Method for monitoring and life prediction of motor for electric hoist based on multi-source data

Abstract

The application provides a motor monitoring and life prediction method for electric hoist based on multi-source data, which comprises the following steps: S1: generating an automatic test instruction sequence according to a national standard work system, and remotely controlling a test bench to perform an unattended cycle life test; S2: synchronously collecting multi-element time series data through a multi-source sensor; S3: transmitting the multi-element time series data to a remote server; S4: performing feature extraction and calculation processing by the remote server to obtain processed feature data; S5: constructing a multi-dimensional damage factor set, and calculating a health state index and a residual life; S6: dynamically evaluating a performance residual value; and S7: generating state information or a multi-level early warning and pushing. The application realizes automatic and unmanned operation of the motor life test for the electric hoist, solves problems such as artificial dependence, single data, one-sided evaluation, early warning lag and the like in the traditional test, and improves test standardization and operation and maintenance efficiency.

Description

Technical Field

[0001] This invention relates to the field of electromechanical equipment testing and maintenance technology, and in particular to a method for monitoring and life prediction of electric motors used in electric hoists based on multi-source data. Background Technology

[0002] As the core power component of electric hoists, the long-term reliability of the electric motor is a key indicator of the overall quality of the hoist. Currently, the main method used in the industry to verify the lifespan and reliability of electric hoist motors is to conduct cyclic operation tests lasting hundreds to thousands of hours on a dedicated test bench, based on the duty cycles specified in national standards. However, existing testing and evaluation methods have the following limitations:

[0003] First, the testing process suffers from low levels of automation and intelligence. Traditional testing relies heavily on manual supervision, requiring operators to manually control equipment startup and shutdown and record key data. This approach is labor-intensive and makes it difficult to guarantee the accuracy and consistency of continuous 24-hour testing.

[0004] Secondly, existing technologies for monitoring the testing process are mostly limited to simple data collection and threshold alarms for a few parameters such as temperature and current. This single-point, threshold-based monitoring cannot comprehensively and deeply reflect the overall state of the equipment under complex cyclic stress. The assessment of performance degradation often relies on destructive testing or performance retesting after the test, resulting in severely delayed results and failing to provide forward-looking insights during the testing process.

[0005] Furthermore, the industry lacks effective means to quantitatively and model-based predict the remaining service life of equipment during testing. It is unable to integrate multi-source and heterogeneous monitoring data into a unified quantitative indicator of equipment health loss, and cannot dynamically assess the residual value of equipment performance based on this. As a result, product reliability design optimization lacks data support, and asset management and maintenance decisions lack a scientific basis. Summary of the Invention

[0006] To address the aforementioned technical problems, this application proposes a method for monitoring and predicting the lifespan of electric motors used in electric hoists based on multi-source data, comprising the following steps:

[0007] S1: Based on the preset national standard working system, generate an automated test instruction sequence and remotely send it to the electric hoist motor life test bench to control the electric hoist motor life test bench to perform unattended cyclic life test.

[0008] The electric hoist motor life test bench is a dedicated life verification test device built according to the national electric motor duty cycle standard for electric hoist motors. It provides a cyclic operation test environment that meets standard working conditions for electric hoist motors and is the hardware carrier for realizing the life test of electric hoist motors.

[0009] Based on the preset national standard working system, the core test requirements are analyzed, and an appropriate sequence of automated test instructions is generated. These instructions are then sent to the local controller of the electric hoist motor life test bench via remote communication, driving the electric hoist motor to automatically execute continuous start-stop and load cycles, ensuring that the test process strictly conforms to the specifications of the national standard working system.

[0010] The core test requirements include load cycle, duty cycle, and cycle requirements;

[0011] Furthermore, the automated test instruction sequence includes a start time, runtime, and stop time; the instruction format of the automated test instruction sequence is adapted to the local controller of the electric hoist motor life test bench.

[0012] By using the national standard working system to drive automated instruction generation, deviations in working conditions caused by manual operation are avoided, ensuring the standardization of the test process and the authenticity of the data; the MQTT protocol ensures the real-time and reliability of instruction transmission, enabling 24-hour unattended cyclic testing, significantly reducing labor costs and solving the problems of traditional testing relying on manual labor and being inefficient.

[0013] S2: During the execution of the cycle life test, multi-source time-series data reflecting the performance of the electric hoist motor are synchronously and in real time collected by multi-source sensors deployed on the electric hoist motor life test bench.

[0014] During the execution of the cycle life test, multi-source sensors deployed on the electric hoist motor life test bench collect multi-dimensional time-series data that can reflect the operating performance and status of the electric hoist at a preset frequency, ensuring the continuity, synchronization and integrity of data collection.

[0015] Furthermore, the multi-source sensor includes a temperature sensor, a current transformer, a voltage sensor, a vibration acceleration sensor, and a noise sensor, wherein the noise sensor has a built-in A-weighted filter.

[0016] The multi-dimensional time-series data includes parameters such as motor winding temperature. Continuous time-domain signals of three-phase operating current and three-phase operating voltage, continuous time-domain signal of vibration acceleration, and A-weighted sound pressure level. .

[0017] Multi-source sensors synchronously collect time-series data on electrical, temperature rise, and mechanical dimensions, covering the core operating status parameters of the electric motor used in electric hoists, ensuring the continuity and integrity of data collection; providing comprehensive and reliable raw data support for subsequent multi-source data fusion and damage assessment, and solving the shortcomings of traditional data collection which is limited in scope and lacks complete information.

[0018] S3: Transmit the multi-dimensional time-series data to a remote server in real time via Internet of Things (IoT) communication.

[0019] The multi-dimensional time-series data is transmitted in real time to a remote server via IoT communication, enabling cross-space data transmission and real-time synchronization, and providing data support for subsequent data processing, analysis and evaluation.

[0020] S4: The remote server receives and stores the multivariate time-series data, and performs feature extraction and calculation on the multivariate time-series data to obtain processed feature data and stores it;

[0021] The remote server receives multi-dimensional time-series data transmitted via the Internet of Things and preprocesses it to obtain processing feature data. Then, it uses a secure and reliable storage method to persistently store the processing feature data to ensure that the data is not lost and is traceable.

[0022] Furthermore, the specific steps for feature extraction and computation to obtain processed feature data include:

[0023] The continuous time-domain signals of the three-phase operating current and three-phase operating voltage are sampled, and the effective value of the three-phase current and the current imbalance are calculated. Total harmonic distortion of current and average active power ;

[0024] The continuous time-domain signal of vibration acceleration is sampled, and the effective value of vibration is calculated. .

[0025] Specifically:

[0026] Within one power frequency cycle T, the continuous time-domain signals of the three-phase operating current and three-phase operating voltage, as well as the continuous time-domain signal of vibration acceleration, are digitally sampled to calculate the effective value of the A-phase current. The formula is as follows: ,in Let N be the instantaneous value of phase A current at the kth discrete sampling time, and N be the number of sampling points in one power frequency cycle;

[0027] Similarly, the other two phases, namely phase B and phase C, are respectively... and The effective value of phase B current was calculated. and the effective value of C-phase current , , These are the instantaneous values ​​of phase B current and phase C current at the k-th discrete sampling time, respectively.

[0028] Through formula = The effective value of vibration was calculated. ,in Let be the instantaneous value of vibration acceleration at the k-th discrete sampling time.

[0029] Current imbalance The ratio of the maximum deviation to the average value of the effective value of the three-phase operating current is used, and the formula is: 100%;

[0030] in, for The maximum value in, for The minimum value in.

[0031] After digitally sampling the continuous time-domain signal of the three-phase operating current, a Fast Fourier Transform (FFT) is used for spectral analysis to extract the effective value of the fundamental current from the spectrum. and the effective values ​​of each harmonic current; subsequently, according to the formula

[0032] 100% calculation of total harmonic distortion of current , The effective value of the nth harmonic current. is the effective value of the fundamental current, and N is the highest harmonic order considered.

[0033] This invention simply applies the Fast Fourier Transform without improving its underlying algorithm; therefore, its specific principles will not be elaborated upon further.

[0034] By comprehensively utilizing the three-phase operating current and three-phase operating voltage, the current imbalance is calculated. and total harmonic distortion of current These parameters reflect the power quality and the internal electrical asymmetry of the motor.

[0035] Calculate the average active power using the instantaneous values ​​of the three-phase operating voltage and the three-phase operating current. The formula is: ;

[0036] in , , Let be the instantaneous values ​​of phase A voltage, phase B voltage, and phase C voltage at the k-th discrete sampling time, respectively. , , These are the instantaneous values ​​of phase A current, phase B current, and phase C current at the kth discrete sampling time, respectively.

[0037] By processing the multivariate time-series data, we obtain data including the effective value of the current and the current imbalance. Total harmonic distortion of current Average active power A-weighted sound pressure level and RMS vibration value It processes feature data to improve data quality and analysis efficiency; and the persistent storage design ensures that data is not lost and is traceable, providing accurate and reliable feature data support for subsequent damage factor construction and lifetime prediction.

[0038] S5: Based on the processed feature data, calculate the current health status index and predicted remaining service life of the electric hoist;

[0039] Step S5 further includes the following sub-steps:

[0040] S51. Calculate the processed feature data to construct a multidimensional damage factor set;

[0041] Regarding motor winding temperature Using formula The temperature rise damage factor was calculated. ;

[0042] in The reference temperature for the motor windings is taken as the rated operating ambient temperature of the motor.

[0043] This represents the maximum permissible operating temperature of the motor winding insulation material, and is an industry standard value.

[0044] Through formula Electrical damage factor was calculated ;

[0045] in, The rated active power of the motor used in electric hoists is obtained directly from the product nameplate or technical manual and reflects the motor's designed output capability.

[0046] According to the formula Calculate the mechanical damage factor ;

[0047] in, The rated allowable value for vibration is obtained from the factory test data of the electric hoist or industry standards, and serves as the benchmark value for vibration damage.

[0048] The noise contribution weighting coefficient is obtained by fitting historical test data and is used to balance the relative contributions of vibration and noise to mechanical damage.

[0049] The A-weighted sound pressure level is the overall operating noise measured at present, which comprehensively reflects the overall noise status of the mechanical components of the electric hoist motor.

[0050] It is obtained by referencing the A-weighted sound pressure level through calibration tests conducted on a brand-new, mechanically undamaged electric hoist under standard no-load conditions.

[0051] Temperature rise damage factor Electrical damage factors and mechanical damage factors These are summarized into a multidimensional set of damage factors.

[0052] S52. Calculate the instantaneous comprehensive damage rate based on the weighted average of the constructed multidimensional damage factor set. ;

[0053] According to the formula Instantaneous comprehensive damage rate was calculated Instantaneous comprehensive damage rate It reflects the instantaneous damage intensity of the electric motor used in the electric hoist at time t.

[0054] in, Let be the weight coefficients of each damage factor, and satisfy the following conditions: =1.

[0055] S53. Regarding the instantaneous comprehensive damage rate By performing integration along the experimental time axis, the cumulative comprehensive damage level from the start of the experiment to the current moment is obtained. ;

[0056] Through formula The cumulative comprehensive damage degree was calculated. The cumulative comprehensive damage The value range is [0,1], which is used to quantify the cumulative damage degree of the electric motor used in electric hoists. The closer the value is to 1, the more serious the cumulative damage degree of the electric motor used in electric hoists.

[0057] in The preset failure damage threshold represents the critical total damage amount from a brand-new state to functional failure. This threshold is determined by the cumulative comprehensive damage level. achieve The motor's lifespan is determined to have ended at this time;

[0058] This represents the integral of the instantaneous comprehensive damage rate over time from the start of the experiment (time 0) to the current time t, and represents the accumulated original damage amount.

[0059] S54. Calculate the current health status index of the electric hoist motor based on the cumulative comprehensive damage degree. With predicted remaining useful life ;

[0060] The health status index The calculation formula is: = 1- The value ranges from [0,1], and the closer the value is to 1, the better the health status of the electric motor used in the electric hoist.

[0061] The predicted remaining useful life The calculation formula is: ,in The design target total lifespan of the electric motor used in electric hoists is determined by the product design indicators; based on the linear correlation between the health status index and the design lifespan, the remaining service life can be accurately predicted.

[0062] This method aims to infer the health status and remaining service life of electric motors used in electric hoists, thereby enabling accurate assessment of the motor's condition and scientific prediction of its lifespan.

[0063] Step S5 constructs a multi-dimensional damage factor set encompassing temperature rise, electrical, and mechanical factors to comprehensively cover the damage types of the core system of the electric motor used in electric hoists, avoiding the one-sidedness of single-dimensional assessment; and based on the comprehensive damage rate and cumulative damage degree calculation of the damage factors, it achieves quantitative characterization of health status and accurate prediction of remaining life.

[0064] S6: Select key performance parameters from the multivariate time-series data and processing feature data, and dynamically evaluate the residual performance value of the electric motor for the electric hoist based on the calculated value and design rated value of the key performance parameters, combined with the predicted remaining service life.

[0065] Based on the multivariate time-series data collected in step S2 and the processing feature data obtained in step S4, key performance output parameters that directly characterize the core working capability of the electric hoist motor are selected. Through weighted fusion calculation, the performance residual value of the electric hoist motor at the current moment is dynamically evaluated and output, quantifying the performance retention degree of the electric hoist motor in the current test stage.

[0066] Furthermore, the performance residual value is expressed as a performance residual value rate. Perform quantitative characterization, A higher value indicates a better retention of the electric motor's performance in the electric hoist. The calculation formula is as follows:

[0067] ;

[0068] in, , and For the weighting coefficients, satisfying ;

[0069] The cumulative effective runtime of the test refers to the effective duration of the cycle life test actually performed by the electric hoist motor from the start of the test to the current moment; k is the number of key performance parameters.

[0070] Furthermore, the key performance parameters include at least the effective value of vibration. Current imbalance and motor winding temperature It covers three aspects: electrical, mechanical, and temperature rise, to ensure a comprehensive assessment.

[0071] The calculated value of the j-th key performance parameter is the data collected in step S2 and calculated in step S4.

[0072] The design rating of the j-th critical performance parameter serves as the benchmark value for performance evaluation, including the permissible vibration rating. Motor winding reference temperature and permissible value of current imbalance ;

[0073] in Determined based on factory test data of electric hoists or industry standards.

[0074] The performance residual value is used to quantify the degree to which the performance of the electric motor used in the electric hoist is retained during the test, and to verify whether it still meets the design requirements.

[0075] The three core key performance parameters of electrical, mechanical and temperature rise are selected and combined with the health status index and remaining service life for weighted integration to achieve dynamic quantitative assessment of performance residual value; the assessment logic covers current operating capacity, cumulative damage status and life reserve potential, and comprehensively reflects the performance retention degree of electric motors used in electric hoists.

[0076] S7: Based on the comparison results of the multivariate time-series data, health status index, performance residual value, and predicted remaining service life with their respective preset safety thresholds, generate status information or multi-level early warning information and push it to the remote terminal.

[0077] The multi-level early warning information includes the following three levels of early warning:

[0078] Level 1 warning: When the instantaneous value of any parameter in the multi-dimensional time-series data exceeds the corresponding preset real-time safety threshold, a real-time fault alarm is triggered; Level 1 warning requires immediate attention and may require remote emergency shutdown;

[0079] Level 2 warning: When the rate of decline of the health status index or performance residual value exceeds the corresponding preset rate of change threshold within a continuous preset time period, a performance degradation warning is triggered; the level 2 warning indicates potential problems and suggests planned maintenance;

[0080] Level 3 warning: When the predicted remaining service life is lower than the preset safe service life threshold, a lifespan pre-termination warning is triggered; Level 3 warning is used for long-term asset management and replacement planning.

[0081] If the comparison results do not meet the triggering conditions for any level of warning, status information is generated, including device identification information, multi-source time-series data collected in real time by multiple sensors, and calculated health status index, predicted remaining service life, and performance residual value.

[0082] Finally, the status information or multi-level early warning information is pushed to the designated remote terminal through remote communication, so as to realize real-time notification of equipment status and timely early warning of risks, which facilitates remote intervention by operation and maintenance personnel.

[0083] This step establishes a three-tiered differentiated early warning mechanism to match different risk scenarios such as real-time failures, performance degradation, and premature end of life, enabling graded response and precise control of risks; status information comprehensively presents the equipment's operating status and evaluation results, and the remote push design ensures that maintenance personnel can obtain information in a timely manner, supports remote intervention, and improves test safety and maintenance efficiency.

[0084] The beneficial effects of the electric hoist remote monitoring and life prediction method based on multi-source data fusion of the present invention are as follows: (1) According to the national standard working system, the precise test instruction sequence is automatically generated and remotely sent to the test bench controller to drive the equipment to automatically perform cyclic tests, replacing manual duty and ensuring that the test process is strict, continuous and reproducible; (2) The system collects and processes multi-dimensional data such as electrical, mechanical and thermal data in a systematic way, and through the original comprehensive damage degree model, these heterogeneous parameters are nonlinearly fused to calculate the health status index and predicted remaining service life that can comprehensively reflect the instantaneous stress and cumulative damage of the equipment; so that the evaluation no longer depends on the single parameter exceeding the limit, but is based on the overall health degradation trend of the equipment to achieve accurate prediction of the equipment health status; (3) By integrating key performance parameters, health status index and predicted remaining service life, a multi-dimensional weighted evaluation logic is formed to ensure that the residual value evaluation result is highly consistent with the actual performance retention degree, and to provide a quantitative basis for the test conclusion; (4) Define multi-level early warning rules from real-time faults, performance degradation to life pre-termination, and push the information to the remote terminal for processing, realizing the refined hierarchical management of risks. Attached Figure Description

[0085] Figure 1 This is a schematic diagram of the overall workflow of the monitoring and life prediction method for electric motors used in electric hoists based on multi-source data according to the present invention. Detailed Implementation

[0086] To provide a further understanding of the purpose, structure, features, and functions of the present invention, detailed descriptions are provided below with reference to specific embodiments.

[0087] Example: Figure 1 As shown, the present invention provides a method for monitoring and predicting the lifespan of an electric motor used in an electric hoist based on multi-source data, comprising the following steps:

[0088] S1: Based on the preset national standard working system, generate an automated test instruction sequence and remotely send it to the electric hoist motor life test bench to control the electric hoist motor life test bench to perform unattended cyclic life test.

[0089] A life test bench for electric hoists conforming to national standards was constructed as the hardware platform for this method. On a remote server, the target national standard working cycle was preset, and the core test requirements, including load cycle, duty cycle, and number of cycles, were analyzed. A suitable automated test instruction sequence was then automatically generated.

[0090] The automated test instruction sequence is a timing program that clearly defines the start time, runtime, and stop time for each loop.

[0091] The automated test command sequence is sent to the local programmable logic controller (PLC) of the electric hoist motor life test bench via a remote communication network. The PLC receives and parses the automated test command sequence, precisely drives the electric hoist to automatically and continuously execute start-stop and load cycles in accordance with national standards, and the entire process requires no manual on-site intervention.

[0092] The remote communication network adopts the MQTT protocol and has a QoS level of 1 to ensure the real-time performance and reliability of the transmission of automated test command sequences.

[0093] S2: During the execution of the cycle life test, multi-source time-series data reflecting the performance of the electric hoist motor are synchronously and in real time collected by multi-source sensors deployed on the electric hoist motor life test bench.

[0094] Multi-source sensors collect multi-dimensional time-series data that reflect the operating performance and status of the electric motor used in electric hoists at preset frequencies, ensuring the continuity, synchronization and integrity of data acquisition.

[0095] Furthermore, the multi-source sensor includes a temperature sensor, a current transformer, a voltage sensor, a vibration acceleration sensor, a laser displacement sensor, and a noise sensor;

[0096] Specifically:

[0097] Temperature is collected by a temperature sensor embedded in the motor windings. ;

[0098] The continuous time-domain signal of the three-phase operating current is acquired through a current transformer.

[0099] The continuous time-domain signal of the three-phase operating voltage is acquired through a voltage sensor.

[0100] A triaxial vibration acceleration sensor mounted on the motor housing is used to collect continuous time-domain vibration acceleration signals.

[0101] A noise sensor with a built-in A-weighted filter, pointing towards the hook's operating area, is used to collect the A-weighted sound pressure level of the overall operating noise, reflecting the overall mechanical operating state of the electric hoist's motor. .

[0102] Data from all sensors is collected synchronously and timestamped to form high-fidelity multi-dimensional time-series data.

[0103] S3: Transmit the multi-dimensional time-series data to a remote server in real time via Internet of Things (IoT) communication.

[0104] The IoT gateway of the electric hoist motor life test bench packages the multi-dimensional time-series data collected in step S2 and transmits it in real time to a remote server deployed on a cloud platform or data center through an enterprise VPN private network or a secure 4G / 5G network. This enables cross-space data transmission and real-time synchronization, providing data support for subsequent data processing, analysis and evaluation.

[0105] S4: The remote server receives and stores the multivariate time-series data, and performs feature extraction and calculation on the multivariate time-series data to obtain processed feature data and stores it;

[0106] After receiving the multi-dimensional time-series data transmitted via the Internet of Things, the remote server first stores the multi-dimensional time-series data in a database for persistent storage. Then, it initiates the feature extraction and computation processing flow.

[0107] Within one power frequency cycle T, the continuous time-domain signals of the three-phase operating current and three-phase operating voltage, as well as the continuous time-domain signal of vibration acceleration, are digitally sampled to calculate the effective value of the A-phase current. The formula is as follows: ,in Let N be the instantaneous value of phase A current at the kth discrete sampling time, and N be the number of sampling points in one power frequency cycle;

[0108] Similarly, the other two phases, namely phase B and phase C, are respectively... and The effective value of phase B current was calculated. and the effective value of C-phase current , , These are the instantaneous values ​​of phase B current and phase C current at the k-th discrete sampling time, respectively.

[0109] Through formula = The effective value of vibration was calculated. ,in Let be the instantaneous value of vibration acceleration at the k-th discrete sampling time.

[0110] Current imbalance The ratio of the maximum deviation to the average value of the effective value of the three-phase operating current is used, and the formula is: 100%;

[0111] in, for The maximum value in, for The minimum value in.

[0112] After digitally sampling the continuous time-domain signal of the three-phase operating current, a Fast Fourier Transform (FFT) is used for spectral analysis to extract the effective value of the fundamental current from the spectrum. and the effective values ​​of each harmonic current;

[0113] Subsequently, according to the formula 100% calculation of total harmonic distortion of current ,in The effective value of the nth harmonic current. is the effective value of the fundamental current, and N is the highest harmonic order considered.

[0114] This invention simply applies the Fast Fourier Transform without improving its underlying algorithm; therefore, its specific principles will not be elaborated upon further.

[0115] By comprehensively utilizing the three-phase operating current and three-phase operating voltage, the current imbalance is calculated. and total harmonic distortion of current These parameters reflect the power quality and the internal electrical asymmetry of the motor.

[0116] Calculate the average active power using the instantaneous values ​​of the three-phase operating voltage and the three-phase operating current. The formula is: ;

[0117] in , , Let be the instantaneous values ​​of phase A voltage, phase B voltage, and phase C voltage at the k-th discrete sampling time, respectively. , , These are the instantaneous values ​​of phase A current, phase B current, and phase C current at the kth discrete sampling time, respectively.

[0118] S5: Based on the processed feature data, calculate the current health status index and predicted remaining service life of the electric hoist motor;

[0119] Specifically, step S5 includes the following sub-steps:

[0120] S51. Calculate the processed feature data to construct a multidimensional damage factor set;

[0121] Regarding motor winding temperature Using formula The temperature rise damage factor was calculated. ;

[0122] in The reference temperature for the motor windings is taken as the rated operating ambient temperature of the motor.

[0123] In this embodiment, the F-class insulation material represents the maximum permissible operating temperature of the motor winding insulation material. The value is 150℃, and it is an H-class insulating material. The value is 180℃.

[0124] Through formula Electrical damage factor was calculated ;

[0125] in, The rated active power of the motor used in electric hoists is obtained directly from the product nameplate or technical manual and reflects the motor's designed output capability.

[0126] According to the formula Calculate the mechanical damage factor ;

[0127] in, The rated allowable value for vibration is obtained from the factory test data of the electric hoist or industry standards, and serves as the benchmark value for vibration damage.

[0128] The noise contribution weighting coefficient is obtained by fitting historical test data and is used to balance the relative contributions of vibration and noise to mechanical damage.

[0129] The A-weighted sound pressure level is the overall operating noise measured at present, which comprehensively reflects the overall noise status of the mechanical components of the electric hoist motor.

[0130] The A-weighted sound pressure level is obtained through calibration tests on brand-new, undamaged electric hoists under standard no-load conditions. The average A-weighted sound pressure level of the newly acquired equipment during no-load operation is used as the noise benchmark for the undamaged state. According to the definition of sound pressure level... and All are dimensionless logarithmic scale values.

[0131] Temperature rise damage factor Electrical damage factors and mechanical damage factors These are summarized into a multidimensional set of damage factors.

[0132] S52. Calculate the instantaneous comprehensive damage rate based on the weighted average of the constructed multidimensional damage factor set. ;

[0133] According to the formula Instantaneous comprehensive damage rate was calculated Instantaneous comprehensive damage rate It reflects the instantaneous damage intensity of the electric motor used in the electric hoist at time t.

[0134] in, Let be the weight coefficients of each damage factor, and satisfy the following conditions: =1; Utilizing the inherent properties of the exponential function, the linear weighted sum of the three damage factors is mapped to a nonlinear damage rate. When the equipment is in good condition, the damage rate is low and grows slowly; when the equipment deteriorates, the damage rate accelerates exponentially.

[0135] S53. Regarding the instantaneous comprehensive damage rate By performing integration along the experimental time axis, the cumulative comprehensive damage level from the start of the experiment to the current moment is obtained. ;

[0136] Through formula The cumulative comprehensive damage degree was calculated. The cumulative comprehensive damage The value range is [0,1], which is used to quantify the cumulative damage degree of the electric motor used in electric hoists. The closer the value is to 1, the more serious the cumulative damage degree of the electric motor used in electric hoists.

[0137] in The preset failure damage threshold represents the critical total damage amount from a brand-new state to functional failure. This threshold is determined by the cumulative comprehensive damage level. achieve The motor's lifespan is determined to have ended at this time;

[0138] This represents the integral of the instantaneous comprehensive damage rate over time from the start of the experiment (time 0) to the current time t, and represents the accumulated amount of original damage.

[0139] This represents the proportion of the equipment's lifespan that has been consumed by time t. Obviously, when the equipment is brand new, i.e., t=0... =0; When the equipment reaches the failure damage threshold. = ,but =1.

[0140] S54. Calculate the current health status index of the electric hoist motor based on the cumulative comprehensive damage degree. With predicted remaining useful life ;

[0141] The health status index The calculation formula is: = 1- The value ranges from [0,1], and the closer the value is to 1, the better the health status of the electric motor used in the electric hoist.

[0142] The predicted remaining useful life The calculation formula is: ,in The target total lifespan of the electric motor used in electric hoists is determined by the product design specifications.

[0143] This method aims to infer the health status and remaining service life of electric motors used in electric hoists, thereby enabling accurate assessment of the motor's condition and scientific prediction of its lifespan.

[0144] S6: Select key performance parameters from the multivariate time-series data and processing feature data, and dynamically evaluate the residual performance value of the electric motor for the electric hoist based on the calculated value and design rated value of the key performance parameters, combined with the predicted remaining service life.

[0145] In this embodiment, the effective value of vibration is selected. Current imbalance and motor winding temperature As key performance parameters, they represent the current state of the three core systems: mechanical, electrical, and thermal.

[0146] The performance residual value is expressed as a performance residual rate. Perform quantitative characterization, A higher value indicates a better retention of the electric motor's performance in the electric hoist. The calculation formula is as follows:

[0147] ;

[0148] in, , and For the weighting coefficients, satisfying ;

[0149] The cumulative effective running time of the test refers to the actual time that the electric hoist motor has been performing the cycle life test from the start of the test to the current moment;

[0150] The calculated value of the j-th key performance parameter is the data collected in step S2 and calculated in step S4.

[0151] The design rating of the j-th critical performance parameter serves as the benchmark value for performance evaluation, including the permissible vibration rating. Motor winding reference temperature and permissible value of current imbalance ;

[0152] in Determined based on factory test data or industry standards for electric motors used in electric hoists.

[0153] The performance residual value rate can be directly used as a quantitative standard to determine whether the motor of the electric hoist still meets the design requirements, effectively avoiding premature termination or invalid continuation of the test due to the lack of clear quantitative indicators, and significantly improving the test efficiency and reliability of the conclusions. From the perspective of operation and maintenance and asset management, the dynamic changes in performance residual value can reveal the trend of equipment performance degradation in advance, providing operation and maintenance personnel with accurate timing for planned maintenance, and providing a scientific basis for the replacement planning of old equipment and asset value assessment, reducing test interruptions or safety risks caused by sudden performance failure. From the perspective of product optimization, through comparative analysis of performance residual values ​​at different test stages, the weak links in the performance of the electric hoist motor can be located in reverse, providing data support for product iteration and upgrading.

[0154] S7: Based on the comparison results of the multivariate time-series data, health status index, performance residual value, and predicted remaining service life with their respective preset safety thresholds, generate status information or multi-level early warning information and push it to the remote terminal.

[0155] A three-tiered early warning rule base was established and connected to a remote terminal APP.

[0156] Level 1 warning: When the instantaneous value of any parameter in the multi-dimensional time-series data exceeds the corresponding preset real-time safety threshold, a real-time fault alarm is triggered;

[0157] Level 2 warning: When the health status index A decrease greater than 0.05 within 24 consecutive hours, or a performance residual value rate A performance degradation warning is triggered when the daily decline rate exceeds 2%.

[0158] Level 3 warning: Triggered when the predicted remaining useful life (RUL) is less than 168 hours.

[0159] If no warning is triggered, the APP interface will normally display status information including device identification information, multi-source time-series data collected in real time by multiple sensors, as well as calculated health status index, predicted remaining service life and performance residual value.

[0160] After receiving the status information, the APP pushes a summary of the information in a non-intrusive way, and the operator can click to view the details;

[0161] After receiving a Level 1 alert, the APP will trigger a pop-up window, vibration, and ringtone notification. It is recommended to immediately shut down the device remotely and provide a quick remote operation entry point. After verifying permissions, commands can be issued.

[0162] After receiving a level-two alert, the app will send vibration and ringtone notifications to indicate performance degradation and provide maintenance suggestions. Operators can view the past 72 hours of data. and Changes in line chart and maintenance recommendations;

[0163] After receiving a Level 3 alert, the app will trigger a pop-up notification indicating that the device's lifespan is about to end. Operators can then view the device details and directly initiate a device disposal request.

[0164] The present invention has been described in the above-described embodiments; however, these embodiments are merely examples for implementing the present invention. It must be noted that the disclosed embodiments do not limit the scope of the present invention. Conversely, any modifications and refinements made without departing from the spirit and scope of the present invention are within the scope of patent protection of the present invention.

Claims

1. A method for monitoring and lifespan prediction of electric motors used in electric hoists based on multi-source data, characterized in that, Includes the following steps: S1: Based on the preset national standard working system, generate an automated test instruction sequence and remotely send it to the electric hoist motor life test bench to control the electric hoist motor life test bench to perform unattended cyclic life test. The electric hoist motor life test bench is a special life verification test device built according to the national motor duty cycle standard for electric hoist motors. It provides a cyclic operation test environment that meets the standard working conditions for electric hoist motors and is the hardware carrier for realizing the life test of electric hoist motors. S2: During the execution of the cycle life test, multi-source time-series data reflecting the performance of the electric hoist motor are synchronously and in real time collected by multi-source sensors deployed on the electric hoist motor life test bench. The multi-source sensors include a temperature sensor, a current transformer, a voltage sensor, a vibration acceleration sensor, and a noise sensor, wherein the noise sensor has a built-in A-weighted filter; the multi-source time-series data includes the following parameters: motor winding temperature. Continuous time-domain signals of three-phase operating current and three-phase operating voltage, continuous time-domain signal of vibration acceleration, and A-weighted sound pressure level. ; S3: Transmit the multi-dimensional time-series data to a remote server in real time via Internet of Things (IoT) communication. S4: The remote server receives and stores the multivariate time-series data, and performs feature extraction and calculation on the multivariate time-series data to obtain processed feature data and stores it; The feature extraction and computational processing to obtain the processed feature data specifically includes: sampling the continuous time-domain signals of the three-phase operating current and the three-phase operating voltage, and calculating the effective value of the three-phase current and the current imbalance. Total harmonic distortion of current and average active power The continuous time-domain signal of vibration acceleration is sampled, and the effective value of vibration is calculated. ; S5: Based on the processed feature data, calculate the current health status index and predicted remaining service life of the electric hoist motor; Specifically, step S5 includes the following sub-steps: S51. Calculate the processed feature data to construct a multidimensional damage factor set; S52. Calculate the instantaneous comprehensive damage rate based on the weighted average of the constructed multidimensional damage factor set. ; The instantaneous comprehensive damage rate The instantaneous damage intensity of the electric motor used in the electric hoist at time t; S53. Regarding the instantaneous comprehensive damage rate By performing integration along the experimental time axis, the cumulative comprehensive damage level from the start of the experiment to the current moment is obtained. ; The cumulative comprehensive damage Used to quantify the cumulative damage level of the electric motor used in electric hoists; S54. Calculate the current health status index of the electric hoist motor based on the cumulative comprehensive damage degree. With predicted remaining useful life ; S6: Select key performance parameters from the multivariate time-series data and processing feature data, and dynamically evaluate the residual performance value of the electric motor for the electric hoist based on the calculated value and design rated value of the key performance parameters, combined with the predicted remaining service life. The key performance parameters include the effective value of vibration. Current imbalance and motor winding temperature It covers three core systems: electrical, mechanical, and temperature rise, ensuring a comprehensive assessment; The performance residual value is expressed as a performance residual rate. Perform quantitative characterization, A higher value indicates a better retention of the electric motor's performance in the electric hoist. The calculation formula is as follows: ； in, , and For the weighting coefficients, satisfying ; The cumulative effective running time of the test refers to the effective duration of the actual cycle life test performed by the electric hoist motor from the start of the test to the current moment. The design target total lifespan of the electric motor used in electric hoists is determined by the product design specifications. k represents the number of key performance parameters, which include the effective value of vibration. Current imbalance and motor winding temperature ; The calculated value of the j-th key performance parameter is the data collected in step S2 and calculated in step S4. The design rating of the j-th critical performance parameter serves as the benchmark value for performance evaluation, including the permissible vibration rating. Motor winding reference temperature and permissible value of current imbalance ;in Determined based on the factory test data of the electric hoist or industry standards; S7: Based on the comparison results of the multivariate time-series data, health status index, performance residual value, and predicted remaining service life with their respective preset safety thresholds, generate status information or multi-level early warning information and push it to the remote terminal. If the comparison result does not meet the triggering conditions for triggering an early warning, status information is generated, including device identification information, multi-source time-series data collected in real time by multiple sensors, and calculated health status index, predicted remaining service life, and performance residual value.

2. The method for monitoring and lifespan prediction of electric motors for electric hoists based on multi-source data according to claim 1, characterized in that, Step S1 specifically includes: parsing the load cycle, duty cycle and cycle requirements of the preset national standard working system, generating a timing control program that includes start time, running time and stop time; and sending the timing control program to the local controller of the life test bench through a remote communication channel to drive the electric hoist to automatically execute continuous start-stop and load cycles.

3. The method for monitoring and lifespan prediction of electric motors for electric hoists based on multi-source data according to claim 1, characterized in that, In step S51, the multidimensional damage factor set includes temperature rise damage factors. Electrical damage factors and mechanical damage factors ; Specifically: using the formula The temperature rise damage factor was calculated. ;in The reference temperature for the motor windings is taken as the rated operating ambient temperature of the motor. This is the maximum permissible operating temperature of the motor winding insulation material, which is an industry standard value. Through formula Electrical damage factor was calculated ;in, The rated active power of the motor used in electric hoists is obtained directly from the product nameplate or technical manual and reflects the motor's designed output capacity. According to the formula Calculate the mechanical damage factor ;in, The rated allowable value for vibration is obtained from the factory test data of the electric hoist or industry standards, and serves as the benchmark value for vibration damage. The noise contribution weighting coefficient is obtained by fitting historical test data and is used to balance the relative contributions of vibration and noise to mechanical damage. The A-weighted sound pressure level is the overall operating noise measured at present, which comprehensively reflects the overall noise status of the mechanical components of the electric hoist motor. It is obtained by referencing the A-weighted sound pressure level through calibration tests conducted on a brand-new, mechanically undamaged electric hoist under standard no-load conditions.

4. The method for monitoring and lifespan prediction of electric motors for electric hoists based on multi-source data according to claim 3, characterized in that, The instantaneous comprehensive damage rate in step S52 The calculation formula is: ; in, Let be the weight coefficients of each damage factor, and satisfy the following conditions: =1.

5. The method for monitoring and lifespan prediction of electric motors for electric hoists based on multi-source data according to claim 1, characterized in that, In step S53, the cumulative comprehensive damage degree The calculation formula is ;in The preset failure damage threshold represents the critical total damage amount from a brand-new state to functional failure. This threshold is determined by the cumulative comprehensive damage level. achieve The motor's lifespan is determined to have ended at this time.

6. The method for monitoring and lifespan prediction of electric motors for electric hoists based on multi-source data according to claim 1, characterized in that, In step S54, the health status index The calculation formula is: = 1- The value ranges from [0,1], and the closer the value is to 1, the better the health status of the electric motor used in the electric hoist. The formula for calculating the predicted remaining useful life (RUL) is as follows: .

7. The method for monitoring and lifespan prediction of electric motors for electric hoists based on multi-source data according to claim 1, characterized in that, In step S7, the multi-level early warning information includes the following three levels of early warning: Level 1 warning: When the instantaneous value of any parameter in the multi-dimensional time-series data exceeds the corresponding preset real-time safety threshold, a real-time fault alarm is triggered; Level 1 warning requires immediate attention and may require remote emergency shutdown; Level 2 warning: When the rate of decline of the health status index or performance residual value exceeds the corresponding preset rate of change threshold within a continuous preset time period, a performance degradation warning is triggered; A level 2 warning indicates a potential problem and suggests planned maintenance. Level 3 warning: When the predicted remaining service life is lower than the preset safe service life threshold, a lifespan pre-termination warning is triggered; Level 3 early warning is used for long-term asset management and replacement planning.

Images