Noise-based stepper motor fault detection method and system

Abstract

The present invention relates to the field of fault detection technology, and specifically discloses a stepper motor fault detection method and system based on noise, the method comprising obtaining the working state of the stepper motor, obtaining the audio information of the stepper motor under different working states based on the noise detection point, creating a running audio model as an audio feature; counting the working state and the audio feature, creating a mapping relationship from the audio feature to the working state; in the actual application stage, obtaining the working period, obtaining the actual audio information based on the noise detection point, determining the audio feature according to the actual audio information and the working period, and obtaining the working parameters based on the mapping relationship as the prediction result. The present invention installs an audio detector at the noise detection point, and the audio detector obtains the working audio of the stepper motor, and then compares it with the preset conditions to determine in real time whether there is a fault; in this process, the preset conditions can be adjusted by the user, thereby improving the accuracy.

Description

Technical Field

[0001] The invention relates to the technical field of fault detection, and in particular to a stepper motor fault detection method and system based on noise. Background Art

[0002] A stepper motor is an electric motor that converts electrical pulse signals into corresponding angular or linear displacements. Each step of its movement corresponds to a fixed angle, which makes the stepper motor very suitable for use in applications that require precise positioning; in actual scenarios, stepper motors are widely used in printers, CNC machine tools, robots, and other equipment that requires precise position control.

[0003] The stepper motor has a great impact on the production process, so fault detection is needed. Existing fault detection solutions are mostly built-in detection solutions, that is, the stepper motor has its own detection module. When this detection module detects a problem, the stepper motor has basically failed and there is no advance detection. How to provide a non-built-in, external fault detection solution so that the fault trigger standard can be controlled by the user and improve the fault detection accuracy is the technical problem that the technical solution of the present invention wants to solve. Summary of the invention

[0004] The object of the present invention is to provide a stepper motor fault detection method and system based on noise to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A stepper motor fault detection method based on noise, the method comprising:

[0007] Create a three-dimensional space model with the stepper motor as the center, and set noise detection points in the three-dimensional space model;

[0008] Simulate background noise sources at different working hours, obtain audio information based on noise detection points, and create a background audio model based on a three-dimensional space model;

[0009] Obtain the working state of the stepper motor, obtain the audio information of the stepper motor under different working states based on the noise detection point, and create a running audio model as the audio feature;

[0010] Count the working status and audio features, and create a mapping relationship from audio features to working status;

[0011] In the actual application stage, the working period is obtained, the actual audio information is obtained based on the noise detection point, the audio features are determined according to the actual audio information and the working period, and the working parameters are obtained based on the mapping relationship as the prediction result.

[0012] As a further solution of the present invention: the step of creating a three-dimensional space model with the stepper motor as the center and setting noise detection points in the three-dimensional space model includes:

[0013] Acquire a body model of the stepper motor, and determine a model boundary based on the body model and a preset length;

[0014] Obtaining object arrangement information in the model boundary, and creating a mapping object in the model boundary according to the object arrangement information;

[0015] Taking the centroid of the stepper motor as the center, obtain the nearest mapping object in each direction, and set the noise detection point at the intersection;

[0016] The distance between the noise detection point and the core of the stepper motor is obtained, the noise detection point is simplified according to the distance, and the simplified noise detection point is used as the final noise detection point.

[0017] As a further solution of the present invention: the step of obtaining the distance between the noise detection point and the core of the stepper motor, simplifying the noise detection point according to the distance, and using the simplified noise detection point as the final noise detection point includes:

[0018] Count all distances according to the preset distance span to obtain the number of distances in each distance span;

[0019] Simplifying the distance quantity based on a preset simplification function;

[0020] For each noise detection point corresponding to a distance span, a simplified number of noise points are randomly selected for retention;

[0021] The retained noise detection point is used as the final noise detection point;

[0022] The simplified function is:

[0023] Where N ′ is the number after simplification, N is the number before simplification, and α and β are preset constants.

[0024] As a further solution of the present invention: the steps of simulating background noise sources in different working periods, acquiring audio information based on noise detection points, and creating a background audio model in combination with a three-dimensional space model include:

[0025] Check the background noise sources at different working hours;

[0026] Activate background noise sources and obtain audio information based on noise detection points;

[0027] Insert the audio information into the created three-dimensional space model to obtain a background audio model;

[0028] The process of inserting audio information into the created three-dimensional space model is as follows:

[0029]

[0030] Where C(i,j,z) is the value at point (i,j,z) in the three-dimensional space model, and E k represents the instantaneous energy of the audio information obtained at the kth noise detection point, K is the total number of noise detection points, f(E k ) is the default about E k The correction function is γ, which is the preset value change rate; dst((i,j,z),(x k ,y k ,z k ) is the point (i,j,z) and the point (x k ,y k ,z k ) between points (x k ,y k ,z k ) is the coordinate of the kth noise detection point;

[0031] dst((i,j,z),(x k ,y k ,z k ) is determined by determining the point (i, j, z) and the point (x k ,y k ,z k ), obtain the noise source closest to the straight line, take the direction away from the nearest noise source as the positive direction, calculate the distance between point (i, j, z) and point (x k ,y k ,z k ) to get the distance of dst((i,j,z),(x k ,y k ,z k ); specifically:

[0032]

[0033] d1 represents the distance between point (i, j, z) and the nearest noise source; d2 represents the distance between point (x k ,y k ,z k ) to the nearest noise source.

[0034] As a further solution of the present invention: the step of obtaining the working state of the stepper motor, obtaining the audio information of the stepper motor under different working states based on the noise detection point, and creating a running audio model as an audio feature includes:

[0035] Obtain the working parameters of the stepper motor in different working periods;

[0036] activating the stepper motor based on the operating parameters and acquiring audio information based on the noise detection points;

[0037] Insert the audio information into the created three-dimensional space model to obtain a running audio model as an audio feature;

[0038] The insertion method is the same as the generation process of the background audio model.

[0039] As a further solution of the present invention: in the actual application stage, the working period is obtained, the actual audio information is obtained based on the noise detection point, the audio features are determined according to the actual audio information and the working period, and the working parameters are obtained based on the mapping relationship, and the steps of taking the prediction results include:

[0040] In the actual application stage, the current working period of the stepper motor is obtained;

[0041] querying a background audio model according to the working period;

[0042] Acquire actual audio information based on noise detection points, create an actual audio model, and remove the background audio model from the actual audio model;

[0043] The actual audio model without the background audio model is input into the mapping relationship to obtain the predicted working parameters as the prediction result.

[0044] The technical solution of the present invention also provides a noise-based stepper motor fault detection system, the system comprising:

[0045] A detection point setting module is used to create a three-dimensional space model with the stepper motor as the center and set noise detection points in the three-dimensional space model;

[0046] Background sound acquisition module, used to simulate background noise sources at different working hours, obtain audio information based on noise detection points, and create a background audio model in combination with a three-dimensional space model;

[0047] The operating sound acquisition module is used to obtain the working status of the stepper motor, obtain the audio information of the stepper motor under different working statuses based on the noise detection point, and create an operating audio model as an audio feature;

[0048] A mapping relationship creation module is used to count the working status and audio features and create a mapping relationship from audio features to working status;

[0049] The mapping relationship application module is used to obtain the working period in the actual application stage, obtain the actual audio information based on the noise detection point, determine the audio characteristics according to the actual audio information and the working period, and obtain the working parameters based on the mapping relationship as the prediction result.

[0050] As a further solution of the present invention: the detection point setting module includes:

[0051] A boundary determination unit, used for acquiring a body model of the stepper motor, and determining a model boundary based on the body model and a preset length;

[0052] A mapping object creation unit, used to obtain object arrangement information in the model boundary, and create a mapping object in the model boundary according to the object arrangement information;

[0053] An execution unit is set to obtain the nearest mapping object in each direction with the core of the stepper motor as the center, and a noise detection point is set at the intersection;

[0054] The detection point simplification unit is used to obtain the distance between the noise detection point and the core of the stepper motor, simplify the noise detection point according to the distance, and use the simplified noise detection point as the final noise detection point.

[0055] As a further solution of the present invention: the background sound acquisition module includes:

[0056] A noise source query unit, used to query the background noise sources in different working hours;

[0057] A noise source activation unit, used to activate the background noise source and obtain audio information based on the noise detection point;

[0058] A first audio insertion unit, used for inserting audio information into the created three-dimensional space model to obtain a background audio model;

[0059] The process of inserting audio information into the created three-dimensional space model is as follows:

[0060]

[0061] Where C(i,j,z) is the value at point (i,j,z) in the three-dimensional space model, and E k represents the instantaneous energy of the audio information obtained at the kth noise detection point, K is the total number of noise detection points, f(E k ) is the default about E k The correction function is γ, which is the preset value change rate; dst((i,j,z),(x k ,y k ,z k ) is the point (i,j,z) and the point (x k,y k ,z k ) between points (x k ,y k ,z k ) is the coordinate of the kth noise detection point;

[0062] dst((i,j,z),(x k ,y k ,z k ) is determined by determining the point (i, j, z) and the point (x k ,y k ,z k ), obtain the noise source closest to the straight line, take the direction away from the nearest noise source as the positive direction, calculate the distance between point (i, j, z) and point (x k ,y k ,z k ) to get the distance of dst((i,j,z),(x k ,y k ,z k ); specifically:

[0063]

[0064] d1 represents the distance between point (i, j, z) and the nearest noise source; d2 represents the distance between point (x k ,y k ,z k ) to the nearest noise source.

[0065] As a further solution of the present invention: the running sound acquisition module includes:

[0066] A working parameter acquisition unit, used to acquire working parameters of the stepper motor in different working periods;

[0067] a motor activation unit for activating the stepper motor based on the operating parameters and obtaining audio information based on the noise detection points;

[0068] A second audio insertion unit is used to insert the audio information into the created three-dimensional space model to obtain a running audio model as an audio feature;

[0069] The insertion method is the same as the generation process of the background audio model.

[0070] Compared with the prior art, the beneficial effects of the present invention are: the present invention sets a noise detection point in the outside world, installs an audio detector at the noise detection point, and uses the audio detector to obtain the working audio of the stepper motor, which is then compared with the preset conditions to determine in real time whether there is a fault; in this process, the preset conditions can be adjusted by the user to improve the accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0072] Figure 1 The flowchart of the noise-based stepper motor fault detection method.

[0073] Figure 2 This is a block diagram of the first sub-process of the noise-based stepper motor fault detection method.

[0074] Figure 3 This is a block diagram of the second sub-process of the noise-based stepper motor fault detection method.

[0075] Figure 4 This is the third sub-flow chart of the noise-based stepper motor fault detection method.

[0076] Figure 5 This is a block diagram of the fourth sub-process of the noise-based stepper motor fault detection method.

[0077] Figure 6 This is the structural block diagram of the noise-based stepper motor fault detection system. DETAILED DESCRIPTION

[0078] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0079] Figure 1 The flowchart of the stepper motor fault detection method based on noise is shown in the figure. In an embodiment of the present invention, a stepper motor fault detection method based on noise is provided. The method includes:

[0080] Step S100: creating a three-dimensional space model with the stepper motor as the center, and setting noise detection points in the three-dimensional space model;

[0081] Get the three-dimensional model of the stepper motor (no details are required), and create a three-dimensional space model based on the three-dimensional model of the stepper motor. The creation process generally starts from the center of the three-dimensional model and extends the preset distance in all directions to obtain the three-dimensional space model. The actual significance of the three-dimensional space model is to characterize the installation range of the noise detection point, that is, the noise detection point is generally installed in the three-dimensional space model. After the noise detection point is determined, the three-dimensional space model needs to be inserted.

[0082] Step S200: simulating background noise sources at different working hours, acquiring audio information based on noise detection points, and creating a background audio model in combination with a three-dimensional space model;

[0083] In the actual application stage, the working process of the stepper motor is pre-set, and it has different working states in different working periods. In addition, the background noise is different in different working periods; therefore, the audio detection process of the present application is related to the working period.

[0084] Specifically, for different working hours, the background noise sources of each working period are simulated, and then an audio detector is installed at the noise detection point. The audio detector obtains audio information and inserts the audio information into the three-dimensional space model. The obtained three-dimensional space model containing audio information is called the background audio model.

[0085] It should be noted that the process of inserting audio information into the three-dimensional space model involves numerical conversion. The audio information needs to be converted into a numerical value within a preset range (such as 0 to 255) and then inserted into the three-dimensional space model.

[0086] Step S300: obtaining the working state of the stepper motor, obtaining audio information of the stepper motor in different working states based on the noise detection point, and creating a running audio model as an audio feature;

[0087] For the stepper motor, the working state of the stepper motor is obtained, and the audio information of the stepper motor under different working states is obtained based on the audio detector at the noise detection point. The same generation process as the background audio model is adopted, and the obtained model is called the running audio model, also called the audio feature.

[0088] Step S400: Counting the working status and audio features, and creating a mapping relationship from the audio features to the working status;

[0089] Step S200 and step S300 are actually the preprocessing stage. Before being put into use, the noise in different environments is tested to obtain the background audio model, which is used in the subsequent background elimination process to improve the audio detection effect; the audio of the motor in different working states is tested to obtain the running audio model, thereby constructing the correspondence between the working state and the running audio model; in actual applications, the running audio model can be obtained through audio detection equipment, and with the help of the corresponding relationship, the working state can be inferred.

[0090] It should be noted that based on the established correspondence, it is very likely that the working status cannot be identified. At this time, it is regarded as a problem and a risk warning is issued.

[0091] In addition, the corresponding relationship is the mapping relationship between the working status and the audio features in the above content. In the actual application stage, the audio features are input and the working status is output.

[0092] Step S500: in the actual application stage, the working period is obtained, the actual audio information is obtained based on the noise detection point, the audio feature is determined according to the actual audio information and the working period, and the working parameter is obtained based on the mapping relationship as a prediction result;

[0093] Step S500 is the actual application stage. First, the current working period of the stepper motor and the background noise affected by the working period are obtained to obtain the background audio model. Then, the actual audio information is obtained based on the noise detection point to obtain the actual audio model. The background audio model is subtracted from the actual audio model to obtain the audio model generated by the stepper motor. The audio model is input into the determined mapping model to obtain the predicted working parameters, which are then used as the prediction results.

[0094] Furthermore, if the working parameters are identifiable and safe working parameters, it means that there is no fault. If the identification result is unsafe or the working parameters cannot be identified, it is considered that a fault exists and a fault prompt containing the fault type is generated; among them, unidentified risk is a type of fault, as a result of all working parameters being unable to be identified.

[0095] Figure 2 The first sub-flow chart of the noise-based stepper motor fault detection method is as follows. The three-dimensional space model is created with the stepper motor as the center, and the steps of setting noise detection points in the three-dimensional space model include:

[0096] Step S101: obtaining a body model of the stepper motor, and determining a model boundary based on the body model and a preset length;

[0097] Step S102: obtaining object arrangement information in the model boundary, and creating a mapping object in the model boundary according to the object arrangement information;

[0098] Step S103: Taking the core of the stepper motor as the center, obtain the nearest mapping object in each direction, and set a noise detection point at the intersection;

[0099] Step S104: obtaining the distance between the noise detection point and the core of the stepper motor, simplifying the noise detection point according to the distance, and using the simplified noise detection point as the final noise detection point.

[0100] The above content limits the process of setting the noise detection point, obtains the body model of the stepper motor, and equidistantly enlarges the body model according to the preset length to obtain the model boundary, obtains the object layout information in the model boundary, and sets some virtual objects inside the model boundary according to the relative position relationship with the stepper motor; then, with the centroid of the stepper motor as the center, obtains its nearest mapping object in each direction, where each direction refers to a ray starting from the centroid, and the angle between any two adjacent rays is a preset value, so that all rays are uniform; specifically, the way to uniformly set the rays can be to create a sphere with the centroid as the center of the sphere, uniformly select points on the spherical surface, establish lines with the centroid, and obtain rays; obtain the nearest mapping object on each ray, and record the intersection of the ray and the mapping object as the noise detection point.

[0101] Specifically, the step of obtaining the distance between the noise detection point and the core of the stepper motor, simplifying the noise detection point according to the distance, and using the simplified noise detection point as the final noise detection point includes:

[0102] Count all distances according to the preset distance span to obtain the number of distances in each distance span;

[0103] Simplifying the distance quantity based on a preset simplification function;

[0104] For each noise detection point corresponding to a distance span, a simplified number of noise points are randomly selected for retention;

[0105] The retained noise detection point is taken as the final noise detection point.

[0106] There may be a large number of noise detection points. Some distance spans are set in advance, and different noise detection points are classified by the distance spans. This process is equivalent to grading the distances to make the distances more regular. Then, for each type of distance span, the number is simplified and the simplified number is retained as the final noise detection point.

[0107] The simplified function is:

[0108] Where N ′ is the number after simplification, N is the number before simplification, and α and β are preset constants.

[0109] The simplification function is used to simplify the quantity. The more the quantity, the more the quantity retained. However, the retained quantity also needs to be in a smaller range. Therefore, this application adopts a logarithmic function for simplification.

[0110] Figure 3The second sub-flow chart of the noise-based stepper motor fault detection method, wherein the steps of simulating background noise sources in different working periods, acquiring audio information based on noise detection points, and creating a background audio model in combination with a three-dimensional space model include:

[0111] Step S201: querying background noise sources in different working hours;

[0112] Step S202: activating the background noise source and acquiring audio information based on the noise detection point;

[0113] Step S203: inserting the audio information into the created three-dimensional space model to obtain a background audio model;

[0114] In an example of the technical solution of the present invention, the process of determining the background audio model is limited. For a certain working period, the background noise source of the current period is queried, the background noise source is activated, and audio information is obtained based on an audio acquirer installed at a noise detection point. The audio information is inserted into the created three-dimensional space model. The insertion method is to assign values ​​to each point in the three-dimensional space model. The final three-dimensional space model containing values ​​is called the background audio model.

[0115] The process of inserting audio information into the created three-dimensional space model is as follows:

[0116]

[0117] Where C(i,j,z) is the value at point (i,j,z) in the three-dimensional space model, and E k represents the instantaneous energy of the audio information obtained at the kth noise detection point, K is the total number of noise detection points, f(E k ) is the default about E k The correction function is γ, which is the preset value change rate; dst((i,j,z),(x k ,y k ,z k ) is the point (i,j,z) and the point (x k ,y k ,z k ) between points (x k ,y k ,z k ) is the coordinate of the kth noise detection point;

[0118] dst((i,j,z),(x k ,y k ,z k ) is determined by determining the point (i, j, z) and the point (x k ,y k ,zk ), obtain the noise source closest to the straight line, take the direction away from the nearest noise source as the positive direction, calculate the distance between point (i, j, z) and point (x k ,y k ,z k ) to get the distance of dst((i,j,z),(x k ,y k ,z k ); specifically:

[0119]

[0120] d1 represents the distance between point (i, j, z) and the nearest noise source; d2 represents the distance between point (x k ,y k ,z k ) to the nearest noise source.

[0121] The above content is actually the conversion process of audio information. For each point, it is necessary to obtain the influence of the audio information at all noise detection points on the point. For a certain noise detection point, its instantaneous energy is calculated, and then corrected. After correction, its influence on the point is calculated again. The influence process adopts a linear function. The default influence process of this application is that the instantaneous energy gradually decreases from the noise source to the outside. Therefore, after obtaining the instantaneous energy of a certain noise detection point, the instantaneous energy at the point (i, j, z) can be reversed according to the reduction ratio (γ) (the instantaneous energy is converted into a corrected value after passing through the correction function).

[0122] Specifically, the method of selecting the noise source is to determine a straight line by point (i, j, z) and the kth noise detection point, select the noise source closest to the straight line as the corresponding noise source, and then, according to the coordinates of point (i, j, z), the kth noise detection point and the noise source, determine the total value, which represents the influence of the kth noise detection point on the point (i, j, z).

[0123] Figure 4 The third sub-flow chart of the noise-based stepper motor fault detection method, wherein the steps of obtaining the working state of the stepper motor, obtaining the audio information of the stepper motor under different working states based on the noise detection point, and creating a running audio model as an audio feature include:

[0124] Step S301: obtaining working parameters of the stepper motor in different working periods;

[0125] Step S302: activating the stepper motor based on the working parameters, and acquiring audio information based on the noise detection point;

[0126] Step S303: insert the audio information into the created three-dimensional space model to obtain a running audio model as an audio feature.

[0127] The above content is somewhat similar to steps S201 to S203, with the difference that the activated sound source is a stepper motor, not a noise source, and the working parameters of the stepper motor in different working periods are obtained. The stepper motor is activated based on the working parameters, and then the audio information is obtained based on the noise detection point. The same scheme as the background audio model is used to insert the obtained audio information into the three-dimensional space model. The obtained model is called the running audio model, and is also used as an audio feature. That is, the method of inserting the audio information of the stepper motor is the same as the generation process of the background audio model.

[0128] Figure 5 The fourth sub-flow chart of the stepper motor fault detection method based on noise, in the actual application stage, the working period is obtained, the actual audio information is obtained based on the noise detection point, the audio feature is determined according to the actual audio information and the working period, and the working parameters are obtained based on the mapping relationship. The steps as the prediction results include:

[0129] Step S501: in the actual application stage, obtaining the current working period of the stepper motor;

[0130] Step S502: querying a background audio model according to the working period;

[0131] Step S503: acquiring actual audio information based on the noise detection point, creating an actual audio model, and removing the background audio model from the actual audio model;

[0132] Step S504: input the actual audio model with the background audio model removed into the mapping relationship to obtain predicted working parameters as the prediction result.

[0133] In the actual application stage, the current working period of the stepper motor is queried, and the background audio model corresponding to the working period is queried. Then, the actual audio information is obtained according to the noise detector installed at the noise detection point. Then, the same information insertion method is adopted, and the obtained model is called the actual audio model; the actual audio model includes the audio generated by the stepper motor and the audio generated by the background. The background audio model is eliminated from the actual audio model, and the obtained model can be regarded as corresponding to the audio generated by the stepper motor. The actual audio model without the background audio model is input into the determined mapping relationship, and the working parameters can be obtained. The obtained working parameters are predicted working parameters, that is, the working parameters of the stepper motor obtained by reverse deduction based on the audio, thereby realizing the monitoring of the operating status of the stepper motor.

[0134] It should be noted that the background audio model is determined according to the working hours. In actual applications, the background noise source may be affected by a variety of random factors. In the standardized production process, the working hours have the greatest impact. Therefore, this application only simulates the background audio models corresponding to different working hours, and does not and cannot consider those random factors.

[0135] The solution of the present application is actually an external detection solution. There is no need to add sensors inside the stepper motor. A noise detector can be installed on or near its housing to perform fault detection on the working state of the stepper motor.

[0136] Figure 6 1 is a structural block diagram of a stepper motor fault detection system based on noise. In an embodiment of the present invention, a stepper motor fault detection system based on noise, the system 10 includes:

[0137] A detection point setting module 11 is used to create a three-dimensional space model with the stepping motor as the center and set noise detection points in the three-dimensional space model;

[0138] The background sound acquisition module 12 is used to simulate the background noise sources in different working periods, obtain audio information based on the noise detection points, and create a background audio model in combination with the three-dimensional space model;

[0139] The operating sound acquisition module 13 is used to acquire the working state of the stepper motor, acquire the audio information of the stepper motor under different working states based on the noise detection point, and create an operating audio model as an audio feature;

[0140] A mapping relationship creation module 14, used to count the working status and audio features, and create a mapping relationship from the audio features to the working status;

[0141] The mapping relationship application module 15 is used to obtain the working period in the actual application stage, obtain the actual audio information based on the noise detection point, determine the audio characteristics according to the actual audio information and the working period, and obtain the working parameters based on the mapping relationship as the prediction result.

[0142] Furthermore, the detection point setting module 11 includes:

[0143] A boundary determination unit, used for acquiring a body model of the stepper motor, and determining a model boundary based on the body model and a preset length;

[0144] A mapping object creation unit, used to obtain object arrangement information in the model boundary, and create a mapping object in the model boundary according to the object arrangement information;

[0145] An execution unit is set to obtain the nearest mapping object in each direction with the core of the stepper motor as the center, and a noise detection point is set at the intersection;

[0146] The detection point simplification unit is used to obtain the distance between the noise detection point and the core of the stepper motor, simplify the noise detection point according to the distance, and use the simplified noise detection point as the final noise detection point.

[0147] Specifically, the background sound acquisition module 12 includes:

[0148] A noise source query unit, used to query the background noise sources in different working hours;

[0149] A noise source activation unit, used to activate the background noise source and obtain audio information based on the noise detection point;

[0150] A first audio insertion unit, used for inserting audio information into the created three-dimensional space model to obtain a background audio model;

[0151] The process of inserting audio information into the created three-dimensional space model is as follows:

[0152]

[0153] Where C(i,j,z) is the value at point (i,j,z) in the three-dimensional space model, and E k represents the instantaneous energy of the audio information obtained at the kth noise detection point, K is the total number of noise detection points, f(E k ) is the default about E k The correction function is γ, which is the preset value change rate; dst((i,j,z),(x k ,y k ,z k ) is the point (i,j,z) and the point (x k ,y k ,z k ) between points (x k ,y k ,z k ) is the k The coordinates of the noise detection points;

[0154] dst((i,j,z),(x k ,y k ,z k ) is determined by determining the point (i, j, z) and the point (x k ,y k ,z k ), obtain the noise source closest to the straight line, take the direction away from the nearest noise source as the positive direction, calculate the distance between point (i, j, z) and point (x k ,y k ,z k) to get the distance of dst((i,j,z),(x k ,y k ,z k ); specifically:

[0155]

[0156] d1 represents the distance between point (i, j, z) and the nearest noise source; d2 represents the distance between point (x k ,y k ,z k ) to the nearest noise source.

[0157] Furthermore, the running sound acquisition module 13 includes:

[0158] A working parameter acquisition unit, used to acquire working parameters of the stepper motor in different working periods;

[0159] a motor activation unit for activating the stepper motor based on the operating parameters and obtaining audio information based on the noise detection points;

[0160] A second audio insertion unit is used to insert the audio information into the created three-dimensional space model to obtain a running audio model as an audio feature;

[0161] The insertion method is the same as the generation process of the background audio model.

[0162] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A stepper motor fault detection method based on noise, characterized in that: The method comprises: Create a three-dimensional space model with the stepper motor as the center, and set noise detection points in the three-dimensional space model; Simulate background noise sources at different working hours, obtain audio information based on noise detection points, and create a background audio model based on a three-dimensional space model; Obtain the working state of the stepper motor, obtain the audio information of the stepper motor under different working states based on the noise detection point, and create a running audio model as the audio feature; Count the working status and audio features, and create a mapping relationship from audio features to working status; In the actual application stage, the working period is obtained, the actual audio information is obtained based on the noise detection point, the audio features are determined according to the actual audio information and the working period, and the working parameters are obtained based on the mapping relationship as the prediction result; The steps of simulating background noise sources at different working hours, acquiring audio information based on noise detection points, and creating a background audio model in combination with a three-dimensional space model include: Check the background noise sources at different working hours; Activate background noise sources and obtain audio information based on noise detection points; Insert the audio information into the created three-dimensional space model to obtain a background audio model; The process of inserting audio information into the created three-dimensional space model is as follows: Where C(i, j, z) is the value at point (i, j, z) in the three-dimensional space model, and E k represents the instantaneous energy of the audio information obtained at the kth noise detection point, K is the total number of noise detection points, f(E k ) is the default about E k The correction function is γ, which is the preset value change rate; dst((i, j, z), (x k ,y k , z k ) is the point (i, j, z) and the point (x k ,y k , z k ) between points (x k ,y k , z k ) is the coordinate of the kth noise detection point; dst((i, j, z), (x k ,y k , z k ) is determined by determining the point (i, j, z) and the point (x k ,y k , z k ), obtain the noise source closest to the straight line, take the direction away from the nearest noise source as the positive direction, calculate the distance between point (i, j, z) and point (x k ,y k , z k ) to get the distance of dst((i, j, z), (x k ,y k , z k ); specifically: d1 represents the distance between point (i, j, z) and the nearest noise source; d2 represents the distance between point (x k ,y k ,z k ) to the nearest noise source.

2. The noise-based stepper motor fault detection method according to claim 1, characterized in that: The step of creating a three-dimensional space model with the stepper motor as the center and setting noise detection points in the three-dimensional space model includes: Acquire a body model of the stepper motor, and determine a model boundary based on the body model and a preset length; Obtaining object arrangement information in the model boundary, and creating a mapping object in the model boundary according to the object arrangement information; Taking the centroid of the stepper motor as the center, obtain the nearest mapping object in each direction, and set the noise detection point at the intersection; The distance between the noise detection point and the core of the stepper motor is obtained, the noise detection point is simplified according to the distance, and the simplified noise detection point is used as the final noise detection point.

3. The noise-based stepper motor fault detection method according to claim 2, characterized in that: The step of obtaining the distance between the noise detection point and the core of the stepper motor, simplifying the noise detection point according to the distance, and using the simplified noise detection point as the final noise detection point includes: Count all distances according to the preset distance span to obtain the number of distances in each distance span; Simplifying the distance quantity based on a preset simplification function; For each noise detection point corresponding to a distance span, a simplified number of noise points are randomly selected for retention; The retained noise detection point is used as the final noise detection point; The simplified function is: Wherein, N' is the number after simplification, N is the number before simplification, and α and β are preset constants.

4. The noise-based stepper motor fault detection method according to claim 1, characterized in that: The steps of obtaining the working state of the stepper motor, obtaining the audio information of the stepper motor under different working states based on the noise detection point, and creating a running audio model as an audio feature include: Obtain the working parameters of the stepper motor in different working periods; activating the stepper motor based on the operating parameters and acquiring audio information based on the noise detection points; Insert the audio information into the created three-dimensional space model to obtain a running audio model as an audio feature; The insertion method is the same as the generation process of the background audio model.

5. The noise-based stepper motor fault detection method according to claim 1, characterized in that: In the actual application stage, the steps of obtaining the working period, obtaining the actual audio information based on the noise detection point, determining the audio features according to the actual audio information and the working period, and obtaining the working parameters based on the mapping relationship as the prediction result include: In the actual application stage, the current working period of the stepper motor is obtained; querying a background audio model according to the working period; Acquire actual audio information based on noise detection points, create an actual audio model, and remove the background audio model from the actual audio model; The actual audio model without the background audio model is input into the mapping relationship to obtain the predicted working parameters as the prediction result.

6. A noise-based stepper motor fault detection system, characterized in that: The system comprises: A detection point setting module is used to create a three-dimensional space model with the stepper motor as the center and set noise detection points in the three-dimensional space model; Background sound acquisition module, used to simulate background noise sources at different working hours, obtain audio information based on noise detection points, and create a background audio model in combination with a three-dimensional space model; The operating sound acquisition module is used to obtain the working status of the stepper motor, obtain the audio information of the stepper motor under different working statuses based on the noise detection point, and create an operating audio model as an audio feature; A mapping relationship creation module is used to count the working status and audio features and create a mapping relationship from audio features to working status; A mapping relationship application module is used to obtain the working period in the actual application stage, obtain the actual audio information based on the noise detection point, determine the audio features according to the actual audio information and the working period, and obtain the working parameters based on the mapping relationship as the prediction result; The background sound acquisition module includes: A noise source query unit, used to query the background noise sources in different working hours; A noise source activation unit, used to activate the background noise source and obtain audio information based on the noise detection point; A first audio insertion unit, used for inserting audio information into the created three-dimensional space model to obtain a background audio model; The process of inserting audio information into the created three-dimensional space model is as follows: Where C(i, j, z) is the value at point (i, j, z) in the three-dimensional space model, and E k represents the instantaneous energy of the audio information obtained at the kth noise detection point, K is the total number of noise detection points, f(E k ) is the default about E k The correction function is γ, which is the preset value change rate; dst((i, j, z), (x k ,y k , z k ) is the point (i, j, z) and the point (x k ,y k , z k ) between points (x k ,y k , z k ) is the coordinate of the kth noise detection point; dst((i, j, z), (x k ,y k , z k ) is determined by determining the point (i, j, z) and the point (x k ,y k , z k ), obtain the noise source closest to the straight line, take the direction away from the nearest noise source as the positive direction, calculate the distance between point (i, j, z) and point (x k ,y k , z k ) to get the distance of dst((i, j, z), (x k ,y k , z k ); specifically: d1 represents the distance between point (i, j, z) and the nearest noise source; d2 represents the distance between point (x k ,y k , z k ) to the nearest noise source.

7. The noise-based stepper motor fault detection system according to claim 6, characterized in that: The detection point setting module includes: A boundary determination unit, used for acquiring a body model of the stepper motor, and determining a model boundary based on the body model and a preset length; A mapping object creation unit, used to obtain object arrangement information in the model boundary, and create a mapping object in the model boundary according to the object arrangement information; An execution unit is set to obtain the nearest mapping object in each direction with the core of the stepper motor as the center, and a noise detection point is set at the intersection; The detection point simplification unit is used to obtain the distance between the noise detection point and the core of the stepper motor, simplify the noise detection point according to the distance, and use the simplified noise detection point as the final noise detection point.

8. The noise-based stepper motor fault detection system according to claim 6, characterized in that: The running sound acquisition module includes: A working parameter acquisition unit, used to acquire working parameters of the stepper motor in different working periods; a motor activation unit for activating the stepper motor based on the operating parameters and obtaining audio information based on the noise detection points; A second audio insertion unit is used to insert the audio information into the created three-dimensional space model to obtain a running audio model as an audio feature; The insertion method is the same as the generation process of the background audio model.

Images