A monitoring and diagnosing method and system for electrical safety faults of a hoisting machinery device

Abstract

This invention discloses a monitoring and diagnosis method and system for electrical safety faults in lifting machinery, relating to the field of lifting machinery safety monitoring technology. The method includes: continuously collecting multi-source electrical data from the lifting machinery's electrical system at a preset sampling frequency; generating a standardized data sequence through preprocessing; calculating four operational risk characterization quantities; obtaining a comprehensive risk index through weighted fusion; and classifying the system's operating status based on preset thresholds. When a warning or fault state is determined, a risk vector is constructed based on weight coefficients, matched with vectors in a fault feature model library, and the fault type with the smallest distance is selected as the diagnostic result. Alarm information is simultaneously output and the data is stored. The weight coefficients and fault feature model library are iteratively optimized based on historical data to continuously improve fault diagnosis accuracy. Compared to existing systems that only provide alarm information, this application can clearly distinguish different fault types, providing maintenance personnel with a more intuitive and accurate basis for fault location.

Description

Technical Field

[0001] This invention relates to the field of safety monitoring technology for lifting machinery, and specifically to a method and system for monitoring and diagnosing electrical safety faults in lifting machinery equipment. Background Technology

[0002] As key equipment in construction, port loading and unloading, and industrial production, the safe operation of lifting machinery directly affects personnel safety and construction efficiency. Within the overall system of lifting machinery, the electrical system undertakes core functions such as power drive, control execution, and safety protection, including the power supply system, motor drive unit, control circuit, and various detection and protection devices. The stability and reliability of the electrical system's operating status are crucial foundations for ensuring the safe operation of lifting machinery. In actual operation, the electrical system of lifting machinery is constantly exposed to complex working conditions, such as frequent start-stop cycles, large load impacts, ambient temperature changes, and power grid fluctuations, all of which can affect the electrical equipment and lead to electrical faults. Common electrical faults include motor overload, abnormal power supply voltage, deteriorated line insulation, poor contact, and control circuit abnormalities. These faults are often sudden and insidious; if not detected in time, they may lead to equipment damage or even safety accidents.

[0003] In existing technologies, the monitoring of the operating status of lifting machinery mainly focuses on mechanical structure and load, while the monitoring of electrical systems often adopts a single-parameter threshold judgment method, such as setting fixed alarm thresholds for current, voltage, or temperature. An alarm is triggered when the detected value exceeds the threshold. However, this type of method typically only judges a single parameter independently, making it difficult to comprehensively reflect the overall operating status of the electrical system, and is prone to false alarms or missed alarms. Although existing technologies have introduced multi-parameter monitoring methods, in terms of data processing, these methods still rely mainly on simple superposition or empirical judgment, lacking systematic analysis of the correlation between different electrical parameters, failing to effectively distinguish between normal fluctuations and abnormal changes, and struggling to accurately identify different types of faults. Furthermore, existing systems typically lack the ability to dynamically optimize based on historical data, making it difficult for diagnostic models to adapt to complex and changing actual working conditions. Regarding fault identification, most systems can only provide abnormal alarms, but cannot differentiate fault types in detail, making it difficult to provide maintenance personnel with clear fault location criteria, thus increasing the difficulty of repair and maintenance costs.

[0004] Therefore, there is an urgent need for a monitoring and diagnostic method for electrical safety faults in lifting machinery to solve these problems. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for monitoring and diagnosing electrical safety faults in lifting machinery and equipment.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] In a first aspect, this application discloses a method for monitoring and diagnosing electrical safety faults in lifting machinery, comprising the following steps:

[0008] S1, in a length of Within the monitoring time window, multi-source electrical data of the lifting machinery equipment's electrical system are continuously collected at a preset sampling frequency, including power supply voltage values. Operating current value Equipment temperature value and switch status value , where i represents the index value at the sampling time;

[0009] S2. Preprocess the multi-source electrical data collected in step S1 to obtain a standardized data sequence;

[0010] S3. Based on the standardized parameters obtained in step S2, calculate four risk quantities used to characterize the operational risk of the electrical system of lifting machinery and equipment, including voltage anomaly risk quantity. Current overload risk Risk of temperature rise and the risk of switching action Specifically, it includes:

[0011] For voltage deviation coefficient Through formula Calculate the risk of voltage anomalies This is used to reflect the overall degree to which the voltage deviates from the rated value within the current time window;

[0012] For current load factor Considering the portion exceeding the rated value, the formula is used. Calculate the risk of current overload Used to measure the degree of overloading;

[0013] For the temperature rise normalization coefficient Through formula Calculate the risk of temperature rise This is used to reflect the heat load level of the equipment within the current time window;

[0014] For the change in switch state Through formula Calculate the risk of switch action This is used to reflect the frequency of operations of the control system, such as the risk of switching actions. If the preset switch action threshold is exceeded, it indicates abnormal switch control or poor contact.

[0015] S4. The four risk quantities obtained in step S3 are weighted and integrated to obtain a comprehensive risk index. Then, based on the comprehensive risk index and the preset threshold, the operating status of the electrical system of the lifting machinery equipment is divided into normal status, early warning status and fault status.

[0016] S5. When the system is in a warning state or a fault state, the four risk quantities are constructed into a risk vector by weighting coefficients. The risk vector is matched with the fault feature vector in the pre-established fault feature model library to obtain the matching degree, and the fault type with the smallest distance is selected as the diagnosis result.

[0017] S6. Output alarm information based on the diagnostic results of step S5, and store the operating data, status assessment results, fault diagnosis results, and alarm records in the database; wherein, the operating data includes power supply voltage data, operating current data, equipment temperature data, and switch status data, the status assessment results include a comprehensive risk index and operating status level, and the fault diagnosis results include fault type, matching distance, and fault confidence; and optimize and adjust the weighting coefficients and fault feature model library based on historical operating data and historical fault data in the database.

[0018] Based on the first aspect, in step S1, the switch state value... If it is a binary variable, This indicates that the switch is closed. This indicates that the switch is off.

[0019] Based on the first aspect, step S2 includes: filtering the multi-source electrical data using a combination of moving average filtering and low-pass filtering to reduce random noise and high-frequency interference; then, uniformly calibrating the filtered multi-source electrical data using a time synchronization unit to align all the multi-source electrical data on the same time axis; then, identifying and removing abnormal data points in the uniformly calibrated multi-source electrical data using a preset statistical threshold; finally, normalization processing is performed, specifically including:

[0020] For the supply voltage value Define voltage deviation coefficient This is used to indicate the relative deviation of the actual voltage from the rated voltage. ,in, Indicates the rated supply voltage value;

[0021] For operating current value Considering the different rated currents under different operating conditions, a current load factor is defined. It is used to reflect the ratio of the current load level to the rated load. ,in Indicates the first The rated current value corresponding to each sampling time under the operating condition;

[0022] For equipment temperature value Define the temperature rise normalization coefficient. This is used to reflect the position of the equipment temperature relative to the allowable temperature range. ,in, Indicates ambient temperature; Indicates the maximum allowable operating temperature of the equipment;

[0023] For switch status values Through formula Calculate the change in switching state between adjacent time points. ,in This represents the switch state value at the (i-1)th sampling time, where the change in switch state is... The value is 1 when a switching action occurs, and 0 otherwise, which is used to reflect the frequency of the control system's actions.

[0024] Based on the first aspect, step S4 specifically includes: using the formula By performing weighted fusion, a comprehensive risk index is obtained. ,in, Indicates the risk of voltage anomalies The weighting coefficients, Indicates the risk of current overload. The weighting coefficients, Indicates the risk of temperature rise The weighting coefficients, Indicates the risk level of switch operation The weight coefficients satisfy the weight normalization condition, i.e. Based on comprehensive risk indicators The operating status of the electrical system of lifting machinery and equipment Quantitative description is performed, and states are divided according to preset thresholds, i.e. ;in This indicates the preset threshold for early warning. This indicates the preset threshold for the fault.

[0025] Based on the first aspect, step S5 specifically includes:

[0026] When the operating status of the electrical system of the lifting machinery equipment In case of early warning or fault conditions, based on the voltage anomaly risk level. Current overload risk Risk of temperature rise and the risk of switching action Constructed as a risk vector , This is then matched with the fault feature vectors in a pre-established fault feature model library. For the first fault feature vector in the fault feature model library... Fault class, fault feature vector for ; Indicates the first Fault feature vectors corresponding to different types of faults; Indicates the fault type number. Indicates the total number of fault types; Indicates the first Voltage anomaly characteristic values ​​corresponding to this type of fault. Indicates the first The current overload characteristic value corresponding to this type of fault. Indicates the first Temperature rise characteristic values ​​corresponding to this type of fault; Indicates the first The characteristic values ​​of the switching action corresponding to the fault type are then determined by the formula. Calculate the distance between the current state and each fault mode. This is used as the degree of matching, and the fault type with the smallest distance is selected. As a diagnostic result, Combined with comprehensive risk indicators Matching results Calculate the fault confidence level , It is used to measure the reliability of fault determination results.

[0027] Based on the first aspect, a fault feature model library is constructed using historical operational data and fault samples, specifically including the following steps:

[0028] Step 1: Extract normal operation data and fault operation data from the historical operation database of the electrical system of the lifting machinery and equipment. The fault operation data includes historical sample data corresponding to overload faults, short circuit or overcurrent faults, insulation deterioration faults, control abnormality faults, and poor contact faults. The historical sample data includes power supply voltage data, operating current data, equipment temperature data, and switch status data. At the same time, the corresponding operating conditions, fault type, and maintenance confirmation results at the time of the fault are recorded.

[0029] The second step is to preprocess the acquired historical sample data, including filtering and noise reduction, time synchronization, outlier removal, and normalization.

[0030] The third step is to extract features from the preprocessed historical sample data to obtain feature data reflecting the operating status of the electrical system of the lifting machinery and equipment, including voltage anomaly features, current load features, temperature rise features, and switch action features. Statistical analysis is then performed on the feature data corresponding to multiple historical samples under the same fault type to calculate the corresponding distribution range, average level, and variation pattern, forming standard feature description information.

[0031] Step 4: Classify and store the standard feature description information to form a fault feature model library; then, during the operation of the electrical system of the lifting machinery equipment, add the newly added fault data and manual confirmation results to the fault feature model library for updating, and regularly update and correct the feature data.

[0032] Secondly, this application discloses a monitoring and diagnostic system for electrical safety faults in lifting machinery and equipment, which utilizes the aforementioned monitoring and diagnostic method for electrical safety faults in lifting machinery and equipment, including:

[0033] The data acquisition module is used to continuously acquire multi-source electrical data of the electrical system of the lifting machinery equipment at a preset sampling frequency within a preset monitoring time window, including power supply voltage value, operating current value, equipment temperature value and switch status value;

[0034] The data preprocessing module is used to preprocess the multi-source electrical data collected by the data acquisition module to obtain a standardized data sequence with a unified time series.

[0035] The feature extraction module is used to calculate four risk quantities to characterize the operational risks of the electrical system of lifting machinery and equipment based on the standardized data sequence obtained by the data preprocessing module. These include voltage anomaly risk quantity, current overload risk quantity, temperature rise risk quantity, and switch action risk quantity.

[0036] The status assessment module is used to perform weighted fusion or mapping calculations on the voltage anomaly risk, current overload risk, temperature rise risk and switch action risk extracted by the feature extraction module to construct electrical operation status assessment indicators and quantitatively assess the current operation status of the electrical system of lifting machinery and equipment.

[0037] The fault diagnosis module is used to establish a fault feature model library, match the electrical operating status evaluation index and its corresponding multi-dimensional feature parameters with the fault feature model library, and determine and identify the corresponding electrical safety fault type.

[0038] The alarm output module is used to output alarm information based on the identified fault type and its severity, and to record and transmit the data remotely.

[0039] The beneficial effects of this invention are:

[0040] 1) This application constructs a multi-source electrical data acquisition and fusion analysis mechanism to uniformly process multiple electrical parameters such as power supply voltage, operating current, equipment temperature, and switch status, and conducts comprehensive analysis on the same time scale. Compared with the existing method of threshold judgment based on only a single parameter, it can more comprehensively reflect the operating status of the lifting machinery electrical system, thereby effectively improving the accuracy of abnormal status identification and reducing false alarms or missed alarms caused by fluctuations of a single parameter.

[0041] 2) This application constructs a comprehensive risk index for electrical operation by weighted fusion of standardized multidimensional feature parameters, and realizes hierarchical judgment of operating status based on the risk index. This transforms the electrical system from the traditional binary judgment of "whether it is abnormal" to a multi-level status identification mode of "normal-early warning-fault". This enables early warning of potential risks before a fault occurs, improves the safety and operational stability of the system, and provides more valuable status information for equipment maintenance.

[0042] 3) This application establishes a fault feature model library and uses the matching relationship between risk vectors and various fault modes for analysis to achieve automatic identification and confidence assessment of electrical fault types. Compared with existing systems that can only provide alarm information, it can clearly distinguish different fault types such as overload, short circuit, insulation degradation and control abnormality, thereby providing maintenance personnel with more intuitive and accurate fault location basis, reducing troubleshooting time, improving maintenance efficiency, and having the ability to continuously optimize the diagnostic model through historical data. Attached Figure Description

[0043] Figure 1 This is a schematic diagram illustrating the steps of a method for monitoring and diagnosing electrical safety faults in lifting machinery according to an embodiment of the present invention.

[0044] Figure 2 This is a schematic diagram of the structure of a monitoring and diagnosis system for electrical safety faults in lifting machinery according to an embodiment of the present invention. Detailed Implementation

[0045] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] For example, this application discloses a method for monitoring and diagnosing electrical safety faults in lifting machinery and equipment, the steps of which are illustrated in the diagram below. Figure 1 As shown, the specific steps include:

[0047] S1. Continuously collect multi-source electrical data of the electrical system of lifting machinery and equipment at a preset sampling frequency;

[0048] S2. Preprocess the multi-source electrical data collected in step S1 to obtain a standardized data sequence;

[0049] S3. Based on the standardized parameters obtained in step S2, calculate the four risk quantities used to characterize the operational risk of the electrical system of lifting machinery and equipment.

[0050] S4. The four risk quantities obtained in step S3 are weighted and integrated to obtain a comprehensive risk index. Then, based on the comprehensive risk index and the preset threshold, the operating status of the electrical system of the lifting machinery equipment is divided into normal status, early warning status and fault status.

[0051] S5. When the system is in a warning state or a fault state, the four risk quantities are constructed into a risk vector by weighting coefficients. The risk vector is matched with the fault feature vector in the pre-established fault feature model library to obtain the matching degree, and the fault type with the smallest distance is selected as the diagnosis result.

[0052] S6. Output alarm information based on the diagnostic results of step S5, and store the operating data, status assessment results, fault diagnosis results, and alarm records in the database; wherein, the operating data includes power supply voltage data, operating current data, equipment temperature data, and switch status data, the status assessment results include a comprehensive risk index and operating status level, and the fault diagnosis results include fault type, matching distance, and fault confidence; and based on the historical operating data and historical fault data in the database, optimize and adjust the weighting coefficients and fault feature model library to improve the system's diagnostic accuracy and environmental adaptability.

[0053] For example, step S1 specifically includes: in a length of Within the monitoring time window, multi-source electrical data of the lifting machinery equipment's electrical system are continuously collected at a preset sampling frequency, including power supply voltage values. Operating current value Equipment temperature value and switch status (insulation resistance or insulation status data and values ​​in the control system) values Where i represents the index value of the sampling time, and the sampling period is set between 0.5 seconds and 2 seconds to balance real-time performance and data stability. The switch state value... If it is a binary variable, This indicates that the switch is closed. This indicates that the switch is off.

[0054] For example, to avoid bias in the results caused by direct fusion, the original data is first standardized. Step S2 includes: filtering the multi-source electrical data using a combination of moving average filtering and low-pass filtering to reduce random noise and high-frequency interference; then, uniformly calibrating the filtered multi-source electrical data using a time synchronization unit to align all the multi-source electrical data on the same time axis; then, identifying and removing outlier data points in the uniformly calibrated multi-source electrical data using a preset statistical threshold (e.g., based on the mean and standard deviation range of historical data); finally, normalization is performed to map the data to a uniform numerical range for subsequent fusion calculations. The normalization process specifically includes:

[0055] For the supply voltage value Define voltage deviation coefficient This is used to indicate the relative deviation of the actual voltage from the rated voltage. ,in, Indicates the rated supply voltage value;

[0056] For operating current value Considering the different rated currents under different operating conditions, a current load factor is defined. It is used to reflect the ratio of the current load level to the rated load. ,in Indicates the first The rated current value corresponding to each sampling time under the operating condition;

[0057] For equipment temperature value Define the temperature rise normalization coefficient. This is used to reflect the position of the equipment temperature relative to the allowable temperature range. ,in, Indicates ambient temperature; Indicates the maximum allowable operating temperature of the equipment;

[0058] For switch status values Through formula Calculate the change in switching state between adjacent time points. ,in This represents the switch state value at the (i-1)th sampling time, where the change in switch state is... The value is 1 when a switching action occurs, and 0 otherwise, which is used to reflect the frequency of the control system's actions.

[0059] For example, after obtaining the above standardized parameters, statistical processing is further performed on various parameters to construct a single indicator that can characterize the operational risk of the system; step S3 specifically includes:

[0060] For voltage deviation coefficient Through formula Calculate the risk of voltage anomalies This value reflects the overall degree to which the voltage deviates from the rated value within the current time window; the larger the value, the worse the power supply stability.

[0061] For current load factor Considering the portion exceeding the rated value, the formula is used. Calculate the risk of current overload Used to measure the degree of overload; the higher the value, the more severe the overload of the electrical system of the lifting machinery and equipment.

[0062] For the temperature rise normalization coefficient Through formula Calculate the risk of temperature rise This is used to reflect the heat load level of the equipment within the current time window;

[0063] For the change in switch state Through formula Calculate the risk of switch action This is used to reflect the frequency of operations of the control system, such as the risk of switching actions. If the preset switch action threshold is exceeded, it indicates that the switch control is abnormal or the contact is poor.

[0064] For example, step S4 specifically includes: using the formula By performing weighted fusion, a comprehensive risk index is obtained. ,in, Indicates the risk of voltage anomalies The weighting coefficients, Indicates the risk of current overload. The weighting coefficients, Indicates the risk of temperature rise The weighting coefficients, Indicates the risk level of switch operation The weight coefficients satisfy the weight normalization condition, i.e. Based on comprehensive risk indicators The operating status of the electrical system of lifting machinery and equipment Quantitative description is performed, and states are divided according to preset thresholds, i.e. ;in This indicates the preset threshold for early warning. This indicates the preset threshold for the fault.

[0065] For example, step S5 specifically includes:

[0066] When the operating status of the electrical system of the lifting machinery equipment In case of early warning or fault conditions, based on the voltage anomaly risk level. Current overload risk Risk of temperature rise and the risk of switching action Constructed as a risk vector , This is then matched with the fault feature vectors in a pre-established fault feature model library. For the first fault feature vector in the fault feature model library... Fault class, fault feature vector for ; Indicates the first Fault feature vectors corresponding to different types of faults; Indicates the fault type number, and Indicates the total number of fault types; Indicates the first Voltage anomaly characteristic values ​​corresponding to this type of fault. Indicates the first The current overload characteristic value corresponding to this type of fault. Indicates the first Temperature rise characteristic values ​​corresponding to this type of fault; Indicates the first The characteristic values ​​of the switching action corresponding to the fault type are then determined by the formula. Calculate the distance between the current state and each fault mode. This is used as the degree of matching, and the fault type with the smallest distance is selected. As a diagnostic result, Combined with comprehensive risk indicators Matching results Calculate the fault confidence level , This value is used to measure the reliability of fault determination results. The larger the value, the higher the system risk and the clearer the matching result.

[0067] For example, the fault feature model library is constructed in the form of feature parameter combination rules or historical fault sample data models, including overload fault modes, short circuit or overcurrent fault modes, insulation degradation fault modes, control anomaly fault modes, and poor contact fault modes. Different fault types correspond to different feature combination modes. For example, overload faults are usually characterized by persistently high current and a significant increase in temperature; short circuit or overcurrent faults are characterized by a sudden increase in current and a decrease in voltage; insulation degradation faults are characterized by a gradual decrease in insulation parameters; and control anomaly faults are characterized by abnormal switching frequency, etc.

[0068] For example, the specific construction process of the fault feature model library includes the following steps:

[0069] Step 1: First, extract normal operation data and fault operation data from the historical operation database of the lifting machinery. The fault operation data includes at least historical sample data corresponding to overload faults, short circuit or overcurrent faults, insulation deterioration faults, control abnormality faults, and poor contact faults. The historical sample data includes power supply voltage data, operating current data, equipment temperature data, and switch status data. At the same time, the operating conditions, fault type, and maintenance confirmation results at the time of the corresponding fault are recorded.

[0070] The second step is to preprocess the acquired historical sample data using the same data processing methods as in step S2, including filtering and noise reduction, time synchronization, outlier removal, and normalization, to ensure that the historical data and real-time running data have a unified data scale and data structure, thereby avoiding deviations in subsequent diagnostic results due to differences in data sources.

[0071] The third step: After completing the data preprocessing, further feature extraction is performed on the historical sample data to obtain characteristic data reflecting the operating status of the electrical system, including voltage anomaly characteristics, current load characteristics, temperature rise characteristics, and switch action characteristics. Statistical analysis is then performed on the characteristic data corresponding to multiple historical samples under the same fault type. By calculating the typical distribution range, average level, and variation patterns of various characteristic data under the same fault state, corresponding standard characteristic description information is formed.

[0072] Step 4: Classify and store the standard feature description information to form a fault feature model library. Overload faults typically correspond to persistently high current accompanied by a significant increase in temperature; short circuit or overcurrent faults typically manifest as a sudden surge in current and a drop in voltage; insulation degradation faults typically manifest as a gradual decrease in insulation performance accompanied by a slow increase in temperature; control anomaly faults typically manifest as abnormal switching frequency or intervals; poor contact faults typically manifest as increased voltage fluctuations and unstable control status. During the long-term operation of the electrical system of the lifting machinery, newly added fault data and manual confirmation results are continuously added to the fault feature model library, and each fault feature is regularly updated and corrected. This allows the fault feature model library to adaptively optimize itself according to changes in operating conditions and equipment status, thereby improving fault identification accuracy and system adaptability.

[0073] For example, this application discloses a monitoring and diagnosis system for electrical safety faults in lifting machinery and equipment. Utilizing the aforementioned method for monitoring and diagnosing electrical safety faults in lifting machinery and equipment, the system is integrated within a control cabinet and communicates with a remote monitoring platform via industrial Ethernet. A schematic diagram of the system is shown below. Figure 2As shown, it includes a data acquisition module, a data preprocessing module, a feature extraction module, a status assessment module, a fault diagnosis module, and an alarm output module. Each module runs in an embedded controller or industrial computer and transmits data through a unified data bus.

[0074] For example, the data acquisition module is used to continuously or periodically acquire multi-source electrical data during the operation of the electrical system of the lifting machinery at a preset sampling frequency; the data acquisition module includes a voltage sensor, a current sensor, a temperature sensor, and an insulation monitoring device. The voltage sensor is set at the power input terminal to acquire the power supply voltage in real time; the current sensor is arranged in the motor drive circuit to acquire the load current change; the temperature sensor is arranged in the motor housing and inside the control cabinet to monitor the temperature rise of the equipment; the insulation monitoring device is connected to the critical electrical circuit to monitor the change of insulation performance; and the switch status acquisition unit is connected to the control circuit to record the action information of switching elements such as relays and contactors.

[0075] For example, the data preprocessing module is used to perform filtering and noise reduction, time synchronization, outlier removal and normalization on the multi-source electrical data to obtain a standardized data sequence with unified time sequence; it includes a filtering unit, a time synchronization unit and a normalization processing unit, wherein the filtering unit adopts one or more of the following methods: moving average filtering, low-pass filtering or Kalman filtering, to eliminate random noise and transient interference signals generated during the acquisition process.

[0076] For example, the feature extraction module is used to extract multi-dimensional feature parameters reflecting the electrical operating status based on the standardized data sequence, including voltage anomaly risk. Current overload risk Risk of temperature rise and the risk of switching action For current data, extract its variation amplitude and fluctuation frequency per unit time to determine the voltage anomaly risk level. Characterize load changes; for voltage data, calculate the degree of deviation from the rated voltage and the fluctuation range, to assess the risk of current overload. Reflecting power supply stability; for temperature data, analyzing its changing trends and the rate of temperature rise per unit time, in order to assess the risk of temperature rise. Reflects the thermal state of the equipment; for switch status data, it counts the number of actions and the time interval between actions to assess the risk of switch actions. It reflects the operating frequency and stability of the control system.

[0077] For example, the status assessment module assigns different weights to each feature based on its importance to electrical safety and calculates a comprehensive assessment index to obtain the current operational health status of the system. Based on preset threshold ranges, the operating status is divided into normal, warning, and fault states. When the assessment result enters the warning or fault range, subsequent diagnostic procedures are triggered. The weights of each multi-dimensional feature parameter are determined through training based on historical operating data and fault sample data or set according to empirical rules to reflect the degree of influence of different features on the electrical operating status.

[0078] For example, the fault diagnosis module is used to match the electrical operating status evaluation indicators and corresponding multi-dimensional feature parameters with a preset fault feature model to determine and identify the corresponding electrical safety fault type. The fault feature model library is constructed in the form of feature parameter combination rules or historical fault sample data models. The model library includes at least overload fault mode, short circuit or overcurrent fault mode, insulation degradation fault mode, control abnormality fault mode, and poor contact fault mode. In this embodiment, the similarity between the current multi-dimensional feature parameters and each fault mode is calculated based on distance metric or similarity matching algorithm, and the fault mode with the highest similarity is selected as the diagnosis result.

[0079] For example, the alarm output module is used to output alarm information according to the identified fault type and its severity, and to record and remotely transmit data; it includes an audible and visual alarm unit and a remote communication unit, wherein the remote communication unit is used to send fault type information, fault level information and handling suggestions to the remote monitoring platform, and to store historical operating data and fault records.

[0080] The above description is merely a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the concept described herein through the above teachings or related technologies or knowledge. Modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A method for monitoring and diagnosing electrical safety faults in lifting machinery, characterized in that, Includes the following steps: S1, in a length of Within the monitoring time window, multi-source electrical data of the lifting machinery equipment's electrical system are continuously collected at a preset sampling frequency, including power supply voltage values. Operating current value Equipment temperature value and switch status value , where i represents the index value at the sampling time; S2. Preprocess the multi-source electrical data collected in step S1 to obtain a standardized data sequence; S3. Based on the standardized parameters obtained in step S2, calculate four risk quantities used to characterize the operational risk of the electrical system of lifting machinery and equipment, including the voltage anomaly risk quantity. Current overload risk Risk of temperature rise and the risk of switching action ; Specifically, it includes: For voltage deviation coefficient Through formula Calculate the risk of voltage anomalies This is used to reflect the overall degree to which the voltage deviates from the rated value within the current time window; For current load factor Considering the portion exceeding the rated value, the formula is used. Calculate the risk of current overload Used to measure the degree of overloading; For the temperature rise normalization coefficient Through formula Calculate the risk of temperature rise This is used to reflect the heat load level of the equipment within the current time window; For the change in switch state Through formula Calculate the risk of switch action This is used to reflect the frequency of operations of the control system, such as the risk of switching actions. If the preset switch action threshold is exceeded, it indicates abnormal switch control or poor contact. S4. The four risk quantities obtained in step S3 are weighted and integrated to obtain a comprehensive risk index. Then, based on the comprehensive risk index and the preset threshold, the operating status of the electrical system of the lifting machinery equipment is divided into normal status, early warning status and fault status. S5. When the system is in a warning state or a fault state, the four risk quantities are constructed into a risk vector by weighting coefficients. The risk vector is matched with the fault feature vector in the pre-established fault feature model library to obtain the matching degree, and the fault type with the smallest distance is selected as the diagnosis result. S6. Output alarm information based on the diagnostic results of step S5, and store the operating data, status assessment results, fault diagnosis results, and alarm records in the database; wherein, the operating data includes power supply voltage data, operating current data, equipment temperature data, and switch status data, the status assessment results include a comprehensive risk index and operating status level, and the fault diagnosis results include fault type, matching distance, and fault confidence; and optimize and adjust the weighting coefficients and fault feature model library based on historical operating data and historical fault data in the database.

2. The method for monitoring and diagnosing electrical safety faults in lifting machinery and equipment according to claim 1, characterized in that: In step S1, the switch state value If it is a binary variable, then This indicates that the switch is closed. This indicates that the switch is off.

3. The method for monitoring and diagnosing electrical safety faults in lifting machinery and equipment according to claim 2, characterized in that, Step S2 includes: filtering the multi-source electrical data using a combination of moving average filtering and low-pass filtering to reduce random noise and high-frequency interference; then, uniformly calibrating the filtered multi-source electrical data using a time synchronization unit to align all the multi-source electrical data on the same time axis; next, identifying and removing outlier data points in the uniformly calibrated multi-source electrical data using a preset statistical threshold; finally, normalization processing is performed, specifically including: For the supply voltage value Define voltage deviation coefficient This is used to indicate the relative deviation of the actual voltage from the rated voltage. ,in, Indicates the rated supply voltage value; For operating current value Considering the different rated currents under different operating conditions, a current load factor is defined. It is used to reflect the ratio of the current load level to the rated load. ,in Indicates the first The rated current value corresponding to each sampling time under the operating condition; For equipment temperature value Define the temperature rise normalization coefficient. It is used to reflect the position of the equipment temperature relative to the allowable temperature range. ,in, Indicates ambient temperature; Indicates the maximum allowable operating temperature of the equipment; For switch status values Through formula Calculate the change in switching state between adjacent time points. ,in This represents the switch state value at the (i-1)th sampling time, where the change in switch state is... The value is 1 when a switching action occurs, and 0 otherwise, which is used to reflect the frequency of the control system's actions.

4. The method for monitoring and diagnosing electrical safety faults in lifting machinery and equipment according to claim 3, characterized in that, Step S4 specifically includes: using the formula By performing weighted fusion, a comprehensive risk index is obtained. ,in, Indicates the risk of voltage anomalies The weighting coefficients, Indicates the risk of current overload. The weighting coefficients, Indicates the risk of temperature rise The weighting coefficients, Indicates the risk level of switch operation The weight coefficients satisfy the weight normalization condition, i.e. Based on comprehensive risk indicators The operating status of the electrical system of lifting machinery and equipment Quantitative description is performed, and states are divided according to preset thresholds, i.e. ;in This indicates the preset threshold for early warning. This indicates the preset threshold for the fault.

5. The method for monitoring and diagnosing electrical safety faults in lifting machinery and equipment according to claim 4, characterized in that, Step S5 specifically includes: When the operating status of the electrical system of the lifting machinery equipment In case of early warning or fault conditions, based on the voltage anomaly risk level. Current overload risk Risk of temperature rise and the risk of switching action Constructed as a risk vector , This is then matched with the fault feature vectors in a pre-established fault feature model library. For the first fault feature vector in the fault feature model library... Fault class, fault feature vector for ; Indicates the first Fault feature vectors corresponding to different types of faults; Indicates the fault type number. Indicates the total number of fault types; Indicates the first Voltage anomaly characteristic values ​​corresponding to this type of fault. Indicates the first The current overload characteristic value corresponding to this type of fault. Indicates the first Temperature rise characteristic values ​​corresponding to this type of fault; Indicates the first The characteristic values ​​of the switching action corresponding to the fault type are then determined by the formula. Calculate the distance between the current state and each failure mode. This is used as the degree of matching, and the fault type with the smallest distance is selected. As a diagnostic result, Combined with comprehensive risk indicators Matching results Calculate the fault confidence level , It is used to measure the reliability of fault determination results.

6. The method for monitoring and diagnosing electrical safety faults in lifting machinery and equipment according to claim 5, characterized in that: The fault feature model library is constructed using historical operational data and fault samples, specifically including the following steps: Step 1: Extract normal operation data and fault operation data from the historical operation database of the electrical system of the lifting machinery and equipment. The fault operation data includes historical sample data corresponding to overload faults, short circuit or overcurrent faults, insulation deterioration faults, control abnormality faults, and poor contact faults. The historical sample data includes power supply voltage data, operating current data, equipment temperature data, and switch status data. At the same time, the corresponding operating conditions, fault type, and maintenance confirmation results at the time of the fault are recorded. The second step is to preprocess the acquired historical sample data, including filtering and noise reduction, time synchronization, outlier removal, and normalization. The third step is to extract features from the preprocessed historical sample data to obtain feature data reflecting the operating status of the electrical system of the lifting machinery and equipment, including voltage anomaly features, current load features, temperature rise features, and switch action features. Statistical analysis is then performed on the feature data corresponding to multiple historical samples under the same fault type to calculate the corresponding distribution range, average level, and variation pattern, forming standard feature description information. Step 4: Classify and store the standard feature description information to form a fault feature model library; then, during the operation of the electrical system of the lifting machinery equipment, add the newly added fault data and manual confirmation results to the fault feature model library for updating, and regularly update and correct the feature data.

7. A monitoring and diagnostic system for electrical safety faults in lifting machinery, employing the monitoring and diagnostic method for electrical safety faults in lifting machinery as described in any one of claims 1-6, characterized in that, include: The data acquisition module is used to continuously acquire multi-source electrical data of the electrical system of the lifting machinery equipment at a preset sampling frequency within a preset monitoring time window, including power supply voltage value, operating current value, equipment temperature value and switch status value; The data preprocessing module is used to preprocess the multi-source electrical data collected by the data acquisition module to obtain a standardized data sequence with a unified time series. The feature extraction module is used to calculate four risk quantities to characterize the operational risks of the electrical system of lifting machinery and equipment based on the standardized data sequence obtained by the data preprocessing module. These include voltage anomaly risk quantity, current overload risk quantity, temperature rise risk quantity, and switch action risk quantity. The status assessment module is used to perform weighted fusion or mapping calculations on the voltage anomaly risk, current overload risk, temperature rise risk and switch action risk extracted by the feature extraction module to construct electrical operation status assessment indicators and quantitatively assess the current operation status of the electrical system of lifting machinery and equipment. The fault diagnosis module is used to establish a fault feature model library, match the electrical operating status evaluation index and its corresponding multi-dimensional feature parameters with the fault feature model library, and determine and identify the corresponding electrical safety fault type. The alarm output module is used to output alarm information based on the identified fault type and its severity, and to record and remotely transmit the data.

Images