A method for purifying the rotor center orbit

Abstract

The present invention discloses a method for purifying a rotor axis trajectory, comprising the following steps: S1, obtaining rotor rotation frequency information; S2, setting a first direction and a second direction, and fitting the expected signals of the displacement signals of the rotor axis in the first direction and the second direction, respectively, based on the rotor rotation frequency information; S3, solving and obtaining a filter based on the expected signals of the displacement signals in the first direction and the second direction; S4, inputting the displacement signals in the first direction and the second direction into the filters solved in step S3 to obtain filtered signals; S5, drawing a rotor axis trajectory diagram based on the filtered signals. The present invention effectively utilizes the characteristics of the displacement signal itself and adopts an adaptive filter with a feedback structure for digital filtering, which can significantly improve the noise suppression capability and obtain a clear and realistic rotor axis trajectory diagram.

Description

Technical Field

[0001] The present invention relates to the field of mechanical health monitoring and fault diagnosis, and in particular to a method for purifying a rotor axis trajectory. Background Art

[0002] The shaft of the equipment that performs rotational motion must have a load-bearing part, called a bearing, and the rotor is a rotating body supported by the bearing.

[0003] The ideal axis trajectory of the rotor is a point, but with the mechanical wear caused by the operation of the equipment, the axis trajectory of the rotor deviates from the ideal point.

[0004] When the equipment is operating normally, the primary frequency component of the rotor displacement signal is a harmonic frequency. During actual operation, the axis's trajectory often forms an ellipse with a relatively small difference between the major and minor axes. When the rotor exhibits misalignment, the axis's trajectory can become crescent-shaped or banana-shaped. As the fault worsens, it can become an "8" shape. When the rotor experiences oil vortex, the axis's trajectory often forms an inward-facing "8."

[0005] Therefore, the rotor axis trajectory contains a wealth of information about the equipment's status, with different axis trajectory shapes reflecting different rotor health states. However, in harsh operating environments (such as continuous rolling mills), the bracket can cause the displacement sensor mounted on it to vibrate. Furthermore, when hardware issues such as low sensor accuracy and interference occur, the collected rotor axis displacement signals in both directions can contain significant noise, resulting in distorted composite axis trajectory and severely impacting axis trajectory recognition.

[0006] To obtain a clear and realistic rotor axis trajectory image, numerous scholars have conducted extensive research on rotor axis trajectory purification. Common rotor axis trajectory purification methods include digital or analog low-pass filtering, wavelet transform and wavelet packet transform, harmonic window, particle swarm optimization (PSO), EMD denoising, and mathematical morphology filtering. However, these methods require the prediction of the rotor fault characteristic frequency band in advance. They then reconstruct the signal in the time domain based on the frequency band of the useful components in the signal. The selection of different frequency bands and basis functions directly affects the purification effect and is not conducive to the automatic implementation of the purification algorithm. EMD-based axis trajectory purification methods are constrained by the EMD algorithm's inherent problems of modal aliasing and endpoint effects, and are purely digitally driven, resulting in a high computational load. Mathematical morphology filtering-based purification methods suffer from statistical bias, resulting in outputs that are biased either too high or too low due to the switching operation. This can lead to a certain deviation between the filter output and the actual output. Summary of the Invention

[0007] The purpose of the present invention is to provide a rotor axis trajectory purification method, which can weaken the noise component in the axis trajectory signal, help identify the operating status of the rotor, and indirectly identify the working status of the equipment.

[0008] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0009] A method for purifying a rotor axis trajectory comprises the following steps:

[0010] S1. Obtaining rotor frequency information;

[0011] S2. Setting a first direction and a second direction, and fitting expected signals of the displacement signals of the rotor axis in the first direction and the second direction respectively according to the rotation frequency information of the rotor;

[0012] S3. Obtain a filter based on expected signals of the displacement signals in the first direction and the second direction;

[0013] S4, inputting the displacement signals in the first direction and the second direction into the filter obtained in step S3 to obtain filtered signals;

[0014] S5. Draw a diagram of the rotor axis trajectory according to the filtered signal.

[0015] Furthermore, in step S2, an expected signal of the displacement signal of the rotor axis in one direction is fitted by the following steps:

[0016] The displacement signal of the rotor axis in one direction is collected, which is recorded as x = [x(1), x(2), ..., x(N)], and the sampling frequency is f s , the number of sampling points is N, record t=[1 / f s ,2 / f s ,…,N / f s ] is the sampling time of signal x, and the desired frequency component f is set i =f*w i , where 1≤i≤m, w i is the set multiple of the i-th desired frequency component relative to the rotational frequency, m is the number of frequencies contained in the set desired frequency component, and f is the rotor rotational frequency information obtained in step S1;

[0017] Assuming the expected signal of pre-fitting is d, its expression is: The coefficient A i 、 Calculated by the following formula:

[0018] Formula Find the partial derivative and establish the equation system according to the following formula:

[0019]

[0020] And solve the system of equations to get The corresponding value is used to determine the calculation formula of the pre-fitted expected signal d.

[0021] Furthermore, step S3 includes: correcting the filter weight coefficient to obtain a corrected filter, including the following steps:

[0022] Establishing the initialization filter to be corrected;

[0023] The input signal vector of the filter is set to: α(n) = [α(n), α(n-1), ..., α(nL)] T , where the filter order is L+1, n is an integer greater than L, L<N number of sampling points, [] T is the transposed matrix;

[0024] Set the L+1 weight coefficient vectors of the filter to δ(n) = [δ0(n), δ1(n), ..., δ L (n)] T , then the calculation formula of the output signal y(n) of the filter is:

[0025]

[0026] According to the calculation formula of the error signal: e(n)=d(n)-y(n)=d(n)-δ T (n)*α(n),

[0027] The correction direction of the filter parameters is calculated as:

[0028]

[0029] The filter weight coefficient is modified according to the following formula, and n is automatically increased by l during each modification process. step , until it reaches n′, then the correction ends, where n′ is the preset correction end integer, and n′≤N:

[0030] δ(n+l step )=δ(n)+2μ*e(n)*α(N), where 0<μ<1, l step is the increment step of n.

[0031] Furthermore, in step S1, the rotation frequency information of the rotor is obtained through the following steps:

[0032] If the device where the rotor is located is equipped with a tachometer, the rotational frequency of the rotor is directly obtained through the tachometer;

[0033] If the speed of the device where the rotor is located is constant at r, then the rotor's rotational frequency f=r / 60 is calculated;

[0034] Otherwise, the rotor frequency is estimated using the fast Fourier transform and integral transform algorithms.

[0035] Furthermore, the displacement signal of the rotor axis in the first direction is x1, the displacement signal of the rotor axis in the second direction is x2, the expected signal fitting the displacement signal of the rotor axis in the first direction is d1, and the expected signal fitting the displacement signal of the rotor axis in the second direction is d2;

[0036] The corrected weight coefficient of the first filter corresponding to the first direction is δ1, and the corrected weight coefficient of the second filter corresponding to the second direction is δ2. The order of the first filter is the same as the order of the second filter. The displacement signal x1 is input into the first filter with a weight coefficient of δ1 to obtain the displacement filter signal of the rotor axis in the first direction; the displacement signal x2 is input into the second filter with a weight coefficient of δ2 to obtain the displacement filter signal of the rotor axis in the second direction.

[0037] Furthermore, a plane coordinate system is established with the first direction and the second direction as coordinate axes, and the coordinate points corresponding to the displacement filter signals of the rotor axis in the first direction and the second direction are determined in the plane coordinate system, and adjacent coordinate points are connected in sequence to obtain the axis trajectory diagram of the rotor.

[0038] Furthermore, the angle between the first direction and the second direction is 90°.

[0039] Furthermore, after step S5, the following steps are further included:

[0040] S6. Determine whether the axis trajectory of the rotor meets a preset abnormality rule. If so, determine that the working state of the rotor is abnormal.

[0041] Furthermore, if there are two points on the axis trajectory diagram of the rotor whose straight-line distance exceeds a preset distance threshold, it is determined that the working state of the rotor is abnormal.

[0042] Furthermore, if the axis trajectory of the rotor is crescent-shaped or figure-8-shaped, it is determined that the working state of the rotor is abnormal.

[0043] The beneficial effects brought about by the technical solution provided by the present invention are as follows: by effectively utilizing the characteristics of the displacement signal itself and adopting an adaptive filter with a feedback structure for digital filtering, the noise suppression capability can be greatly improved, and a clear and realistic rotor axis trajectory diagram can be obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0045] Figure 1 A schematic flow chart of a rotor axis trajectory purification method provided by an exemplary embodiment of the present invention;

[0046] Figure 2 A schematic diagram of a flow chart for obtaining rotor rotation frequency provided by an exemplary embodiment of the present invention;

[0047] Figure 3 A schematic diagram of a graphical interface showing the collected original displacement signal of the rotor shaft center and the corresponding shaft center trajectory provided by an exemplary embodiment of the present invention;

[0048] Figure 4 A schematic structural diagram of an adaptive filter provided as an exemplary embodiment of the present invention;

[0049] Figure 5 for Figure 3 Schematic diagram of the graphical interface of the original displacement signal and axis trajectory after filtering. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0051] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0052] When performing health monitoring and fault diagnosis on rotating mechanical equipment, the rotor axis trajectory contains rich information about the equipment's operating status. For example, when the rotor is normal, the corresponding axis trajectory is an ellipse with a small difference between the major and minor axes. The axis trajectory corresponding to rotor misalignment is a banana or outer "8" shape. The axis trajectory caused by oil film vortex is an inner "8" shape. The present invention proposes a method for purifying the rotor axis trajectory, which can weaken the noise introduced to the axis trajectory by problems such as harsh industrial site environment, low accuracy of the acquisition device, and hardware interference, and help accurately identify the rotor's operating status. Two mutually perpendicular displacement sensors are arranged on the same cross-section of the rotor, and a digital acquisition device is used to acquire the displacement of the rotor in these two mutually perpendicular directions. The expected signals of the two displacement signals are fitted to correct the filter, and the acquired displacement signal is filtered using the corrected filter. The two filtered displacement signals are then synthesized into a rotor axis trajectory diagram in a Cartesian rectangular coordinate system. The present invention effectively utilizes the characteristics of the displacement signal itself (calculates the expected displacement signal) and adopts an adaptive filter with a feedback structure for digital filtering, which can greatly improve the noise suppression capability and obtain a clear and approximately true rotor axis trajectory diagram.

[0053] In one embodiment of the present invention, a method for purifying the rotor axis trajectory is provided. The trajectory purification in this embodiment is a trajectory signal denoising / noise reduction processing method, that is, denoising / noise reduction processing is performed on the original trajectory signal obtained, and a signal closer to the true trajectory is obtained after processing. Figure 1 The rotor axis trajectory purification method comprises the following steps:

[0054] S1. Obtain the rotor's rotation frequency information.

[0055] Specifically, see Figure 2 If the device where the rotor is located is equipped with a tachometer, the rotational frequency of the rotor is directly obtained through the tachometer;

[0056] If the device where the rotor is located is not equipped with a tachometer and the speed of the device where the rotor is located is constant at rrpm, then the rotational frequency of the rotor is calculated as f=r / 60;

[0057] Otherwise, the rotor frequency is estimated using the fast Fourier transform and integral transform algorithms. Based on the prior knowledge that "among the frequency components of the displacement signal of a rotor (a rotor supported by sliding bearings), the amplitude of one times the rotational frequency is often the highest," we attempt to estimate the rotor frequency f based on the characteristics of the displacement signal itself. The specific method is as follows:

[0058] Set the first direction and the second direction, perform FFT transformation on the displacement signals x1 and x2 in the two directions, and obtain the spectrum X1 and X2. The frequencies with the largest amplitude in X1 and X2 are recorded as f max1and f max2 If f max1 =f max2 , then the rotor frequency f=f max1 =f max2 If f max1 ≠f max2 , perform integral transformation on x1 and x2 to remove high frequency noise and obtain x1′ and x2′, then perform FFT transformation on x1′ and x2′ to obtain X1' and X2', and record the frequencies with the largest amplitude in X1' and X2' as f max3 and f max4 , if f max3 =f max4 , then the rotor frequency f=f max3 =f max4 If f max3 ≠f max4 , then mark the data collected this time as abnormal, recollect the displacement signal and perform the above operation. In one embodiment of the present invention, the rotation frequency f=5.6 Hz is obtained in this way.

[0059] S2. Fit expected signals of the displacement signals of the axis of the rotor in the first direction and the second direction respectively according to the rotation frequency information of the rotor.

[0060] In one embodiment of the present invention, a device uses a sliding bearing to collect displacement signals of the rotor in two mutually perpendicular directions (i.e., the angle between the first direction and the second direction is 90°). The two original displacement signals and the axis trajectory are as follows: Figure 3 As shown, it can be seen that there are obvious burrs on both the original displacement signal and the axis trajectory.

[0061] Therefore, it is necessary to first fit the expected signals of the two displacement signals. Specifically, the expected signal of the displacement signal of the rotor axis in the first direction is fit as an example: the displacement signal of the rotor axis in the first direction is collected, denoted as x = [x(1), x(2), ..., x(N)], and the sampling frequency is f s , the number of sampling points is N, record t=[1 / f s ,2 / f s ,…,N / f s ] is the sampling time of signal x, and the desired frequency component f is set i =f*w i , where 1≤i≤m, w i is the set multiple of the i-th desired frequency component relative to the rotational frequency, m is the number of frequencies contained in the set desired frequency component, and f is the rotor rotational frequency information obtained in step S1;

[0062] Sliding bearing shaft system failures are often caused by shaft imbalance, misalignment, oil film turbulence, and rotor rubbing. The frequency components contained in these healthy conditions mainly include 1 / 3, 1, 2, 3, 4, and 5 times the frequency. Therefore, in one embodiment of the present invention, w1 = 1 / 3, w2 = 1, w3 = 2, w4 = 3, w5 = 4, and w6 = 5 are used to fit the signal, and the fitted signal is used as the expected signal of the adaptive filter.

[0063] Assuming the expected signal of pre-fitting is d, its expression is:

[0064]

[0065] The coefficient A i 、 Calculated by the following formula:

[0066] When the following formula 2 obtains the minimum value, the fitting effect of formula 1 is the best:

[0067]

[0068] In order to minimize Formula 2, according to the minimum theorem of continuous functions, the following partial derivative formula needs to be established, and the equation system is established according to the following Formula 3:

[0069]

[0070] And solve the system of equations to get The corresponding value is substituted into Formula 1 to determine the calculation formula of the pre-fitted expected signal d, and the expected displacement corresponding to each sampling point in the first direction is obtained.

[0071] The same steps are used to fit the expected signal of the displacement signal of the rotor axis in the second direction.

[0072] S3. Obtain a filter based on the expected signals of the displacement signals in the first direction and the second direction.

[0073] The filter weight coefficient is modified to obtain a modified filter. In this embodiment, the LMS adaptive algorithm is used to adaptively filter the two displacement signals in the first direction and the second direction respectively to establish the initialization filter to be modified. The structure of the filter is as follows: Figure 4 As shown, the order of the filter is L+1, L is much smaller than the number of sampling points N of the displacement signal.

[0074] The input signal vector of the filter is set to: α(n) = [α(n), α(n-1), ..., α(nL)] T , where n is an integer greater than L, L<N, the number of sampling points, [] Tis the transposed matrix;

[0075] Set the L+1 weight coefficient vectors of the filter to δ(n) = [δ0(n), δ1(n), ..., δ L (n)] T , then the calculation formula of the output signal y(n) of the filter is:

[0076]

[0077] According to the calculation formula of the error signal: e(n)=d(n)-y(n)=d(n)-δ T (n)*α(n) (Formula 5),

[0078] The correction direction of the filter parameters is calculated as:

[0079]

[0080] The filter weight coefficient is modified according to the following formula 7, and n is automatically increased by l during each modification process. step , until it reaches n′, then the correction ends, where n′ is the preset correction end integer, and n′≤N:

[0081] δ(n+l step )=δ(n)+2μ*e(n)*α(N) (Formula 7)

[0082] Among them, 0<μ<1, l step is the increment step of n.

[0083] In this embodiment, the correction starting point integer n is L+1, and the correction end point integer n′ is N. In the process of correcting the filter weight coefficient, the step length of n from L+1 to N can be selected as 1. The advantage of this setting is that the filter weight coefficient can be corrected to the maximum extent to improve the authenticity of the subsequent filtered signal. It should be noted that the present invention does not limit the correction starting point integer and the correction end point integer as well as the step length l step Specific value of .

[0084] The present invention is not limited to the LMS adaptive algorithm. In other embodiments of the present invention, LMS-based variant adaptive filter algorithms such as NLMS and RLS may be used to replace the LMS adaptive algorithm.

[0085] S4. Input the displacement signals in the first direction and the second direction into the filter obtained in step S3 to obtain filtered signals.

[0086] The displacement signal of the rotor axis in the first direction is x1, the displacement signal of the rotor axis in the second direction is x2, the expected signal fitting the displacement signal of the rotor axis in the first direction is d1, and the expected signal fitting the displacement signal of the rotor axis in the second direction is d2;

[0087] S5. Draw a diagram of the rotor axis trajectory according to the filtered signal.

[0088] The modified weight coefficient of the first filter corresponding to the first direction (X-axis) is δ1, and the modified weight coefficient of the second filter corresponding to the second direction (Y-axis) is δ2. The order of the first filter is the same as the order of the second filter. The displacement signal x1 is input into the first filter with the weight coefficient δ1 to obtain the displacement filter signal of the rotor axis in the X-axis direction, as shown in FIG. Figure 5 The displacement signal in the X direction after adaptive filtering in the adaptive filtering; the displacement signal x2 is input into the second filter with a weight coefficient of δ2 to obtain the displacement filter signal of the rotor axis in the Y coordinate axis, such as Figure 5 The displacement signal in the Y direction after adaptive filtering.

[0089] A plane coordinate system is established with the first direction and the second direction as coordinate axes. Coordinate points corresponding to the displacement filter signals of the rotor axis in the first direction and the second direction are determined in the plane coordinate system. Adjacent coordinate points are connected in sequence to obtain a rotor axis trajectory diagram, such as Figure 5 The purified rotor axis trajectory in .

[0090] S6. Determine whether the axis trajectory of the rotor meets a preset abnormality rule. If so, determine that the working state of the rotor is abnormal.

[0091] The preset exception rules include:

[0092] <1> There are two points on the rotor axis trajectory diagram whose straight-line distance exceeds a preset distance threshold;

[0093] <2> If the axis trajectory of the rotor is crescent-shaped or figure-8-shaped;

[0094] The abnormality rule can be used alone or in combination to determine that the working state of the rotor is abnormal. If the working state of the rotor is determined to be abnormal, a prompting device can be used to issue a prompt message, which can be an alarm, a display screen, or a touch screen.

[0095] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0096] The above is only a specific implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for purifying a rotor axis trajectory, characterized in that: The following steps are involved: S1. Obtaining rotor frequency information; S2. Setting a first direction and a second direction, and fitting expected signals of the displacement signals of the rotor axis in the first direction and the second direction respectively according to the rotation frequency information of the rotor; The expected signal of the displacement signal of the rotor axis in one direction is fitted by the following steps: the displacement signal of the rotor axis in one direction is collected, denoted as x=[x(1), x(2),…, x(N)], and the sampling frequency is f s , the number of sampling points is N, record t=[1 / f s ,2 / f s ,…,N / f s ] is the sampling time of signal x, and the desired frequency component f is set i =f*w i , where 1≤i≤m, w i is the set multiple of the i-th desired frequency component relative to the rotation frequency, m is the number of frequencies contained in the set desired frequency component, and f is the rotor rotation frequency information obtained in step S1; the pre-fitted desired signal is set to d, and its expression is: The coefficient A i 、 It is calculated by the following formula: Find the partial derivative and establish the equation system according to the following formula: And solve the system of equations to get The corresponding value is used to determine the calculation formula of the pre-fitted expected signal d; S3. Obtaining a filter based on the expected signals of the displacement signals in the first direction and the second direction; and correcting the filter weight coefficients to obtain a corrected filter, comprising the following steps: establishing an initialization filter to be corrected; and setting the input signal vector of the filter to: α(n) = [α(n), α(n-1), ..., α(nL)] T , where the filter order is L+1, n is an integer greater than L, L<N number of sampling points, [] T is the transposed matrix; set the L+1 weight coefficient vectors of the filter to δ(n)=[δ0(n),δ1(n),...,δ L (n)] T , then the calculation formula of the output signal y(n) of the filter is: According to the calculation formula of the error signal: e(n)=d(n)-y(n)=d(n)-δ T (n)*α(n), the correction direction of the filter parameters is calculated as: The filter weight coefficient is modified according to the following formula, and n is automatically increased by l during each modification process. step , until n' is reached, the correction is ended, where n' is the preset correction end integer, and n ′ ≤N:δ(n+l step )=δ(n)+2μ*e(n)*α(N), where 0<μ<1, l step is the self-increasing step size of n; S4, inputting the displacement signals in the first direction and the second direction into the filter obtained in step S3 to obtain filtered signals; S5. Draw a diagram of the rotor axis trajectory according to the filtered signal.

2. The rotor axis trajectory purification method according to claim 1, characterized in that: In step S1, the rotor frequency information is obtained through the following steps: If the device where the rotor is located is equipped with a tachometer, the rotational frequency of the rotor is directly obtained through the tachometer; If the speed of the device where the rotor is located is constant at r, then the rotor's rotational frequency f=r / 60 is calculated; Otherwise, the rotor frequency is estimated using the fast Fourier transform and integral transform algorithms.

3. The rotor axis trajectory purification method according to claim 1, characterized in that: The displacement signal of the rotor axis in the first direction is x1, the displacement signal of the rotor axis in the second direction is x2, the expected signal fitting the displacement signal of the rotor axis in the first direction is d1, and the expected signal fitting the displacement signal of the rotor axis in the second direction is d2; The corrected weight coefficient of the first filter corresponding to the first direction is δ1, and the corrected weight coefficient of the second filter corresponding to the second direction is δ2. The order of the first filter is the same as the order of the second filter. The displacement signal x1 is input into the first filter with a weight coefficient of δ1 to obtain the displacement filter signal of the rotor axis in the first direction; the displacement signal x2 is input into the second filter with a weight coefficient of δ2 to obtain the displacement filter signal of the rotor axis in the second direction.

4. The rotor axis trajectory purification method according to claim 3, characterized in that: A plane coordinate system is established with the first direction and the second direction as coordinate axes, coordinate points corresponding to the displacement filter signals of the axis of the rotor in the first direction and the second direction are determined in the plane coordinate system, and adjacent coordinate points are connected in sequence to obtain the axis trajectory diagram of the rotor.

5. The rotor axis trajectory purification method according to any one of claims 1 to 4, characterized in that: The angle between the first direction and the second direction is 90°.

6. The rotor axis trajectory purification method according to claim 1, characterized in that: After step S5, the following steps are also included: S6. Determine whether the axis trajectory of the rotor meets a preset abnormality rule. If so, determine that the working state of the rotor is abnormal.

7. The rotor axis trajectory purification method according to claim 6, characterized in that: If there are two points on the axis trajectory diagram of the rotor whose straight-line distance exceeds a preset distance threshold, it is determined that the working state of the rotor is abnormal.

8. The rotor axis trajectory purification method according to claim 6, characterized in that: If the axis trajectory of the rotor is crescent-shaped or figure-8-shaped, it is determined that the working state of the rotor is abnormal.

Images