Industrial equipment monitoring method, apparatus, device, and storage medium

Abstract

This application discloses an industrial equipment monitoring method, apparatus, equipment, and storage medium, relating to the field of equipment monitoring technology. The disclosed industrial equipment monitoring method includes: collecting operating status data of industrial equipment within a target area; determining whether the industrial equipment is in a preset target operating condition based on the operating status data; and if the industrial equipment is in the target operating condition, analyzing and processing the operating status data to obtain equipment status monitoring results. This application can improve equipment operation and maintenance efficiency.

Description

Technical Field

[0001] This application relates to the field of equipment monitoring technology, and in particular to industrial equipment monitoring methods, devices, equipment and storage media. Background Technology

[0002] In industrial production environments, industrial equipment such as motors, pumps, fans, speed reducers, and conveying devices are the infrastructure that ensures continuous production. The maintenance of these industrial equipment mainly relies on regular inspections, planned maintenance, and post-maintenance by inspection personnel, and is highly dependent on human experience overall.

[0003] With the development of monitoring technology, some industrial equipment has deployed online monitoring systems and implemented over-limit alarms through fixed thresholds. However, the alarm thresholds still need to be set manually. If the settings are not set properly, it is easy to cause missed alarms or false alarms. Moreover, after the alarm occurs, manual fault diagnosis and analysis are still required, resulting in low equipment operation and maintenance efficiency.

[0004] In summary, improving equipment operation and maintenance efficiency has become a pressing technical problem that needs to be solved in this field. Summary of the Invention

[0005] The main purpose of this application is to provide an industrial equipment monitoring method, device, equipment and storage medium, which aims to improve equipment operation and maintenance efficiency.

[0006] To achieve the above objectives, this application proposes an industrial equipment monitoring method, which includes: Collect operational status data of industrial equipment within the target area; Determine whether the industrial equipment is in a preset target operating condition based on the operating status data; If the industrial equipment is in the target operating condition, the operating status data is analyzed and processed to obtain the equipment status monitoring results.

[0007] In one embodiment, before the step of determining whether the industrial equipment is in a preset target operating condition based on the operating status data, the method further includes: Based on the changes in the rotational speed signal of the industrial equipment, the operating conditions of the industrial equipment are divided into shutdown state, startup state, steady-state operation state, and variable speed operation state. The target operating condition of the industrial equipment is determined by at least one of the following: the shutdown state, the startup state, the steady-state operation state, and the variable-speed operation state.

[0008] In one embodiment, the operating status data includes vibration waveforms, rotational speed signals, and temperature signals. The step of analyzing and processing the operating status data to obtain equipment status monitoring results if the industrial equipment is in the target operating condition includes: If the industrial equipment is in the target operating condition, the vibration waveform is resampled at equal angles using the rotation speed signal to obtain an angular domain signal. The spectral analysis of the angular domain signal yields the order spectrum; Extract feature values ​​associated with equipment mechanical faults from the order spectrum; The feature value and the temperature signal are matched with a preset fault feature database, and the equipment status monitoring result of the industrial equipment is obtained based on the matching result.

[0009] In one embodiment, after the step of extracting feature values ​​associated with equipment mechanical faults from the order spectrum, the method further includes: A dynamic baseline model is established based on data from the historical healthy operation phases of the industrial equipment. Calculate the deviation between the feature value and the corresponding feature value in the dynamic baseline model; The equipment status monitoring results of the industrial equipment are determined based on the range of the deviation.

[0010] In one embodiment, the step of collecting operational status data of industrial equipment within the target area includes: Vibration waveforms of industrial equipment within the target area are collected using a preset vibration sensor, rotation speed signals of the industrial equipment are collected using a preset rotation speed sensor, and temperature signals of the industrial equipment are collected using a preset temperature sensor. Send self-test commands to the vibration sensor, the speed sensor and the temperature sensor respectively, and receive the self-test status information returned by each sensor in response to the self-test command; The working status of each sensor is determined based on the self-test status information.

[0011] In one embodiment, after the step of analyzing and processing the operating status data to obtain equipment status monitoring results if the industrial equipment is in the target operating condition, the method further includes: Based on the equipment status monitoring results, generate equipment diagnostic reports and equipment maintenance work orders.

[0012] In one embodiment, the method further includes: The operating status data and equipment status monitoring results of multiple industrial devices are uploaded to a cloud server so that the cloud server can construct an equipment fault knowledge graph. In response to a knowledge graph query request, the device fault knowledge graph is retrieved from the cloud server.

[0013] Furthermore, to achieve the above objectives, this application also proposes an industrial equipment monitoring device, which includes: The data acquisition module is used to collect operating status data of industrial equipment within the target area; The operating condition determination module is used to determine whether the industrial equipment is in a preset target operating condition based on the operating status data. The data analysis module is used to analyze and process the operating status data to obtain equipment status monitoring results if the industrial equipment is in the target operating condition.

[0014] In addition, to achieve the above objectives, this application also proposes an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the industrial equipment monitoring method described above.

[0015] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the industrial equipment monitoring method described above.

[0016] This application proposes an industrial equipment monitoring method. The method involves collecting operating status data of industrial equipment within a target area; determining whether the industrial equipment is under a preset target operating condition based on the operating status data; and analyzing and processing the operating status data to obtain the equipment status monitoring results if the industrial equipment is under the target operating condition.

[0017] In summary, this application collects industrial equipment operating status data and determines whether it is in a target operating condition. Under the target operating condition, the operating status data of the equipment is analyzed and processed to obtain equipment status monitoring results. This reduces the over-reliance on human experience, avoids the problems of missed or false alarms that may be caused by human judgment, helps to detect potential equipment failures in advance, reduces the impact of sudden equipment failures on production, and thus effectively improves equipment operation and maintenance efficiency. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating an embodiment of the industrial equipment monitoring method of this application. Figure 2This is a flowchart illustrating Embodiment 2 of the industrial equipment monitoring method of this application; Figure 3 This is another schematic diagram of the process provided in Embodiment 2 of the industrial equipment monitoring method of this application; Figure 4 This is a schematic diagram of the module structure of the industrial equipment monitoring device according to an embodiment of this application; Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the industrial equipment monitoring method in this application embodiment.

[0021] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0023] In industrial production environments, industrial equipment such as motors, pumps, fans, speed reducers, and conveying devices are the infrastructure that ensures continuous production. The maintenance of these industrial equipment mainly relies on regular inspections, planned maintenance, and post-maintenance by inspection personnel, and is highly dependent on human experience overall.

[0024] With the development of monitoring technology, some industrial equipment has deployed online monitoring systems and implemented over-limit alarms through fixed thresholds. However, the alarm thresholds still need to be set manually. If the settings are not set properly, it is easy to cause missed alarms or false alarms. Moreover, after the alarm occurs, manual fault diagnosis and analysis are still required, resulting in low equipment operation and maintenance efficiency.

[0025] In summary, improving equipment operation and maintenance efficiency has become a pressing technical problem that needs to be solved in this field.

[0026] This application provides a solution that collects the operating status data of industrial equipment within a target area; determines whether the industrial equipment is in a preset target operating condition based on the operating status data; and if the industrial equipment is in the target operating condition, analyzes and processes the operating status data to obtain the equipment status monitoring results.

[0027] In summary, the embodiments of this application collect industrial equipment operating status data and determine whether it is in a target operating condition. Under the target operating condition, the operating status data of the equipment is analyzed and processed to obtain the equipment status monitoring results. This reduces the over-reliance on human experience, avoids the problems of missed or false alarms that may be caused by human judgment, helps to discover potential equipment failures in advance, reduces the impact of sudden equipment failures on production, and thus effectively improves equipment operation and maintenance efficiency.

[0028] It should be noted that the executing entity in this embodiment can be a general-purpose or special-purpose computing device with program execution capabilities, such as a server, personal computer, or mobile phone. An industrial equipment monitoring system (hereinafter referred to as the system) can be deployed on this device, and the industrial equipment monitoring method of this embodiment is implemented by running this system. This system can be implemented in various software forms, such as through a standalone client application, as a web-based service provided through a browser, or integrated into existing professional software tools as a plugin, module, or functional component.

[0029] Based on this, the embodiments of this application provide an industrial equipment monitoring method, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the industrial equipment monitoring method of this application.

[0030] In this embodiment, the industrial equipment monitoring method includes steps S10 to S30: Step S10: Collect operating status data of industrial equipment within the target area; It should be noted that industrial equipment refers to the infrastructure used in industrial production environments, such as motors, pumps, fans, speed reducers, and conveying devices, to ensure continuous production. The target area refers to the specific physical space range where equipment monitoring is required, such as a production workshop, a unit of equipment, or a production line. Operating status data refers to various physical quantity signals that can reflect the current working status of industrial equipment, specifically including but not limited to vibration waveforms, speed signals, and temperature signals.

[0031] Sensors are pre-installed on key components of industrial equipment to collect operational status data. For example, vibration sensors are installed on vibration-sensitive parts such as bearing housings and casings to collect vibration waveforms during operation; speed sensors, such as Hall effect current sensors, photoelectric encoders, or magnetoelectric sensors, are installed near the rotating shafts to collect speed signals; and temperature sensors, such as thermocouples, resistance temperature detectors (RTDs), or infrared temperature sensors, are installed on the surface or inside the equipment to collect temperature signals. These sensors collect operational status data of the industrial equipment in real time and transmit the collected data wirelessly to a data processing terminal with a deployed system.

[0032] Step S20: Determine whether the industrial equipment is in the preset target operating condition based on the operating status data; It should be noted that the target operating condition refers to the working state that is set to be suitable for equipment status analysis during the monitoring process. Specifically, it can be a steady-state operating state or a specific variable-speed operating state, etc.

[0033] Based on the collected operational status data, the speed signal is first analyzed. Specifically, by monitoring the trend of the speed signal over time, the current operating stage of the equipment is identified. For example, when the speed signal is zero or close to zero, the equipment is determined to be in a stopped state; when the speed signal rises from zero and gradually stabilizes, the equipment is determined to be in a starting state; when the speed signal fluctuates stably around the rated speed, the equipment is determined to be in a steady-state operating state; and when the speed signal continuously rises, falls, or exhibits periodic changes, the equipment is determined to be in a variable-speed operating state. The identified current operating state is compared with preset target operating conditions. If the current state meets the characteristic requirements of the target operating condition, the industrial equipment is determined to be in the target operating condition.

[0034] In one feasible embodiment, steps A10-A20 may be included before step S20: Step A10: Based on the changes in the rotational speed signal of the industrial equipment, the operating conditions of the industrial equipment are divided into shutdown state, startup state, steady-state operation state, and variable speed operation state. It should be noted that the speed signal refers to the electrical signal reflecting the rotational speed of the equipment, collected by a speed sensor, usually measured in revolutions per minute or revolutions per second. Operating condition segmentation refers to dividing the entire operating process of the equipment into stages with different characteristics based on the changing patterns of the speed signal. The shutdown state refers to the state where the equipment speed is zero or close to zero, at which point the equipment is stationary. The startup state refers to the process of the equipment accelerating from a standstill to its rated speed, during which the speed signal shows an upward trend. The steady-state operating state refers to the state where the equipment operates stably near its rated speed, with the speed signal fluctuating within a certain range but without a significant trend change. The variable-speed operating state refers to the process where the equipment speed continuously changes over time, which may include acceleration, deceleration, or periodic speed changes.

[0035] By continuously receiving speed signals collected by speed sensors, a curve showing the speed signal changing over time is established. Specifically, the speed curve can be segmented and identified. A sliding window algorithm is used to monitor the derivative and rate of change of the speed. When the speed value is detected to be lower than a preset shutdown threshold and the duration exceeds a set time, the period is marked as a shutdown state. When the speed is detected to be rising continuously from the shutdown threshold and the rate of increase exceeds the startup threshold, the period is marked as a startup state. When the speed is detected to be fluctuating around the rated speed and the rate of change is always lower than the steady-state threshold, the period is marked as a steady-state operation state. When the speed is detected to be changing continuously and the rate of change exceeds the speed change threshold, the period is marked as a speed change operation state. In this way, the automatic segmentation and marking of each operating condition period within the entire operating cycle of the equipment is completed.

[0036] Step A20: Determine at least one of the following states as the target operating conditions of the industrial equipment: shutdown state, startup state, steady-state operation state, and variable-speed operation state.

[0037] It should be noted that determining the target operating condition means selecting one or more operating conditions from the segmented operating conditions as suitable for subsequent analysis, based on the monitoring purpose and the actual operating characteristics of the equipment. That is, the target operating condition can be a single operating condition type or a combination of multiple operating condition types.

[0038] The system receives user-configured target operating condition selection commands via a human-machine interface. For example, users can select the type of operating condition to be analyzed based on their monitoring needs. For equipment requiring vibration analysis, steady-state operation is typically selected as the target operating condition because the vibration signal is relatively stable under steady-state conditions, making it easier to extract stable fault characteristics. For equipment requiring analysis of fault characteristics during speed change, speed change operation is selected as the target operating condition, such as analyzing gear faults in an elevator during acceleration and deceleration. For equipment requiring monitoring of starting impact, the starting state can be selected as the target operating condition. For situations requiring confirmation of a completely stopped equipment state, the stopped state can be selected as the target operating condition. After setting the target operating condition, the configuration parameters are stored in a preset storage area for subsequent steps when determining the operating condition.

[0039] Through the above steps, suitable analysis conditions can be flexibly selected according to actual monitoring needs, avoiding the complexity and uncertainty brought about by data processing under all operating conditions. At the same time, the customized configuration of target operating conditions meets the diverse needs of different equipment and different monitoring purposes, thereby improving the flexibility of equipment monitoring.

[0040] Step S30: If the industrial equipment is in the target operating condition, the operating status data is analyzed and processed to obtain the equipment status monitoring results.

[0041] It should be noted that equipment status monitoring results refer to diagnostic conclusions about the health status of equipment obtained by analyzing operational status data. Specifically, this may include information such as whether the equipment has a fault, the type of fault, and the severity of the fault.

[0042] After determining that the industrial equipment is in the target operating condition, the operating status data is analyzed and processed to determine the equipment status monitoring results, such as determining whether the equipment is in a healthy state or a faulty state, and identifying the specific fault type, such as rolling bearing failure or misalignment failure. After the analysis is completed, the equipment status monitoring results are output and displayed in a preset format.

[0043] In one feasible embodiment, step S30 may be followed by step S40: Step S40: Generate an equipment diagnostic report and an equipment maintenance work order based on the equipment status monitoring results.

[0044] It should be noted that the equipment diagnostic report can be a structured document containing information such as the equipment's health status, fault type, fault severity, and recommended maintenance measures, while the equipment maintenance work order can be a task instruction containing a maintenance task description, maintenance priority, recommended maintenance time, and required spare parts information.

[0045] The generated equipment status monitoring results, including fault characteristic data, matched fault types, and deviation data, are formatted and processed to generate an equipment diagnostic report according to a preset template. Maintenance priorities are set based on the severity of faults in the diagnostic results; for example, severe faults are set to high priority, and minor anomalies to low priority. Furthermore, the required spare parts are retrieved from the spare parts database based on the commonly used spare parts information associated with the fault type, generating a spare parts demand list. Finally, all of the above information is integrated to form an equipment maintenance work order. After the diagnostic report and maintenance work order are generated, they can be sent to relevant maintenance personnel via email, SMS, or push notifications from the maintenance platform to guide subsequent maintenance work.

[0046] Thus, by generating diagnostic reports and maintenance work orders, monitoring results are directly transformed into executable maintenance tasks, shortening the time from fault discovery to maintenance response, thereby improving the accuracy of equipment monitoring and operational efficiency.

[0047] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description and will not be repeated hereafter. In addition, the operating status data includes vibration waveforms, speed signals, and temperature signals, such as... Figure 2 As shown, step S30 may include steps S301 to S304: Step S301: If the industrial equipment is in the target operating condition, the vibration waveform is resampled at equal angles using the rotation speed signal to obtain the angular domain signal. It should be noted that a vibration waveform refers to the curve showing the change in the vibration amplitude of equipment over time, collected by a vibration sensor, typically plotted with time on the x-axis and vibration amplitude on the y-axis. A rotational speed signal refers to a synchronously acquired signal showing the change in the equipment's rotational speed over time. Equal-angle resampling is a signal processing technique that converts the vibration waveform, originally sampled at equal time intervals, into a signal sampled at equal angular intervals based on changes in the rotational speed signal. An angular domain signal refers to a signal plotted with the rotational angle on the x-axis and vibration amplitude on the y-axis.

[0048] Once the industrial equipment is determined to be in the preset target operating condition, synchronously acquired vibration waveforms and rotational speed signals are obtained. First, the rotational speed signal is processed to extract the instantaneous rotational speed value corresponding to each moment. Then, based on the instantaneous rotational speed value, the time interval required for the equipment's rotating shaft to rotate through a unit angle is calculated, i.e., the angle sampling time sequence. Next, an interpolation algorithm is used to calculate the vibration amplitude corresponding to each angle sampling moment on the original vibration waveform with equal time intervals; for example, linear interpolation or spline interpolation methods can be used. The calculated vibration amplitudes are arranged in order of rotational angle to form an angular domain signal. Through equal-angle resampling, the vibration signal corresponds to the equipment's rotational angle, eliminating the influence of rotational speed fluctuations on the vibration signal, allowing subsequent analysis to focus on fault characteristics related to the rotational angle.

[0049] Step S302: Perform spectral analysis on the angular domain signal to obtain the order spectrum; It should be noted that spectrum analysis refers to signal processing methods that convert signals in the time or angular domain to the frequency or order domain, which can be achieved using Fourier transform. An order spectrum is a graph with the rotational order as the horizontal axis and the vibration amplitude as the vertical axis. The order is a multiple of the equipment's reference rotational speed; for example, a first harmonic corresponds to the equipment's rotational frequency, and a second harmonic corresponds to twice the rotational frequency.

[0050] A Fast Fourier Transform (FFT) is performed on the angular domain signal to transform it into the order domain. The result is a series of discrete order components, each corresponding to a specific order value and the vibration amplitude at that order. These order components are arranged in ascending order of order value and plotted as an order spectrum. The order spectrum clearly shows the vibration components related to the equipment's rotational frequency. For example, for rolling bearing failures, energy peaks will appear at specific fault characteristic orders, and for misalignment failures, significant peaks will appear at the second harmonic.

[0051] Step S303: Extract feature values ​​associated with equipment mechanical faults from the order spectrum; It should be noted that equipment mechanical failure refers to abnormal states related to the mechanical structure that occur during the operation of industrial equipment, specifically including rolling bearing failure, misalignment, imbalance, and looseness. Eigenvalues ​​are quantitative indicators extracted from the order spectrum that characterize a specific type of failure, such as amplitude, sideband energy, and harmonic energy ratio at a specific order.

[0052] Based on pre-configured fault feature extraction rules, the order positions corresponding to various mechanical faults are located in the order spectrum. For example, for rolling bearing faults, the fault feature order is calculated based on the bearing's geometric parameters, and the amplitude of that order and its harmonics is extracted from the order spectrum. For misalignment faults, the amplitude at the second harmonic is extracted; for imbalance faults, the amplitude at the first harmonic is extracted; and for loosening faults, the amplitude and sideband energy of the harmonic sequence are extracted. After extraction, a set of feature value vectors associated with various faults are formed for subsequent diagnostic use.

[0053] Step S304: Match the feature values ​​and temperature signals with a preset fault feature database, and obtain the equipment status monitoring results of the industrial equipment based on the matching results.

[0054] It should be noted that the fault feature database is a pre-built database that stores the characteristic value ranges, characteristic value combination patterns, and temperature change patterns corresponding to various mechanical faults. Matching refers to comparing the extracted characteristic values ​​and temperature signals with the fault patterns in the database to find the closest fault type.

[0055] The system accesses a pre-defined fault feature database, which stores typical feature value ranges and temperature ranges for various faults such as rolling bearing faults, misalignment faults, imbalance faults, and looseness faults. The extracted feature value vector and synchronously acquired temperature signal are matched against the typical feature value ranges and temperature ranges in the database. Specifically, the similarity or matching degree between the current feature value and the corresponding feature value range for each fault mode in the database is calculated, as is the similarity or matching degree between the temperature signal and the temperature range. Euclidean distance, cosine similarity, or rule-based logical judgment methods can be used. The fault mode with the highest matching degree is taken as the diagnostic result. It is worth noting that, depending on the actual application scenario, feature value matching can be used as the primary judgment criterion, while temperature signals can be used for auxiliary judgment. For example, if the bearing fault feature value is obvious but the temperature is normal, it may be judged as an early, minor fault; if the feature value is obvious and the temperature is rising, it may be judged as a serious fault. Based on the matching results, the equipment condition monitoring results for the industrial equipment are determined, such as outputting conclusions like "early wear of rolling bearings, continued observation recommended" or "misalignment fault, alignment correction required."

[0056] Therefore, by using the speed signal to resample the vibration waveform at equal angles, the influence of speed fluctuations on the vibration signal can be effectively eliminated, thereby improving the accuracy and efficiency of equipment fault diagnosis under variable speed conditions.

[0057] In one feasible embodiment, such as Figure 3 As shown, steps S303 may be followed by steps S305 to S307: Step S305: Establish a dynamic baseline model based on data from the historical healthy operation phases of the industrial equipment; It should be noted that the historical healthy operation phase refers to the period during which industrial equipment was in a fault-free and normal operating state over a predetermined period of time. The dynamic baseline model is a reference model built based on data from the equipment's normal operating period, reflecting the changing patterns of characteristic values ​​of the equipment in a healthy state. This model can be dynamically updated over time and with the accumulation of new data.

[0058] First, operational data of industrial equipment during its healthy operating phase is selected from historical databases. Selection criteria can include data immediately after maintenance, periods manually confirmed as fault-free, or early-stage operational data. Feature value sequences for each analysis point are extracted from these healthy-phase data. Statistical analysis is performed on similar feature values, calculating their mean, standard deviation, percentiles, and other statistical indicators. These indicators are used as baseline parameters to construct an initial dynamic baseline model. As the equipment continues to operate, new healthy-phase data is continuously collected. A sliding window or recursive algorithm is used to dynamically update the baseline model, enabling it to adapt to normal fluctuations caused by equipment aging and environmental changes.

[0059] Step S306: Calculate the deviation between the eigenvalue and the corresponding eigenvalue in the dynamic baseline model; It should be noted that deviation refers to the degree of difference between the currently extracted feature value and the corresponding feature value in the dynamic baseline model. It can be represented by various quantitative indicators, such as absolute difference, relative percentage change, and standard deviation multiple.

[0060] For each extracted feature value, the corresponding baseline value is found in the dynamic baseline model, such as the mean or median of similar feature values. The difference between the current feature value and the baseline value is calculated to obtain the absolute deviation. The absolute deviation is divided by the standard deviation of the baseline value to obtain the deviation factor in units of standard deviation. Alternatively, the percentage change of the current feature value relative to the baseline value can be calculated, i.e., the current value minus the baseline value and then divided by the baseline value. The various deviation indices obtained from the above calculations together constitute a quantitative assessment of the degree of anomaly of the feature value.

[0061] Step S307: Determine the equipment status monitoring results of the industrial equipment based on the range of deviation.

[0062] It should be noted that the deviation range refers to the pre-set threshold interval used to classify the health level of the equipment. For example, multiple thresholds can be set to classify the deviation into different levels such as normal, attention, warning, and danger.

[0063] Multiple deviation thresholds are preset, such as a first threshold, a second threshold, and a third threshold. The calculated deviation is then compared with these thresholds. If the deviation of all feature values ​​is less than the first threshold, the device is determined to be in a healthy state, and the monitoring result "Device operating normally" is output. If the deviation of any feature value is between the first and second thresholds, the device is determined to be in a state of alert, and the monitoring result "Slight deviation of feature value, close monitoring recommended" is output. If the deviation of any feature value is between the second and third thresholds, the device is determined to be in a state of warning, and the monitoring result "Significant deviation of feature value, inspection recommended" is output. If the deviation of any feature value is greater than the third threshold, the device is determined to be in a dangerous state, and the monitoring result "Severe deviation of feature value, immediate shutdown and maintenance required" is output. Furthermore, the deviation of multiple feature values ​​can be combined for a comprehensive judgment; for example, if multiple feature values ​​exceed the limit simultaneously, the alarm level is increased.

[0064] Therefore, by establishing a dynamic baseline model using the equipment's own historical health data, the individual characteristics and normal fluctuation patterns of each device can be fully reflected, avoiding potential misjudgments that might occur when using a uniform threshold on different devices. By calculating the deviation and classifying it into levels, a quantitative assessment and tiered early warning of equipment health status can be achieved, improving the accuracy of equipment status monitoring.

[0065] In one feasible embodiment, step S10 may include steps S101 to S103: Step S101: Collect the vibration waveform of the industrial equipment in the target area through a preset vibration sensor, collect the rotation speed signal of the industrial equipment through a preset speed sensor, and collect the temperature signal of the industrial equipment through a preset temperature sensor. It should be noted that a pre-installed vibration sensor refers to a sensing device pre-installed at key measurement points of industrial equipment to collect vibration signals, such as a piezoelectric accelerometer. A pre-installed speed sensor refers to a sensing device pre-installed near the rotating shaft of the equipment to collect speed signals, such as a Hall current sensor, photoelectric encoder, or magnetoelectric speed sensor. A pre-installed temperature sensor refers to a sensing device pre-installed on or inside the surface of the equipment to collect temperature signals, such as a thermocouple, resistance temperature detector (RTD), or infrared temperature sensor.

[0066] Various sensors are installed at key parts of industrial equipment according to a pre-designed measurement point scheme. For example, vibration sensors are installed on vibration-sensitive parts such as bearing housings and housings, ensuring close contact between the sensor and the equipment surface. Speed ​​sensors are installed near the rotating shaft; for motor equipment, Hall current sensors can be used to indirectly obtain the speed by measuring the current frequency, while for equipment with a rotating shaft, photoelectric encoders or magnetoelectric sensors can be directly installed to measure the speed. Temperature sensors are installed on the equipment surface or inserted into pre-machined temperature measurement holes, ensuring good thermal contact with the measured part. All sensors acquire corresponding signals in real time according to the set sampling frequency and triggering method.

[0067] Step S102: Send self-test commands to the vibration sensor, speed sensor and temperature sensor respectively, and receive the self-test status information returned by each sensor in response to the self-test command; It should be noted that the self-test command refers to the control signal that triggers the sensor to perform its internal self-diagnostic function, and the self-test status information refers to the data returned by the sensor after completing the self-test, reflecting its own health status. Specifically, it may include parameters such as the sensor's operating voltage, signal strength, internal circuit status, and temperature compensation status.

[0068] According to a preset self-test cycle or before each data acquisition, a self-test command is broadcast or unicast to all connected sensors. The self-test command contains specific command codes and parameters, instructing the sensors to initiate a self-diagnostic program. Upon receiving the self-test command, each sensor executes its internal self-diagnostic functions, such as detecting the resistance or capacitance of sensitive components, measuring internal reference signals, checking the power supply voltage, and testing the communication link. After completing the self-test, the sensor encapsulates the self-test results into a self-test status information data packet and returns it to the system through the communication interface. The system receives and parses this self-test status information, extracting the key parameters.

[0069] Step S103: Determine the working status of each sensor based on the self-test status information.

[0070] It should be noted that the working status of a sensor refers to whether the sensor can complete the measurement task normally and accurately, including normal status and abnormal status. Abnormal status can be further subdivided into specific types such as sensor failure, decreased accuracy, communication failure, and power supply abnormality.

[0071] The received self-test status information is compared with the preset normal operating range. For example, if the operating voltage value returned by the sensor is within the allowable error range of the nominal voltage, the power supply is considered normal; otherwise, the power supply is considered abnormal. If the signal strength value is higher than the preset minimum signal threshold, the signal is considered normal; otherwise, the signal is considered weak or there is a communication failure. If the internal circuit status code matches the normal value, the circuit is considered normal; otherwise, the circuit is considered faulty. If the temperature compensation status is normal, the sensor's temperature compensation function is considered normal. Based on the above judgment results, a conclusion is drawn about the sensor's operating status. For sensors determined to be abnormal, an abnormality mark can be added to the data record, or an alarm can be triggered to notify maintenance personnel to check or replace the sensor.

[0072] By following the steps above, while collecting equipment operating status data, the working status of the sensors themselves is verified, which can promptly detect sensor faults or performance degradation, avoid data distortion and misdiagnosis caused by sensor problems, and thus improve the reliability of equipment monitoring data.

[0073] In one feasible embodiment, the industrial equipment monitoring method may further include steps B10-B20: Step B10: Upload the operating status data and equipment status monitoring results of multiple industrial devices to the cloud server so that the cloud server can build a knowledge graph of equipment faults. It should be noted that a cloud server refers to a remote server deployed in a cloud computing environment that has data storage and processing capabilities. A device fault knowledge graph is a data model that organizes knowledge in a graph structure, containing device types, fault types, fault characteristics, and the relationships between fault cases. Entities are typically represented by nodes, and relationships between entities are represented by edges.

[0074] The collected equipment operating status data and the analyzed equipment status monitoring results are uploaded to the cloud server periodically or in real time. The uploaded data includes raw data such as vibration waveforms, speed signals, and temperature signals, as well as extracted feature values, diagnostic conclusions, and health levels. The cloud server receives and stores massive amounts of data from multiple devices and multiple factory areas. Based on this data, knowledge extraction technology is used to identify entities and relationships. Entities can be equipment models, fault types, fault characteristics, etc. The correspondence between feature values ​​and fault types is extracted from the operating data. These entities and relationships are stored in the form of a graph database to construct an equipment fault knowledge graph. This knowledge graph records various faults occurring in different equipment types, typical characteristic patterns corresponding to each fault, and successfully handled fault cases.

[0075] Step B20: In response to the knowledge graph query request, retrieve the device fault knowledge graph from the cloud server.

[0076] It should be noted that a knowledge graph query request refers to an instruction initiated by a user to obtain specific fault information or equipment information from the knowledge graph. Users can input query conditions through a graphical interface or submit structured query statements through an API (Application Programming Interface).

[0077] Maintenance personnel access the knowledge graph query interface on the cloud server through client devices, inputting query conditions, such as selecting the equipment type as "water pump" and the fault type as "bearing fault," or inputting a specific range of fault characteristic values. The client sends the query request to the cloud server, which parses the query conditions, executes the query operation in the graph database, and retrieves nodes and relationships that match the conditions. The query results are presented graphically, displaying relevant equipment nodes, fault nodes, characteristic nodes, and their connections. For example, the query results may show other bearing fault cases that have occurred on similar equipment, the characteristic value range corresponding to each case, the handling measures taken, and the handling effects. Maintenance personnel can click on nodes to view detailed information and obtain reference for fault diagnosis and handling.

[0078] Through the above steps, cross-device fault knowledge accumulation and sharing are realized, and fault cases and experiences scattered across multiple devices and multiple factory areas are aggregated into a structured knowledge graph. Through the knowledge graph query function, maintenance personnel can quickly retrieve historical similar cases when encountering new faults, obtain fault characteristics and handling experience, and provide reference for current fault diagnosis, thereby improving the level of intelligence in fault diagnosis and the efficiency of knowledge reuse.

[0079] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the industrial equipment monitoring method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0080] This application also provides an industrial equipment monitoring device; please refer to... Figure 4 Industrial equipment monitoring devices include: Data acquisition module 10 is used to collect operating status data of industrial equipment within the target area; The operating condition determination module 20 is used to determine whether the industrial equipment is in a preset target operating condition based on the operating status data. The data analysis module 30 is used to analyze and process the operating status data of industrial equipment if the equipment is in the target operating condition, and obtain the equipment status monitoring results.

[0081] Optionally, the operating condition determination module 20 is also used for: Based on the changes in the speed signal of the industrial equipment, the operating conditions of the industrial equipment are divided into shutdown state, startup state, steady-state operation state and variable speed operation state. At least one of the following states—stop, start, steady-state, and variable-speed operation—is defined as the target operating condition for industrial equipment.

[0082] Optionally, the operating status data includes vibration waveforms, rotational speed signals, and temperature signals. The data analysis module 30 is also used for: If the industrial equipment is in the target operating condition, the vibration waveform is resampled at equal angles using the rotation speed signal to obtain the angular domain signal. Spectral analysis of the angular domain signal yields the order spectrum; Extract feature values ​​associated with equipment mechanical failures from the order spectrum; The characteristic values ​​and temperature signals are matched with a preset fault characteristic database, and the equipment status monitoring results of the industrial equipment are obtained based on the matching results.

[0083] Optionally, the data analysis module 30 is also used for: A dynamic baseline model is established based on data from the historical healthy operating phases of industrial equipment. Calculate the deviation between the eigenvalue and the corresponding eigenvalue in the dynamic baseline model; The equipment condition monitoring results of industrial equipment are determined based on the range of deviation.

[0084] Optionally, the data acquisition module 10 is also used for: Vibration waveforms of industrial equipment within the target area are collected using a preset vibration sensor, rotation speed signals of industrial equipment are collected using a preset speed sensor, and temperature signals of industrial equipment are collected using a preset temperature sensor. Send self-test commands to the vibration sensor, speed sensor and temperature sensor respectively, and receive the self-test status information returned by each sensor in response to the self-test command; The working status of each sensor is determined based on the self-test status information.

[0085] Optionally, the data analysis module 30 is also used for: Equipment diagnostic reports and equipment maintenance work orders are generated based on the equipment condition monitoring results.

[0086] Optionally, the data analysis module 30 is also used for: The operating status data and equipment status monitoring results of multiple industrial devices are uploaded to the cloud server so that the cloud server can build a knowledge graph of equipment faults. In response to a knowledge graph query request, the device fault knowledge graph is retrieved from the cloud server.

[0087] The industrial equipment monitoring device provided in this application, employing the industrial equipment monitoring method described in the above embodiments, can improve equipment operation and maintenance efficiency. Compared with the prior art, the beneficial effects of the industrial equipment monitoring device provided in this application are the same as those of the industrial equipment monitoring method provided in the above embodiments, and other technical features in the industrial equipment monitoring device are the same as those disclosed in the industrial equipment monitoring method of the above embodiments, and will not be repeated here.

[0088] This application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the industrial equipment monitoring method in the first embodiment described above.

[0089] The following is for reference. Figure 5 The diagram illustrates a structural schematic of an electronic device suitable for implementing embodiments of this application. The electronic devices in these embodiments may include, but are not limited to, mobile terminals such as laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 5 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0090] like Figure 5As shown, the electronic device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the electronic device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. The communication device 1009 allows the electronic device to exchange data with other devices wirelessly or via wired communication. Although the diagrams show electronic devices with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented alternatively.

[0091] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0092] The electronic device provided in this application, employing the industrial equipment monitoring method described in the above embodiments, can improve equipment operation and maintenance efficiency. Compared with the prior art, the beneficial effects of the electronic device provided in this application are the same as those of the industrial equipment monitoring method provided in the above embodiments, and other technical features of the electronic device are the same as those disclosed in the industrial equipment monitoring method of the previous embodiment, and will not be repeated here.

[0093] It should be understood that the various parts disclosed in the embodiments of this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0094] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0095] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the industrial equipment monitoring method in the above embodiments.

[0096] The computer-readable storage medium provided in this application embodiment may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0097] The aforementioned computer-readable storage medium may be included in an electronic device or may exist independently without being assembled into an electronic device.

[0098] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by an electronic device, the electronic device causes the electronic device to: collect operating status data of industrial equipment within a target area; determine whether the industrial equipment is in a preset target operating condition based on the operating status data; and if the industrial equipment is in the target operating condition, analyze and process the operating status data to obtain equipment status monitoring results.

[0099] Computer program code for performing the operations of the embodiments of this application can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0100] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0101] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0102] The readable storage medium provided in this application embodiment is a computer-readable storage medium. This computer-readable storage medium stores computer-readable program instructions (i.e., a computer program) for executing the above-described industrial equipment monitoring method, which can improve equipment operation and maintenance efficiency. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application embodiment are the same as the beneficial effects of the industrial equipment monitoring method provided in the above embodiments, and will not be repeated here.

[0103] The above are only some embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for monitoring industrial equipment, characterized in that, The industrial equipment monitoring method includes: Collect operational status data of industrial equipment within the target area; Determine whether the industrial equipment is in a preset target operating condition based on the operating status data; If the industrial equipment is in the target operating condition, the operating status data is analyzed and processed to obtain the equipment status monitoring results.

2. The industrial equipment monitoring method as described in claim 1, characterized in that, Before the step of determining whether the industrial equipment is in a preset target operating condition based on the operating status data, the method further includes: Based on the changes in the rotational speed signal of the industrial equipment, the operating conditions of the industrial equipment are divided into shutdown state, startup state, steady-state operation state, and variable speed operation state. The target operating condition of the industrial equipment is determined by at least one of the following: the shutdown state, the startup state, the steady-state operation state, and the variable-speed operation state.

3. The industrial equipment monitoring method as described in claim 1, characterized in that, The operating status data includes vibration waveforms, rotational speed signals, and temperature signals. The step of analyzing and processing the operating status data to obtain equipment status monitoring results if the industrial equipment is in the target operating condition includes: If the industrial equipment is in the target operating condition, the vibration waveform is resampled at equal angles using the rotation speed signal to obtain an angular domain signal. The spectral analysis of the angular domain signal yields the order spectrum; Extract feature values ​​associated with equipment mechanical faults from the order spectrum; The feature value and the temperature signal are matched with a preset fault feature database, and the equipment status monitoring result of the industrial equipment is obtained based on the matching result.

4. The industrial equipment monitoring method as described in claim 3, characterized in that, Following the step of extracting feature values ​​associated with equipment mechanical faults from the order spectrum, the method further includes: A dynamic baseline model is established based on data from the historical healthy operation phases of the industrial equipment. Calculate the deviation between the feature value and the corresponding feature value in the dynamic baseline model; The equipment status monitoring results of the industrial equipment are determined based on the range of the deviation.

5. The industrial equipment monitoring method as described in claim 1, characterized in that, The step of collecting operational status data of industrial equipment within the target area includes: Vibration waveforms of industrial equipment within the target area are collected using a preset vibration sensor, rotation speed signals of the industrial equipment are collected using a preset rotation speed sensor, and temperature signals of the industrial equipment are collected using a preset temperature sensor. Send self-test commands to the vibration sensor, the speed sensor and the temperature sensor respectively, and receive the self-test status information returned by each sensor in response to the self-test command; The working status of each sensor is determined based on the self-test status information.

6. The industrial equipment monitoring method as described in claim 1, characterized in that, After the step of analyzing and processing the operating status data to obtain the equipment status monitoring results if the industrial equipment is in the target operating condition, the method further includes: Based on the equipment status monitoring results, generate equipment diagnostic reports and equipment maintenance work orders.

7. The industrial equipment monitoring method according to any one of claims 1 to 6, characterized in that, The method further includes: The operating status data and equipment status monitoring results of multiple industrial devices are uploaded to a cloud server so that the cloud server can construct an equipment fault knowledge graph. In response to a knowledge graph query request, the device fault knowledge graph is retrieved from the cloud server.

8. An industrial equipment monitoring device, characterized in that, The industrial equipment monitoring device includes: The data acquisition module is used to collect operating status data of industrial equipment within the target area; The operating condition determination module is used to determine whether the industrial equipment is in a preset target operating condition based on the operating status data. The data analysis module is used to analyze and process the operating status data to obtain equipment status monitoring results if the industrial equipment is in the target operating condition.

9. An electronic device, characterized in that, The electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the industrial equipment monitoring method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the industrial equipment monitoring method as described in any one of claims 1 to 7.

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