Sectional type PMLSM monitoring system and method

Abstract

The invention discloses a sectional type PMLSM monitoring system and method, relates to the technical field of sectional type PMLSM monitoring, and solves the problem that the prior art ignores the important influence of environmental factors on the performance of a motor. And the technical problem that the health assessment result of the motor is not comprehensive and accurate enough due to the fact that the prior art only depends on historical operation data and current operation data and lacks comprehensive analysis of multi-dimensional data is solved. The method comprises the following steps: acquiring thrust data of each segment of a segmented PMLSM in real time, and acquiring environmental data of the segmented PMLSM in an operation process; calculating an early warning evaluation coefficient of each segment based on the thrust data; constructing a correction model based on the environment data to correct the early warning evaluation coefficient of each segment, and obtaining a target early warning evaluation coefficient of each segment; and judging whether each segment in the segmented PMLSM needs early warning or not based on the target early warning evaluation coefficient. The above problem is solved.

Description

Technical Field

[0001] The invention belongs to the field of segmented PMLSM monitoring, and in particular relates to a segmented PMLSM monitoring system and method. Background Art

[0002] With the development of industrial automation and precision manufacturing technology, permanent magnet linear synchronous motor (PMLSM) has been widely used in various high-precision and high-performance application scenarios due to its high precision, high efficiency and high response speed.

[0003] The prior art (invention patent with publication number CN110222765B) discloses a permanent magnet synchronous motor health status monitoring method and system to accurately detect the health status of the permanent magnet motor; the method of the invention includes obtaining the historical normal operation data of the permanent magnet synchronous motor to be analyzed as the original data set and dividing it into K subsets; collecting the current operation data of the permanent magnet synchronous motor to be analyzed, and finding p subsets similar to the current operation data from the K subsets to form a training data set; selecting k data similar to the current operation data from the training data set, constructing a training data matrix, and constructing a covariance matrix and a cross-covariance matrix based on the training data matrix; establishing a correlation model based on the covariance matrix and the cross-covariance matrix; generating a residual signal, calculating statistics and The corresponding threshold is set, and the statistic is compared with the threshold. If the statistic does not exceed the threshold, it is considered that the permanent magnet motor to be analyzed is in a healthy state, otherwise it is in an abnormal state. The prior art detects the health state of the permanent magnet synchronous motor by analyzing the correlation between the operating data of the permanent magnet synchronous motor, and can detect motor abnormalities in a timely manner. The prior art mainly relies on the operating data of the motor, ignoring the important influence of environmental factors on the performance of the motor. These environmental factors will have a significant impact on the material, magnetic properties, heat dissipation and lubrication of the motor, thereby affecting the operating stability and life of the motor. Secondly, the prior art only relies on historical operating data and current operating data, lacks comprehensive analysis of multi-dimensional data, resulting in incomplete and inaccurate health assessment results for the motor.

[0004] Therefore, the present invention proposes a segmented PMLSM monitoring system and method to solve the above problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a segmented PMLSM monitoring system and method, which are used to solve the problem that the prior art mainly relies on the operating data of the motor and ignores the important influence of environmental factors on the performance of the motor; these environmental factors will have a significant impact on the material, magnetic properties, heat dissipation and lubrication of the motor, thereby affecting the operating stability and life of the motor; secondly, the prior art only relies on historical operating data and current operating data, lacks comprehensive analysis of multi-dimensional data, resulting in the technical problem that the health assessment results of the motor are not comprehensive and accurate enough.

[0006] To achieve the above-mentioned object, a first aspect of the present invention provides a segmented PMLSM monitoring system, comprising: a data acquisition module, a data analysis module, a correction module and an early warning module;

[0007] Data acquisition module: used to collect the thrust data of each segment of the segmented PMLSM in real time, as well as the environmental data of the segmented PMLSM during operation;

[0008] Data analysis module: calculates the early warning assessment coefficient of each segment based on thrust data;

[0009] Correction module: Based on the environmental data, a correction model is constructed to correct the early warning evaluation coefficient of each segment to obtain the target early warning evaluation coefficient of each segment;

[0010] Warning module: Determines whether each segment in the segmented PMLSM needs warning based on the target warning evaluation coefficient.

[0011] Preferably, the data acquisition module communicates with and / or is electrically connected to the data analysis module and the correction module respectively; the correction module communicates with and / or is electrically connected to the data analysis module and the early warning module respectively.

[0012] Preferably, the real-time acquisition of thrust data of each segment of the segmented PMLSM, and the acquisition of environmental data of the segmented PMLSM during operation, include:

[0013] The thrust of each segmented motor in the segmented PMLSM is collected in real time through force sensors;

[0014] The environmental data of the segmented PMLSM during operation is collected through data collection equipment; the environmental data includes: ambient temperature, ambient humidity and dust concentration in the air.

[0015] It should be noted that the force sensor is installed at the connection between each segmented primary mover and the load, that is, the place where the primary mover is connected to the external load (such as a workbench, a slider, etc.).

[0016] Preferably, the calculation of the warning assessment coefficient of each segment based on the thrust data includes:

[0017] The thrust of each segment is marked as Fi; where i is the number of segments in the segmented PMLSM; j is the number of collected data points;

[0018] By formula: The average thrust of each segment is calculated; where Fi avg Refers to the average thrust of each segment; N is the number of thrust data points collected; Fi j It refers to the thrust value of the jth collected data point in the i-th segment;

[0019] The early warning evaluation coefficient of each segment is calculated by the formula: Pi = A × e^(Zi / Zi+1); wherein Pi refers to the early warning evaluation coefficient of the i-th segment; A is the proportional coefficient, and 0<A<1; Zi refers to the fluctuation degree of the thrust of the i-th segment, and

[0020] It should be noted that the thrust data point refers to the data point of the thrust data of each segment of the segmented PMLSM collected within a preset time interval, specifically the thrust value collected within the preset time interval; the number of thrust data points refers to the total number of data points collected within a specific time period, for example, if the sampling period is 1 millisecond within a 1-second time period, then 1000 sampling points will be recorded within this 1 second, that is, N=1000;

[0021] The greater the fluctuation of thrust, the significantly reduced operating stability, position and speed control accuracy of the permanent magnet synchronous linear motor will be, leading to increased energy consumption, increased mechanical wear, increased control system complexity, and increased failure risk, which in turn affects the reliability and service life of the equipment and limits its use in high-precision and high-performance application scenarios.

[0022] The present invention can more accurately evaluate the operating status of the segmented PMLSM and improve the accuracy of the monitoring system by calculating the average thrust and thrust fluctuation degree of each segment; by real-time monitoring of the early warning evaluation coefficient, potential problems can be discovered in time before a failure occurs, reducing the risk of equipment damage; by quantitative analysis of the thrust fluctuation degree, problems such as increased mechanical wear can be identified, which helps to optimize the design and parameter configuration of the control system, thereby extending the life of the equipment. In addition, through early warning and preventive maintenance, downtime caused by equipment failure can be reduced, the overall production efficiency of the production line can be improved, and costs can be reduced.

[0023] Preferably, the correction model is constructed based on the environmental data to correct the early warning assessment coefficient of each segment, including:

[0024] S1: mark the ambient temperature as W, the ambient humidity as H, and the dust concentration in the air as C;

[0025] S2: Take environmental data as the independent variable, and the calculated result as the dependent variable, and mark it as the correction coefficient f;

[0026] S3: Fit the independent variables and dependent variables by polynomial fitting to establish a revised model; the revised model is specifically:

[0027] f=α×e^(|W-ZW| / ZW)+β×e^(|H-ZH| / ZH)+[ln(C+1)^θ] / tanh(C);

[0028] Among them, α, β, θ are proportional coefficients, and 0<α<1, 0<β<1, 0<θ<1, ZW is the optimum temperature of the segmented PMLSM during operation; ZH is the optimum humidity of the segmented PMLSM during operation;

[0029] S4: By adding the correction coefficient to the warning assessment coefficient of each segment, the target warning assessment coefficient of each segment is obtained.

[0030] It should be noted that the ambient temperature has a significant impact on the performance of permanent magnet synchronous linear motors. The higher the temperature, the greater the thermal stress of the motor's internal materials, the lower the magnetic properties of the permanent magnets, and the greater the need for heat dissipation, which may lead to overheating protection or even damage to the motor; the lower the temperature, the more brittle the material may become, the higher the viscosity of the lubricant, affecting startup and operation, and condensation is more likely to occur when the temperature difference is large, affecting insulation performance;

[0031] The higher the humidity, the greater the risk of corrosion of the metal parts inside the motor. The insulation material absorbs moisture, resulting in reduced insulation performance. Condensation may cause water to accumulate inside the motor. The lower the humidity, the greater the possibility of static electricity accumulation, resulting in discharge, affecting the normal operation of electronic components. At the same time, the material may become dry and brittle, affecting mechanical properties.

[0032] The dust concentration in the air has a direct impact on the cleanliness and operating stability of the motor; the higher the dust concentration, the greater the pollution and blockage inside and outside the motor, the lower the heat dissipation effect, the more severe the wear of moving parts, and the poor contact of electrical connectors.

[0033] The prior art mainly focuses on the operating data of the motor and ignores the impact of environmental factors; the present invention introduces environmental data and comprehensively considers the impact of ambient temperature, humidity and dust concentration on motor performance, making the evaluation result more accurate and reliable.

[0034] Preferably, judging whether each segment in the segmented PMLSM needs an early warning based on the target early warning evaluation coefficient includes:

[0035] Determine whether the target warning evaluation coefficient of each segment in the segmented PMLSM is greater than the preset evaluation threshold; if yes, generate a warning and send the warning information to the client for reminder; if not, continue to monitor and judge; among which, the preset evaluation threshold is set by experts based on experience.

[0036] A second aspect of the present invention provides a segmented PMLSM monitoring method, comprising:

[0037] Step 1: Collect the thrust data of each segment of the segmented PMLSM in real time, and collect the environmental data of the segmented PMLSM during operation;

[0038] Step 2: Calculate the warning assessment coefficient of each segment based on the thrust data;

[0039] Step 3: Construct a correction model based on environmental data to correct the early warning evaluation coefficient of each segment to obtain the target early warning evaluation coefficient of each segment;

[0040] Step 4: Determine whether each segment in the segmented PMLSM needs warning based on the target warning evaluation coefficient.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The prior art mainly relies on the operation data of the motor, ignoring the important influence of environmental factors on the performance of the motor; these environmental factors will have a significant impact on the material, magnetic properties, heat dissipation and lubrication of the motor, thereby affecting the operation stability and life of the motor; secondly, the prior art only relies on historical operation data and current operation data, lacks comprehensive analysis of multi-dimensional data, resulting in the technical problem that the health assessment result of the motor is not comprehensive and accurate enough; the present invention collects the thrust data of each segment in real time through a force sensor, and calculates the average thrust and thrust fluctuation degree of each segment, and then obtains the early warning evaluation coefficient; by constructing a correction model, taking environmental data as an independent variable, establishing a correction model by polynomial fitting, correcting the early warning evaluation coefficient of each segment, and obtaining the target early warning evaluation coefficient; this correction model can dynamically adjust the threshold to ensure the accuracy and reliability of the evaluation result; by judging whether the target early warning evaluation coefficient of each segment in the segmented PMLSM is greater than the preset evaluation threshold, an early warning is issued, which not only improves the accuracy and response speed of the monitoring system, but also effectively solves the problem of ignoring environmental factors and a single data source in the prior art, and significantly improves the operation stability and reliability of the segmented PMLSM. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A schematic diagram of a system module according to an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of a specific process of an embodiment of the present invention;

[0046] Figure 3 Schematic diagram of the method steps of an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] See also Figure 1-Figure 2 , the first aspect of the present invention provides a segmented PMLSM monitoring system, including: a data acquisition module, a data analysis module, a correction module and an early warning module;

[0049] Data acquisition module: used to collect the thrust data of each segment of the segmented PMLSM in real time, as well as the environmental data of the segmented PMLSM during operation;

[0050] Data analysis module: calculates the early warning assessment coefficient of each segment based on thrust data;

[0051] Correction module: Based on the environmental data, a correction model is constructed to correct the early warning evaluation coefficient of each segment to obtain the target early warning evaluation coefficient of each segment;

[0052] Warning module: Determines whether each segment in the segmented PMLSM needs warning based on the target warning evaluation coefficient.

[0053] Collect thrust data of each segment of the segmented PMLSM in real time, as well as the environmental data of the segmented PMLSM during operation, including:

[0054] The thrust of each segmented motor in the segmented PMLSM is collected in real time through force sensors;

[0055] The environmental data of the segmented PMLSM during operation is collected through data collection equipment; the environmental data includes: ambient temperature, ambient humidity and dust concentration in the air.

[0056] The early warning assessment coefficient for each segment is calculated based on the thrust data, including:

[0057] The thrust of each segment is marked as Fi; where i is the number of segments in the segmented PMLSM; j is the number of collected data points;

[0058] By formula: The average thrust of each segment is calculated; where Fi avg Refers to the average thrust of each segment; N is the number of thrust data points collected; Fi j It refers to the thrust value of the jth collected data point in the i-th segment;

[0059] The early warning evaluation coefficient of each segment is calculated by the formula: Pi = A × e^(Zi / Zi+1); wherein Pi refers to the early warning evaluation coefficient of the i-th segment; A is the proportional coefficient, and 0<A<1; Zi refers to the fluctuation degree of the thrust of the i-th segment, and

[0060] Based on the environmental data, a correction model is built to correct the early warning assessment coefficient of each segment, including:

[0061] S1: mark the ambient temperature as W, the ambient humidity as H, and the dust concentration in the air as C;

[0062] S2: Take environmental data as the independent variable, and the calculated result as the dependent variable, and mark it as the correction coefficient f;

[0063] S3: Fit the independent variables and dependent variables by polynomial fitting to establish a revised model; the revised model is specifically:

[0064] f=α×e^(|W-ZW| / ZW)+β×e^(|H-ZH| / ZH)+[ln(C+1)^θ] / tanh(C);

[0065] Among them, α, β, θ are proportional coefficients, and 0<α<1, 0<β<1, 0<θ<1, ZW is the optimum temperature of the segmented PMLSM during operation; ZH is the optimum humidity of the segmented PMLSM during operation;

[0066] S4: By adding the correction coefficient to the warning assessment coefficient of each segment, the target warning assessment coefficient of each segment is obtained.

[0067] Based on the target warning evaluation coefficient, it is determined whether each segment in the segmented PMLSM needs warning, including:

[0068] Determine whether the target warning evaluation coefficient of each segment in the segmented PMLSM is greater than the preset evaluation threshold; if yes, generate a warning and send the warning information to the client for reminder; if not, continue to monitor and judge; among which, the preset evaluation threshold is set by experts based on experience.

[0069] For example, suppose there is a segmented permanent magnet linear synchronous motor (PMLSM), which is divided into three segments (i = 1, 2, 3), each segment is equipped with a force sensor to monitor the thrust changes during operation. At the same time, the system is equipped with a data acquisition device that can collect data on ambient temperature (W), humidity (H) and dust concentration (C).

[0070] The collected data needs to be monitored as follows:

[0071] 1. Data acquisition module;

[0072] Thrust data were collected in each segment within 1 second with a sampling period of 1 millisecond (N = 1000);

[0073] Thrust data:

[0074] Segment 1: F1 = [1000N, 1005N, 998N, …, 1002N] (a total of 1000 data points);

[0075] Segment 2: F2 = [995N, 1000N, 996N, ..., 998N] (a total of 1000 data points);

[0076] Segment 3: F3 = [1002N, 1003N, 1001N, ..., 1004N] (a total of 1000 data points);

[0077] Environmental data:

[0078] Ambient temperature: W = 35°C;

[0079] Ambient humidity: H = 60%;

[0080] Dust concentration in the air: C = 10 mg / m 3 ;

[0081] 2. Data analysis module;

[0082] Calculate the average thrust;

[0083] For each segment, the formula: Calculate the average thrust for each segment;

[0084] Assume F1avg =1000N;

[0085] F2 avg =980N;

[0086] F3 avg =950N;

[0087] Calculate early warning assessment coefficients;

[0088] For each segment, the formula: Calculate the fluctuation degree of thrust in each segment;

[0089] Assume Z1 = 5N;

[0090] Z2=6N;

[0091] Z3=4N;

[0092] The early warning assessment coefficient is calculated as follows:

[0093] Assume that the proportionality factor A = 0.8;

[0094] P1 = 0.8 × e^(5 / 5+1)≈1.84;

[0095] P2 = 0.8 × e^(6 / 6+1)≈1.90;

[0096] P3 = 0.8 × e^(4 / 4+1)≈1.78;

[0097] 3. Correction module;

[0098] Constructing a revised model;

[0099] Construct a correction model based on environmental data and calculate the correction coefficient f;

[0100] Assume that the proportionality coefficients α = 0.6, β = 0.7, θ = 0.5;

[0101] Optimum temperature ZW = 20°C;

[0102] Optimum humidity ZH = 50%;

[0103] f=0.6×e^(|35-20| / 20)+0.7×e^(|60-50| / 50)+[ln(10+1)^0.5] / tanh(10)

[0104] ≈3.3;

[0105] Modify the early warning assessment coefficient;

[0106] Add the correction factor f to the warning evaluation coefficient of each segment to obtain the target warning evaluation coefficient of each segment, expressed as MPi:

[0107] MP1 = 3.3 + 1.84 = 5.14;

[0108] MP1 = 3.3 + 1.90 = 5.2;

[0109] MP1 = 3.3 + 1.78 = 5.08;

[0110] 4. Early warning module;

[0111] Determine whether an early warning is needed;

[0112] Assuming the preset evaluation threshold is 5.1, compare the target warning evaluation coefficient of each segment with the threshold:

[0113] Segment 1: Since the target warning evaluation coefficient (5.14) of segment 1 is greater than the preset evaluation threshold (5.1), a warning message is sent to the client;

[0114] Segment 2: Since the target warning evaluation coefficient (5.2) of segment 2 is greater than the preset evaluation threshold (5.1), a warning message is sent to the client;

[0115] Segment 3: Since the target warning evaluation coefficient (5.08) of segment 3 is less than the preset evaluation threshold (5.1), no warning is issued;

[0116] Upon receiving the early warning information of segment 1 and segment 2, the motors of segment 1 and segment 2 are checked immediately.

[0117] The present invention collects thrust data and environmental data of each segment of the segmented PMLSM in real time, calculates and corrects the early warning evaluation coefficient of each segment, can timely discover potential problems before failure occurs, reduce the risk of equipment damage, improve the accuracy and response speed of the monitoring system, and significantly improve the operating stability and reliability of the segmented PMLSM, thereby extending the life of the equipment, reducing downtime, improving the overall production efficiency of the production line, and reducing costs.

[0118] See also Figure 3 The second aspect of the present invention provides a segmented PMLSM monitoring method, comprising:

[0119] Step 1: Collect the thrust data of each segment of the segmented PMLSM in real time, and collect the environmental data of the segmented PMLSM during operation;

[0120] Step 2: Calculate the warning assessment coefficient of each segment based on the thrust data;

[0121] Step 3: Construct a correction model based on environmental data to correct the early warning evaluation coefficient of each segment to obtain the target early warning evaluation coefficient of each segment;

[0122] Step 4: Determine whether each segment in the segmented PMLSM needs warning based on the target warning evaluation coefficient.

[0123] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0124] Working principle of the present invention:

[0125] The present invention collects thrust data of each segment of a segmented PMLSM in real time, and collects environmental data of the segmented PMLSM during operation; calculates a warning evaluation coefficient of each segment based on the thrust data; and constructs a correction model based on the environmental data to correct the warning evaluation coefficient of each segment to obtain a target warning evaluation coefficient of each segment; and judges whether each segment in the segmented PMLSM needs a warning based on the target warning evaluation coefficient, thereby solving the problem that the prior art mainly relies on the operating data of the motor and ignores the important influence of environmental factors on the performance of the motor; these environmental factors will have a significant influence on the material, magnetic properties, heat dissipation and lubrication of the motor, thereby affecting the operating stability and life of the motor; secondly, the prior art only relies on historical operating data and current operating data, lacks a comprehensive analysis of multi-dimensional data, resulting in the problem that the health assessment result of the motor is not comprehensive and accurate enough.

[0126] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A segmented PMLSM monitoring system, characterized in that: include: Data collection module, data analysis module, correction module and early warning module; Data acquisition module: used to collect the thrust data of each segment of the segmented PMLSM in real time, as well as the environmental data of the segmented PMLSM during operation; Data analysis module: calculates the early warning assessment coefficient of each segment based on thrust data; Correction module: Based on the environmental data, a correction model is constructed to correct the early warning evaluation coefficient of each segment to obtain the target early warning evaluation coefficient of each segment; Warning module: Determines whether each segment in the segmented PMLSM needs warning based on the target warning evaluation coefficient.

2. A segmented PMLSM monitoring system according to claim 1, characterized in that: The real-time collection of thrust data of each segment of the segmented PMLSM and the collection of environmental data of the segmented PMLSM during operation include: The thrust of each segmented motor in the segmented PMLSM is collected in real time through force sensors; The environmental data of the segmented PMLSM during operation is collected through data collection equipment; the environmental data includes: ambient temperature, ambient humidity and dust concentration in the air.

3. A segmented PMLSM monitoring system according to claim 1, characterized in that: The calculation based on the thrust data to obtain the early warning assessment coefficient of each segment includes: The thrust of each segment is marked as Fi; where i is the number of segments in the segmented PMLSM; j is the number of collected data points; By formula: The average thrust of each segment is calculated; where Fi avg Refers to the average thrust of each segment; N is the number of thrust data points collected; Fi j It refers to the thrust value of the jth collected data point in the i-th segment; The early warning evaluation coefficient of each segment is calculated by the formula: Pi = A × e^(Zi / Zi+1); wherein Pi refers to the early warning evaluation coefficient of the i-th segment; A is the proportional coefficient, and 0<A<1; Zi refers to the fluctuation degree of the thrust of the i-th segment, and 4. A segmented PMLSM monitoring system according to claim 1, characterized in that: The correction model is constructed based on the environmental data to correct the early warning assessment coefficient of each segment, including: S1: mark the ambient temperature as W, the ambient humidity as H, and the dust concentration in the air as C; S2: Take environmental data as the independent variable, and the calculated result as the dependent variable, and mark it as the correction coefficient f; S3: Fit the independent variables and dependent variables by polynomial fitting to establish a revised model; the revised model is specifically: f=α×e^(|W-ZW| / ZW)+β×e^(|H-ZH| / ZH)+[ln(C+1)^θ] / tanh(C); Among them, α, β, θ are proportional coefficients, and 0<α<1, 0<β<1, 0<θ<1, ZW is the optimum temperature of the segmented PMLSM during operation; ZH is the optimum humidity of the segmented PMLSM during operation; S4: By adding the correction coefficient to the warning assessment coefficient of each segment, the target warning assessment coefficient of each segment is obtained.

5. A segmented PMLSM monitoring system according to claim 1, characterized in that: The step of judging whether each segment in the segmented PMLSM needs an early warning based on the target early warning evaluation coefficient includes: Determine whether the target warning evaluation coefficient of each segment in the segmented PMLSM is greater than the preset evaluation threshold; if yes, generate a warning and send the warning information to the client for reminder; if not, continue to monitor and judge.

6. A segmented PMLSM monitoring method, applied to a segmented PMLSM monitoring system according to any one of claims 1 to 5, characterized in that: include: Step 1: Collect the thrust data of each segment of the segmented PMLSM in real time, and collect the environmental data of the segmented PMLSM during operation; Step 2: Calculate the warning assessment coefficient of each segment based on the thrust data; Step 3: Construct a correction model based on environmental data to correct the early warning evaluation coefficient of each segment to obtain the target early warning evaluation coefficient of each segment; Step 4: Determine whether each segment in the segmented PMLSM needs warning based on the target warning evaluation coefficient.

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