Maglev train suspension system electromagnet fault detection algorithm

Abstract

The invention discloses a maglev train suspension system electromagnet fault detection algorithm, which comprises the following steps: establishing a suspension system model of a maglev train, and designing a state observer; an electromagnet signal is collected and processed, and a residual value is obtained; the residual value is compared with a threshold value, and fault information of the electromagnet of the maglev train suspension system is obtained; and fault information of the electromagnet is reconstructed. The fault of the electromagnet can be effectively detected and reconstructed by constructing the third-order linear extended state observer (LSEO), system uncertainty, noise and the like can be dealt with through the self-adaption and self-calibration functions of the third-order linear extended state observer, robustness is enhanced, and detection reliability is improved. Fault information of the magnetic levitation system can be quantitatively estimated through LSEO for fault reconstruction, the fault type and position can be accurately identified, and false alarm and missing alarm can be avoided. Meanwhile, fault reconstruction provides an important basis for maintenance, and maintenance personnel can quickly position and take targeted measures, so that the maintenance efficiency is improved, and the cost is reduced.

Description

Technical Field

[0001] The invention relates to the technical field of maglev trains, and in particular to an electromagnet fault detection algorithm for a maglev train suspension system. Background Art

[0002] Maglev train technology is a technology that uses electromagnetic force to overcome gravity to suspend vehicles. It is an important achievement of modern science and technology. In the maglev system, electromagnets are the core components for achieving train suspension and drive. They generate a strong magnetic field to enable the train to suspend stably above the track and rely on magnetic force to drive the train forward. Maglev trains have significant advantages such as high speed, low noise, low vibration, environmental protection and energy saving, and are one of the important directions for future transportation development.

[0003] Electromagnets are key components of maglev trains. Failure of electromagnets may lead to a decline in train performance, such as weakened suspension force and unstable guiding force, which in turn affects the running speed and stability of the train. Their performance status is directly related to the running safety of the train. By performing fault detection on electromagnets, potential hidden faults can be discovered in a timely manner, and safety accidents such as train derailment and parking caused by electromagnet failure can be prevented, thereby ensuring the safety of passengers and goods.

[0004] At present, in practical applications, the impact of sensor failure in the suspension system on train operation is often more direct and significant; therefore, in the existing technology, the fault detection of the maglev system sensor has been extensively studied because of its urgency, and the detection technology is relatively mature. However, there are relatively few studies on electromagnets. With the continuous development and improvement of maglev train technology, the increase in speed requirements and the further pursuit of riding experience, the fault detection and diagnosis of electromagnets is no longer a problem that can be ignored, and the fault detection of electromagnets is also a technical problem that needs to be solved urgently. Summary of the invention

[0005] According to an embodiment of the present invention, in order to solve the above-mentioned deficiencies in the prior art, a magnetic levitation train suspension system electromagnet fault detection algorithm is provided, comprising the following steps: Step 1: Establish the suspension system model of the maglev train and design the state observer; Step 2: Collect and process the electromagnet signal to obtain the residual value; Step 3: Compare the residual value with the threshold value to obtain the fault information of the electromagnet of the maglev train suspension system; Step 4: Reconstruct the fault information of the electromagnet.

[0006] Preferably, in step three, when the residual value is less than a threshold value, the electromagnet is not faulty; when the residual value is greater than the threshold value, the electromagnet is faulty.

[0007] Preferably, the state observer is a third-order linear extended state observer.

[0008] Preferably, step 2 further comprises the following steps: Based on the third-order linear extended state observer, a third-order linear fault detection observer model and observer error model are established; Establish the fault model of the suspension system and make a difference with the fault detection observer model to get the residual model; Based on the observer error model and the residual model, the steady-state solution is obtained as the residual value.

[0009] Preferably, step 4 further comprises the following steps: Based on the fault model of the suspension system, establishing a fault reconstruction model of the electromagnet; Based on the fault reconstruction model, obtaining a fault estimation value of the state observer in steady state; The state signal of the electromagnet in the operation of the magnetic levitation system is collected and compared with the fault estimation value, and the fitting condition is judged to determine whether the magnetic levitation system has an electromagnet fault.

[0010] Preferably, the establishment of the fault reconstruction model of the electromagnet further comprises the following sub-steps: Establish a steady-state fault model when the state observer is in steady state; The steady-state fault model is integrated and solved within a set convergence time.

[0011] According to the electromagnetic fault detection algorithm of the maglev train suspension system of the embodiment of the present invention, a third-order linear extended state observer (LSEO) is constructed to effectively detect and reconstruct the electromagnetic fault. LSEO has the ability to reconstruct and estimate the state of unknown nonlinear systems, which means that it can deal with the uncertainty, noise and interference of the system to a certain extent. In the traditional electromagnetic fault detection of maglev trains, these uncertain factors may cause the failure of traditional detection methods. However, LSEO can enhance the robustness of the system and improve the reliability of fault detection through its self-adaptation, self-calibration and self-correction functions. At the same time, the specific fault information of the maglev system is quantitatively estimated by the extended state observer, which can more accurately identify the fault type, location and severity of the electromagnet, thereby avoiding the false alarm and missed alarm problems that may occur in the traditional fault detection method; at the same time, it also provides an important basis for formulating scientific and reasonable maintenance strategies. Maintenance personnel can quickly locate the fault point according to the results of fault reconstruction and take targeted maintenance measures, thereby improving maintenance efficiency and reducing maintenance costs.

[0012] It is to be understood that both the foregoing general description and the following detailed description are exemplary, and are intended to provide further explanation of the technology as claimed. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A flow chart of an electromagnet fault detection algorithm for a maglev train suspension system according to an embodiment of the present invention; Figure 2 It is a simulation experiment diagram of the electromagnetic fault detection algorithm of the maglev train suspension system according to the embodiment of the present invention when a fault occurs; Figure 3 This is a simulation experiment diagram of multiplicative fault reconstruction of an actuator of an electromagnet fault detection algorithm for a maglev train suspension system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings to further illustrate the present invention.

[0015] In order to ensure the high stability and safety of the maglev train during operation. The present application embodiment provides an algorithm for detecting electromagnet faults in the suspension system of a maglev train, such as Figure 1 As shown, it includes the following steps: Step 1: Establish the suspension system model of the maglev train and design the state observer; Step 2: Collect and process the electromagnet signal to obtain the residual value; Step 3: Compare the residual with the threshold to obtain the fault information of the electromagnet of the maglev train suspension system; Step 4: Reconstruct the fault information of the electromagnet.

[0016] The algorithm can accurately observe and estimate the state changes of the system in real time by establishing an accurate suspension system model and state observer. At the same time, by using the comparison method of residual value and threshold, it can effectively detect the fault of the electromagnet, which helps to improve the operation efficiency and service quality of maglev trains, and enhance the public's trust and acceptance of maglev trains.

[0017] Preferably, in step 3, under normal operation, thanks to the high precision of the observer and the stability of the system, the difference between the actual state of the system and the estimated state of the observer (i.e., the residual value) will be maintained in a small range, which is defined by a preset threshold. Specifically, if the residual value is less than the threshold, we can consider that the electromagnet is in a normal state and no fault occurs, otherwise it is considered that the electromagnet has a fault, which can be effectively judged.

[0018] Preferably, the state observer is a third-order extended state observer LESO. A third-order linear extended state observer (LESO) is used as the state observer of the maglev train suspension system. By expanding the second-order dynamic model of the second-order maglev system, the state observer model is established as follows: ; It achieves an accurate estimation of the real state of the system by fully capturing the dynamic behavior of the system and tracking unknown disturbances. By comparing the residuals with the actual measured values ​​of the electromagnet, the observer can capture the tiny state changes of the electromagnet fault at an early stage, significantly improving the accuracy and sensitivity of fault detection, and providing solid technical support for the safe operation of maglev trains.

[0019] Furthermore, step three includes the data processing link in the electromagnet fault detection of the maglev train suspension system, which specifically includes the following sub-steps: First, a fault detection observer model and an observer error model are constructed based on a third-order linear extended state observer. The error model is: ; Secondly, the fault model of the suspension system is established as: ; In the formula, and Indicates the location and mode of fault occurrence respectively. Indicates an unknown fault.

[0020] Then, by comparing it with the fault detection observer model, we get the residual model: ; Then, when the electromagnet fails, the steady-state residual value is obtained by combining the observer error model and the residual model: ; When an actuator fault occurs, the observer error is not equal to zero, and the residual is not zero either. Therefore, the fault can be detected by setting a reasonable threshold. The algorithm can accurately capture and quantify the electromagnet fault signal through precise modeling and comparative analysis. Its beneficial technical effect is that it significantly improves the accuracy and sensitivity of fault detection, can detect potential faults earlier, and provides strong technical support for the safe operation of maglev trains.

[0021] Preferably, step 4 further comprises the following steps: based on the established suspension system fault model, calculating the fault value of the state observer in a steady state, specifically: When the extended observer converges to a stable state, that is, Time residual , then the steady-state fault model in steady state is: ; This step uses the model's predictive capabilities to pre-set the boundary conditions of the system under normal conditions, providing a benchmark for subsequent fault detection and then solving the fault value. This process ensures that the algorithm has enough time to capture and quantify potential fault signs, improving the accuracy and stability of detection.

[0022] Assume that the system starts running from 0 and the convergence time is , after integration operation, we can get: ; The setting of convergence time is based on the dynamic characteristics of the system to solve the fault, ensuring that quantitative analysis of fault detection can be implemented.

[0023] By further transforming the fault value according to the specific location of the fault, the fault reconstruction can be completed. For example, if a multiplicative fault may occur in the electromagnet, the reconstructed fault is as follows: ; Based on this, the fault model is reconstructed to obtain the fault estimate. The multiplicative fault model takes into account the multiplier effect of the fault on the system output and can more accurately simulate the impact of the electromagnet fault on the system performance. By reconstructing the model, the detection strategy can be dynamically adjusted to deal with different types of fault modes; the state signal of the electromagnet in the operation of the maglev system is obtained in real time and compared with the fault estimate to determine whether it is a multiplicative electromagnet fault.

[0024] The above sub-steps combine theoretical prediction with actual observation, realize fault reconstruction and quantitative estimation, and can locate faults in a timely and accurate manner, further improving the real-time and accuracy of detection and the safety and reliability of maglev trains.

[0025] The embodiment of the present invention provides an electromagnet fault detection algorithm for a maglev train suspension system. By reconstructing the fault model and setting the convergence time, we can more accurately capture and quantify the fault signs of the electromagnet, reducing the possibility of false alarms and missed alarms. This algorithm can cope with different types of fault modes, improving the versatility and adaptability of detection; by obtaining the state signal of the electromagnet in real time and comparing it with the fault estimate, technicians can promptly discover and handle the fault, ensuring the safe operation of the maglev train.

[0026] Specifically, according to the method of the embodiment of the present invention, a simulation experiment is carried out, and parameters are set and operated accordingly according to common data of maglev trains, and the following results are obtained: Figure 2 The results shown show that the residual amplitude suddenly changes and exceeds the threshold value of 19.421 at about 5s, which means that the sensor fault is detected at this time and is consistent with the preset fault time, indicating that this method has a good diagnostic effect on electromagnet multiplicative faults. Similarly, the fault is reconstructed, such as Figure 3 As shown, it can be seen that the input value reconstructed based on the estimated value is basically consistent with the actual input value, and the reconstruction effect is good.

[0027] Above, refer to Figures 1 to 3 The electromagnet fault detection algorithm of the suspension system of a maglev train according to an embodiment of the present invention is described, and a third-order linear extended state observer (LSEO) is constructed to effectively detect and reconstruct electromagnet faults. LSEO has the ability to reconstruct and estimate the state of an unknown nonlinear system, which means that it can deal with the uncertainty, noise and interference of the system to a certain extent. In the traditional electromagnet fault detection of maglev trains, these uncertain factors may cause the failure of traditional detection methods. However, LSEO can enhance the robustness of the system and improve the reliability of fault detection through its self-adaptation, self-calibration and self-correction functions. At the same time, the specific fault information of the maglev system is quantitatively estimated by the extended state observer, further improving the safety and reliability. By reconstructing the electromagnet fault, detailed fault information can be obtained, and the quantitative estimation can more accurately identify the fault type, location and severity of the electromagnet, thereby avoiding the false alarm and missed alarm problems that may occur in the traditional fault detection method; at the same time, it also provides an important basis for formulating a scientific and reasonable maintenance strategy. Maintenance personnel can quickly locate the fault point according to the results of the fault reconstruction and take targeted maintenance measures, thereby improving maintenance efficiency and reducing maintenance costs.

[0028] In the description of the present invention, it should be noted that, unless otherwise specified, “plurality” means two or more than two; the orientations or positional relationships indicated by terms such as “upper”, “lower”, “left”, “right”, “inner”, “outer”, “top”, and “bottom” are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0029] It should be noted that, in this specification, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprises..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0030] Although the content of the present invention has been described in detail through the above preferred embodiments, it should be appreciated that the above description should not be considered as a limitation of the present invention. After reading the above content, it will be apparent to those skilled in the art that various modifications and substitutions of the present invention will occur. Therefore, the protection scope of the present invention should be limited by the appended claims.

Claims

1. An algorithm for detecting electromagnet faults in a maglev train suspension system, characterized in that: Include Step 1: Establish a suspension system model of a maglev train and design a state observer, wherein the state observer is a third-order linear extended state observer; Step 2: Collect and process the electromagnet signal of the suspension system to obtain the residual value; Step 3: Compare the residual value with a threshold value to obtain fault information of the electromagnet of the maglev train suspension system; when the residual value is less than the threshold value, the electromagnet is not faulty; when the residual value is greater than the threshold value, the electromagnet is faulty; Step 4: Reconstruct the fault information of the electromagnet.

2. The electromagnetic fault detection algorithm for the maglev train suspension system according to claim 1, characterized in that: Step 2 further comprises the following steps: Based on the third-order linear extended state observer, a third-order linear fault detection observer model and an observer error model are established; Establishing the fault model of the suspension system and subtracting it from the fault detection observer model to obtain a residual model; A steady-state solution is obtained based on the observer error model and the residual model as the residual value.

3. The electromagnetic fault detection algorithm for the maglev train suspension system as claimed in claim 2, characterized in that: Step 4 further includes the following steps: Based on the fault model of the suspension system, establishing a fault reconstruction model of the electromagnet; Based on the fault reconstruction model, obtaining a fault estimation value of the state observer in steady state; The state signal of the electromagnet in the operation of the suspension system is collected and compared with the fault estimation value, and the fitting condition is judged to determine whether the electromagnet fault occurs in the magnetic suspension system.

4. The electromagnetic fault detection algorithm for the maglev train suspension system as claimed in claim 3 is characterized in that: The establishment of the fault reconstruction model of the electromagnet further comprises the following sub-steps: Establish a steady-state fault model when the state observer is in steady state; The steady-state fault model is integrated and solved within a set convergence time.

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