A motor state monitoring method and system based on big data

Abstract

The application discloses a motor state monitoring method and system based on big data, and particularly relates to the technical field of motor testing, which comprises the following steps: reading a real-time rotating speed of a motor to calculate a rotating speed variation, adjusting a multi-parameter acquisition period based on a threshold comparison result, and marking a unified timestamp; calculating a timestamp deviation of parameters in the same batch, screening time sequence effective parameters, and sorting; grouping according to parameter types, assigning the same time sequence node to parameters with the same timestamp, and generating a time sequence parameter package through parallel processing; and performing a ratio operation on the parameters and rated parameters to complete state determination. The motor state monitoring method and system based on big data solves the problems of asynchronous parameter acquisition and excessive redundant data under variable working conditions through linkage acquisition and whole-process time sequence control, improves data reliability and determination accuracy, and adapts to the monitoring requirements of the whole life cycle of the motor.

Description

Technical Field

[0001] This invention relates to the field of motor testing technology, and in particular to a method and system for monitoring motor condition based on big data. Background Technology

[0002] Engine testing technology includes technologies related to performance testing, fault diagnosis, and operational status assessment of engines and their associated motors. Its core content is to obtain parameter information during motor operation through various testing methods, and to analyze and determine the health status of the motor's operating conditions. This technical field covers multiple technical aspects such as static characteristic testing, dynamic operation monitoring, fault early warning, and life prediction of motors, and involves systematic technical content such as motor operating parameter acquisition, data recording, operating condition analysis, and status determination.

[0003] Among them, the motor condition monitoring method and system based on big data refers to the construction of a big data processing flow based on the current, voltage, speed, temperature and vibration parameters collected during motor operation. The system collects multi-source motor operating parameters through a big data storage architecture, processes the collected parameter data in parallel using a big data distributed computing framework, and matches and compares the processed parameter data according to preset motor condition judgment rules to form the motor condition monitoring results.

[0004] However, in practical industrial applications, existing motor condition monitoring technologies still have many unavoidable technical defects. Most existing technologies adopt a fixed-cycle parameter acquisition mode, which cannot adaptively adjust the acquisition strategy according to the actual operating conditions of the motor. In scenarios with fluctuating operating conditions such as motor start-stop, load change, and speed increase / decrease, the fixed acquisition cycle cannot fully capture the dynamic changes of parameters. On the other hand, continuously using a high-frequency acquisition mode will generate a large amount of invalid redundant data during the steady-state operation of the motor, which will significantly increase the resource consumption for data storage and processing. At the same time, the acquisition of multi-source parameters in existing technologies often uses independent clock sources, which is prone to the problem of parameter timing asynchrony. This leads to the misalignment of the time base for subsequent multi-parameter correlation analysis, which directly affects the accuracy of the condition determination results. Summary of the Invention

[0005] The main objective of this invention is to provide a method and system for monitoring motor condition based on big data, which can effectively solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for monitoring motor condition based on big data includes the following steps: The system reads the real-time speed parameters of the motor in real time, calculates the difference between the speed parameters of the current cycle and the previous cycle to obtain the speed change, compares the speed change with the preset speed step threshold, and synchronously adjusts the acquisition cycle of the motor temperature, current, voltage and vibration parameters. It also marks all acquired parameters with a unified timestamp consistent with the speed reading time. Calculate the pairwise deviation values ​​of the timestamps of parameters in the same batch, compare the deviation values ​​with the preset synchronization deviation threshold, filter the valid time series parameters and sort them in ascending order of timestamp; Valid parameters are grouped according to parameter type, and the same time sequence node is assigned to each group of parameters with the same timestamp. After synchronous parallel processing, a time sequence parameter packet is generated. The parameters extracted from the timing parameter package are compared with the corresponding rated parameters of the motor. Based on the calculation results and the determination of the motor status within the preset threshold range, the motor status monitoring is completed.

[0007] Preferably, when the change in rotational speed is greater than or equal to a preset step threshold, the acquisition period of all acquired parameters is shortened by a preset ratio, and an encrypted acquisition timer is started. The shortened acquisition period is continuously executed within the encrypted acquisition timer period. When the change in rotational speed is less than the preset step threshold and the encrypted acquisition timer period ends, the acquisition period of all acquired parameters is restored to the initial acquisition period.

[0008] Preferably, the collected parameters include real-time temperature parameters of the motor stator winding, real-time current parameters of the motor three-phase, real-time voltage parameters of the motor three-phase, radial vibration parameters of the motor drive end, radial vibration parameters of the motor non-drive end, and axial vibration parameters of the motor. The timestamps of all parameters are marked with a clock source of the same origin as the rotational speed reading time, with the marking accuracy uniform to the millisecond level, and are bound to the collection start time of the corresponding parameter.

[0009] Preferably, the pairwise timestamp deviation is the absolute value of the difference between the timestamps of any two parameters in the same batch; when the pairwise timestamp deviations of all parameters in the same batch are less than the preset synchronization deviation threshold, the parameters in that batch are marked as time-series valid parameters; when any set of timestamp deviations in the same batch is greater than or equal to the preset synchronization deviation threshold, all parameters in that batch are removed, and a supplementary sampling instruction for the corresponding parameter is triggered, and the supplementary sampling parameter re-executes the timestamp marking and deviation verification operation.

[0010] Preferably, the parameter groups are strictly grouped independently according to the types of rotational speed parameters, temperature parameters, current parameters, voltage parameters, radial vibration parameters, and axial vibration parameters, and each group of parameters retains complete timestamp information; the timing nodes are divided according to the millisecond interval of the timestamps, and all grouped parameters corresponding to the same timestamp are assigned to the same parallel processing timing node to ensure that the processing timing of different types of parameters is completely aligned.

[0011] Preferably, the synchronous parallel processing involves performing a second-order timestamp consistency check on all group parameters within the same time-series node. If the check passes, all parameters within the time-series node are arranged and encapsulated in order of type to generate a time-series parameter package with a unique timestamp identifier. Time-series node parameters that fail the check are directly removed and do not proceed to the subsequent judgment process.

[0012] Preferably, the rated parameters of the motor are the rated speed parameters, rated winding temperature parameters, rated three-phase current parameters, rated three-phase voltage parameters, and rated vibration amplitude parameters marked on the motor nameplate; the ratio calculation is the ratio of each parameter in the same sequence parameter package to the corresponding rated parameter, and each ratio is compared with a preset multi-level proportion threshold range to determine its classification, marking the compliance level of the corresponding parameter, counting the number of parameters in the same sequence parameter package that meet the compliance level, comparing the statistical results with a preset compliance quantity threshold, and outputting the judgment result of the corresponding motor status.

[0013] A motor condition monitoring system based on big data, applying any of the above-mentioned motor condition monitoring methods based on big data, the system comprising a linkage acquisition unit, a timing verification unit, a distributed parallel processing unit, a condition determination unit, and a unified clock source module communicating with each unit; The linkage acquisition unit is used to read the real-time speed parameters of the motor, complete the calculation of speed change, adjust the acquisition cycle of multiple types of parameters according to the comparison results, mark all acquired parameters with a unified timestamp in conjunction with the unified clock source module, and transmit the timestamped parameters to the timing verification unit. The timing verification unit is used to calculate the timestamp deviation value of parameters in the same batch, filter the valid timing parameters according to the comparison results, sort the valid parameters in ascending order of timestamps, and transmit the sorted parameters to the distributed parallel processing unit. The distributed parallel processing unit is used to complete the type grouping of valid parameters, allocate the same time sequence node to the grouped parameters with the same timestamp, generate a time sequence parameter packet after performing synchronous parallel processing, and transmit the time sequence parameter packet to the state determination unit. The state determination unit is used to extract parameters from the timing parameter package, perform the ratio calculation of the parameters with the corresponding rated parameters of the motor, and complete the monitoring and determination of the motor state and output the results based on the calculation results and the classification of the preset threshold range.

[0014] Compared with the prior art, the present invention has the following beneficial effects: This invention addresses the core pain points of existing motor condition monitoring technologies from the data acquisition source: asynchronous acquisition of multiple operating parameters under varying operating conditions, misaligned timing references, and a high proportion of invalid and redundant data. This is achieved by constructing a multi-parameter linkage acquisition mechanism centered on motor speed changes, coupled with a unified timestamp marking and timing validity verification process using a common clock source. By triggering adaptive adjustments to the acquisition cycle based on speed changes, the invention ensures parameter acquisition density during motor condition fluctuations while avoiding invalid data redundancy during steady-state operation. Furthermore, the unified timing reference control and invalid data removal mechanism throughout the entire process significantly improves the reliability and timing consistency of the base data entering subsequent processing flows, providing stable data support for accurate motor condition determination. This invention addresses the problems of misaligned processing times for multiple types of motor parameters, high processing latency under large data volumes, and excessive reliance on complex algorithms and dedicated models in existing technologies by using an execution flow that independently groups parameters by type and processes them synchronously and in parallel with sequential nodes. By dividing parallel processing time nodes according to timestamps, the processing times of different parameter types are completely aligned, avoiding distortion of parameter relationships caused by misaligned processing times. Furthermore, the distributed parallel processing architecture significantly improves parameter processing efficiency in scenarios with multiple devices and large data volumes. The entire processing process performs standardized operations based solely on the timing attributes of the parameters, eliminating the need for complex signal processing algorithms and data models. The processing logic is clear and traceable, significantly reducing the deployment threshold and maintenance costs of the technical solution. This invention addresses the industry pain points of existing motor condition monitoring technologies by constructing a multi-dimensional quantitative judgment mechanism based on the motor's own rated parameters. These pain points include over-reliance on historical databases and expert experience bases, inconsistent judgment benchmarks, poor generalization, and susceptibility to misjudgments caused by single-parameter fluctuations. The entire judgment process uses only the inherent rated parameters marked on the motor nameplate as the sole benchmark, eliminating the need to introduce external experience data or historical operating data. The judgment benchmark is unified and traceable. Furthermore, the statistical judgment method based on multiple parameter compliance levels effectively avoids misjudgments caused by occasional fluctuations in single parameters, significantly improving the accuracy and stability of motor condition judgment. The closed-loop technical architecture can stably adapt to the continuous monitoring needs of the entire motor lifecycle, possessing strong industrial application potential and technological scalability. Attached Figure Description

[0015] Figure 1 This is a diagram illustrating the multi-source operating parameter linkage acquisition and unified timing marking of motors according to the present invention. Figure 2 This is a diagram illustrating the timing validity verification and effective parameter selection for the data acquisition parameters of this invention. Figure 3 This is a diagram illustrating the effective parameter type grouping and synchronous parallel processing of the present invention; Figure 4This is a graph illustrating the quantitative calculation and monitoring judgment of motor operating status according to the present invention; Figure 5 This is a block diagram of the motor condition monitoring system of the present invention. Detailed Implementation

[0016] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0017] Example 1, see Figure 1 This embodiment is mainly used to realize the linkage acquisition and unified timing marking of multi-source operating parameters of motor, so as to provide basic acquisition data with the same timing for subsequent monitoring processes; In the specific implementation process, the motor used is a three-phase asynchronous motor with a rated speed of 1480 r / min, a rated stator winding temperature of 130℃, a rated three-phase current of 25A, a rated three-phase voltage of 380V, and a rated vibration amplitude of 2.8 mm / s. First, the real-time speed parameters of the motor are read in real time, and the difference between the speed parameters of the current cycle and the previous cycle is calculated to obtain the speed change. The speed change is compared with the preset speed step threshold, and the acquisition cycle of the motor temperature, current, voltage, and vibration parameters is adjusted synchronously. All acquired parameters are marked with a unified timestamp consistent with the speed reading time. When the speed change is greater than or equal to the preset speed step threshold, the acquisition cycle of all acquired parameters is shortened by a preset ratio. Simultaneously, encrypted acquisition timing is initiated. Within the encrypted acquisition timing period, the shortened acquisition cycle is continuously executed. When the speed change is less than the preset speed step threshold and the encrypted acquisition timing period ends, the acquisition cycle of all acquisition parameters is restored to the initial acquisition cycle. The acquisition parameters include the real-time temperature parameters of the motor stator winding, the real-time current parameters of the motor three-phase, the real-time voltage parameters of the motor three-phase, the radial vibration parameters of the motor drive end, the radial vibration parameters of the motor non-drive end, and the axial vibration parameters of the motor. The timestamps of all parameters are marked with the same clock source as the speed reading time, and the marking accuracy is uniformly set to the millisecond level. They are also bound to the acquisition start time of the corresponding parameters. In this embodiment, the preset speed step threshold is 100 r / min, the initial acquisition cycle is 100 ms, the preset shortening ratio is one-fifth, the shortened acquisition cycle is 20 ms, the encrypted acquisition timing period is 5 s, and the speed reading cycle is consistent with the initial acquisition cycle of 100 ms. During operation, the motor speed read in the previous cycle was 1480 r / min, and the motor speed read in the current cycle is 1350 r / min. The calculated speed change is 130 r / min, which is greater than or equal to the preset speed step threshold of 100 r / min. This triggers an adjustment of the acquisition cycle, shortening the acquisition cycle of all parameters from 100 ms to 20 ms. At the same time, a 5-second encrypted acquisition timer is started. Within the timer period, parameter acquisition is continuously performed at a 20 ms cycle. After the timer period ends, the read motor speed returns to 1470 r / min, and the current speed change is 10 r / min, which is less than the preset speed step threshold. The initial acquisition cycle of 100 ms is then restored. The same hardware clock chip is used to provide a unified clock signal for speed reading and all parameter acquisition actions, ensuring that the timestamp reference of all parameters is completely consistent and avoiding timing misalignment caused by clock deviations of different acquisition modules.

[0018] Example 2, see Figure 2 This embodiment, based on the completion of multi-source motor operation parameter linkage acquisition and unified timing marking in Embodiment 1, further realizes timing validity verification and effective parameter screening of the acquired parameters, eliminates invalid data with asynchronous timing, and ensures timing consistency of subsequent processed data; In the specific implementation process, the batch of collected parameters marked with a unified timestamp in Example 1 are received, the pairwise deviation value of the timestamps of the parameters in the same batch is calculated, the deviation value is compared with the preset synchronization deviation threshold, the time-series valid parameters are filtered and sorted in ascending order of timestamp, the pairwise deviation value of timestamp is the absolute value of the difference between the timestamps of any two parameters in the same batch, when the pairwise deviation value of timestamps of all parameters in the same batch is less than the preset synchronization deviation threshold, the parameters of the batch are marked as time-series valid parameters, when any set of timestamp deviation values ​​in the same batch is greater than or equal to the preset synchronization deviation threshold, all parameters of the batch are removed, and the corresponding parameter supplementation instruction is triggered. The supplemented parameters re-execute the timestamp marking and deviation verification operation. In this example, the preset synchronization deviation threshold is 5ms. During the actual operation, the timestamps for the first batch of collected parameters were as follows: rotational speed parameter 10:00:00.000, temperature parameter 10:00:00.002, current parameter 10:00:00.001, voltage parameter 10:00:00.003, radial vibration parameter at the drive end 10:00:00.002, radial vibration parameter at the non-drive end 10:00:00.004, and axial vibration parameter 10:00:00.003. The absolute value of the difference between each pair of timestamps was calculated, and the maximum deviation was 4ms, which was less than the preset synchronization deviation threshold of 5ms. This batch... The parameters were marked as time-series valid parameters. In the second batch of acquired parameters, the timestamp of the axial vibration parameter was 10:00:00.006. The absolute difference between the timestamp of the rotational speed parameter and the timestamp of the axial vibration parameter was 6ms, which was greater than or equal to the preset synchronization deviation threshold of 5ms. All parameters in this batch were directly discarded. At the same time, a supplementary acquisition command was triggered to the acquisition unit. The parameters that were supplemented were re-performed with timestamp marking and deviation verification until the verification was passed. All marked time-series valid parameters were arranged in ascending order of timestamp, that is, sorted in the order of acquisition time from earliest to latest, to provide a time-series coherent basic data for subsequent processing.

[0019] Example 3, see Figure 3 Based on the effective timing parameter filtering and sorting completed in Example 2, this embodiment further realizes the grouping of effective parameters by type and synchronous parallel processing, generating a timing parameter package with a unified timing identifier to ensure that the processing timing of multiple types of parameters is completely aligned. In the specific implementation process, the valid timing parameters arranged in ascending order of timestamps as in Example 2 are received, and the valid parameters are grouped according to parameter type. The same timing node is assigned to each group of parameters with the same timestamp. After synchronous parallel processing, a timing parameter package is generated. The parameter grouping is strictly based on the type of rotation speed parameter, temperature parameter, current parameter, voltage parameter, radial vibration parameter, and axial vibration parameter. Each group of parameters retains complete timestamp information. The timing node is divided according to the millisecond interval of the timestamp. All grouped parameters corresponding to the same timestamp are assigned to the same parallel processing timing node to ensure that the processing timing of different types of parameters is completely aligned. Synchronous parallel processing involves performing a second-order timestamp consistency check on all grouped parameters within the same time-series node. If the check passes, all parameters within that time-series node are arranged and encapsulated in order of type to generate a time-series parameter package with a unique timestamp identifier. Time-series node parameters that fail the check are directly removed and do not proceed to the subsequent decision-making process. In the actual operation, the sorted time-series valid parameters are independently grouped into 6 parameter types. Each group of parameters retains the original timestamp information completely. Time-series nodes are divided according to the millisecond interval of the timestamps. The 6 groups of parameters with the timestamp 10:00:00.000 are all assigned to time-series node 000, the 6 groups of parameters with the timestamp 10:00:00.020 are all assigned to time-series node 020, and so on. The parameters within each time-series node are all from the same source data at the same acquisition time. A second timestamp consistency check is performed on the grouped parameters within each time-series node. If the timestamp deviation of all parameters in timing node 000 is less than 5ms, the verification is passed. The parameters in this node are arranged and packaged in the order of rotational speed, temperature, current, voltage, radial vibration, and axial vibration, generating a timing parameter package with a unique timestamp of 10:00:00.000. If a second verification of a timing node finds that the parameter timestamp deviation exceeds 5ms, all parameters of that node are directly removed and do not enter the subsequent judgment process. Parameters of different timing nodes can be processed synchronously in parallel without waiting for the previous node to complete its processing, which greatly improves the processing efficiency under large data volume.

[0020] Example 4, see Figure 4 This embodiment, based on the generation of timing parameter packages with unified timing identifiers in Embodiment 3, further realizes the quantitative calculation and monitoring judgment of motor operating status, and completes the core process of the entire motor status monitoring. In the specific implementation process, the timing parameter package generated in Example 3 is received, the parameters in the timing parameter package are extracted and the ratio is calculated with the corresponding rated parameters of the motor. Based on the calculation result and the classification judgment of the preset threshold range, the motor status monitoring judgment is completed. The rated parameters of the motor are the rated speed parameters, rated winding temperature parameters, rated three-phase current parameters, rated three-phase voltage parameters, and rated vibration amplitude parameters marked on the motor nameplate. The ratio calculation is the ratio of each parameter in the timing parameter package to the corresponding rated parameter. Each ratio is classified with the preset multi-level proportion threshold range, and the compliance level of the corresponding parameter is marked. The number of parameters in the timing parameter package that meet the compliance level is counted. The statistical result is compared with the preset compliance number threshold, and the judgment result of the corresponding motor status is output. In this example, the preset compliance proportion threshold range is 80%-120%, that is, parameters with ratio results in the range of 0.8 to 1.2 are marked as compliant, and parameters outside this range are marked as non-compliant. The preset compliance number threshold is 5. During the actual operation, the parameters in the timing parameter package with the timestamp of 10:00:00.000 were extracted. The specific values ​​were: real-time speed 1450 r / min, real-time stator winding temperature 115℃, real-time three-phase current 24A, real-time three-phase voltage 375V, radial vibration at the drive end 2.2 mm / s, radial vibration at the non-drive end 2.3 mm / s, and axial vibration 2.1 mm / s. The ratio was calculated sequentially, and the speed ratio was 1450 ÷ 1480 ≈ 0.98, which is within the 80%-120% range and is marked as compliant. The temperature ratio is approximately 0.88 (115 ÷ 130), which falls within the 80%-120% range and is therefore compliant. The current ratio is 24÷25=0.96, which is within the 80%-120% range and is therefore marked as compliant. The voltage ratio is approximately 0.99 (375 ÷ 380 ≈ 0.99), which falls within the 80%-120% range and is therefore compliant. The ratios of the three vibration parameters were set to 2.3 mm / s, 2.4 mm / s, and 2.3 mm / s respectively. Calculations showed that 2.3 ÷ 2.8 ≈ 0.82 and 2.4 ÷ 2.8 ≈ 0.86, all within the 80%-120% range, and all were compliant. The system detects 6 compliant parameters, which is greater than or equal to the preset compliance threshold of 5. The system outputs a result indicating normal motor operation. In another set of timing parameters, the real-time three-phase current is 32A, and the calculated current ratio is 32÷25=1.28, exceeding the upper limit of 120%, thus marking it as non-compliant. The other 5 parameters are compliant, and the system detects 5 compliant parameters, which is equal to the preset compliance threshold. The system outputs a result indicating normal motor operation. In the third set of timing parameters, the real-time current is 32A and the real-time winding temperature is 160℃, with a calculated temperature ratio of 160÷130≈1.23. Both parameters are non-compliant, and the system detects 4 compliant parameters, which is less than the preset compliance threshold. The system outputs a result indicating abnormal motor operation. The entire detection process relies solely on the motor's nameplate rated parameters and real-time acquired parameters, without the need for external experience bases or historical databases, ensuring the independence and traceability of the detection process.

[0021] Example 5, see Figure 5This embodiment describes a big data-based motor condition monitoring system applying the monitoring methods described in Embodiments 1 to 4 above. Specifically, it implements the entire process of hardware deployment and unit collaborative operation for motor condition monitoring. Specifically, the system includes a linkage acquisition unit, a timing verification unit, a distributed parallel processing unit, a condition determination unit, and a unified clock source module communicating with each unit. The linkage acquisition unit reads real-time motor speed parameters, calculates speed changes, adjusts the acquisition cycle of multiple parameter types based on comparison results, and, in conjunction with the unified clock source module, marks all acquired parameters with a unified timestamp. The timestamped parameters are then transmitted to the timing verification unit. The timing verification unit calculates the timestamp deviation of parameters in the same batch, filters valid timing parameters based on comparison results, sorts the valid parameters by timestamp in ascending order, and transmits the sorted parameters to the distributed parallel processing unit. The distributed parallel processing unit groups the valid parameters by type and assigns the same timing sequence to grouped parameters with the same timestamp. Each node performs synchronous parallel processing to generate a timing parameter package, which is then transmitted to the status determination unit. The status determination unit extracts the parameters from the timing parameter package, performs a ratio calculation between the parameters and the corresponding rated parameters of the motor, and determines the motor status based on the calculation results and the preset threshold range. The unified clock source module uses a high-precision hardware clock chip to provide all units with a consistent millisecond-level clock signal, ensuring the consistency of the time base throughout the process. The linkage acquisition unit is equipped with speed sensors, temperature sensors, current transformers, voltage transformers, and vibration sensors, which respectively collect the motor's speed, winding temperature, three-phase current, three-phase voltage, and radial and axial vibration parameters. All sensors are connected to the unified clock source module to ensure the timing synchronization of the acquisition actions. The timing verification unit, distributed parallel processing unit, and status determination unit are all deployed in an industrial-grade server, which can support the synchronous monitoring data processing of multiple motors and adapt to the monitoring needs of large-scale motor clusters in industrial scenarios.

[0022] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended technical solutions and their equivalents.

Claims

1. A method for monitoring motor condition based on big data, characterized in that, Includes the following steps: The system reads the real-time speed parameters of the motor in real time, calculates the difference between the speed parameters of the current cycle and the previous cycle to obtain the speed change, compares the speed change with the preset speed step threshold, and synchronously adjusts the acquisition cycle of the motor temperature, current, voltage and vibration parameters. It also marks all acquired parameters with a unified timestamp consistent with the speed reading time. Calculate the pairwise deviation values ​​of the timestamps of parameters in the same batch, compare the deviation values ​​with the preset synchronization deviation threshold, filter the valid time series parameters and sort them in ascending order of timestamp; Valid parameters are grouped according to parameter type, and the same time sequence node is assigned to each group of parameters with the same timestamp. After synchronous parallel processing, a time sequence parameter packet is generated. The parameters extracted from the timing parameter package are compared with the corresponding rated parameters of the motor. Based on the calculation results and the determination of the motor status within the preset threshold range, the motor status monitoring is completed.

2. The motor condition monitoring method based on big data according to claim 1, characterized in that, When the speed change is greater than or equal to the preset speed step threshold, the acquisition period of all acquisition parameters is shortened by a preset ratio, and an encrypted acquisition timer is started. The shortened acquisition period is continuously executed within the encrypted acquisition timer period. When the speed change is less than the preset speed step threshold and the encrypted acquisition timer period ends, the acquisition period of all acquisition parameters is restored to the initial acquisition period.

3. The motor condition monitoring method based on big data according to claim 1, characterized in that, The collected parameters include real-time temperature parameters of the motor stator winding, real-time current parameters of the motor three-phase, real-time voltage parameters of the motor three-phase, radial vibration parameters of the motor drive end, radial vibration parameters of the motor non-drive end, and axial vibration parameters of the motor. The timestamps of all parameters are marked with the same clock source as the speed reading time, with the marking accuracy uniformly at the millisecond level, and are bound to the collection start time of the corresponding parameter.

4. The motor condition monitoring method based on big data according to claim 1, characterized in that, The pairwise deviation of the timestamps is the absolute value of the difference between the timestamps of any two parameters in the same batch. When the pairwise deviation of the timestamps of all parameters in the same batch is less than the preset synchronization deviation threshold, the parameters in that batch are marked as time-series valid parameters. When any set of timestamp deviations in the same batch is greater than or equal to the preset synchronization deviation threshold, all parameters in that batch are removed, and a supplementary sampling instruction for the corresponding parameter is triggered. The supplementary parameters are then re-executed with timestamp marking and deviation verification.

5. The motor condition monitoring method based on big data according to claim 1, characterized in that, The parameters are strictly grouped independently according to the types of rotational speed parameters, temperature parameters, current parameters, voltage parameters, radial vibration parameters, and axial vibration parameters. Each group of parameters retains complete timestamp information. The timing nodes are divided according to the millisecond interval of the timestamps. All grouped parameters corresponding to the same timestamp are assigned to the same parallel processing timing node to ensure that the processing timing of different types of parameters is completely aligned.

6. The motor condition monitoring method based on big data according to claim 5, characterized in that, The synchronous parallel processing involves performing a second-order timestamp consistency check on all group parameters within the same time-series node. If the check passes, all parameters within the time-series node are arranged and encapsulated in order of type to generate a time-series parameter package with a unique timestamp identifier. Time-series node parameters that fail the check are directly removed and do not proceed to the subsequent judgment process.

7. The motor condition monitoring method based on big data according to claim 1, characterized in that, The rated parameters of the motor are the rated speed, rated winding temperature, rated three-phase current, rated three-phase voltage, and rated vibration amplitude parameters marked on the motor nameplate. The ratio calculation is the ratio of each parameter in the same sequence parameter package to the corresponding rated parameter. Each ratio is compared with a preset multi-level proportion threshold range to determine its classification, and the compliance level of the corresponding parameter is marked. The number of parameters in the same sequence parameter package that meet the compliance level is counted. The statistical results are compared with the preset compliance quantity threshold, and the judgment result of the corresponding motor status is output.

8. A motor condition monitoring system based on big data, characterized in that, The motor status monitoring method based on big data according to any one of claims 1-7, the system includes a linkage acquisition unit, a timing verification unit, a distributed parallel processing unit, a status determination unit, and a unified clock source module that is communicatively connected to each unit; The linkage acquisition unit is used to read the real-time speed parameters of the motor, complete the calculation of speed change, adjust the acquisition cycle of multiple types of parameters according to the comparison results, mark all acquired parameters with a unified timestamp in conjunction with the unified clock source module, and transmit the timestamped parameters to the timing verification unit. The timing verification unit is used to calculate the timestamp deviation value of parameters in the same batch, filter the valid timing parameters according to the comparison results, sort the valid parameters in ascending order of timestamps, and transmit the sorted parameters to the distributed parallel processing unit. The distributed parallel processing unit is used to complete the type grouping of valid parameters, allocate the same time sequence node to the grouped parameters with the same timestamp, generate a time sequence parameter packet after performing synchronous parallel processing, and transmit the time sequence parameter packet to the state determination unit. The state determination unit is used to extract parameters from the timing parameter package, perform the ratio calculation of the parameters with the corresponding rated parameters of the motor, and complete the monitoring and determination of the motor state and output the results based on the calculation results and the classification of the preset threshold range.

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