum inertia weight, the maximum iteration number and the current iteration number. For example, in practical applications, T =200,w may be set min ＝0.2，w max =0.8, the specific value of the parameter can be adjusted according to actual requirements, which is not limited in the present invention.
It can be understood that the optimization method of the probability neural network smoothing factor by using the improved firefly algorithm is similar to that of the standard firefly algorithm, only the position updating formula of the firefly needs to be replaced, and specific steps are not described herein.
According to the fault diagnosis method for the rolling bearing of the coal mining machine, provided by the invention, the digital twin model of the rolling bearing of the coal mining machine in the process industry is constructed, the digital twin technology is combined with the probabilistic neural network to construct the fault diagnosis model of the rolling bearing, and the model is optimized by adopting the improved firefly algorithm.
Optionally, according to the fault diagnosis method for the rolling bearing of the coal mining machine provided by the invention, the improved firefly algorithm is that inertia weight is added in a standard firefly algorithm and step-size factors are improved;
the step factor calculation formula is:
in the above formula, t is the current iteration number, a 0 Is the initial step size factor.
Specifically, sinx was found, where x =1.5707, the function value was 1 and decreased as x decreased, thus introducing a sinusoidal function into the step size to enhance the global search capability. The improved firefly algorithm adds inertia weight and improves a step size factor in the standard firefly algorithm.
The step factor calculation formula is:
in the above formula, t is the current iteration number, a 0 Is the initial step size factor, and T is the maximum number of iterations.
According to the fault diagnosis method for the rolling bearing of the coal mining machine, provided by the invention, the digital twin model of the rolling bearing of the coal mining machine in the process industry is constructed, the digital twin technology is combined with the probabilistic neural network to construct the fault diagnosis model of the rolling bearing, and the model is optimized by adopting the improved firefly algorithm.
Optionally, according to the fault diagnosis method for the rolling bearing of the coal mining machine provided by the invention, the probability neural network smoothing factor is optimized according to an improved firefly algorithm, and an optimal smoothing factor is determined, which specifically includes:
initializing basic parameters of the firefly, and taking a probability neural network smoothing factor as a firefly individual;
calculating the fitness value of the firefly individual according to the initial position of the firefly;
comparing the brightness of the firefly based on the fitness value, determining the brightest position as a new position of the firefly, and updating the position of the firefly according to a position updating formula;
and iteratively updating the fitness value and the position of the firefly until a preset optimizing condition is met, outputting the optimal firefly position, and determining an optimal smoothing factor.
Specifically, optimizing the probabilistic neural network smoothing factor according to an improved firefly algorithm to determine an optimal smoothing factor, specifically comprising:
initializing basic parameters of the firefly, including the initial population scale N of the firefly, the position of the firefly, the light absorption coefficient gamma, the step factor alpha, the attraction degree beta, the maximum iteration number T and the like, taking the probability neural network smoothing factor as a firefly individual, initializing the probability neural network, and carrying out normalization processing on fault sample data.
And calculating the fitness value of the firefly individual according to the firefly initial position and a firefly relative brightness formula, wherein the fitness value reflects the brightness of the firefly.
The relative brightness of firefly I is formulated as:
in the above formula, I 0 Represents the fluorescence brightness when the distance of the firefly is 0, that is, the brightness of the brightest firefly; γ represents a light absorption coefficient; r is ij Represents the distance between fireflies i and j. Comparing the brightness of the fireflies based on the fitness value, determining the brightest position as a new position of the fireflies, and updating the positions of the fireflies according to a position updating formula;
and iteratively updating the fitness value and the position of the firefly until a preset optimizing condition is met, outputting the optimal firefly position, and obtaining the optimal smoothing factor obtained by the improved firefly algorithm.
According to the fault diagnosis method for the rolling bearing of the coal mining machine, provided by the invention, the digital twin model of the rolling bearing of the coal mining machine in the process industry is constructed, the digital twin technology is combined with the probabilistic neural network to construct the fault diagnosis model of the rolling bearing, and the model is optimized by adopting the improved firefly algorithm.
Optionally, according to the fault diagnosis method for the rolling bearing of the coal mining machine provided by the invention, the preset optimization condition includes:
the iteration times are greater than a preset iteration threshold; or
The result of the objective function is smaller than a preset expected threshold value;
wherein the objective function formula is:
in the above formula, E i For actual output, R i For the desired output, N is the total number of fireflies.
Specifically, as shown in fig. 4, when the optimal smoothing factor is determined by using the improved firefly algorithm, a preset optimization condition needs to be set.
The iteration times are greater than a preset iteration threshold; or
The result of the objective function is smaller than a preset expected threshold value;
when the objective function is PNN network effect detection, the mean square error MSE of an output value and a real value is obtained, and the objective function formula is as follows:
in the above formula, E i For actual output, R i For the desired output, N is the total number of fireflies.
And solving a smoothing factor by adopting an improved firefly algorithm and assigning the smoothing factor to a probabilistic neural network, and recording the position and the brightness value of the current firefly to obtain the optimal smoothing factor when the accuracy of fault diagnosis is highest, namely the target function result is smaller than a preset expected threshold value or the maximum iteration number is reached.
According to the fault diagnosis method for the rolling bearing of the coal mining machine, provided by the invention, the digital twin model of the rolling bearing of the coal mining machine in the process industry is constructed, the digital twin technology is combined with the probabilistic neural network to construct the fault diagnosis model of the rolling bearing, and the model is optimized by adopting the improved firefly algorithm.
To further illustrate the practical effects of the present invention, matlab2020b software was used to perform simulation verification of the performance comparison between the FA method and the MFA method.
Considering uncertainty, the population number of the fireflies is set to be 30, the maximum iteration number T =200, and an initial step factor alpha 0 =0.97, initial attraction degree β 0 =0.8, minimum attraction degree β min =0.2, and the light absorption coefficient γ =1.
After repeated debugging, fig. 5 is a graph of the probabilistic neural network prediction effect optimized based on the firefly algorithm provided by the present invention, fig. 6 is a schematic diagram of the probabilistic neural network training error optimized based on the firefly algorithm provided by the present invention, and the prediction effect and the training error of the PNN network optimized by the FA algorithm are shown in fig. 5-6.
Fig. 7 is a diagram of the prediction effect of the probabilistic neural network optimized based on the improved firefly algorithm provided by the present invention, fig. 8 is a diagram of the training error of the probabilistic neural network optimized based on the improved firefly algorithm provided by the present invention, and fig. 7-8 show the prediction effect and the training error of the PNN network optimized by the MFA algorithm.
In fig. 5 and 7, the abscissa indicates the sample number, the ordinate indicates the prediction result, pre indicates the predicted value, and Real indicates the true value. In fig. 6 and 8, the abscissa represents the sample number, and the ordinate represents the sample type error. The result shows that compared with the traditional FA algorithm, the improved MFA algorithm provided by the invention has a obviously lower prediction error rate and greatly improves the accuracy of fault diagnosis.
Fig. 9 is a schematic structural view of a fault diagnosis system for a rolling bearing of a coal mining machine provided by the present invention, and as shown in fig. 9, the present invention further provides a fault diagnosis system for a rolling bearing of a coal mining machine, including: a model construction unit 901, a model determination unit 902, and a fault diagnosis unit 903;
the model building unit 901 is used for building a digital twin model of the rolling bearing of the target coal mining machine and a corresponding fault diagnosis model of the rolling bearing based on the rolling bearing of the target coal mining machine;
the model determining unit 902 is used for determining a target rolling bearing fault diagnosis model based on the digital twin model and the rolling bearing fault diagnosis model according to a firefly optimization algorithm; the rolling bearing fault diagnosis model is determined based on a probabilistic neural network;
and the fault diagnosis unit 903 is used for determining a fault result of the rolling bearing of the target coal mining machine based on the fault diagnosis model of the target rolling bearing.
Fig. 2 is a structural schematic diagram of a digital twin model provided by the invention, and as shown in fig. 2, in order to ensure the accuracy of modeling a rolling bearing of a target coal mining machine, the actual operation condition of the rolling bearing needs to be analyzed first to establish the digital twin model.
The model building unit 901 is used for building a digital twin model of the rolling bearing of the coal mining machine by utilizing Unity3D based on the rolling bearing of the target coal mining machine. The digital twin model of the coal mining machine rolling bearing comprises a geometric model, a physical model, a behavior model and a rule model.
The geometric model of the coal mining machine rolling bearing comprises parameters such as geometric size and shape of each part, position relation between equipment and the like; the physical model is the real-time mapping of the physical state of the rolling bearing, such as strain force, damage and the like, of the rolling bearing; the behavior model comprises the action response of the rolling bearing to internal and external environments and system instructions, so that the digital twin body can simulate actual running conditions in a superrealistic manner to perform fault diagnosis and analysis; the rule model summarizes experience, historical data, real-time data and the running state of the equipment based on expert knowledge, and excavates fault rules of the equipment, so that a fault prediction function is provided.
And establishing a corresponding rolling bearing fault diagnosis model by combining expert knowledge, machine learning and the like on the basis of the digital twin model of the rolling bearing of the target coal mining machine. It is understood that, among other things, the rolling bearing fault diagnosis model is determined based on a Probabilistic Neural Network (PNN) for achieving predictive fault diagnosis of the target shearer rolling bearing.
It should be noted that, the specific construction method and the actual structure of the digital twin model and the corresponding rolling bearing fault diagnosis model in the present invention can be adjusted according to the actual application requirements of the present invention, which is not limited in the present invention.
And the model determining unit 902 is used for optimizing an initial rolling bearing fault diagnosis model according to a firefly optimization algorithm based on the digital twin model and the rolling bearing fault diagnosis model, and determining a target rolling bearing fault diagnosis model.
The digital twin model is real-time mapping of a physical entity in a virtual model, monitors the running state of the rolling bearing entity in real time, and transmits simulation data of the rolling bearing entity to a database in real time to provide sample data for training of the rolling bearing fault diagnosis model.
After the failure diagnosis model is successfully trained, the failure diagnosis unit 903 is configured to perform predictive failure diagnosis on the rolling bearing according to data information of the target rolling bearing when the target rolling bearing needs to be diagnosed based on the target rolling bearing failure diagnosis model, and determine a failure result of the target coal mining machine rolling bearing.
It can be understood that, during fault diagnosis, real-time fault prediction may be performed by using current actual data information of the target rolling bearing, or fault detection may be performed by using analog data obtained by the digital twin model, and a specific type of the diagnosis data may be adjusted according to actual requirements, which is not limited in the present invention.
According to the fault diagnosis system for the rolling bearing of the coal mining machine, provided by the invention, the digital twinning model of the rolling bearing of the coal mining machine in the process industry is constructed, the digital twinning technology is combined with the probabilistic neural network to construct the fault diagnosis model of the rolling bearing, the model is optimized by adopting a firefly algorithm, the rapid training of the model is realized, and the problem of low accuracy of a prediction result caused by inaccurate modeling is effectively solved. The rolling bearing fault diagnosis model realizes the predictive fault diagnosis of the rolling bearing, can effectively improve the accuracy of the predictive fault diagnosis of the rolling bearing of the coal mining machine, and reduces the operation cost of the coal mining machine.
It should be noted that, the fault diagnosis system for the rolling bearing of the coal mining machine provided by the invention is used for executing the fault diagnosis method for the rolling bearing of the coal mining machine, and the specific implementation mode is consistent with the implementation mode of the method, and is not described herein again.
Fig. 10 is a schematic physical structure diagram of an electronic device provided in the present invention, and as shown in fig. 10, the electronic device may include: a processor (processor) 101, a communication interface (communication interface) 102, a memory (memory) 103 and a communication bus 104, wherein the processor 101, the communication interface 102 and the memory 103 complete communication with each other through the communication bus 104. The processor 101 may invoke logic instructions in the memory 103 to perform a shearer rolling bearing fault diagnosis method, the method comprising: establishing a digital twin model of the rolling bearing of the target coal mining machine and a corresponding rolling bearing fault diagnosis model based on the rolling bearing of the target coal mining machine; the rolling bearing fault diagnosis model is determined based on a probabilistic neural network; determining a target rolling bearing fault diagnosis model based on a digital twin model and a rolling bearing fault diagnosis model according to a firefly optimization algorithm; and determining a fault result of the rolling bearing of the target coal mining machine based on the fault diagnosis model of the target rolling bearing.
In addition, the logic instructions in the memory 103 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In another aspect, the present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions, when the program instructions are executed by a computer, the computer being capable of executing the method for diagnosing the fault of the rolling bearing of the coal mining machine provided by the above methods, the method comprising: establishing a digital twin model of the rolling bearing of the target coal mining machine and a corresponding rolling bearing fault diagnosis model based on the rolling bearing of the target coal mining machine; the rolling bearing fault diagnosis model is determined based on a probabilistic neural network; determining a target rolling bearing fault diagnosis model based on a digital twin model and a rolling bearing fault diagnosis model according to a firefly optimization algorithm; and determining a fault result of the rolling bearing of the target coal mining machine based on the fault diagnosis model of the target rolling bearing.
In still another aspect, the present invention also provides a non-transitory computer readable storage medium, on which a computer program is stored, the computer program being implemented by a processor to perform the above-provided fault diagnosis method for a rolling bearing of a coal mining machine, the method comprising: establishing a digital twin model of the rolling bearing of the target coal mining machine and a corresponding rolling bearing fault diagnosis model based on the rolling bearing of the target coal mining machine; the rolling bearing fault diagnosis model is determined based on a probabilistic neural network; determining a target rolling bearing fault diagnosis model based on a digital twin model and a rolling bearing fault diagnosis model according to a firefly optimization algorithm; and determining a fault result of the rolling bearing of the target coal mining machine based on the fault diagnosis model of the target rolling bearing.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. Based on the understanding, the above technical solutions substantially or otherwise contributing to the prior art may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method of various embodiments or some parts of embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A fault diagnosis method for a rolling bearing of a coal mining machine is characterized by comprising the following steps:
establishing a digital twin model of a rolling bearing of the target coal mining machine and a corresponding rolling bearing fault diagnosis model based on the rolling bearing of the target coal mining machine; wherein the rolling bearing fault diagnosis model is determined based on a probabilistic neural network;
determining a target rolling bearing fault diagnosis model based on the digital twin model and the rolling bearing fault diagnosis model according to a firefly optimization algorithm;
and determining a fault result of the rolling bearing of the target coal mining machine based on the fault diagnosis model of the target rolling bearing.
2. The fault diagnosis method for the rolling bearing of the coal mining machine according to claim 1, wherein the determining of the fault diagnosis model for the target rolling bearing based on the digital twin model and the fault diagnosis model for the rolling bearing according to a firefly optimization algorithm specifically comprises:
determining a fault sample training set according to fault sample data based on the digital twin model;
training the rolling bearing fault diagnosis model based on the fault sample training set;
optimizing the smoothing factor of the probabilistic neural network according to a firefly algorithm to determine an optimal smoothing factor;
and determining a fault diagnosis model of the target rolling bearing based on the optimal smoothing factor.
3. The fault diagnosis method for the rolling bearing of the coal mining machine according to claim 2, wherein the determining a fault diagnosis model for the target rolling bearing based on the optimal smoothing factor specifically comprises:
determining an optimized probabilistic neural network according to the optimal smoothing factor;
testing the optimized probabilistic neural network based on a fault sample test set, and judging whether the optimized probabilistic neural network can accurately carry out fault diagnosis;
if the optimized probabilistic neural network is determined to be incapable of accurately diagnosing faults, the steps of training and testing the rolling bearing fault diagnosis model based on the fault sample training set are repeated until the optimized probabilistic neural network is determined to be capable of accurately diagnosing faults and determining a target probabilistic neural network;
and determining a target rolling bearing fault diagnosis model based on the target probability neural network.
4. The fault diagnosis method for the rolling bearing of the coal mining machine according to claim 2 or 3, wherein the probability neural network smoothing factor is optimized according to a firefly algorithm to determine an optimal smoothing factor, and specifically comprises the following steps:
optimizing the probability neural network smoothing factor according to an improved firefly algorithm to determine an optimal smoothing factor;
wherein, the improved firefly algorithm adds inertia weight to the standard firefly algorithm;
the improved firefly algorithm is characterized in that the position updating formula of the firefly i attracted to move towards the firefly j is as follows:
x i (t+1)＝w(t)x i (t)+β(x j (t)-x i (t))+α new (rand-0.5)；
in the above formula, x i (t) and x j (t) are the positions of firefly i and firefly j after the t-th displacement in space, w (t) is the inertial weight, alpha new For the purpose of the step-size factor,beta is the attraction of firefly j to firefly i, and rand is [0,1 ]]Uniformly distributed random factors are subjected to;
the inertia weight calculation formula is as follows:
in the above formula, w max Is the maximum inertial weight, w min T is the maximum number of iterations for the minimum inertial weight.
5. The coal mining machine rolling bearing fault diagnosis method according to claim 4, characterized in that the improved firefly algorithm is to add inertial weight and improve step size factor in a standard firefly algorithm;
the step factor calculation formula is as follows:
in the above formula, t is the current iteration number, a 0 Is the initial step size factor.
6. The method for diagnosing the fault of the rolling bearing of the coal mining machine according to claim 5, wherein the probability neural network smoothing factor is optimized according to an improved firefly algorithm to determine an optimal smoothing factor, and specifically comprises the following steps:
initializing basic parameters of the firefly, and taking the probability neural network smoothing factor as a firefly individual;
calculating the fitness value of the firefly individual according to the initial position of the firefly;
comparing the brightness of the fireflies based on the fitness value, determining the brightest position as a new position of the fireflies, and updating the positions of the fireflies according to the position updating formula;
and iteratively updating the fitness value and the position of the firefly until a preset optimizing condition is met, outputting the optimal firefly position, and determining an optimal smoothing factor.
7. The fault diagnosis method for the rolling bearing of the coal mining machine according to claim 6, wherein the preset optimizing condition comprises:
the iteration times are greater than a preset iteration threshold; or
The result of the objective function is less than a preset expected threshold;
wherein the objective function formula is:
in the above formula, E i For actual output, R i For the desired output, N is the total number of fireflies.
8. A coal mining machine rolling bearing fault diagnosis system is characterized by comprising: the system comprises a model building unit, a model determining unit and a fault diagnosis unit;
the model building unit is used for building a digital twin model of the rolling bearing of the target coal mining machine and a corresponding rolling bearing fault diagnosis model based on the rolling bearing of the target coal mining machine;
the model determining unit is used for determining a target rolling bearing fault diagnosis model based on the digital twin model and the rolling bearing fault diagnosis model according to a firefly optimization algorithm; wherein the rolling bearing fault diagnosis model is determined based on a probabilistic neural network;
and the fault diagnosis unit is used for determining a fault result of the rolling bearing of the target coal mining machine based on the fault diagnosis model of the target rolling bearing.
9. An electronic device, comprising a memory and a processor, wherein the processor and the memory communicate with each other via a bus; the memory stores program instructions executable by the processor, the processor calling the program instructions to be able to execute the shearer rolling bearing fault diagnosis method according to any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the shearer rolling bearing fault diagnosis method according to any one of claims 1 to 7.
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