Sintering pallet motor fault monitoring system

Abstract

The invention belongs to the technical field of sintering pallets, and particularly discloses a sintering pallet motor fault monitoring system which comprises a data acquisition module, a model construction module, a hidden danger judgment module, an early warning module, a cloud collaboration module and a database. The data acquisition module is used for acquiring temperature, vibration, current and rotating speed data and analyzing the characteristics of temperature rise rate, spectrum asymmetry, harmonic content, rotating speed fluctuation and the like; the model construction module extracts a characteristic set of winding overheating, bearing wear, current imbalance and rotating speed abnormity based on historical fault data, and establishes an intelligent identification model; the hidden danger judgment module analyzes and determines the fault risk according to the matching degree of the real-time operation data and the model, and the early warning module evaluates the fault type and grade according to the matching degree. According to the system, through multi-source data fusion and intelligent model matching, real-time monitoring, accurate diagnosis and active early warning of motor faults are realized, and the operation reliability of equipment is effectively improved.

Description

Technical Field

[0001] The invention relates to the technical field of sintering trolleys, and in particular to a sintering trolley motor fault monitoring system. Background Art

[0002] In the modern steel production process, sintering process is a vital link. As the core equipment of the sintering process, the sintering trolley carries the key task of mixing and pelletizing various powdered iron-containing raw materials and sintering them into bulk sintered ore at high temperature.

[0003] Due to the harsh operating environment of the sintering trolley, which is exposed to high temperature, high dust and heavy load for a long time, its motor is very prone to failure. Once the motor fails, it will not only cause the sintering trolley to stop running, but also directly affect the output and quality of sintered ore, and then cause a serious chain reaction in the entire steel production process, increasing production costs. Therefore, it is extremely important to implement effective fault monitoring and management of the sintering trolley motor.

[0004] Existing sintering trolley technologies mostly focus on the optimization and improvement of equipment structure or functional expansion to improve sintering efficiency and product quality. For example, document CN117367124A discloses a sintering trolley that can improve sintering quality. The air leakage is reduced by the matching design of the sealing groove and the driving gear disc. At the same time, a driving motor is used to drive the stirring rod to achieve mineral powder uniformity, and an exhaust fan is used to improve combustion efficiency. Although this solution optimizes the sealing performance and combustion effect of the sintering trolley, its application to the driving motor is still limited to functional execution, and does not involve real-time monitoring and fault analysis of the motor's operating status. Similarly, document CN119289690A discloses a trolley sintering furnace with an automatic discharging function, which realizes automatic discharging of products by driving the trolley body through a driving motor, and reduces impurity pollution with the help of a protective plate. However, this technology also does not monitor the health status of the driving motor, and only improves the discharging flexibility and finished product quality through structural improvements.

[0005] The above-mentioned prior art shows that the current research and development direction for sintering carts is mainly focused on mechanical structure optimization and process improvement, while there is an obvious gap in the intelligent monitoring and management of the motor operating status. Since the sintering cart is in high temperature and high load conditions for a long time, its drive motor is prone to failure due to overheating, abnormal vibration or current fluctuation, which in turn leads to equipment shutdown, reduced sintering efficiency and even production accidents. The existing technology lacks real-time collection, analysis and fault warning mechanisms for motor operating parameters, making it difficult to achieve early diagnosis and preventive maintenance of faults. Therefore, there is an urgent need for an intelligent monitoring system for sintering cart motors to make up for the shortcomings of the existing technology and improve equipment operation reliability and production continuity. Summary of the invention

[0006] In view of the above problems, the present invention proposes a sintering trolley motor fault monitoring system to realize the function of monitoring the sintering trolley.

[0007] The technical solution adopted by the present invention to solve its technical problem is: the present invention provides a sintering trolley motor fault monitoring system, including: a data acquisition module: real-time acquisition of multi-source operation data of the motor, including temperature data, vibration data, current data and speed data, and pre-processing.

[0008] Model building module: Based on the operating data of the motor's historical faults, the feature set of winding overheating, bearing wear, current imbalance, and unstable speed faults is extracted to build identification models for various faults.

[0009] Hidden danger judgment module: The real-time operation data of the motor is input into the fault identification model for matching analysis to determine whether the motor has hidden dangers of fault.

[0010] Early warning module: When there is a potential fault in the motor, the fault type is analyzed and the fault level is evaluated, and a corresponding fault early warning signal is generated.

[0011] Cloud collaboration module: encrypts and transmits the motor's multi-source operating data and fault warning signals to the cloud server, and receives fault optimization instructions issued by the cloud.

[0012] Database: Stores the operating data of the motor when it has historically failed.

[0013] Based on the above embodiment, the data acquisition module includes a temperature data acquisition unit, a vibration data acquisition unit, a current data acquisition unit and a speed data acquisition unit, wherein the specific working process of the temperature data acquisition unit is: setting the duration of the monitoring cycle, and setting each sampling time point within the monitoring cycle according to a preset time interval.

[0014] The temperature of the motor casing and three-phase winding at each sampling time point during the monitoring period is collected by an infrared temperature sensor, and a temperature change trend curve of the motor casing and three-phase winding during the monitoring period is drawn.

[0015] According to the temperature change trend curves of the motor housing and the three-phase winding during the monitoring period, the peak value of the temperature rise rate and the peak value of the temperature fluctuation are obtained respectively, and the temperature rise data and temperature fluctuation data of the motor are obtained accordingly.

[0016] The temperature change trend curves of the motor casing and the three-phase winding during the monitoring period are longitudinally compared to obtain the peak temperature difference between the motor casing and the winding and the peak temperature difference between the three-phase windings during the monitoring period to obtain the temperature distribution data of the motor.

[0017] The temperature rise data, temperature fluctuation data and temperature distribution data of the motor are summarized to obtain the temperature data of the motor.

[0018] On the basis of the above-mentioned embodiment, the specific working process of the vibration data acquisition unit is: collecting radial, axial and tangential vibration spectra of the motor bearing during the monitoring period through a three-axis vibration sensor.

[0019] According to the radial vibration spectrum of the motor bearing during the monitoring period, the amplitude of each frequency component and the phase relationship between each frequency component are obtained, and then substituted into the preset relationship comparison table between the amplitude of the frequency component, the phase relationship between the frequency components and the degree of vibration asymmetry to obtain the degree of asymmetry of the radial vibration of the motor bearing.

[0020] Similarly, according to the analysis method of the asymmetry degree of the radial vibration of the motor bearing, the asymmetry degree of the axial and tangential vibrations of the motor bearing is obtained.

[0021] The vibration data of the motor is constructed based on the radial, axial and tangential vibration spectra and the degree of vibration asymmetry of the motor bearing.

[0022] Based on the above embodiment, the specific working process of the current data acquisition unit is: collecting the three-phase current waveform of the motor during the monitoring period through the Hall current sensor, further obtaining the harmonic content, instantaneous current fluctuation peak value, and current waveform distortion rate of the three-phase current of the motor during the monitoring period, and constructing the current data of the motor.

[0023] Based on the above embodiment, the specific working process of the speed data acquisition unit is: monitor the speed of the motor through a photoelectric encoder, capture the speed fluctuation peak value when the motor runs at a constant speed within the monitoring period, and collect the overshoot peak value and response time peak value when the motor speed switches, to construct the speed data of the motor.

[0024] Compared with the prior art, the sintering trolley motor fault monitoring system described in the present invention has the following beneficial effects: 1. Real-time monitoring of multi-source data to improve the comprehensiveness of fault detection: The present invention can comprehensively cover the key operating parameters of the motor through real-time collection and preprocessing of motor temperature, vibration, current, and speed operating data, avoiding the limitations of a single data source and ensuring early detection of potential fault hazards.

[0025] 2. Accurate fault identification model driven by historical data: The present invention extracts features and constructs an identification model based on the historical fault data of the motor. It can accurately distinguish typical fault types such as winding overheating, bearing wear, current imbalance, and unstable speed, thereby improving the pertinence and accuracy of diagnosis.

[0026] 3. Dynamic matching degree analysis and hidden danger classification warning: The present invention adopts deviation matching degree analysis, combined with preset thresholds and comprehensive fault probability mapping rules, to realize dynamic quantitative evaluation and classification of fault hidden dangers, which helps to prioritize high-risk faults and reduce the risk of sudden downtime. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0028] Figure 1 This is a system module connection diagram of the present invention.

[0029] Figure 2 This is a composition diagram of the multi-source operation data acquisition module of the present invention.

[0030] Figure 3 This is a flow chart of the sintering trolley motor fault monitoring and analysis of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.

[0032] See also Figure 1 and Figure 3 As shown, the present invention provides a sintering trolley motor fault monitoring system, including a data acquisition module, a model building module, a hidden danger judgment module, an early warning module, a cloud collaboration module, and a database.

[0033] The model building module is connected to the data acquisition module and the hidden danger judgment module respectively, the early warning module is connected to the hidden danger judgment module and the cloud collaboration module respectively, and the database is connected to the model building module.

[0034] The data acquisition module acquires multi-source operation data of the motor in real time, including temperature data, vibration data, current data and rotation speed data, and performs pre-processing.

[0035] Further, see Figure 2 As shown, the data acquisition module includes a temperature data acquisition unit, a vibration data acquisition unit, a current data acquisition unit and a speed data acquisition unit, wherein the specific working process of the temperature data acquisition unit is: setting the duration of the monitoring cycle, and setting each sampling time point within the monitoring cycle according to a preset time interval.

[0036] The temperature of the motor casing and three-phase winding at each sampling time point during the monitoring period is collected by an infrared temperature sensor, and a temperature change trend curve of the motor casing and three-phase winding during the monitoring period is drawn.

[0037] According to the temperature change trend curves of the motor housing and the three-phase winding during the monitoring period, the peak value of the temperature rise rate and the peak value of the temperature fluctuation are obtained respectively, and the temperature rise data and temperature fluctuation data of the motor are obtained accordingly.

[0038] It should be noted that the peak values ​​of the temperature rise rates of the motor housing and the three-phase winding during the monitoring period are obtained by obtaining the temperature rise rates corresponding to the data points on the temperature change trend curve according to the temperature change trend curve of the motor housing and the three-phase winding, and comparing them with each other to obtain the peak values ​​of the temperature rise rates of the motor housing and the three-phase winding.

[0039] It should be noted that the peak value of the temperature fluctuation of the motor casing and the three-phase winding during the monitoring period is obtained by the following method: according to the temperature change trend curve of the motor casing and the three-phase winding, the temperature fluctuation of each data point on the temperature change trend curve relative to the adjacent previous data point is obtained, and the temperature fluctuations are compared with each other to obtain the peak value of the temperature fluctuation of the motor casing and the three-phase winding.

[0040] The temperature change trend curves of the motor casing and the three-phase winding during the monitoring period are longitudinally compared to obtain the peak temperature difference between the motor casing and the winding and the peak temperature difference between the three-phase windings during the monitoring period to obtain the temperature distribution data of the motor.

[0041] The temperature rise data, temperature fluctuation data and temperature distribution data of the motor are summarized to obtain the temperature data of the motor.

[0042] In a specific embodiment, the infrared temperature sensor is installed on the surface of the motor housing and the three-phase winding.

[0043] As a preferred solution, detection position points can be respectively arranged in the motor housing and the three-phase winding, and the average values ​​of the temperatures corresponding to the detection position points in the motor housing and the three-phase winding at each sampling time point during the monitoring period are calculated and used as the temperatures of the motor housing and the three-phase winding at each sampling time point during the monitoring period.

[0044] As a preferred solution, a temperature change trend curve of the motor housing and three-phase winding during the monitoring period is drawn. The specific method is: a coordinate system is established with the sampling time point as the horizontal axis and the temperature as the vertical axis. According to the temperature of the motor housing and the three-phase winding at each sampling time point during the monitoring period, the corresponding data points are marked in the coordinate system. The mathematical model establishment method is used to draw a trend curve of the temperature change of the motor housing and the three-phase winding over time during the monitoring period, and it is recorded as the temperature change trend curve of the motor housing and the three-phase winding during the monitoring period.

[0045] As a preferred solution, the peak temperature difference between the motor casing and the winding during the monitoring period is obtained. The specific method is: calculate the temperature difference between the motor casing temperature and the three-phase winding temperature at the same sampling time point during the monitoring period, and then select the maximum temperature difference as the temperature difference between the motor casing and the winding corresponding to the corresponding acquisition time point.

[0046] The temperature difference between the motor casing and the winding at each sampling time point during the monitoring period is compared, and the maximum temperature difference is selected as the peak temperature difference between the motor casing and the winding during the monitoring period.

[0047] As a preferred solution, the peak temperature difference between the three-phase windings during the monitoring period is obtained. The specific method is: compare the temperatures of the three-phase windings at each sampling time point during the monitoring period, obtain the maximum temperature difference between the three-phase windings at each sampling time point during the monitoring period, compare them with each other, and further obtain the peak temperature difference between the three-phase windings during the monitoring period.

[0048] It should be noted that the present invention can accurately identify local overheating or heat dissipation anomalies by extracting multi-dimensional temperature data such as temperature rise rate, temperature fluctuation, and temperature difference peak, thereby avoiding misjudgment of traditional single temperature monitoring.

[0049] Furthermore, the specific working process of the vibration data acquisition unit is: collecting radial, axial and tangential vibration spectra of the motor bearing during the monitoring period through a three-axis vibration sensor.

[0050] According to the radial vibration spectrum of the motor bearing during the monitoring period, the amplitude of each frequency component and the phase relationship between each frequency component are obtained, and then substituted into the preset relationship comparison table between the amplitude of the frequency component, the phase relationship between the frequency components and the degree of vibration asymmetry to obtain the degree of asymmetry of the radial vibration of the motor bearing.

[0051] Similarly, according to the analysis method of the asymmetry degree of the radial vibration of the motor bearing, the asymmetry degree of the axial and tangential vibrations of the motor bearing is obtained.

[0052] The vibration data of the motor is constructed based on the radial, axial and tangential vibration spectra and the degree of vibration asymmetry of the motor bearing.

[0053] It should be noted that the three-axis vibration sensor is installed on the motor bearing seat.

[0054] It should be noted that the asymmetry of the radial, axial and tangential vibrations of the motor bearing refers to the fact that during the operation of the motor bearing, the radial, axial and tangential vibrations thereof present uneven and asymmetric characteristics in different directions.

[0055] It should be noted that when the radial vibration of the motor bearing is asymmetric, the vibration intensity in different directions is different, which will cause the amplitude of certain frequency components in the vibration spectrum to change. For example, in the direction of stronger vibration, the amplitude of the relevant frequency component will increase significantly, while in the direction of weaker vibration, the amplitude is relatively small. By comparing the difference in spectrum amplitude in different directions, the degree of asymmetry of radial vibration can be intuitively understood.

[0056] It should be noted that when the radial vibration of the motor bearing is asymmetric, the phase of vibration in different directions may change, which will also be reflected in the spectrum. For example, in normal symmetrical vibration, the phase of vibration in each direction may have a certain regularity, but when asymmetric vibration occurs, the phase relationship becomes complicated. By analyzing the change of phase, we can further understand the characteristics of radial vibration asymmetry.

[0057] It should be noted that the present invention can capture subtle features of bearing wear and enhance the early warning capability of bearing failure by utilizing a three-axis vibration spectrum diagram and asymmetry analysis.

[0058] Furthermore, the specific working process of the current data acquisition unit is: collecting the three-phase current waveform of the motor during the monitoring period through the Hall current sensor, further obtaining the harmonic content, instantaneous current fluctuation peak value, and current waveform distortion rate of the three-phase current of the motor during the monitoring period, and constructing the current data of the motor.

[0059] It should be noted that the Hall current sensor is integrated into the motor power supply circuit.

[0060] Furthermore, the specific working process of the speed data acquisition unit is: monitoring the speed of the motor through a photoelectric encoder, capturing the speed fluctuation peak value when the motor runs at a constant speed within the monitoring period, and collecting the overshoot peak value and response time peak value when the motor speed switches, to construct the speed data of the motor.

[0061] It should be noted that the photoelectric encoder is connected to the motor shaft.

[0062] It should be noted that the constant speed operation of the motor means that the motor runs stably at a certain constant speed.

[0063] It should be noted that the overshoot peak value refers to the percentage of the maximum difference between the actual speed and the target speed during the speed switching process of the motor.

[0064] It should be noted that the peak response time refers to the longest time that the motor takes from the issuance of a speed switching command to the actual speed reaching the target speed and stabilizing within a certain error range.

[0065] As a preferred solution, preprocessing the multi-source operation data of the motor refers to preprocessing operations of denoising, normalizing and aligning timestamps on the multi-source operation data.

[0066] It should be noted that the present invention effectively identifies current imbalance and speed abnormality through the collection of dynamic parameters such as harmonic content, waveform distortion rate, speed fluctuation, etc., and enhances adaptability to complex working conditions.

[0067] It should be noted that, through the real-time collection and preprocessing of multi-source operating data such as temperature, vibration, current, and speed, the system of the present invention can comprehensively cover the key operating parameters of the motor, avoid the limitations of a single data source, and ensure the early discovery of potential fault hazards.

[0068] The model building module extracts the feature set of winding overheating, bearing wear, current imbalance, and speed instability faults based on the operating data of the motor's historical faults, and builds recognition models for various faults.

[0069] Furthermore, the specific working process of the model building module is: extracting the operating data of the motor during historical faults stored in the database, screening the temperature data of the motor during historical winding overheating, the vibration data during bearing wear, the current data during current imbalance, and the speed data during speed instability.

[0070] According to the temperature data of the motor winding overheating in history, the peak values ​​of the temperature rise rate of the motor casing and the three-phase winding, the peak value of the temperature fluctuation, the peak value of the temperature difference between the motor casing and the winding, and the peak value of the temperature difference between the three-phase windings are obtained when the motor winding overheats in history, and the feature set of the winding overheating fault is obtained. An identification model of the winding overheating fault is constructed with the input as the fault feature and the output as the fault type.

[0071] Similarly, an identification model for bearing wear, current imbalance, and speed instability faults is constructed.

[0072] It should be noted that the construction process of the motor winding overheating fault identification model is as follows: S1, data collection and labeling; collecting historical temperature data when the motor is running, including data in normal state and winding overheating fault state, where the data contains timestamp, motor housing temperature, three-phase winding temperature and corresponding fault type label.

[0073] S2. Feature extraction: Extract the following key features from the temperature data: peak value of temperature rise rate, peak value of temperature fluctuation, peak value of temperature difference between casing and winding, and peak value of temperature difference between three-phase windings.

[0074] S3. Data preprocessing: standardize or normalize the features, eliminate dimensional differences, and divide the training set and test set (such as an 8:2 ratio).

[0075] S4. Model selection and training: Select a classification model (such as random forest, SVM, neural network, etc.), with the input as a feature set and the output as a fault type label, and train the model through supervised learning.

[0076] S5. Model evaluation: Use indicators such as accuracy and confusion matrix to evaluate model performance, and optimize hyperparameters through cross-validation.

[0077] It should be noted that a feasible simulation process, assuming that the peak temperature rise rate of the motor housing and the three-phase winding is 5°C / min, the peak temperature fluctuation is 3.0°C, the peak temperature difference between the motor housing and the winding is 8°C, and the peak temperature difference between the three-phase winding is 2°C, can obtain the fault type identification result, see Table 1 for details.

[0078] Table 1. Winding overheat fault type identification results

[0079]

[0080] It should be noted that the feature set of bearing wear faults includes radial, axial and tangential vibration spectra and the degree of vibration asymmetry of the motor bearing.

[0081] It should be noted that the feature set of the current imbalance fault includes the harmonic content of the three-phase current of the motor, the instantaneous current fluctuation peak value, and the current waveform distortion rate.

[0082] It should be noted that the characteristic set of the speed unstable fault includes the speed fluctuation peak value when the motor is running at a constant speed, the overshoot peak value when the speed is switched, and the response time peak value.

[0083] It should be noted that the present invention extracts features and constructs an identification model based on the historical fault data of the motor, which can accurately distinguish typical fault types such as winding overheating, bearing wear, current imbalance, and unstable speed, thereby improving the pertinence and accuracy of diagnosis.

[0084] The hidden danger judgment module inputs the real-time operation data of the motor into the fault recognition model for matching analysis to judge whether the motor has hidden dangers of faults.

[0085] Furthermore, the specific working process of the hidden danger judgment module includes: inputting the temperature data of the motor into the identification model of the winding overheating fault, obtaining the difference between each sub-item in the motor temperature data and the corresponding sub-item in the winding overheating fault identification model, recording it as the deviation of each sub-item in the motor temperature data relative to the model, and inputting the deviation into a preset temperature data relative model deviation-winding overheating fault identification model matching relationship model, outputting the matching degree of the motor and the winding overheating fault identification model, and the relationship model contains a quantitative mapping relationship between the temperature data relative model deviation and the winding overheating fault identification model matching degree.

[0086] Similarly, the matching degree of the motor and bearing wear fault identification model, the current imbalance fault identification model, and the speed instability fault identification model is obtained.

[0087] It should be noted that a feasible simulation process analyzes the matching degree of the motor and winding overheat fault identification model based on the deviation of each sub-item in the motor temperature data relative to the model. The specific implementation process is as follows: Steps 1. Prepare data: Obtain a set of real-time motor temperature data, and calculate the deviation of each sub-item relative to the corresponding sub-item in the model. Please refer to Table 2 for details.

[0088] Table 2. Deviation of each sub-item of real-time temperature data relative to the corresponding sub-item in the model

[0089]

[0090] Steps 2. Determine the matching relationship model: Assume that the preset temperature data relative model deviation-winding overheat fault identification model matching relationship model is a linear relationship: matching degree = 1-Σ (absolute value of deviation / model value).

[0091] Steps3. Calculate the matching degree: According to the above formula: Σ(absolute value of deviation / model value) = (0.5 / 5) + (0.2 / 6) + (0.2 / 3) + (0.4 / 4) + (0.5 / 8) + (0.2 / 2) ≈ 0.4625; matching degree = 1-0.4625 = 0.5375, expressed as a percentage of 53.75%.

[0092] Steps4. Final table presentation, see Table 3.

[0093] Table 3. Calculation results

[0094]

[0095] It should be noted that the deviation of the radial, axial and tangential vibration spectrum diagrams of the motor bearing in the motor vibration data relative to the model is obtained by: obtaining the similarity between the radial vibration spectrum diagram of the motor bearing in the motor vibration data and the radial vibration spectrum diagram of the bearing in the bearing wear fault identification model, substituting it into the preset comparison relationship between the similarity of the vibration spectrum diagram and the deviation of the vibration spectrum diagram relative to the model, and obtaining the deviation of the radial vibration spectrum diagram of the motor bearing relative to the model.

[0096] Similarly, obtain the deviation of the axial and tangential vibration spectra of the motor bearing relative to the model.

[0097] Furthermore, the specific working process of the hidden danger judgment module also includes: comparing the matching degree between the motor and various fault identification models with a preset matching degree threshold; if the matching degree between the motor and various fault identification models is less than the preset matching degree threshold, the motor has no hidden fault hazards; otherwise, the motor has hidden fault hazards.

[0098] The early warning module analyzes the fault type and evaluates the fault level when there is a potential fault in the motor, and generates a corresponding fault early warning signal.

[0099] Furthermore, the specific working process of the early warning module includes: if the matching degree between the motor and a certain fault identification model is greater than or equal to a preset matching degree threshold, the fault is classified into the fault type of the motor, and the motor fault type set is obtained by statistics.

[0100] Furthermore, the specific working process of the early warning module also includes: setting a relationship comparison table between the matching degree between the motor and various fault identification models and the comprehensive fault probability of the motor according to preset rules, and screening out the comprehensive fault probability of the motor according to the matching degree between the motor and various fault identification models in the fault type set.

[0101] The comprehensive fault probability of the motor is substituted into the preset mapping relationship between the comprehensive fault probability and the fault level to obtain the fault level of the motor and generate a corresponding fault warning signal.

[0102] The cloud collaboration module encrypts and transmits the multi-source operating data and fault warning signals of the motor to the cloud server, and receives the fault optimization instructions issued by the cloud.

[0103] It should be noted that the fault optimization instructions may specifically be parameter dynamic adjustment instructions, operation mode switching instructions, maintenance and repair strategy instructions, fault suppression and fault-tolerant control instructions, system-level collaborative control instructions, etc.

[0104] It should be noted that the present invention realizes dynamic quantitative evaluation and classification of fault hazards by adopting deviation matching analysis, combined with preset thresholds and comprehensive fault probability mapping rules, which helps to prioritize high-risk faults and reduce the risk of sudden downtime.

[0105] The database stores the operation data of the motor when it has historical faults.

[0106] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. A sintering trolley motor fault monitoring system, characterized in that: include: Data acquisition module: collects multi-source operation data of the motor in real time, including temperature data, vibration data, current data and speed data, and performs pre-processing; Model building module: Based on the operating data of the motor's historical faults, the temperature rise, temperature fluctuation and temperature distribution characteristics when the winding is overheated are extracted; the spectrum and asymmetric characteristics of vibration in all directions when the bearing is worn; the harmonic, instantaneous current and waveform distortion rate characteristics when the current is unbalanced; Speed ​​fluctuation and speed switching overshoot and response time characteristics when the speed is unstable; build identification models for various faults; Hidden danger judgment module: input the real-time operation data of the motor into the fault identification model for matching analysis to determine whether the motor has hidden dangers of faults; Early warning module: analyzes the fault type and evaluates the fault level when there is a potential fault in the motor, and generates a corresponding fault early warning signal; Cloud collaboration module: encrypts and transmits the motor's multi-source operating data and fault warning signals to the cloud server, and receives fault optimization instructions issued by the cloud; Database: Stores the operating data of the motor when it has historically failed.

2. A sintering trolley motor fault monitoring system according to claim 1, characterized in that: The data acquisition module includes a temperature data acquisition unit, a vibration data acquisition unit, a current data acquisition unit and a speed data acquisition unit, wherein the specific working process of the temperature data acquisition unit is as follows: Set the duration of the monitoring cycle and set each sampling time point within the monitoring cycle according to the preset time interval; The temperature of the motor housing and the three-phase winding at each sampling time point during the monitoring period is collected by an infrared temperature sensor, and a temperature change trend curve of the motor housing and the three-phase winding during the monitoring period is drawn; According to the temperature change trend curves of the motor housing and the three-phase winding during the monitoring period, the peak value of the temperature rise rate and the peak value of the temperature fluctuation are obtained respectively, and the temperature rise data and temperature fluctuation data of the motor are obtained accordingly; Compare the temperature change trend curves of the motor housing and the three-phase windings in the monitoring period longitudinally, obtain the peak temperature difference between the motor housing and the windings, and the peak temperature difference between the three-phase windings in the monitoring period, and obtain the temperature distribution data of the motor; The temperature rise data, temperature fluctuation data and temperature distribution data of the motor are summarized to obtain the temperature data of the motor.

3. A sintering trolley motor fault monitoring system according to claim 2, characterized in that: The specific working process of the vibration data acquisition unit is as follows: The radial, axial and tangential vibration spectra of the motor bearings during the monitoring period are collected by a three-axis vibration sensor; According to the radial vibration spectrum of the motor bearing during the monitoring period, the amplitude of each frequency component and the phase relationship between each frequency component are obtained, and the amplitude and the phase relationship between the frequency components are substituted into the preset relationship comparison table between the amplitude of the frequency component, the phase relationship between the frequency components and the degree of vibration asymmetry, and the degree of asymmetry of the radial vibration of the motor bearing is obtained by matching; Similarly, according to the analysis method of the asymmetric degree of radial vibration of the motor bearing, the asymmetric degree of axial and tangential vibration of the motor bearing is obtained; The vibration data of the motor is constructed based on the radial, axial and tangential vibration spectra and the degree of vibration asymmetry of the motor bearing.

4. A sintering trolley motor fault monitoring system according to claim 2, characterized in that: The specific working process of the current data acquisition unit is as follows: The three-phase current waveform of the motor during the monitoring period is collected by the Hall current sensor, and the harmonic content, instantaneous current fluctuation peak value, and current waveform distortion rate of the three-phase current of the motor during the monitoring period are further obtained to construct the current data of the motor.

5. The sintering trolley motor fault monitoring system according to claim 2 is characterized in that: The specific working process of the speed data acquisition unit is as follows: The motor speed is monitored by a photoelectric encoder to capture the peak value of the speed fluctuation when the motor is running at a constant speed within the monitoring period, and the overshoot peak value and response time peak value when the motor speed is switched are collected to construct the motor speed data.

6. A sintering trolley motor fault monitoring system according to claim 2, characterized in that: The specific working process of the model building module is as follows: Extract the running data of the motor when it has a historical fault stored in the database, and filter the temperature data when the motor has a historical winding overheating, the vibration data when the bearing is worn, the current data when the current is unbalanced, and the speed data when the speed is unstable; According to the temperature data of the motor winding overheating in the past, the peak values ​​of the temperature rise rate of the motor housing and the three-phase winding, the peak value of the temperature fluctuation, the peak value of the temperature difference between the motor housing and the winding, and the peak value of the temperature difference between the three-phase windings are obtained when the motor winding is overheating in the past, and the feature set of the winding overheating fault is obtained, and an identification model of the winding overheating fault is constructed with the input as the fault feature and the output as the fault type; Similarly, an identification model for bearing wear, current imbalance, and speed instability faults is constructed.

7. A sintering trolley motor fault monitoring system according to claim 6, characterized in that: The specific working process of the hidden danger judgment module includes: Input the temperature data of the motor into the identification model of the winding overheat fault, obtain the difference between each sub-item in the motor temperature data and the corresponding sub-item in the winding overheat fault identification model, record it as the deviation of each sub-item in the motor temperature data relative to the model, and input the deviation into a preset temperature data relative model deviation-winding overheat fault identification model matching degree relationship model, output the matching degree of the motor and the winding overheat fault identification model, the relationship model includes a quantitative mapping relationship between the temperature data relative model deviation and the winding overheat fault identification model matching degree; Similarly, the matching degree of the motor and bearing wear fault identification model, the current imbalance fault identification model, and the speed instability fault identification model is obtained.

8. A sintering trolley motor fault monitoring system according to claim 7, characterized in that: The specific working process of the hidden danger judgment module also includes: The matching degree between the motor and various fault identification models is compared with a preset matching degree threshold. If the matching degree between the motor and various fault identification models is less than the preset matching degree threshold, the motor has no hidden fault risk. Otherwise, the motor has hidden fault risk.

9. The sintering trolley motor fault monitoring system according to claim 7, characterized in that: The specific working process of the early warning module includes: If the matching degree between the motor and a certain fault identification model is greater than or equal to a preset matching degree threshold, the fault is classified as a fault type of the motor, and a set of motor fault types is obtained by statistics.

10. A sintering trolley motor fault monitoring system according to claim 9, characterized in that: The specific working process of the early warning module also includes: According to preset rules, a comparison table of the relationship between the motor's matching degree with various fault identification models and the motor's comprehensive fault probability is set, and the motor's comprehensive fault probability is screened out according to the matching degree between the motor and various fault identification models in the fault type set; The comprehensive fault probability of the motor is substituted into the preset mapping relationship between the comprehensive fault probability and the fault level to obtain the fault level of the motor and generate a corresponding fault warning signal.

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