Method and system for identifying multi-source error parameters of coded disc of encoder
By collecting and analyzing the instantaneous angular velocity signals of the encoder code disk at different speeds, using artificial lemming optimization algorithm to identify the eccentricity and inclination angle of the code disk, the problem of multi-source error in traditional encoder manufacturing is solved, and manufacturing accuracy and fault recognition capabilities are improved.
Patent Information
- Application Number
- CN202510587450.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-08
- Publication Date
- 2025-08-19
AI Technical Summary
During the manufacturing process of traditional encoders, mechanical errors such as eccentricity and inclination of the code disk are difficult to eliminate, resulting in the superposition of multi-source error coupling and superposition, affecting manufacturing accuracy and fault signal extraction, especially in low-speed or high-precision scenarios, the error harmonic component can easily conceal the key fault characteristics.
The instantaneous angular velocity signals of the same code disk under multiple sets of different speed conditions are collected, the minimum fluctuation signals are filtered through peak-to-peak values, the mean value is calculated and the error parameters are initialized, and the optimization objective function is constructed, and the iterative method is used to generate the theoretical model instantaneous angular velocity error signal, and the eccentricity and inclination angle of the code disk are identified.
It improves the manufacturing accuracy of the encoder, optimizes the manufacturing process flow, enhances the fault identification effect and efficiency, and provides a technical basis for judging whether the error covers up key fault characteristics.
Smart Images
Figure CN120508874A_ABST
Abstract
Description
Technical Field
[0001] The present invention belongs to the technical field of encoders, and relates to a method and system for identifying multi-source error parameters of an encoder code disk. Background Art
[0002] During the encoder manufacturing process, the installation and adjustment accuracy of the code disk directly affects its final measurement performance. Traditional processes rely on manual experience or simple geometric model adjustment methods, which make it difficult to eliminate mechanical errors such as installation eccentricity and code disk tilt. This leads to the coupling and superposition of multi-source errors (such as installation eccentricity, dynamic tilt and structural deformation). Such errors manifest as periodic angular velocity fluctuations during high-speed rotation, seriously restricting the improvement of manufacturing accuracy.
[0003] At the same time, the multi-source errors of the encoder will interfere with the extraction of fault signals, especially in low-speed or high-precision detection scenarios. The harmonic components caused by the errors are prone to overlap with weak fault characteristic frequency bands, but there is no clear judgment mechanism whether the errors will cover up the key fault frequency bands. Summary of the Invention
[0004] The purpose of the present invention is to provide a method and system for identifying multi-source error parameters of an encoder code disk, so as to complete the identification of error parameters of the error code disk.
[0005] In order to achieve the above object, the basic scheme of the present invention is: a method for identifying multi-source error parameters of an encoder code disk, comprising the following steps:
[0006] S1, collects instantaneous angular velocity signals of the same encoder under multiple groups of different speed conditions, calculates the peak-to-peak value of each group of signals, and selects the minimum fluctuation signal;
[0007] S2, calculate the mean of the minimum fluctuation signal, initialize the error parameters, and generate the theoretical error signal;
[0008] S3, constructing the optimization objective function based on the theoretical error signal;
[0009] S4, performing optimization algorithm iteration according to the principle of artificial lemming optimization algorithm or particle swarm optimization algorithm, and outputting the global optimal parameters if the convergence condition is met, otherwise returning to step S2;
[0010] S5, based on the global optimal parameters, generates the instantaneous angular velocity error signal of the theoretical model.
[0011] The working principle and beneficial effects of this basic solution are as follows: This technical solution collects multiple sets of instantaneous angular velocity signals under different speed conditions, obtains a reference signal with minimal speed fluctuation through peak-to-peak screening, and calculates its mean. Based on the theoretical model, it generates an instantaneous angular velocity error signal corresponding to the error type, and uses the reference mean as the model speed input.
[0012] An optimization function is constructed to minimize the root mean square logarithm of the difference between the frequency domain amplitude spectra of the experimental and theoretical signals. The artificial lemming optimization algorithm is used to optimize the parameters. The final output is the globally optimal code disk eccentricity and tilt angle parameters, which remain consistent at different rotational speeds.
[0013] By identifying the eccentricity and tilt angle of the encoder disc, or the eccentricity and tilt angle under misalignment, from the instantaneous angular velocity error signal, the error parameters of the same error encoder disc under different speed conditions can be determined. This method not only helps improve encoder manufacturing accuracy but also provides a technical basis for determining whether errors will mask key fault characteristics.
[0014] By combining the instantaneous angular velocity error signal with the smallest speed fluctuation among the multiple sets of collected signals and the theoretical instantaneous angular velocity error signals under different error parameters with an optimization algorithm, the eccentricity and tilt angle of the code disk, or the eccentricity and tilt angle under misalignment, can be identified from the instantaneous angular velocity error signal. Ultimately, the error parameters of the same error code disk under different speed conditions can be determined. This method not only helps improve encoder manufacturing accuracy and optimize its manufacturing process, but also provides a technical basis for determining whether errors will mask key fault characteristics, further improving the effectiveness and efficiency of fault identification.
[0015] Furthermore, calculate the peak-to-peak value V of each signal group pp , screening minimum fluctuation signal IAS k for:
[0016] V pp =V max -V min
[0017] IAS k =arg min[V pp (IAS1,IAS2,…,IAS n )]
[0018] Among them, V max , V min Indicates the maximum and minimum values of each group of signals; IAS n is the nth fluctuation signal.
[0019] The calculation is simple, the corresponding parameters are obtained, and the influence of the rotational speed fluctuation on the instantaneous angular velocity error signal generated by the theoretical model is reduced.
[0020] Furthermore, the method of calculating the mean of the minimum fluctuation signal, initializing the error parameters, and generating the theoretical error signal is as follows:
[0021] Calculate the mean of the minimum instantaneous angular velocity fluctuation signal, recorded as IAS k_m , which is the speed reference in the theoretical error model:
[0022] IAS k_m =mean(IAS k )
[0023] Among them, IAS k is the minimum fluctuation signal;
[0024] If the code disc is known to have a single error type, generate the instantaneous angular velocity error signal:
[0025]
[0026] Among them, ω er is the instantaneous angular velocity error of the encoder when the probe is arranged radially in the eccentric state, ω ar is the instantaneous angular velocity error of the code disk when the probe is arranged axially in an eccentric state; ρ r is the radial eccentricity, ρ a is the axial eccentricity; α′ represents the position angle information read by the probe, α is the actual rotation angle; Δr is the eccentricity distance, r is the radius of the code disk, ω(t) is the instantaneous angular velocity of the code disk when it rotates, ω t Indicates the instantaneous angular velocity error of the code disk in the tilted state;
[0027] Otherwise, the instantaneous angular velocity error signal is generated as:
[0028]
[0029] Among them, ω(t) is replaced by IAS k_m , generate a set of theoretical error signals under different error parameters, denoted as IAS e ;
[0030] IAS e =[IAS e1 (ρ1,β1),IAS e2 (ρ2,β2),…,IAS en (ρ n ,β n )]
[0031] Where β is the tilt angle of the code disk, ω misr is the instantaneous angular velocity error when the probe is arranged radially, ω misa Instantaneous angular velocity error when the probe is arranged axially, IAS en is the theoretical error signal calculated under different error parameters, ρ n Indicates the eccentricity error of the code disk, β n Indicates the code disk tilt angle error.
[0032] Generate error parameters and initialize the artificial lemming's search position.
[0033] Furthermore, we choose to measure the instantaneous angular velocity IAS with error m and the theoretical error signal IAS e The minimum value f(ρ,β) of the logarithm of the root mean square value rms of the spectrum of the difference between the two is the optimization objective function, which is:
[0034] f(ρ,β)=min(log(rms(F[IAS m -IAS e ])))
[0035] Here, F represents Fourier transform.
[0036] Due to the phase misalignment between the experimental signal and the theoretical simulation signal, direct subtraction of the two signals in the time domain results in amplitude variations or abnormal energy distribution. However, in the frequency domain amplitude spectrum, the impact of phase variations does not need to be considered, so this optimization objective function is selected.
[0037] Furthermore, according to the ALA algorithm principle, the global optimal parameters are calculated, specifically:
[0038]
[0039] E(t)=4×arctan(1-t / T max )×ln(1 / rand)
[0040] in, is the current optimal solution; F is the sign of changing the search direction; A vector representing random Brownian motion; It is a random number in 1 row and Dim column between [-1,1], which is used to control the movement of the current optimal individual and random individuals in the lemming population; is a randomly selected individual in the population; L is a random number related to this iteration; is a random search individual in the population; G represents the escape coefficient of lemmings, which decreases with the increase of the number of iterations; Levy is Levy flight, which is used to simulate the deceptive behavior of lemmings; E(t) represents the energy coefficient.
[0041] Since the error parameters of the same code disk do not change with the change of the speed condition, the error parameters remain unchanged under other subsequent speed conditions, completing the identification of the error parameters of an error code disk.
[0042] Furthermore, according to the experimentally measured instantaneous angular velocity signal IAS m , select the instantaneous angular velocity signal IAS that has completed error parameter identification i, calculate its mean speed, and generate the theoretical model instantaneous angular velocity error signal IAS at the stable speed of this working condition based on the error of artificial lemming parameter identification [ρ,β]best :
[0043] IAS [ρ,β]best =IAS i_m +IAS e[ρ,β]best
[0044] Among them, IAS [ρ,β]best represents the instantaneous angular velocity signal of the theoretical model generated under the optimal parameters ρ, β; IAS i_m Represents the speed reference in the theoretical error model; IAS e[ρ,β]best represents the instantaneous angular velocity error signal of the theoretical model generated under the optimal parameters ρ, β.
[0045] Generate the instantaneous angular velocity error signal of the theoretical model at the stable speed of this working condition to obtain more accurate error parameters.
[0046] The present invention also provides an encoder code disc multi-source error parameter identification system, comprising a data acquisition module and a processing module, wherein the data acquisition module is used to collect instantaneous angular velocity signals of the same code disc under multiple groups of different speed conditions and transmit them to the processing module;
[0047] The processing module executes the method of the present invention to generate a theoretical model instantaneous angular velocity error signal.
[0048] The system identifies the eccentricity, tilt angle, or eccentricity and tilt angle of the code disc under misalignment from the instantaneous angular velocity error signal, and then determines the error parameters of the same error code disc under different speed conditions. BRIEF DESCRIPTION OF THE DRAWINGS
[0049] Figure 1 1 is a flow chart of a method for identifying multi-source error parameters of an encoder code disk according to the present invention;
[0050] Figure 2 It is a flow chart of the ALA algorithm of the encoder code disk multi-source error parameter identification method of the present invention. DETAILED DESCRIPTION
[0051] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.
[0052] In the description of the present invention, it should be understood that the terms "longitudinal", "transverse", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention.
[0053] In the description of the present invention, unless otherwise specified and limited, it should be noted that the terms "installed", "connected" and "connected" should be understood in a broad sense. For example, it can be a mechanical connection or an electrical connection, or it can be the internal communication between two components. It can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to the specific circumstances.
[0054] The present invention discloses a method for identifying multi-source error parameters of an encoder code disk. By identifying the eccentricity, tilt angle, or eccentricity and tilt angle under misalignment of the code disk from the instantaneous angular velocity error signal, the error parameters of the same error code disk under different speed conditions are determined. This method not only helps to improve the manufacturing accuracy of the encoder, but also provides a technical basis for judging whether the error will mask key fault characteristics. Figure 1 As shown in FIG, the encoder code disc multi-source error parameter identification method includes the following steps:
[0055] S1, collects instantaneous angular velocity signals of the same encoder under multiple groups of different speed conditions, calculates the peak-to-peak value of each group of signals, and selects the minimum fluctuation signal;
[0056] S2, calculate the mean of the minimum fluctuation signal, initialize the error parameters, and generate the theoretical error signal;
[0057] S3, constructing the optimization objective function based on the theoretical error signal;
[0058] S4, performing optimization algorithm iteration according to the principle of artificial lemming optimization algorithm or particle swarm optimization algorithm, and outputting the global optimal parameters if the convergence condition is met, otherwise returning to step S2;
[0059] S5, based on the global optimal parameters, generates the instantaneous angular velocity error signal of the theoretical model.
[0060] In a preferred embodiment of the present invention, the peak-to-peak value V of each group of signals is calculated. pp , screening minimum fluctuation signal IAS k , thereby reducing the impact of speed fluctuations on the instantaneous angular velocity error signal generated by the theoretical model:
[0061] Vpp =V max -V min
[0062] IAS k =arg min[V pp (IAS1,IAS2,…,IAS n )]
[0063] Among them, V max , V min Indicates the maximum and minimum values of each group of signals; IAS n is the nth fluctuation signal.
[0064] In a preferred embodiment of the present invention, the method for calculating the mean of the minimum fluctuation signal, initializing the error parameter, and generating the theoretical error signal is as follows:
[0065] Calculate the mean of the minimum instantaneous angular velocity fluctuation signal, recorded as IAS k_m , which is the speed reference in the theoretical error model:
[0066] IAS k_m =mean(IAS k )
[0067] Among them, IAS k is the minimum fluctuation signal;
[0068] If the code disc is known to have a single error type, generate the instantaneous angular velocity error signal:
[0069]
[0070] Among them, ω er is the instantaneous angular velocity error of the encoder when the probe is arranged radially in the eccentric state, ω ar is the instantaneous angular velocity error of the code disk when the probe is arranged axially in an eccentric state; ρ r is the radial eccentricity, ρ a is the axial eccentricity; α′ represents the position angle information read by the probe, α is the actual rotation angle; Δr is the eccentricity distance, r is the radius of the code disk, ω(t) is the instantaneous angular velocity of the code disk when it rotates, ω t Indicates the instantaneous angular velocity error of the code disk in the tilted state;
[0071] Otherwise, the instantaneous angular velocity error signal is generated as:
[0072]
[0073] Among them, ω(t) is replaced by IAS k_m , generate a set of theoretical error signals under different error parameters, denoted as IASe ;
[0074] IAS e =[IAS e1 (ρ1,β1),IAS e2 (ρ2,β2),…,IAS en (ρ n ,β n )]
[0075] Where β is the tilt angle of the code disk, ω misr is the instantaneous angular velocity error when the probe is arranged radially, ω misa Instantaneous angular velocity error when the probe is arranged axially, IAS en is the theoretical error signal calculated under different error parameters, ρ n Indicates the eccentricity error of the code disk. In the above different formulas, ρ n Expressed as ρ r or a , ρ r , ρ a The expressions differ only because of the different probe arrangements. The specific choice depends on the arrangement of the probes; β n Indicates the code disk tilt angle error.
[0076] The radial arrangement of the probe means that the installation direction of the probe is perpendicular to the rotation direction of the shaft, and the axial arrangement means that the probe is arranged along the axis of the shaft to monitor axial displacement or vibration.
[0077] In a preferred embodiment of the present invention, due to the phase misalignment between the experimental signal and the theoretical simulation signal, the direct subtraction of the two signals in the time domain will result in amplitude changes or abnormal energy distribution. However, in the frequency domain amplitude spectrum, the influence of phase changes does not need to be considered, so the instantaneous angular velocity IAS with errors is measured. m and the theoretical error signal IAS e The minimum value f(ρ,β) of the logarithm of the root mean square value rms of the spectrum of the difference between the two is the optimization objective function, which is:
[0078] f(ρ,β)=min(log(rms(F[IAS m -IAS e ])))
[0079] Here, F represents Fourier transform.
[0080] In a preferred embodiment of the present invention, Figure 2 As shown in the figure, according to the principle of artificial lemming optimization (ALA) algorithm, the global optimal parameters are calculated, which are:
[0081]
[0082] E(t)=4×arctan(1-t / T max )×ln(1 / rand)
[0083] in, is the current optimal solution; F is the sign of changing the search direction; A vector representing random Brownian motion; It is a random number in 1 row and Dim column between [-1,1], which is used to control the movement of the current optimal individual and random individuals in the lemming population; is a randomly selected individual in the population; L is a random number related to this iteration; is a random search individual in the population; G represents the escape coefficient of lemmings, which decreases with the increase of the number of iterations; Levy is Levy flight, which is used to simulate the deceptive behavior of lemmings; E(t) represents the energy coefficient.
[0084] In a preferred embodiment of the present invention, the instantaneous angular velocity signal IAS measured by the experiment is m , select the instantaneous angular velocity signal IAS that has completed error parameter identification i , calculate its mean speed, and generate the theoretical model instantaneous angular velocity error signal IAS at the stable speed of this working condition based on the error of artificial lemming parameter identification [ρ,β]best :
[0085] IAS [ρ,β]best =IAS i_m +IAS e[ρ,β]best
[0086] Among them, IAS [ρ,β]best represents the instantaneous angular velocity signal of the theoretical model generated under the optimal parameters ρ, β; IAS i_m Represents the speed reference in the theoretical error model; IAS e[ρ,β]best represents the instantaneous angular velocity error signal of the theoretical model generated under the optimal parameters ρ, β.
[0087] The present invention also provides an encoder code disc multi-source error parameter identification system, including a data acquisition module and a processing module. The data acquisition module is used to collect instantaneous angular velocity signals of the same code disc under multiple groups of different speed conditions and transmit them to the processing module.
[0088] The processing module executes the method of the present invention to generate a theoretical model instantaneous angular velocity error signal.
[0089] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
[0090] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.
Claims
1. A method for identifying multi-source error parameters of an encoder code disk, characterized in that: The steps include: S1, collects instantaneous angular velocity signals of the same encoder under multiple groups of different speed conditions, calculates the peak-to-peak value of each group of signals, and selects the minimum fluctuation signal; S2, calculate the mean of the minimum fluctuation signal, initialize the error parameters, and generate the theoretical error signal; S3, constructing the optimization objective function based on the theoretical error signal; S4, performing optimization algorithm iteration according to the principle of artificial lemming optimization algorithm or particle swarm optimization algorithm, and outputting the global optimal parameters if the convergence condition is met, otherwise returning to step S2; S5, based on the global optimal parameters, generates the instantaneous angular velocity error signal of the theoretical model.
2. The encoder code disc multi-source error parameter identification method according to claim 1, characterized in that: Calculate the peak-to-peak value V of each signal group pp , screening minimum fluctuation signal IAS k for: V pp =V max -V min IAS k =arg min[V pp (IAS1,IAS2,…,IAS n )] Among them, V max , V min Indicates the maximum and minimum values of each group of signals; IAS n is the nth fluctuation signal.
3. The encoder code disc multi-source error parameter identification method according to claim 2, characterized in that: The method to calculate the mean of the minimum fluctuation signal, initialize the error parameters, and generate the theoretical error signal is: Calculate the mean of the minimum instantaneous angular velocity fluctuation signal, recorded as IAS k_m , which is the speed reference in the theoretical error model: IAS k_m =mean(IAS k ) Among them, IAS k is the minimum fluctuation signal; If the code disc is known to have a single error type, generate the instantaneous angular velocity error signal: Among them, ω er is the instantaneous angular velocity error of the encoder when the probe is arranged radially in the eccentric state, ω ar is the instantaneous angular velocity error of the code disk when the probe is arranged axially in an eccentric state; ρ r is the radial eccentricity, ρ a is the axial eccentricity; α′ represents the position angle information read by the probe, α is the actual rotation angle; Δr is the eccentricity distance, r is the radius of the code disk, ω(t) is the instantaneous angular velocity of the code disk when it rotates, ω t Indicates the instantaneous angular velocity error of the code disk in the tilted state; Otherwise, the instantaneous angular velocity error signal is generated as: Among them, ω(t) is replaced by IAS k_m , generate a set of theoretical error signals under different error parameters, denoted as IAS e ; IAS e =[IAS e1 (ρ1,β1),IAS e2 (ρ2,β2),…,IAS en (r n ,b n )] Where β is the tilt angle of the code disk, ω misr is the instantaneous angular velocity error when the probe is arranged radially, ω misa Instantaneous angular velocity error when the probe is arranged axially, IAS en is the theoretical error signal calculated under different error parameters, ρ n Indicates the eccentricity error of the code disk, β n Indicates the code disk tilt angle error.
4. The encoder code disc multi-source error parameter identification method according to claim 1, characterized in that: Select the instantaneous angular velocity IAS with error measurement m and the theoretical error signal IAS e The minimum value f(ρ,β) of the logarithm of the root mean square value rms of the spectrum of the difference between the two is the optimization objective function, which is: f(ρ,β)=min(log(rms(F[IAS m -IAS e ]))) Here, F represents Fourier transform.
5. The encoder code disc multi-source error parameter identification method according to claim 1, characterized in that: According to the ALA algorithm principle, the global optimal parameters are calculated, specifically: E(t)=4×arctan(1-t / T max )×ln(1 / rand) in, is the current optimal solution; F is the sign of changing the search direction; A vector representing random Brownian motion; It is a random number in 1 row and Dim column between [-1,1], which is used to control the movement of the current optimal individual and random individuals in the lemming population; is a randomly selected individual in the population; L is a random number related to this iteration; is a random search individual in the population; G represents the escape coefficient of lemmings, which decreases with the increase of the number of iterations; Levy is Levy flight, which is used to simulate the deceptive behavior of lemmings; E(t) represents the energy coefficient.
6. The encoder code disc multi-source error parameter identification method according to claim 1, characterized in that: According to the experimentally measured instantaneous angular velocity signal IAS m , select the instantaneous angular velocity signal IAS that has completed error parameter identification i , calculate its mean speed, and generate the theoretical model instantaneous angular velocity signal IAS at the stable speed of this working condition based on the error of artificial lemming parameter identification [ρ,β]best : IAS [ρ,β]best =IAS i_m +IAS e[ρ,β]best Among them, IAS [ρ,β]best represents the instantaneous angular velocity signal of the theoretical model generated under the optimal parameters ρ, β; IAS i_m Represents the speed reference in the theoretical error model; IAS e[ρ,β]best represents the instantaneous angular velocity error signal of the theoretical model generated under the optimal parameters ρ, β.
7. An encoder code disc multi-source error parameter identification system, characterized in that: It includes a data acquisition module and a processing module. The data acquisition module is used to collect instantaneous angular velocity signals of the same encoder under multiple groups of different speed conditions and transmit them to the processing module; The processing module executes the method according to any one of claims 1 to 6 to generate a theoretical model instantaneous angular velocity error signal.