Early fault signal variable scale convex peak phase identification method, terminal and system

Abstract

The application provides a variable scale convex peak phase identification method, a terminal and a system of an early fault signal, first, a to-be-detected signal of a target bearing under a variable scale Duffing system is acquired; then, a phase bifurcation diagram of the to-be-detected signal is drawn with the phase of a reference signal as a benchmark; then, the phase bifurcation diagram is identified to determine a period boundary value in the phase bifurcation diagram; finally, the initial phase of the to-be-detected signal is calculated according to the period boundary value. Through the phase bifurcation diagram and the variable scale convex peak phase identification method, the phase of the early fault signal is identified, so that the accuracy of frequency identification of the fault signal is ensured, and the influence on bearing fault identification is avoided.

Description

Technical Field

[0001] This invention belongs to the field of fault detection technology, and in particular relates to a variable-scale convex peak phase recognition method, terminal and system for early fault signals. Background Technology

[0002] Weak signals are widely present in nature, and their identification has applications in fields such as space exploration, medical diagnosis, and fault detection. How to identify useful weak signals from background noise is a hot research topic. When early failures occur in the wheelset bearings of high-speed trains, the emitted signals are weak and periodic, which can be approximated as harmonic signals. Therefore, the essence of signal identification is to identify the frequency, amplitude, and phase of the harmonic signals. Among these, frequency identification can determine the location of the fault in the high-speed train wheelset bearings.

[0003] Currently, due to the sensitivity of chaotic systems to the initial values ​​of weak signals, they are often used to detect the characteristic parameters of weak signals. Existing methods are based on the assumption that "the phase of the signal to be detected is equal to zero" or ignore the influence of phase on the system. However, in actual engineering signal measurements, it is almost impossible for the initial phase of an early fault signal to be exactly zero. The presence of phase error will lead to errors in frequency identification, which may in turn lead to incorrect fault location identification. Summary of the Invention

[0004] In view of this, the present invention provides a variable-scale convex peak phase recognition method, terminal and system for early fault signals, aiming to solve the problem of bearing fault location recognition error caused by phase error in the prior art.

[0005] A first aspect of this invention provides a method for identifying the phase of a variable-scale convex peak in an early fault signal, comprising:

[0006] Acquire the detection signal of the target bearing in a variable-scale Duffing system;

[0007] Using the phase of the reference signal as a reference, draw the phase bifurcation diagram of the signal to be detected;

[0008] Identify the phase bifurcation diagram and determine the periodic boundary values ​​in the phase bifurcation diagram;

[0009] The initial phase of the signal to be detected is calculated based on the periodic boundary value.

[0010] A second aspect of the present invention provides a variable-scale convex peak phase identification device for early fault signals, comprising:

[0011] The acquisition module is used to acquire the detection signal of the target bearing in the variable-scale Duffing system;

[0012] The drawing module is used to draw the phase bifurcation diagram of the signal to be detected based on the phase of the reference signal;

[0013] The determination module is used to identify the phase bifurcation diagram and determine the periodic boundary values ​​in the phase bifurcation diagram;

[0014] The calculation module is used to calculate the initial phase of the signal to be detected based on the periodic boundary value.

[0015] A third aspect of the present invention provides a terminal including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the variable-scale convex peak phase recognition method for early fault signals as described in the first aspect above.

[0016] A fourth aspect of the present invention provides a detection system, including: an acceleration sensor, a signal acquisition device, and a terminal as described in the third aspect above.

[0017] A fifth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the variable-scale convex peak phase recognition method for early fault signals as described in the first aspect above.

[0018] The method, terminal, and system for identifying early fault signals using variable-scale peak phase recognition provided in this invention first acquire the target bearing signal under a variable-scale Duffing system; then, using the phase of a reference signal as a benchmark, a phase bifurcation diagram of the target signal is drawn; next, the phase bifurcation diagram is identified to determine the periodic boundary values; finally, the initial phase of the target signal is calculated based on the periodic boundary values. By creating the phase bifurcation diagram and using the variable-scale peak phase recognition method, the phase of the early fault signal is identified, thereby ensuring the accuracy of frequency identification of the fault signal and avoiding any impact on bearing fault identification. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is an application scenario diagram of the variable-scale convex peak phase recognition method for early fault signals provided in the embodiments of the present invention;

[0021] Figure 2This is a flowchart illustrating the implementation of the variable-scale convex peak phase recognition method for early fault signals provided in this embodiment of the invention.

[0022] Figure 3(a) is a phase bifurcation diagram when the initial phase is 0 according to an embodiment of the present invention;

[0023] Figure 3(b) is a phase bifurcation diagram when the initial phase is π / 6 according to an embodiment of the present invention;

[0024] Figure 3(c) is a phase bifurcation diagram when the initial phase is π / 2 according to an embodiment of the present invention;

[0025] Figure 3(d) is a phase bifurcation diagram when the initial phase is π according to an embodiment of the present invention;

[0026] Figure 3(e) is a phase bifurcation diagram when the initial phase is -3π / 4 according to an embodiment of the present invention;

[0027] Figure 3(f) is a phase bifurcation diagram when the initial phase is -π / 2 according to an embodiment of the present invention;

[0028] Figure 4(a) is a phase bifurcation diagram when the initial phase is π / 4, provided by the real-time example of the present invention;

[0029] Figure 4(b) is a phase bifurcation diagram when the initial phase is π / 2, provided by the real-time example of the present invention;

[0030] Figure 4(c) is a phase bifurcation diagram when the initial phase is 4π / 5, provided in the real-time example of the present invention;

[0031] Figure 4(d) is a phase bifurcation diagram provided by the real-time example of the present invention when the initial phase is -4π / 5;

[0032] Figure 5(a) is a frequency bifurcation diagram of Example 1 of the present invention;

[0033] Figure 5(b) is a phase bifurcation diagram of Example 1 of the present invention;

[0034] Figure 6(a) is a frequency bifurcation diagram of Example 2 of the present invention;

[0035] Figure 6(b) is a phase bifurcation diagram of Example 2 of the present invention;

[0036] Figure 6(c) is a frequency bifurcation diagram of Example 2 of the present invention after system replacement;

[0037] Figure 7 The graph shows the chaotic threshold of system (1) as a function of the initial phase of the signal to be detected;

[0038] Figure 8(a) is a reference signal amplitude bifurcation diagram of system (21);

[0039] Figure 8(b) is a reference signal amplitude bifurcation diagram of system (1);

[0040] Figure 9(a) is the system phase diagram when the reference signal phase is -0.17;

[0041] Figure 9(b) is the system phase diagram when the reference signal phase is -0.18;

[0042] Figure 9(c) is the system phase diagram when the reference signal phase is 1.75.

[0043] Figure 9(d) is the system phase diagram when the reference signal phase is 1.76;

[0044] Figure 10 This is a schematic diagram of the structure of the variable-scale convex peak phase recognition device for early fault signals provided in an embodiment of the present invention;

[0045] Figure 11 This is a schematic diagram of the terminal provided in an embodiment of the present invention. Detailed Implementation

[0046] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0047] Figure 1 This is an application scenario diagram of the variable-scale convex peak phase recognition method for early fault signals provided in this embodiment of the invention. For example... Figure 1 As shown, in some embodiments, the bearing fault detection system includes: an acceleration sensor 11, a signal acquisition device 12, and a terminal 13.

[0048] An accelerometer 11 is installed at a predetermined position on the top of the bearing end cover to collect weak signals from the bearing and send the collected acceleration signals to a signal acquisition device 12. The signal acquisition device 12 performs preliminary noise reduction and processing on the collected acceleration signals and then transmits them to a terminal 13. The terminal 13 parses the received signals into the form of a Holmes-type variable-scale random Duffing system to calculate the initial phase of the signal to be detected.

[0049] Figure 2 This is a flowchart illustrating the implementation of the variable-scale convex peak phase recognition method for early fault signals provided in this embodiment of the invention. Figure 2 As shown, in some embodiments, the variable-scale convex peak phase recognition method for early fault signals is applied to... Figure 1As shown, the method includes:

[0050] S210, acquire the detection signal of the target bearing under the variable-scale Duffing system.

[0051] The signal to be detected is Figure 1 The weak signals collected from the bearings are used to detect whether there are early failures in the bearings.

[0052] In this embodiment of the invention, the expression for the Holmes-type variable-scale stochastic Duffing system is:

[0053]

[0054] Among them, xx 3 It is a nonlinear term, and μ is the damping ratio. It is a reference signal. σn(t) is the signal to be detected, and σn(t) is Gaussian white noise with noise intensity σ and power spectral density K.

[0055] Consider the influence of the initial phase of the signal to be detected on the frequency identification method of the variable-scale convex peak frequency identification method when the parameters of system (1) change. In system (1), the damping coefficient μ, the reference signal amplitude f, the amplitude r of the signal to be detected, and the frequency ω1 of the signal to be detected are set to values ​​within the effective range, a bifurcation diagram of the reference signal frequency ω is plotted, and the phase of the signal to be detected is measured. The results of taking other values ​​within a cycle and comparing the distance the peak center moves when it is zero are shown in Table 1.

[0056] Table 1. Influence of system (1) parameter variations on frequency identification using the variable-scale peak frequency identification method

[0057]

[0058] As shown in Table 1, when noise and measurement errors are ignored, the shift distance of the peak center is only related to the phase when using the variable-scale peak frequency identification method to identify the frequency of the signal to be detected; that is, frequency identification is only related to the phase. When the damping coefficient of the variable-scale Duffing system, the amplitude and frequency of the reference signal and the signal to be detected are within a reasonable range, the identified phase of the signal to be detected is unique and accurate. Therefore, a variable-scale Duffing system with determined parameters can be used as a phase identification system to identify the initial phase of the signal to be detected.

[0059] S220: Using the phase of the reference signal as a reference, draw the phase bifurcation diagram of the signal to be detected.

[0060] In this embodiment of the invention, the phase of the reference signal is taken as the phase zero point on the horizontal axis, and the horizontal axis range is [-π, π]. This is used in the variable-scale Duffing system. The maximum value X max Using the vertical axis as the ordinate, plot the phase bifurcation diagram of the signal to be detected.

[0061] S230, identify the phase bifurcation diagram and determine the periodic boundary value in the phase bifurcation diagram.

[0062] In this embodiment of the invention, in system (1), there is a state transition phenomenon from chaos to periodicity, and the center of the periodic motion is the initial phase of the signal to be detected. Therefore, it is necessary to determine the periodic boundary value in order to calculate the periodic center. The periodic boundary value can be manually calibrated or obtained by image recognition algorithms such as edge detection algorithms and target recognition algorithms, and is not limited here.

[0063] In some embodiments, S230 may include: identifying a phase bifurcation diagram, determining periodic regions and aperiodic regions in the phase bifurcation diagram; and determining periodic boundary values ​​based on the periodic regions and aperiodic regions.

[0064] In this embodiment of the invention, a threshold segmentation algorithm can be used to segment dense areas of black pixels into aperiodic regions and sparse areas of black pixels into periodic regions by using a preset pixel threshold. The phase value of the contact edge between the periodic region and the aperiodic region is the periodic boundary value.

[0065] In some embodiments, S230 may include: identifying a phase bifurcation diagram and determining periodic and aperiodic regions in the phase bifurcation diagram; correcting the periodic and aperiodic regions based on the length of the second side of the periodic region and the length of the second side of the aperiodic region; wherein the contact side of the periodic and aperiodic regions is the first side; and the non-contact side of the periodic and aperiodic regions is the second side; and determining a periodic boundary value based on the corrected periodic region and the corrected aperiodic region.

[0066] Figure 3(a) is a phase bifurcation diagram when the initial phase is 0 according to an embodiment of the present invention. As shown in Figure 3(a), the pixels in the contact part between the periodic region and the non-periodic region are relatively sparse and the contact line is irregular. Therefore, conventional image recognition algorithms are prone to errors in the recognition of periodic boundary values, which in turn leads to inaccurate initial phase recognition.

[0067] As can be seen from Figure 3(a), the upper boundary of the periodic region is usually slightly higher than the upper boundary of the non-periodic region. Therefore, in addition to segmentation based on pixels, segmentation can also be based on the vertical axis value of pixels on the upper boundary to avoid identification errors of periodic boundary values.

[0068] In this embodiment of the invention, after determining the periodic region and the non-periodic region in the phase bifurcation diagram, the contour curves of the periodic region and the non-periodic region can be roughly drawn to obtain two rectangular contours. The average phase value of each point on the two second sides is the two initially determined periodic boundary values, which are denoted as the first boundary value.

[0069] Calculate the mean of the first side above the periodic region and the non-periodic region, i.e., X. max The mean of the values, denoted as periodic standard A and aperiodic standard B, is the sum of the values ​​of a whole X in the region [-π, π]. max Value curves, especially near the contact points between periodic and aperiodic regions, if their X max If the distance between a point and the periodic standard A is less than the distance to the non-periodic standard B, then the point is considered to belong to the periodic region; otherwise, the point belongs to the non-periodic region. This allows us to determine two more periodic boundary values, denoted as the second boundary value.

[0070] The actual boundary values ​​can be obtained by multiplying the first boundary value and the second boundary value by their respective weights. The weights corresponding to the first boundary value and the second boundary value are summed to one; the specific values ​​are determined based on requirements and are not limited here.

[0071] S240, calculate the initial phase of the signal to be detected based on the periodic boundary value.

[0072] Figure 3(a) is a phase bifurcation diagram when the initial phase is 0 according to an embodiment of the present invention; Figure 3(b) is a phase bifurcation diagram when the initial phase is π / 6 according to an embodiment of the present invention; Figure 3(c) is a phase bifurcation diagram when the initial phase is π / 2 according to an embodiment of the present invention; Figure 3(d) is a phase bifurcation diagram when the initial phase is π according to an embodiment of the present invention; Figure 3(e) is a phase bifurcation diagram when the initial phase is -3π / 4 according to an embodiment of the present invention; Figure 3(f) is a phase bifurcation diagram when the initial phase is -π / 2 according to an embodiment of the present invention.

[0073] As shown in Figures 3(a), 3(b), 3(c), and 3(f), if the periodic motion is continuously located within the range [-π, π], the midpoint of the total length of the periodic motion is the phase of the signal to be detected, i.e.:

[0074]

[0075] As shown in Figure 3(d), if the periodic motion is discontinuously located within [-π, π] and the center of the convex peak is greater than zero, the midpoint value of the total length of the periodic motion is the phase of the signal to be detected, that is:

[0076]

[0077] As shown in Figure 3(e), if the periodic motion is discontinuously located within [-π, π] and the center of the convex peak is less than zero, the midpoint value of the total length of the periodic motion is the phase of the signal to be detected, that is:

[0078]

[0079] In summary, it can be concluded that in some embodiments, the period boundary value includes the left boundary of the period and the right boundary of the period; accordingly, S240 may include:

[0080]

[0081] in, The initial phase of the signal to be detected is denoted as . All are between [-π, π]. The left boundary of the period, The right boundary of the period is k, which is 1 or -1, depending on the period.

[0082] exist At that time, the periodic motion is continuous.

[0083] exist At that time, the periodic motion is interrupted; in When the value is less than 0, the peak center is greater than 0, and k = 1; When the value is greater than 0, the center of the convex peak is less than 0, and k = -1.

[0084] The following embodiment verifies the variable-scale convex peak phase identification method for early fault signals, but it is not intended to be limiting.

[0085] Figure 4(a) is a phase bifurcation diagram when the initial phase is π / 4 provided by the real-time example of the present invention; Figure 4(b) is a phase bifurcation diagram when the initial phase is π / 2 provided by the real-time example of the present invention; Figure 4(c) is a phase bifurcation diagram when the initial phase is 4π / 5 provided by the real-time example of the present invention; Figure 4(d) is a phase bifurcation diagram when the initial phase is -4π / 5 provided by the real-time example of the present invention.

[0086] like Figures 4(a)-4(d) As shown, in this implementation example, signals with known phases of π / 4, π / 2, 4π / 5, and -4π / 5 are taken as the signals to be detected. Phase bifurcation diagrams are drawn according to S220, and then identified according to S230. It can be determined that:

[0087] Figure 4(a) Calculated according to formula (2) The absolute error is 0.0019, and the relative error is 0.24%.

[0088] Figure 4(b) Calculated according to formula (2) The absolute error is 0.001, and the relative error is 0.064%.

[0089] Figure 4(c) Calculated according to formula (3) The absolute error is 0.011, and the relative error is 0.44%.

[0090] Figure 4(d) Calculated according to formula (4) The absolute error is 0.0075, and the relative error is 0.3%.

[0091] In some embodiments, after S240, the method further includes: drawing a frequency bifurcation diagram of the signal to be detected based on the frequency of the reference signal; determining the frequency observation value of the signal to be detected based on the frequency bifurcation diagram; determining the peak center movement distance based on the initial phase of the signal to be detected; calculating the frequency of the signal to be detected based on the frequency observation value and the peak center movement distance; and determining the fault location of the target bearing based on the frequency of the signal to be detected.

[0092] In this embodiment of the invention, the frequency bifurcation diagram is constructed in the same way as the phase bifurcation diagram, except that the horizontal axis is changed from phase to frequency, which will not be described again here. Subsequently, using the same principle as S230 and S240 to calculate the initial phase, the frequency value of the center of the period in the frequency bifurcation diagram is calculated as the frequency observation value.

[0093] In some embodiments, determining the fault location of the target bearing based on the frequency of the signal to be detected includes: determining the fault location of the target bearing based on the frequency of the signal to be detected and the theoretical frequency values ​​of the target bearing under various early fault conditions.

[0094] Two examples are given below to illustrate the frequency of the signal to be detected and the location of the fault in the target bearing, but these are not intended to be limiting. In these two examples, the outer ring of the bearing has a machining fault measuring 5 mm in length, 1 mm in width, and 0.7 mm in depth. The bearing speed is 1200 r / min. In the experiment, the accelerometer is mounted on the top of the bearing end cap. The specific parameters of the bearing are shown in the table below:

[0095] Table 2 Wheelset Bearing Parameters

[0096]

[0097] Table 3 Theoretical values ​​of fault angular frequency

[0098]

[0099] Example 1:

[0100] The system sampling frequency was set to 51200Hz, and this experiment included a total of 5×10 4 There are 10 data points. To avoid excessive noise affecting the system's noise immunity, the data is multiplied by 0.05, compressing the amplitude of both noise and signal.

[0101] Starting from the 102600th data point, the system (1) inputs data and uses the reference signal as a reference to formulate a frequency bifurcation diagram and a phase bifurcation diagram. Figure 5(a) shows the frequency bifurcation diagram of Example 1 of the present invention. Figure 5(b) shows the phase bifurcation diagram of Example 1 of the present invention. From Figure 5(a), the frequency observation value of the signal to be detected can be calculated as ω. s The value is 887. As shown in Figure 5(b), Calculated according to formula (5) That is, 5.9° is within the detectable range of system (1), therefore the peak center shift distance for:

[0102]

[0103] Will Substituting into equation (6), we can obtain The frequency ω1 of the signal to be detected is:

[0104]

[0105] From equation (7), we can obtain ω1 = 886.9, which has an absolute error of 30.1 and a relative error of 3.28% compared with the theoretical fault frequency.

[0106] In this example, the frequency of the signal to be detected is closest to the theoretical frequency ω = 917 when the outer ring malfunctions, so the outer ring may be malfunctioning and should be repaired in time.

[0107] Example 2:

[0108] The system sampling frequency was set to 51200Hz, and this experiment included a total of 5×10 4 There are 10 data points. To avoid excessive noise affecting the system's noise immunity, the data is multiplied by 0.05, compressing the amplitude of both noise and signal.

[0109] Starting from the 102400th data point, the system (1) is input, and a frequency bifurcation diagram and a phase bifurcation diagram are formulated based on the reference signal. Figure 6(a) shows the frequency bifurcation diagram of Example 2 of the present invention. Figure 6(b) shows the phase bifurcation diagram of Example 2 of the present invention. As shown in Figure 5(a), the phase of the signal to be detected is located in the recognition blind region. As shown in Figure 5(b), Calculated according to formula (5) That is, 162°, so the following system (8) is used to input the data and redraw the frequency bifurcation diagram.

[0110]

[0111] Figure 6(c) shows the frequency bifurcation diagram of Example 2 of the present invention after system replacement. As shown in Figure 6(c), the observed frequency value of the signal to be detected is ω. s The value is 886.6, representing the distance the peak center moves when the phase is in the recognition blind zone. for:

[0112]

[0113] Will Substituting into equation (9), we can obtain From equation (7), the frequency ω1 of the signal to be detected is 886.81, with an absolute error of 30.79 and a relative error of 3.36% compared with the theoretical fault frequency.

[0114] In this example, the frequency of the signal to be detected is closest to the theoretical frequency ω = 917 when the outer ring malfunctions, so the outer ring may be malfunctioning and should be repaired in time.

[0115] In some embodiments, after calculating the frequency of the signal to be detected, the method further includes: determining the theoretical value of the initial phase of the signal to be detected based on the frequency of the signal to be detected and the amplitude of the reference signal; if the difference between the initial phase of the signal to be detected and the theoretical value of the initial phase of the signal to be detected is greater than a preset threshold, then the method jumps to the step of identifying the phase bifurcation diagram and determining the periodic boundary value in the phase bifurcation diagram, so as to recalculate the initial phase of the signal to be detected.

[0116] The absolute and relative errors calculated above are all calculated using the method of this invention to calculate the phase and frequency when the signal to be detected is known, and then the errors are calculated based on the known values ​​and the calculated values. In actual detection, the signal to be detected is completely unknown. Therefore, in this embodiment of the invention, to avoid errors causing incorrect bearing fault identification, after each calculation of the frequency of the signal to be detected, the method in this embodiment can be used to verify it. If the verification error is too large, the weights corresponding to the first boundary value and the second boundary value are adjusted, and the initial phase of the signal to be detected is re-determined until the verified error meets the requirements.

[0117] In this embodiment, the theoretical value of the initial phase can be calculated according to the following formula:

[0118]

[0119] Among them, f d and f d ' is the chaos threshold, and r is the amplitude of the signal to be detected. The initial phase of the reference signal.

[0120] The principle of the above formula (10) is as follows:

[0121] System (1) has three singularities: two centers (±1,0) and one saddle point (0,0), and a homologous orbit.

[0122]

[0123] When ω = ω1, for The stochastic Melnikov function of system (1) is:

[0124]

[0125]

[0126]

[0127] in, It is the random part. The first moment is:

[0128]

[0129] The first moment ignores the effect of noise on the threshold, so M must be considered. + The second moment of (t0). Since the transfer function H(ω) is:

[0130]

[0131] Where h(t)=σy + (t) is the impulse response function.

[0132] It can be concluded that The second moment is:

[0133]

[0134] The mean-square system (1) has the following stochastic Melnikov function:

[0135]

[0136] in,

[0137] The necessary and sufficient condition for the existence of a simple zero in the mean-square sense of the stochastic Melnikov function of system (1) is:

[0138]

[0139] It satisfies: ω≠k, t0≠(kπ-θ) / ω, t0≠[kπ+(π / 2)-θ] / ω, t0∈[-π,π], k=0,±1,±2…,±n,….

[0140] From equation (18), we can obtain that when Sometimes:

[0141]

[0142] The chaos threshold of system (1) in the Smale horseshoe sense is:

[0143]

[0144] Specifically, when r = 0, At that time, system (1) becomes:

[0145]

[0146] From equation (9), the chaos threshold of system (10) is:

[0147]

[0148] Then equation (9) simplifies to:

[0149]

[0150] Figure 7 The graph shows the chaotic threshold of system (1) as a function of the initial phase of the signal to be detected. Figure 7 As shown, let μ = 0.5, r = 0.02, ω = 1, σ = 0.01, Initial phase of the signal to be detected The chaos threshold f of system (1) d The phase has an impact. Therefore, when using system (1) to identify the frequency of the signal to be detected, the influence of the phase must be considered. Therefore, equation (10) above can be derived from equation (23).

[0151] Figure 8(a) is a reference signal amplitude bifurcation diagram of system (21); Figure 8(b) is a reference signal amplitude bifurcation diagram of system (1).

[0152] like Figures 8(a)-8(b) As shown, in this embodiment, let μ = 0.5, ω = 1, σ = 0.01, and plot the bifurcation diagram of system (10) with respect to the reference signal amplitude. It can be seen that the reference signal amplitude f of system (10) d =0.3765.

[0153] Let μ=0.5, ω=ω1=1, σ=0.01, r=0.02, The initial phase of the signal to be measured can be set. Drawing a bifurcation diagram of system (1) with respect to the amplitude of the reference signal, it can be seen that the amplitude of the reference signal f′ in system (1) is... d=0.3621. Then, using equation (10), the theoretical value of the initial phase is calculated, and the theoretical value of the initial phase is 0.7860. The absolute error between the theoretical value and the actual value of the initial phase is 0.001, and the relative error is 0.13%.

[0154] It can be concluded that the above equation (10) theoretically provides an accurate expression for the phase of the signal to be detected, thus improving the theory of weak signal parameter identification. However, this formula requires knowledge of the frequency of the signal to be detected and the amplitude of the reference signal of the system containing and without the signal to be detected must be obtained first. It has certain limitations and the process is cumbersome. Therefore, it is only used for the iterative verification process of the method of this invention.

[0155] In addition to the methods described above, this invention can also be verified by generating a system phase diagram, as detailed below:

[0156] Figure 9(a) shows the system phase diagram when the reference signal phase is -0.17; Figure 9(b) shows the system phase diagram when the reference signal phase is -0.18; Figure 9(c) shows the system phase diagram when the reference signal phase is 1.75; Figure 9(d) shows the system phase diagram when the reference signal phase is 1.76. Figures 9(a)-9(d) As shown, let μ=0.5, f=0.815, r=0.02, σ=0.01, ω=ω1=1, Reference signal phase When the values ​​are -0.17, -0.18, 1.75, and 1.76 respectively, draw the phase diagram of system (1).

[0157] As shown in Figures 9(a) and (b), the chaos threshold of system (1) is... As shown in Figures 9(c) and (d), the chaos threshold of system (1) is... The phase of the signal to be detected is calculated according to equation (14). The absolute error is 0.005, and the relative error is 0.64%. This indicates that the variable-scale convex peak phase recognition method accurately identifies the phase of early fault signals.

[0158] In summary, the beneficial effects of this invention are as follows: by creating a phase bifurcation diagram and using a variable-scale convex peak phase recognition method, the phase of early fault signals can be identified, thereby ensuring the accuracy of frequency identification of fault signals and avoiding any impact on bearing fault identification.

[0159] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0160] Figure 10 This is a schematic diagram of the structure of the variable-scale convex peak phase recognition device for early fault signals provided in an embodiment of the present invention. Figure 10 As shown, in some embodiments, the variable-scale convex peak phase recognition device 10 for early fault signals includes:

[0161] The acquisition module 1010 is used to acquire the detection signal of the target bearing under the variable-scale Duffing system;

[0162] The drawing module 1020 is used to draw the phase bifurcation diagram of the signal to be detected based on the phase of the reference signal.

[0163] The determination module 1030 is used to identify the phase bifurcation diagram and determine the periodic boundary values ​​in the phase bifurcation diagram;

[0164] The calculation module 1040 is used to calculate the initial phase of the signal to be detected based on the periodic boundary value.

[0165] Optionally, the periodic boundary values ​​include the left boundary and the right boundary of the period; the calculation module 1040 is used for:

[0166]

[0167] in, The initial phase of the signal to be detected is denoted as . All are between [-π, π]. The left boundary of the period, The right boundary of the period is k, which is 1 or -1, depending on the period.

[0168] Optionally, the determining module 1030 is used to: identify the phase bifurcation diagram, determine the periodic region and non-periodic region in the phase bifurcation diagram; and determine the periodic boundary value based on the periodic region and non-periodic region.

[0169] Optionally, the determining module 1030 is used to: identify the phase bifurcation diagram and determine the periodic and aperiodic regions in the phase bifurcation diagram; correct the periodic and aperiodic regions based on the length of the second side of the periodic region and the second side of the aperiodic region; wherein the contact side of the periodic and aperiodic regions is the first side; and the non-contact side of the periodic and aperiodic regions is the second side; and determine the periodic boundary value based on the corrected periodic region and the corrected aperiodic region.

[0170] Optionally, the variable-scale peak phase identification device 10 for early fault signals further includes: a frequency calculation module, used to draw a frequency bifurcation diagram of the signal to be detected based on the frequency of the reference signal; determine the frequency observation value of the signal to be detected based on the frequency bifurcation diagram; determine the peak center movement distance based on the initial phase of the signal to be detected; and calculate the frequency of the signal to be detected based on the frequency observation value and the peak center movement distance.

[0171] Optionally, the variable-scale convex peak phase recognition device 10 for early fault signals further includes: a fault recognition module, used to determine the fault location of the target bearing based on the frequency of the signal to be detected and the theoretical frequency value of the target bearing under various early faults.

[0172] Optionally, the variable-scale convex peak phase identification device 10 for early fault signals further includes: a verification module, used to determine the theoretical value of the initial phase of the signal to be detected based on the frequency of the signal to be detected and the amplitude of the reference signal; if the difference between the initial phase of the signal to be detected and the theoretical value of the initial phase of the signal to be detected is greater than a preset threshold, then the process jumps to the step of identifying the phase bifurcation diagram and determining the periodic boundary value in the phase bifurcation diagram, so as to recalculate the initial phase of the signal to be detected.

[0173] The variable-scale convex peak phase recognition device for early fault signals provided in this embodiment can be used to execute the above method embodiment. Its implementation principle and technical effect are similar, and will not be described again here.

[0174] Figure 11 This is a schematic diagram of the terminal structure provided in an embodiment of the present invention. Figure 11 As shown, an embodiment of the present invention provides a terminal 11, which includes a processor 1100, a memory 1110, and a computer program 1120 stored in the memory 1110 and executable on the processor 1100. When the processor 1100 executes the computer program 1120, it implements the steps in the embodiments of the variable-scale convex peak phase recognition method for various early fault signals described above, for example... Figure 2 The steps are shown. Alternatively, when processor 1100 executes computer program 1120, it implements the functions of each module / unit in the above system embodiments, for example... Figure 11 The functions of each module are shown.

[0175] For example, computer program 1120 may be divided into one or more modules / units, one or more of which are stored in memory 1110 and executed by processor 1100 to complete the present invention. One or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 1120 in terminal 11.

[0176] Terminal 11 can be a terminal or a server. The terminal can be a mobile phone, MCU, ECU, industrial control computer, etc., without limitation. The server can be a physical server, cloud server, etc., without limitation. Terminal 11 may include, but is not limited to, processor 1100 and memory 1110. Those skilled in the art will understand that... Figure 10This is merely an example of terminal 11 and does not constitute a limitation on terminal 11. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal may also include input / output devices, network access devices, buses, etc.

[0177] The processor 1100 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0178] The memory 1110 can be an internal storage unit of the terminal 11, such as the hard disk or RAM of the terminal 11. The memory 1110 can also be an external storage device of the terminal 11, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the terminal 11. Furthermore, the memory 1110 can include both internal and external storage units of the terminal 11. The memory 1110 is used to store computer programs and other programs and data required by the terminal. The memory 1110 can also be used to temporarily store data that has been output or will be output.

[0179] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps described in the above-described method for identifying variable-scale convex peak phases of early fault signals.

[0180] A computer-readable storage medium stores a computer program 1120. The computer program 1120 includes program instructions. When executed by the processor 1100, the program instructions implement all or part of the processes in the methods described in the above embodiments. Alternatively, the computer program 1120 can instruct related hardware to implement these processes. The computer program 1120 can be stored in a computer-readable storage medium. When executed by the processor 1100, the computer program 1120 can implement the steps of the various method embodiments described above. The computer program 1120 includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0181] The computer-readable storage medium can be an internal storage unit of the terminal in any of the foregoing embodiments, such as the terminal's hard disk or memory. The computer-readable storage medium can also be an external storage device of the terminal, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the terminal. Furthermore, the computer-readable storage medium can include both internal storage units and external storage devices of the terminal. The computer-readable storage medium is used to store computer programs and other programs and data required by the terminal. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0182] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0183] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0184] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0185] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0186] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0187] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0188] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0189] If an integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0190] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for identifying the phase of a variable-scale convex peak in an early fault signal, characterized in that, The method includes: Acquire the detection signal of the target bearing in a variable-scale Duffing system; Using the phase of the reference signal as a reference, draw the phase bifurcation diagram of the signal to be detected; The phase bifurcation diagram is identified to determine the periodic boundary values ​​in the phase bifurcation diagram; The initial phase of the signal to be detected is calculated based on the periodic boundary value. The periodic boundary values ​​include the left and right boundaries of the period; based on the periodic boundary values, the initial phase of the signal to be detected is calculated, including: in, φ 1 represents the initial phase of the signal to be detected. φ 1. φ 左 , φ 右 All are between [-π, π]. φ 左 The left boundary of the period, φ 右 As the right boundary of the period, k It is 1 or -1, depending on ; The step of identifying the phase bifurcation diagram and determining the periodic boundary values ​​in the phase bifurcation diagram includes: The phase bifurcation diagram is identified to determine the periodic and non-periodic regions within it. The periodic boundary value is determined based on the periodic region and the non-periodic region.

2. The method for identifying the phase of early fault signals with varying scale convex peaks according to claim 1, characterized in that, The step of identifying the phase bifurcation diagram and determining the periodic boundary values ​​in the phase bifurcation diagram includes: The phase bifurcation diagram is identified to determine the periodic and non-periodic regions within it. The periodic region and the aperiodic region are modified based on the length of the second side of the periodic region and the length of the second side of the aperiodic region; wherein the contact side between the periodic region and the aperiodic region is the first side; and the non-contact side between the periodic region and the aperiodic region is the second side. The periodic boundary value is determined based on the corrected periodic region and the corrected aperiodic region.

3. The method for identifying the phase of early fault signals with varying scale convex peaks according to claim 1 or 2, characterized in that, After calculating the initial phase of the signal to be detected, the method further includes: Using the frequency of the reference signal as a reference, draw the frequency bifurcation diagram of the signal to be detected; The frequency observation value of the signal to be detected is determined based on the frequency bifurcation diagram. The moving distance of the peak center is determined based on the initial phase of the signal to be detected; The frequency of the signal to be detected is calculated based on the frequency observation value and the moving distance of the peak center. The fault location of the target bearing is determined based on the frequency of the signal to be detected.

4. The variable-scale convex peak phase recognition method for early fault signals according to claim 3, characterized in that, Determining the fault location of the target bearing based on the frequency of the signal to be detected includes: The fault location of the target bearing is determined based on the frequency of the signal to be detected and the theoretical frequency value of the target bearing under various early fault conditions.

5. The method for identifying the phase of early fault signals with varying scale convex peaks according to claim 3, characterized in that, After calculating the frequency of the signal to be detected, the method further includes: Based on the frequency of the signal to be detected and the amplitude of the reference signal, determine the theoretical initial phase value of the signal to be detected; If the difference between the initial phase of the signal to be detected and the theoretical value of the initial phase of the signal to be detected is greater than a preset threshold, the process jumps to the step of identifying the phase bifurcation diagram and determining the periodic boundary value in the phase bifurcation diagram in order to recalculate the initial phase of the signal to be detected.

6. A terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the variable-scale convex peak phase recognition method for early fault signals as described in any one of claims 1 to 5.

7. A detection system, characterized in that, include: An accelerometer, a signal acquisition device, and the terminal as described in claim 6 above.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the variable-scale convex peak phase identification method for early fault signals as described in any one of claims 1 to 5.

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