A turnout fault diagnosis system and method based on vibration spectrum analysis
Through multi-source vibration sensing and signal decomposition technology, combined with dynamic baseline modeling and fault decision-making, the accuracy problem of turnout fault diagnosis is solved, accurate diagnosis and timely maintenance of turnout faults are achieved, and the safety of railway transportation is ensured.
Patent Information
- Application Number
- CN202510355943.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-25
- Publication Date
- 2025-09-30
- Estimated Expiration
- 2045-03-25
AI Technical Summary
Existing turnout fault diagnosis technology cannot effectively decompose vibration signals and has difficulty in accurately distinguishing different vibration sources, resulting in low fault diagnosis accuracy, prone to misjudgments and missed judgments, and unable to meet the railway transportation's demand for accurate diagnosis of turnout faults.
A multi-source vibration sensing module is used to accurately collect vibration signals. The mechanical transmission chain signal decoupling module decomposes the signals into three independent components: motor drive, gear transmission, and locking mechanism impact. Fault judgment is performed in combination with the dynamic baseline modeling module and the fault decision engine module, and the cross-correlation verification module is used to ensure the validity of the decomposition results.
It achieves accurate diagnosis of turnout faults, reduces misjudgments and missed judgments, promptly detects potential faults, provides accurate fault information, ensures the safety and smooth flow of railway transportation, and improves the efficiency and accuracy of fault diagnosis.
Smart Images
Figure CN120197109B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of railway signal equipment fault diagnosis, and in particular to a turnout fault diagnosis system and method based on vibration spectrum analysis. Background Art
[0002] Railway transportation, as an infrastructure and a popular means of transportation, occupies an important position in transportation. Turnouts are line connection equipment that allows locomotives and vehicles to transfer from one track to another in railway lines. The reliability of their operating status is directly related to the safety and efficiency of railway transportation. As railway transportation develops towards high speed and heavy load, the frequency and load of turnouts are increasing, and the probability of failure is also increasing accordingly. Timely and accurate diagnosis of turnout failures is of great significance to ensuring the safety and smooth operation of railway transportation and reducing operating costs.
[0003] At present, in the field of turnout fault diagnosis, traditional vibration spectrum analysis methods mainly rely on simple frequency domain or time domain analysis of the overall vibration signal. This method cannot effectively decompose the vibration signal during the turnout operation according to different mechanical transmission chains, and it is difficult to accurately distinguish the signals generated by different vibration sources such as motor drive, gear transmission, and locking mechanism impact. When faced with faults such as broken teeth on switch gears and lack of oil in the lock, since these faults have certain similarities in the vibration spectrum performance, traditional methods cannot establish a time-frequency domain energy ratio baseline for each independent component, resulting in low fault diagnosis accuracy, prone to misjudgment and missed judgment, and unable to meet the railway transportation's demand for accurate diagnosis of turnout faults.
[0004] In summary, the existing turnout fault diagnosis technology has obvious shortcomings in processing complex vibration signals and accurately distinguishing fault types. Therefore, there is an urgent need to develop a system and method that can effectively decompose vibration signals, establish an accurate fault feature baseline, and achieve accurate fault diagnosis. Summary of the Invention
[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide a turnout fault diagnosis system and method based on vibration spectrum analysis. It can accurately collect vibration signals of different channels at key parts of the turnout through a multi-source vibration sensing module. The mechanical transmission chain signal decoupling module adopts constrained variational modal decomposition signal to decompose the complex vibration signal into three independent components: motor drive, gear transmission, and locking mechanism impact, so that the operating status of each mechanical component can be analyzed more carefully. The dynamic baseline modeling module dynamically updates the threshold according to the number of turnout operations and ambient temperature factors to improve the accuracy of fault judgment. The fault decision engine module constructs a three-dimensional feature space joint judgment rule and compares the characteristic values of each component with the threshold. It can accurately distinguish the fault types of switch gear broken teeth and lock oil deficiency, reducing misjudgment and missed judgment. This can not only timely discover potential faults of the turnout and avoid further deterioration of the fault, but also provide accurate fault information to railway maintenance personnel, thereby effectively ensuring the safety and smooth flow of railway transportation.
[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: On the one hand, a turnout fault diagnosis system based on vibration spectrum analysis is provided, which comprises: a multi-source vibration sensing module, a mechanical transmission chain signal decoupling module, a dynamic baseline modeling module, a fault decision engine module, and a cross-correlation verification module;
[0007] The multi-source vibration sensing module is composed of a triaxial acceleration sensor array arranged on the axial direction of the switch housing, the top of the gearbox, and the root of the locking rod, synchronously collecting three-channel vibration signals of the motor drive, gear transmission, and locking mechanism;
[0008] The mechanical transmission chain signal decoupling module physically decomposes the vibration signal into motor drive component, gear transmission component, and locking impact component, and calculates three types of time-frequency domain features of each component: harmonic distortion rate H, modulation sideband ratio M, and impact period variance S;
[0009] The dynamic baseline modeling module uses a sliding time window to dynamically update the energy ratio baseline threshold H of the three components. th 、M th 、S th ;
[0010] The fault decision engine module generates a fault type diagnosis conclusion based on the exceeding combination of the three types of characteristic values, and the fault type diagnosis conclusion includes gear tooth breakage fault, locking oil shortage fault, and compound fault;
[0011] The cross-correlation verification module determines the validity of the decomposition by comparing the real-time decomposed motor drive component with the cross-correlation function of the preset no-load test template.
[0012] Furthermore, the mechanical transmission chain signal decoupling module synchronously collects three-channel vibration signals, and the physical correspondence between the three-channel vibration signals is:
[0013] Motor drive component: used to reflect low-frequency vibration caused by motor winding imbalance and bearing defects, with a frequency range of 0-500Hz, to determine potential motor failure hazards;
[0014] Gear transmission component: medium-frequency vibration indicating gear meshing impact and wear, with a frequency range of 500Hz-5kHz. This component is used to monitor the health of the gears.
[0015] Locking impact component: used to capture the high-frequency vibration generated by the locking mechanism at the moment of collision, with a frequency greater than 5kHz, to judge the working condition of the locking mechanism.
[0016] Furthermore, the mechanical transmission chain signal decoupling module decomposes the mixed vibration signal collected by the multi-source vibration sensing module by minimizing the function value to achieve mathematical decomposition of the mixed vibration signal and decompose it into K independent modal components {u k} and K = 3, that is, K corresponds to the three components of motor drive, gear transmission, and locking impact, and the center frequency {ω corresponding to each modal component is determined k}, the minimization function value satisfies: Among them, {u k} represents the modal components obtained by decomposition, u k (t) represents the real-time decomposition result, {ω k} represents the center frequency corresponding to each modal component, represents the derivative with respect to time t, δ(t) is the time function, j is the imaginary unit, * represents the convolution operation, α is the balance parameter used to adjust the bandwidth of each modal component, P k It is a constraint operator, which is set according to the characteristics of the mechanical components. Specifically:
[0017] Motor component constraint: P1(u1)=|FFT(u1)| f>500Hz =0, indicating that the motor component u1 is processed by fast Fourier transform FFT, forcing the high-frequency energy with a frequency greater than 500Hz to zero, which is used to detect the low-frequency vibration of the motor drive component;
[0018] Gear component constraints: Indicates that the energy of the gear component u2 in the frequency range of 500Hz to 5kHz is integrated so that this energy accounts for the total energy E total The ratio of is greater than or equal to 80%, so as to ensure the proportion of medium frequency energy in the gear component;
[0019] Locking component constraints: Among them, max(|u3(t)|) represents the maximum value of the locking component u3 in the time domain, A th is the amplitude threshold and A th =0.5g, g is the acceleration of gravity, freq(u3) represents the main frequency of the locking component u3, [f min ,f max ] is the characteristic frequency range of the vibration signal generated by the locking mechanism impact. This constraint requires that the maximum amplitude of the locking component exceeds the set threshold and its main frequency falls within the frequency range, thereby enhancing the detection of locking mechanism impact events.
[0020] Furthermore, the mechanical transmission chain signal decoupling module calculates three types of time-frequency domain features of each component: harmonic distortion rate H, modulation sideband ratio M, and impulse period variance S, where:
[0021] The harmonic distortion rate H is used to measure the content of harmonic components in the signal, reflecting the degree to which the signal deviates from the fundamental wave. For a periodic signal x(t), its harmonic distortion rate is X n represents the amplitude of the nth harmonic component of the signal x(t), and X1 represents the amplitude of the fundamental component of the signal x(t), that is, the normal operating frequency of the equipment;
[0022] The modulation sideband ratio M is used to describe the strength of the modulation phenomenon in the signal. The modulation sideband is the frequency component generated on both sides of the fundamental frequency due to the modulation of the signal. That is, for the modulated signal x(t), its modulation sideband ratio is A s-a Represents the amplitude of the ath modulation sideband component, A c-j The amplitude of the jth carrier component corresponds to the meshing frequency of the gears, N is the number of modulation sidebands, and M is the number of carrier components;
[0023] The shock period variance S is used to describe the periodic stability of shock events in the signal. For a signal x(t) containing shock events, the occurrence time of the shock event in the signal is detected by detecting the occurrence time t1, t2, ..., t n , calculate the time interval T between adjacent impact events i =t i+1 -t i , i=1,2,…,n-1, and obtain the shock period variance T i is the time interval between the ith adjacent shock events, is the average time interval between all adjacent impact events, that is, n is the number of impact events detected.
[0024] Furthermore, the dynamic baseline modeling module is based on the sliding time window T W =30±ΔT Dynamic update threshold, the specific threshold setting is:
[0025] The motor harmonic distortion rate threshold: H th =5%+0.5%·ln(N), where N∈Z + Represents the cumulative number of turnout operations;
[0026] The gear modulation sideband ratio threshold: M th =0.25+0.02(T env -20), where T env ∈[-10,50]℃ is the ambient temperature;
[0027] Locking shock cycle variance threshold: S th =0.1ms 2 Is a fixed value.
[0028] Furthermore, the fault diagnosis conclusion logic in the fault decision engine module is:
[0029]
[0030] Furthermore, the cross-correlation verification module establishes a pure vibration template u of the motor by no-load r (t), calculate the real-time decomposition result u k The normalized correlation coefficient ρ of (t) is Where T represents the length of the time series, It is the pure vibration template of the motor u r The mean of (t), is the real-time decomposition result u k The mean of (t);
[0031] When ρ≥0.9, the real-time decomposition result has a high correlation with the pure vibration template of the motor, the decomposition is effective, and fault judgment is allowed;
[0032] When 0.8≤ρ<0.9, it indicates low correlation and signal interference, which triggers the signal resampling operation;
[0033] When ρ<0.8, it indicates poor correlation, and the sensor calibration instruction is output.
[0034] On the other hand, a turnout fault diagnosis method based on vibration spectrum analysis is provided, wherein the specific steps of the method are as follows:
[0035] S100, using a multi-source vibration sensing module to synchronously collect three-channel vibration signals of the motor, gear, and locking mechanism through a three-axis acceleration sensor array;
[0036] S200, using the mechanical transmission chain signal decoupling module to decompose the signal and calculate the harmonic distortion rate, modulation sideband ratio, and impulse cycle variance;
[0037] S300, using a dynamic baseline modeling module, dynamically updating the energy percentage baseline thresholds of the three components using a sliding time window;
[0038] S400, generating a fault type diagnosis result by comparing the combination of exceeding characteristic values with the threshold value through the fault decision engine module;
[0039] S500: Using a cross-correlation verification module, cross-correlation verification is performed between the real-time decomposition result and the pure driving component template.
[0040] Compared with the existing technology, this turnout fault diagnosis system and method based on vibration spectrum analysis has the following beneficial effects:
[0041] 1. The present invention uses a multi-source vibration sensing module to accurately collect vibration signals from different channels at key locations on the turnout. The mechanical transmission chain signal decoupling module uses constrained variational modal decomposition to decompose complex vibration signals into three independent components: motor drive, gear transmission, and locking mechanism impact. This allows for a more detailed analysis of the operating status of each mechanical component. The dynamic baseline modeling module dynamically updates the threshold based on the number of turnout operations and ambient temperature to improve the accuracy of fault judgment. The fault decision engine module constructs a three-dimensional feature space joint judgment rule, combines the characteristic values of each component with the threshold for comparison, and can accurately distinguish between fault types such as broken teeth on the switch gear and lack of oil in the lock, thereby reducing misjudgments and missed judgments. This not only enables timely detection of potential turnout faults and prevents further deterioration of the faults, but also provides accurate fault information to railway maintenance personnel, thereby effectively ensuring the safety and smooth flow of railway transportation.
[0042] 2. The fault diagnosis system and method of the present invention can quickly and accurately locate the fault location and type, allowing maintenance personnel to directly repair and replace the faulty components, avoiding unnecessary comprehensive inspections and repairs. At the same time, the cross-correlation verification module can promptly detect abnormal conditions of the sensor and trigger calibration instructions, ensuring the stability and reliability of the system and reducing misdiagnosis and unnecessary repairs caused by sensor failures. In addition, the dynamic baseline modeling module dynamically updates the threshold according to the actual operating conditions, allowing the system to better adapt to different working environments and working condition changes, further improving the efficiency and accuracy of fault diagnosis.
[0043] Other advantages, objects and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be learned from the practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS
[0044] To more clearly illustrate the embodiments of the present invention 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 of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort.
[0045] Figure 1 This is an operation flow chart of a turnout fault diagnosis system based on vibration spectrum analysis;
[0046] Figure 2 This is a step diagram of a turnout fault diagnosis method based on vibration spectrum analysis. DETAILED DESCRIPTION
[0047] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the specific implementation methods, structures, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments.
[0048] Example 1
[0049] This embodiment focuses on a turnout fault diagnosis system based on vibration spectrum analysis and elaborates on its specific operation process. It uses a multi-source vibration sensing module to collect vibration signals from different parts of the turnout, a mechanical transmission chain signal decoupling module to decompose the signals and calculate eigenvalues, a dynamic baseline modeling module to update the thresholds, a fault decision engine module to combine eigenvalues and thresholds to diagnose faults, and a cross-correlation verification module to ensure the effectiveness of the decomposition, thereby achieving accurate diagnosis of turnout faults and providing strong support for railway transportation safety.
[0050] In order to accurately monitor the operating status of the turnout at the railway turnout site, this embodiment installs a multi-source vibration sensing module on the turnout. The module consists of a three-axis acceleration sensor array, which is arranged in the axial direction of the switch housing, the top of the gear box, and the root of the locking rod. Because the axial direction of the switch housing can effectively collect the vibration generated by the motor drive, the top of the gear box can obtain the vibration signal during gear transmission, and the root of the locking rod can capture the vibration of the locking mechanism when it is working. After the sensor is installed, the three-channel vibration signals of the motor drive, gear transmission, and locking mechanism are synchronously collected. During the collection process, the sensor converts the mechanical vibration into electrical signals. These electrical signals contain rich information on the operating status of the turnout. Since the turnout will generate vibrations of different frequencies and amplitudes in different working stages (conversion, locking), it is ensured that various vibration signals can be fully collected.
[0051] The vibration signal collected from the multi-source vibration sensing module is a mixed signal, which contains vibration information from different mechanical components and cannot be directly used to accurately judge the operating status of each component of the turnout. Therefore, the mechanical transmission chain signal decoupling module processes it and uses the constrained variational modal decomposition technology to decompose the mixed vibration signal into independent modal components corresponding to the three key parts of the turnout: motor drive, gear transmission, and locking impact. This goal is achieved by minimizing the function value, that is, {u k} represents the various modal components obtained by decomposition, that is, the vibration components corresponding to motor drive, gear transmission, and locking impact, {ω k} represents the center frequency corresponding to each modal component, which reflects the main vibration frequency characteristics of the modal component. represents the derivative with respect to time t, which is used to highlight the rate of change information of the signal. δ(t) is a time function, j is an imaginary unit, * represents the convolution operation, through which the signal can be transformed into the time-frequency domain and feature extraction can be performed. α is a balance parameter, which is used to adjust the bandwidth of each modal component and control the fineness of the decomposition result. If the value of α is too large, the bandwidth of each modal component will be narrowed, resulting in the loss of signal characteristics. If the value is too small, the decomposition effect is poor and different modal components cannot be effectively separated. k It is a constraint operator, which is set according to the characteristics of the mechanical components and is used to further constrain the decomposition results. Its decomposition logic is:
[0052] Motor component constraint: P1(u1)=|FFT(u1)| f>500Hz =0, the motor component u1 is processed through fast Fourier transform (FFT), forcing the high-frequency energy with a frequency greater than 500 Hz to zero. Because the motor drive component is mainly low-frequency vibration, this constraint can effectively remove the high-frequency interference component in the motor vibration signal, highlighting the low-frequency vibration characteristics, which is convenient for subsequent fault analysis of the motor drive part.
[0053] Gear component constraints: Indicates that the energy of the gear component u2 in the frequency range of 500Hz to 5kHz is integrated and required to account for the total energy E total The ratio is greater than or equal to 80%. This is because the vibration generated by the gear transmission is mainly concentrated in this intermediate frequency range. By ensuring the proportion of intermediate frequency energy in the gear component, the working status of the gear can be analyzed more accurately, and whether the gear has meshing impact, wear and other faults can be detected.
[0054] Locking component constraints: Among them, max(|u3(t)|) represents the maximum value of the locking component u3 in the time domain, A th is the amplitude threshold, freq(u3) represents the main frequency of the locking component u3, [f min ,f max ] is the characteristic frequency range of the vibration signal generated by the impact of the locking mechanism. When the maximum amplitude of the locking component exceeds the set threshold and its main frequency falls within a specific frequency range, P3(u3) takes the value of 1, indicating that an impact event of the locking mechanism is detected; otherwise, it takes the value of 0. This constraint condition can strengthen the detection of impact events of the locking mechanism and timely discover faults in the locking mechanism.
[0055] After decomposition, the independent motor drive component, gear transmission component, and locking impact component are obtained, and the three time-frequency domain characteristics of each component, namely, harmonic distortion rate H, modulation sideband ratio M, and impact period variance S, are calculated. Among them, the harmonic distortion rate That is, for a periodic signal x(t), X n Indicates the amplitude of the nth harmonic component of the signal x(t), X1 indicates the amplitude of the fundamental component of the signal x(t), and the harmonic distortion rate H is used to measure the content of harmonic components in the signal, reflecting the degree to which the signal deviates from the fundamental wave. In turnout fault diagnosis, the harmonic distortion rate of the motor drive component can reflect whether the motor winding is balanced, whether there are defects in the bearing, and other issues. If there is a fault in the motor, its harmonic distortion rate will change. By monitoring the change in the harmonic distortion rate, it can be determined whether the motor is operating normally; modulation sideband ratio That is, for the modulated signal x(t), A s-a Represents the amplitude of the ath modulation sideband component, A c-j represents the amplitude of the jth carrier component. The modulation sideband ratio M is used to describe the strength of the modulation phenomenon in the signal. In gear transmission, when the gear is worn, the tooth surface is damaged, and other faults occur, the modulation phenomenon will occur, causing the modulation sideband ratio to change. By monitoring the modulation sideband ratio, the health status of the gear can be judged. For the impact period variance S, the occurrence time of the impact event in the detection signal t1, t2, ..., tn , calculate the time interval T between adjacent impact events i =t i+1 -t i (i=1,2,…,n-1), get the shock period variance in, is the average time interval between all adjacent shock events, n is the number of detected shock events, and the shock cycle variance S is used to describe the periodic stability of shock events in the signal. In a locking mechanism, the shock cycle is relatively stable under normal circumstances. When the locking mechanism fails (such as oil shortage, component wear, etc.), the shock cycle will change, and the shock cycle variance will also change accordingly. By monitoring the shock cycle variance, the working condition of the locking mechanism can be judged.
[0056] The dynamic baseline modeling module is used to determine whether the operating status of each turnout component is normal, and to establish a dynamic baseline modeling module based on the sliding time window T W =30±ΔT Dynamic update threshold. The operating status of the turnout is affected by many factors, such as ambient temperature, the number of turnout operations, etc. The fixed threshold cannot adapt to these changes. By sliding the time window, the changes of these factors are considered in real time, the threshold is updated, and the accuracy of fault judgment is improved. The specific threshold is set as: motor harmonic distortion rate threshold H th =5%+0.5%·ln(N), where N∈Z + Represents the cumulative number of turnout operations. As the number of turnout operations increases, the mechanical components of the motor will gradually wear out, causing the harmonic distortion rate to change. Through this formula, the threshold of the motor harmonic distortion rate can be dynamically adjusted according to the actual number of turnout operations, so as to more accurately determine whether the motor has a fault; the gear modulation sideband ratio threshold M th =0.25+0.02(T env -20), where T env ∈[-10,50]℃ is the ambient temperature. The ambient temperature affects the lubrication performance of the gear, and thus affects the meshing state of the gear. When the temperature changes, the gear modulation sideband ratio will also change. Through this formula, the threshold of the gear modulation sideband ratio can be dynamically adjusted according to the ambient temperature to improve the accuracy of gear fault judgment; the locking impact cycle variance threshold S th =0.1ms 2 It is a fixed value that reflects the effect of mechanical clearance tolerance on the locking impact cycle.
[0057] The fault decision engine module calculates the characteristic values of each component (harmonic distortion rate H, modulation sideband ratio M, impact period variance S) calculated by the mechanical transmission chain signal decoupling module, and the threshold value (H th 、M th 、S th), generate the fault type diagnosis conclusion, the fault diagnosis conclusion logic is: When the modulation sideband ratio M of the gear transmission component is greater than 1.2M th , and the harmonic distortion rate H of the motor drive component is less than 0.8H th , while the impact period variance S of the locking impact component is less than 0.08ms 2 When the gear is broken, it is determined to be a gear tooth failure. This is because when the gear is broken, the gear meshing impact will increase and the modulation sideband ratio will increase; while the motor drive part is normal, the harmonic distortion rate should be within the range, and the impact cycle variance is small when the locking mechanism works normally; when the harmonic distortion rate H of the motor drive component is greater than 1.2H th , the impact period variance S of the locking impact component is greater than 0.15ms 2 , and the modulation sideband ratio M of the gear transmission component is less than 0.8M th When the gear transmission part is normal, the modulation sideband ratio should be within the normal range; when (M>1.2M th ∪H>1.2H th ) and S>0.15ms 2 When the fault occurs, it is determined to be a compound fault, that is, problems exist in the gear, motor and locking mechanism at the same time.
[0058] In order to ensure the validity of the decomposition results of the mechanical transmission chain signal decoupling module, the cross-correlation verification module establishes a pure vibration template u of the motor through a no-load experiment. r (t), this template represents the vibration characteristics of the motor under normal no-load operation, and calculates the real-time decomposition result u k The normalized correlation coefficient ρ of (t) is: Where T represents the length of the time series, It is the pure vibration template of the motor u r The mean of (t), is the real-time decomposition result u kThe mean of (t) is ρ ≥ 0.9, indicating that the real-time decomposition result has a high correlation with the motor's pure vibration template, the decomposition is effective, and fault diagnosis is allowed. This means that the mechanical transmission chain signal decoupling module can accurately separate the motor drive component, and the subsequent fault diagnosis results are highly reliable. When 0.8 ≤ ρ < 0.9, it indicates low correlation and signal interference. In this case, the signal resampling operation is triggered to re-collect the vibration signal to obtain more accurate data and ensure the reliability of the decomposition result. When ρ < 0.8, it indicates poor correlation, which may be a problem with the sensor. In this case, the sensor calibration instruction is output to calibrate the sensor to ensure the accuracy of the sensor data collected, thereby ensuring the normal operation of the entire fault diagnosis system.
[0059] In summary, this embodiment demonstrates in detail the operation process of the turnout fault diagnosis system based on vibration spectrum analysis. The vibration signals of key parts of the turnout are collected through the multi-source vibration sensing module. The mechanical transmission chain signal decoupling module decomposes the signal and calculates the eigenvalue. The dynamic baseline modeling module dynamically updates the threshold according to factors such as the number of turnout operations and the ambient temperature. The fault decision engine module combines the eigenvalue and the threshold for fault diagnosis. The cross-correlation verification module ensures the validity of the decomposition results. The various modules work closely together to achieve accurate diagnosis of turnout faults. This fault diagnosis system can timely detect potential faults of the turnout, reduce misjudgments and missed judgments, provide accurate fault information to railway maintenance personnel, and effectively ensure the safety and smooth operation of railway transportation. At the same time, the system has good adaptability and reliability, and can operate stably under different working environments and working conditions, providing strong technical support for the development of the railway industry.
[0060] Example 2
[0061] like Figure 1 As shown, based on Example 1, this embodiment details a specific process of a turnout fault diagnosis system based on vibration spectrum analysis when performing turnout fault diagnosis. Through the orderly operation of each link of the system, the turnout fault condition can be comprehensively and accurately analyzed. The specific process is as follows:
[0062] (1) Data collection stage
[0063] Sensor installation: The multi-source vibration sensing module installs the three-axis acceleration sensor array in the axial direction of the switch housing, the top of the gearbox and the root of the locking rod. These locations can effectively capture the vibrations generated by the motor drive, gear transmission and locking mechanism.
[0064] Signal acquisition: Start the sensor to synchronously collect three-channel vibration signals of the motor drive, gear transmission, and locking mechanism. The sensor continuously records vibration signals during different working stages of the turnout (such as switching, locking, etc.).
[0065] (2) Signal decoupling stage
[0066] Initial processing: The collected mixed vibration signal is transmitted to the mechanical transmission chain signal decoupling module, and the mixed signal is decomposed into three independent modal components: motor drive, gear transmission, and locking impact.
[0067] Motor component constraint: Process the motor drive components to remove high-frequency interference components and highlight low-frequency vibration characteristics.
[0068] Gear component constraint: Ensure the energy proportion of the gear transmission component within the frequency range of 500Hz to 5kHz to accurately analyze the gear working state.
[0069] Locking component constraint: Detects the maximum amplitude and main frequency of the locking impact component. When the amplitude exceeds the set threshold and the main frequency falls within the range, it is determined that an impact event of the locking mechanism is detected.
[0070] Feature calculation: Calculate the time-frequency domain characteristics of the harmonic distortion rate, modulation sideband ratio, and impulse period variance of each component.
[0071] (3) Threshold setting stage
[0072] Dynamic update considerations: The dynamic baseline modeling module dynamically updates the threshold based on a sliding time window, taking into account the impact of factors such as the cumulative number of turnout operations and ambient temperature on the turnout operating status.
[0073] Motor threshold setting: Set the threshold for the motor harmonic distortion rate based on the cumulative number of turnout operations.
[0074] Gear threshold setting: Set the corresponding threshold for the gear modulation sideband ratio based on the ambient temperature.
[0075] Lockout threshold setting: determines the fixed threshold for the variance of the lockout shock cycle.
[0076] (4) Fault diagnosis stage
[0077] Rule judgment: The fault decision engine module makes judgments based on the characteristic values of each component and the dynamically updated thresholds according to the set fault diagnosis rules.
[0078] Fault type determination: As shown in Example 1, if the modulation sideband ratio of the gear transmission component, the harmonic distortion rate of the motor drive component and the impact period variance of the locking impact component meet the constraint conditions, it is determined to be a gear tooth breakage fault; when the harmonic distortion rate of the motor drive component, the impact period variance of the locking impact component and the modulation sideband ratio of the gear transmission component meet the corresponding conditions, it is determined to be a locking oil shortage fault; when the modulation sideband ratio of the gear transmission component or the harmonic distortion rate of the motor drive component, and the impact period variance of the locking impact component meet the conditions, it is determined to be a compound fault.
[0079] (5) Verification and calibration phase
[0080] Template establishment: A pure vibration template of the motor is established through the cross-correlation verification module. This template represents the vibration characteristics of the motor during normal no-load operation.
[0081] Correlation calculation: Calculate the normalized correlation coefficient between the real-time decomposition results and the motor's pure vibration template to evaluate the effectiveness of the decomposition results.
[0082] Handling different situations: judge the correlation coefficient, perform cross-correlation verification on the real-time decomposition result and the pure driving component template, and output feedback to the multi-source vibration sensing module.
[0083] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as above in terms of a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications to equivalent embodiments using the technical contents disclosed above. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.
Claims
1. A turnout fault diagnosis system based on vibration spectrum analysis, characterized in that: The system consists of: multi-source vibration sensing module, mechanical transmission chain signal decoupling module, dynamic baseline modeling module, fault decision engine module, and cross-correlation verification module; The multi-source vibration sensing module is composed of a triaxial acceleration sensor array arranged in the axial direction of the switch housing, the top of the gearbox, and the root of the locking rod. It synchronously collects three-channel vibration signals of the motor drive, gear transmission, and locking mechanism. The physical correspondence between the three-channel vibration signals is as follows: Motor drive component: used to reflect low-frequency vibration caused by motor winding imbalance and bearing defects, with a frequency range of 0-500Hz, to determine potential motor failure hazards; Gear transmission component: medium-frequency vibration indicating gear meshing impact and wear, with a frequency range of 500Hz-5kHz. This component is used to monitor the health of the gears. Locking impact component: used to capture the high-frequency vibration generated by the locking mechanism at the moment of collision, with a frequency greater than 5kHz, to judge the working condition of the locking mechanism; The mechanical transmission chain signal decoupling module physically decomposes the vibration signal into motor drive component, gear transmission component, and locking impact component, and calculates three types of time-frequency domain features of each component: harmonic distortion rate H, modulation sideband ratio M, and impact period variance S. In the process of decomposing the mixed vibration signal collected by the multi-source vibration sensing module, the mechanical transmission chain signal decoupling module realizes the mathematical decomposition of the mixed vibration signal by minimizing the function value, and decomposes it into K independent modal components {u k } and K = 3, that is, K corresponds to the three components of motor drive, gear transmission, and locking impact, and the center frequency {ω corresponding to each modal component is determined k }, the minimization function value satisfies: Among them, {u k } represents the modal components obtained by decomposition, u k (t) represents the real-time decomposition result, {ω k } represents the center frequency corresponding to each modal component, represents the derivative with respect to time t, δ(t) is the time function, j is the imaginary unit, * represents the convolution operation, α is the balance parameter used to adjust the bandwidth of each modal component, P k It is a constraint operator, which is set according to the characteristics of the mechanical components. Specifically: Motor component constraint: P1(u1)=|FFT(u1)| f>500Hz =0, indicating that the motor component u1 is processed by fast Fourier transform FFT, forcing the high-frequency energy with a frequency greater than 500Hz to zero, which is used to detect the low-frequency vibration of the motor drive component; Gear component constraints: Indicates that the energy of the gear component u2 in the frequency range of 500Hz to 5kHz is integrated so that this energy accounts for the total energy E total The ratio of is greater than or equal to 80%, so as to ensure the proportion of medium frequency energy in the gear component; Locking component constraints: Among them, max(|u3(t)|) represents the maximum value of the locking component u3 in the time domain, A th is the amplitude threshold and A th =0.5g, g is the acceleration of gravity, freq(u3) represents the main frequency of the locking component u3, [f min ,f max ] is the characteristic frequency range of the vibration signal generated by the locking mechanism impact, thereby enhancing the detection of locking mechanism impact events; The mechanical transmission chain signal decoupling module calculates three types of time-frequency domain features of each component: harmonic distortion rate H, modulation sideband ratio M, and impulse period variance S, where: The harmonic distortion rate H is used to measure the content of harmonic components in the signal, reflecting the degree to which the signal deviates from the fundamental wave. For a periodic signal x(t), its harmonic distortion rate is X n represents the amplitude of the nth harmonic component of the signal x(t), and X1 represents the amplitude of the fundamental component of the signal x(t), that is, the normal operating frequency of the equipment; The modulation sideband ratio M is used to describe the strength of the modulation phenomenon in the signal. The modulation sideband is the frequency component generated on both sides of the fundamental frequency due to the modulation of the signal. That is, for the modulated signal x(t), its modulation sideband ratio is A s-a Represents the amplitude of the ath modulation sideband component, A c-j The amplitude of the jth carrier component corresponds to the meshing frequency of the gears, N is the number of modulation sidebands, and M is the number of carrier components; The shock period variance S is used to describe the periodic stability of shock events in the signal. For a signal x(t) containing shock events, the occurrence time of the shock event in the signal is detected by detecting the occurrence time t1, t2, ..., t n , calculate the time interval T between adjacent impact events i =t i+1 -t i , i=1,2,…,n-1, and obtain the shock period variance T i is the time interval between the ith adjacent shock events, is the average time interval between all adjacent impact events, that is, n is the number of impact events detected; The dynamic baseline modeling module uses a sliding time window to dynamically update the energy ratio baseline threshold H of the three components. th 、M th 、S th ; The dynamic baseline modeling module is based on a sliding time window T W =30±ΔT Dynamic update threshold, the specific threshold setting is: The motor harmonic distortion rate threshold: H th =5%+0.5%·ln(N), where N∈Z + Represents the cumulative number of turnout operations; The gear modulation sideband ratio threshold: M th =0.25+0.02(T env -20), where T env ∈[-10,50]℃ is the ambient temperature; Locking shock cycle variance threshold: S th =0.1ms 2 is a fixed value; The fault decision engine module generates a fault type diagnosis conclusion based on the combination of the three types of characteristic values exceeding the standard, and the fault type diagnosis conclusion includes gear tooth breakage fault, locking oil shortage fault, and composite fault; The cross-correlation verification module determines the validity of the decomposition by comparing the real-time decomposed motor drive component with the cross-correlation function of the preset no-load test template.
2. A turnout fault diagnosis system based on vibration spectrum analysis according to claim 1, characterized in that: The fault diagnosis conclusion logic in the fault decision engine module is:
3. A turnout fault diagnosis system based on vibration spectrum analysis according to claim 1, characterized in that: The cross-correlation verification module establishes a pure vibration template u of the motor by no-load r (t), calculate the real-time decomposition result u k The normalized correlation coefficient ρ of (t) is Where T represents the length of the time series, It is the pure vibration template of the motor u r The mean of (t), is the real-time decomposition result u k The mean of (t); When ρ≥0.9, the real-time decomposition result has a high correlation with the pure vibration template of the motor, the decomposition is effective, and fault judgment is allowed; When 0.8≤ρ<0.9, it indicates low correlation and signal interference, which triggers the signal resampling operation; When ρ<0.8, it indicates poor correlation, and the sensor calibration instruction is output.
4. A turnout fault diagnosis method based on vibration spectrum analysis, applicable to a turnout fault diagnosis system based on vibration spectrum analysis according to any one of claims 1 to 3, characterized in that: The specific steps of this method are: S100, using a multi-source vibration sensing module to synchronously collect three-channel vibration signals of the motor, gear, and locking mechanism through a three-axis acceleration sensor array; S200, uses the mechanical transmission chain signal decoupling module to decompose the signal and calculate the harmonic distortion rate, modulation sideband ratio, and impulse cycle variance; S300, using a dynamic baseline modeling module, dynamically updating the energy proportion baseline thresholds of the three types of components using a sliding time window; S400, generating a fault type diagnosis result by comparing the combination of exceeding characteristic values with the threshold value through the fault decision engine module; S500: Using a cross-correlation verification module, cross-correlation verification is performed between the real-time decomposition result and the pure driving component template.