Digital integrated bridge crane fault diagnosis system

Abstract

The invention relates to the technical field of intelligent monitoring, in particular to a digital integrated bridge crane fault diagnosis system which comprises a stress fluctuation monitoring module, a local stress analysis module, an energy flow monitoring module, a fault propagation calculation module and an intelligent early warning module. According to the method, the change trend of the health state of the structure is identified through stress fluctuation data analysis in a continuous period, the real-time control capability of the safety of the bridge crane structure is enhanced, local gridding stress monitoring is implemented based on a high-risk area, the abnormal expansion trend is captured, the fault prediction accuracy is improved, and the fault prediction efficiency is improved. The method comprises the following steps of: determining a part with over-limit loss and an abnormal energy transmission path in a power transmission process, ensuring deep analysis of an equipment operation state, calculating a fault influence range in combination with an input power change rate, identifying continuity of a power loss path, analyzing a propagation trend of a fault, and optimizing fault traceability and diffusion prediction capability. And the comprehensiveness of the operation state evaluation of the bridge crane is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent monitoring, and particularly to a fault diagnosis system for digital integrated bridge cranes. Background Art

[0002] The technical field of intelligent monitoring involves real-time monitoring and data analysis of the operating status of industrial equipment, mechanical systems, and infrastructure to improve operating safety, reduce faults, and optimize maintenance strategies. The core content involves links such as sensor data acquisition, signal analysis, health status assessment, and fault prediction. Through embedded sensing devices, remote data acquisition terminals, and industrial communication networks, the operating parameters of mechanical equipment are obtained, and compared and analyzed with historical data to achieve accurate perception of the equipment status and judgment of the fault trend. Intelligent monitoring technology has been widely applied in many fields such as industrial production, transportation, power systems, and building structure safety monitoring. Its development involves aspects such as data fusion analysis, intelligent diagnosis strategies, and remote monitoring and management, relying on information technology and automation means to improve the reliability and operation and maintenance efficiency of equipment.

[0003] Among them, the fault diagnosis system for digital integrated bridge cranes refers to a system that based on sensor network and data processing technology, monitors the operating status of bridge cranes in real time and identifies potential faults. This system aims at key components such as the structural stress, vibration characteristics, motor status, and braking system of bridge cranes, uses measuring devices such as acceleration sensors, strain gauges, and current sensors to obtain operating parameters, and adopts feature extraction and pattern recognition methods to analyze the data. After the data is collected on-site, it is transmitted to the computing unit through an industrial bus or wireless network for data preprocessing, and then the equipment status is judged according to the classification algorithm to achieve automatic fault diagnosis of bridge cranes.

[0004] In the existing fault diagnosis process of bridge cranes, in terms of stress monitoring, only single-point measurement is relied on, and the analysis of periodic change trends cannot be formed, making it difficult to identify abnormal stress fluctuations in a timely manner, resulting in the potential risk of structural fatigue damage not being fully evaluated. The local stress detection method lacks refined monitoring of high-risk areas and only relies on the global average stress level for judgment, making it difficult to detect local abnormal expansion trends and making it difficult to give early warnings about potential problems in stress concentration areas. In terms of energy flow analysis, the existing methods mainly focus on the energy consumption changes of single components and fail to establish the energy transfer relationship between components, resulting in the source of abnormal energy loss being difficult to trace and affecting the accuracy of fault root cause analysis. The fault propagation assessment does not consider the power change path between adjacent components and lacks accurate modeling of the fault diffusion trend, which may lead to insufficient assessment of the fault impact range, making the formulation of equipment maintenance strategies lack pertinence and reducing operating safety and operation and maintenance efficiency. Summary of the Invention

[0005] The object of the present invention is to solve the drawbacks existing in the prior art, and a digital integrated fault diagnosis system for bridge cranes is proposed.

[0006] To achieve the above object, the present invention adopts the following technical solutions: The digital integrated fault diagnosis system for bridge cranes includes: The stress fluctuation monitoring module obtains the stress data of the bridge crane, calculates the stress wave amplitude at consecutive time points, statistically analyzes the mean value of the wave amplitudes, determines the maximum value of the wave amplitude, compares it with the stress fluctuation threshold, records the frequency of abnormal fluctuations, analyzes the stress fluctuation change trend within a continuous period, marks and outputs the high-risk stress fluctuation area; The local stress analysis module arranges additional stress sensing units at adjacent structural parts according to the high-risk stress fluctuation area, statistically analyzes the local stress data, calculates the stress mean difference, extracts the abnormal stress gradient area, analyzes the abnormal expansion trend, and obtains the local stress abnormal area; The energy flow monitoring module collects the input and output power data of the power unit, transmission unit, and braking unit of the bridge crane based on the local stress abnormal area, calculates the power transfer ratio, compares it with the energy loss threshold, records the abnormal energy transfer path, marks the components with abnormal power loss, and obtains the energy loss abnormal area; The fault propagation calculation module statistically analyzes the input power change rate of adjacent components based on the energy loss abnormal area, calculates the fault influence range, determines whether the power loss path is continuous, and determines the fault propagation path to obtain the fault propagation path area.

[0007] As a further solution of the present invention, the high-risk stress fluctuation area includes the maximum stress wave amplitude data, the frequency of abnormal fluctuations, and the fluctuation change trend; the local stress abnormal area includes the local stress mean difference value, the abnormal stress gradient area, and the abnormal expansion trend; the energy loss abnormal area includes the components with excessive power loss, the abnormal energy transfer path, and the abnormal power transfer ratio data; the fault propagation path area includes the input power change rate, the analysis result of the continuity of the power loss path, and the fault influence range.

[0008] As a further solution of the present invention, the stress fluctuation monitoring module includes: The stress data acquisition sub-module obtains the stress data of the main beam, end beam, welding joints, and support connection points of the bridge crane through stress sensors, continuously acquires the stress values of each monitoring point using stress sensors, records the stress values at each time point, calls the stress data of adjacent time points, calculates the stress change amount at consecutive time points, and obtains the stress data sequence; The stress wave amplitude calculation sub-module calculates the stress wave amplitude at consecutive time points based on the stress data sequence, statistically analyzes the mean value of the stress wave amplitudes at all time points, determines the maximum value by screening all wave amplitude data, and obtains the stress wave amplitude mean value and the maximum value data; Based on the mean and maximum stress wave amplitude data, the stress fluctuation risk assessment sub-module determines whether the stress fluctuation threshold is exceeded, records the number of abnormal fluctuations, and statistically analyzes the fluctuation trend within consecutive periods. The formula used is: ; Calculate the fluctuation change rate , and based on the trend change, determine whether the fluctuation period shortens. If the fluctuation change rate continues to increase and the fluctuation period shortens, mark the target monitoring point as a high-risk area, and comprehensively output the high-risk stress fluctuation area. Among them, represents the number of periods, represents the mean stress wave amplitude of the th period, represents the mean stress wave amplitude of the th period, represents the th period, and represents the number of abnormal fluctuations within the

[0009] As a further aspect of the present invention, the local stress analysis module includes: The additional sensor unit layout sub-module, based on the high-risk stress fluctuation area, arranges additional stress sensor units in the high-risk area and adjacent structural parts, sets a shortened acquisition time interval, and acquires and establishes a local stress data set; The local stress gradient calculation sub-module calculates the difference in the mean stress between adjacent monitoring points within the local grid based on the local stress data set, compares it with the stress gradient threshold, screens the abnormal area, and obtains the abnormal stress gradient area; The stress anomaly expansion analysis sub-module analyzes the expansion trend of the stress anomaly based on the abnormal stress gradient area, using the formula: ; Calculate the stress gradient change rate between adjacent monitoring points within consecutive periods , determine the area where the expansion trend is concentrated, and obtain the local stress anomaly area. Among them, represents the number of monitoring points within the local grid, represents the stress value of the th monitoring point, represents the stress value of the adjacent monitoring point, represents the th monitoring point and the distance to the adjacent monitoring point, represents the number of periods, represents the th period, and represents the number of abnormal stress events within the

[0010] As a further aspect of the present invention, the energy flow monitoring module includes: The input and output power acquisition sub-module synchronously acquires the input power and output power data of the power unit, transmission unit, and braking unit of the bridge crane based on the local stress abnormal area, screens the acquired data based on the sensor detection data and historical operation data, eliminates the abnormal data items, and records and generates the input power and output power data; The power transmission ratio calculation sub-module calculates the power transmission ratio between components ; based on the input power and output power data according to the transmission relationship between components, using the formula: between component and component , compares the transmission conditions between components, and obtains the power loss distribution record. Among them, is the output power of component , is the input power of component ; The energy loss anomaly identification sub-module compares the power loss conditions between components based on the power loss distribution record, determines whether the loss exceeds the energy loss threshold, marks the components with abnormal power loss, records the abnormal energy transfer path, and obtains the energy loss abnormal area.

[0011] As a further solution of the present invention, the fault propagation calculation module includes: The input power change statistics sub-module statistically analyzes the input power data of adjacent components based on the energy loss abnormal area, calculates the input power change rate between adjacent components, screens the abnormal change values, and obtains the input power change rate data; The fault influence range calculation sub-module calculates the fault influence range ; of component based on the input power change rate data, using the formula: , compares the sizes of the influence ranges, and obtains the fault influence range data. Among them, represents the input power of component , represents the input power of component , represents the transmission distance between component and , represents the number of components; The fault propagation path identification sub-module determines whether the power loss path is continuous based on the fault influence range data, analyzes the power loss trend of adjacent components, determines whether the fault influence is coherent, screens the fault propagation direction, and obtains the fault propagation path area.

[0012] As a further solution of the present invention, the system further includes an intelligent warning module; Based on the fault propagation path area, the intelligent warning module calculates the number of affected components of the bridge crane, determines whether the influence range exceeds the critical value. If the number of affected components reaches the warning condition, it determines the fault level and issues a fault warning message; The fault warning message includes the number of affected components, the influence range, and the fault level.

[0013] As a further solution of the present invention, the intelligent warning module includes: Based on the fault propagation path area, the affected component statistics sub-module counts the number of affected components of the bridge crane, calls the power loss situation of each component, screens the components with abnormal power fluctuations, calculates the total number of affected components, analyzes the correlation between components, records the distribution of affected components, and obtains the record of the number of affected components; Based on the record of the number of affected components, the warning condition judgment sub-module determines whether the influence range exceeds the critical value, combines the distribution of affected components, analyzes its coverage range, screens the set of components that exceed the set influence area threshold, and determines whether there is an expanding trend of the fault influence based on the distribution density and power loss trend of the affected components, and determines whether the warning condition is met to obtain the warning trigger status; Based on the warning trigger status, the fault level identification sub-module determines the fault level, screens the corresponding fault severity interval, matches the preset fault level standard, analyzes the number of affected components, loss trend and distribution density, classifies and records the fault level information, and issues a fault warning message.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In the present invention, through the analysis of stress fluctuation data in continuous cycles, the change trend of the structural health state is accurately identified, the accurate positioning of high-risk areas is ensured, the real-time control ability of the structural safety of the bridge crane is enhanced. Based on the identified high-risk areas, local grid stress monitoring is implemented, the acquisition time interval is shortened, the abnormal expansion trend is captured, the early fault symptoms are refined and identified, the accuracy of fault prediction is improved. Through the comparative analysis of the input and output power data of adjacent components, an energy flow model is established to clarify the components with excessive loss and abnormal energy transfer paths during the power transfer process, ensuring in-depth analysis of the equipment operation state. Combining the input power change rate to calculate the fault influence range, identifying the continuity of the power loss path, analyzing the propagation trend of the fault, optimizing the fault tracing and diffusion prediction ability, improving the comprehensiveness of the bridge crane operation state assessment. The multi-level data monitoring method combined with dynamic trend analysis makes the identification of potential faults of the bridge crane more refined, enhances the warning ability, and ensures the timeliness and pertinence of maintenance decisions. Description of the Drawings

[0015] Figure 1 is the system flow chart of the present invention; Figure 2 is the flow chart of the stress fluctuation monitoring module of the present invention; Figure 3 is the flow chart of the local stress analysis module of the present invention; Figure 4 is the flow chart of the energy flow monitoring module of the present invention; Figure 5 is the flow chart of the fault propagation calculation module of the present invention; Figure 6 is the flow chart of the intelligent early warning module of the present invention. Detailed Embodiments

[0016] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0017] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0018] Please refer to Figure 1 , the digital integrated bridge crane fault diagnosis system includes: The stress fluctuation monitoring module obtains the stress data of the main girder, end beam, welding joints, and support connection points of the bridge crane through stress sensors, calculates the stress wave amplitude at consecutive time points, statistically calculates the mean value of all amplitudes within a consecutive period, determines the maximum value of the amplitude, judges whether it exceeds the stress fluctuation threshold, records the abnormal fluctuation frequency, analyzes the stress fluctuation change trend within the consecutive period. If it continues to increase and the fluctuation period shortens, the target monitoring point is marked as a high-risk area, and the high-risk stress fluctuation area is output; The local stress analysis module, based on the high-risk stress fluctuation area, arranges additional stress sensing units in the high-risk stress fluctuation area and adjacent structural parts, shortens the acquisition time interval to statistically calculate the local stress data, calculates the stress mean difference between adjacent monitoring points within the local grid, extracts the abnormal stress gradient area, analyzes the abnormal expansion trend, and obtains the local stress abnormal area; Based on the local stress anomaly area, the energy flow monitoring module collects the input power and output power data of the power unit, transmission unit, and braking unit of the bridge crane, calculates the power transfer ratio between adjacent components, compares whether the power loss exceeds the energy loss threshold, records the abnormal energy transfer path, and marks the components with abnormal power loss to obtain the energy loss abnormal area; Based on the energy loss abnormal area, the fault propagation calculation module statistically analyzes the input power change rate of adjacent components, calculates the fault influence range, judges whether the power loss path is continuous, and determines the fault propagation path to obtain the fault propagation path area; Based on the fault propagation path area, the intelligent warning module calculates the number of affected components of the bridge crane, judges whether the influence range exceeds the critical value. If the number of affected components reaches the warning condition, it determines the fault level and issues a fault warning message.

[0019] The high-risk stress fluctuation area includes the maximum stress amplitude data, abnormal fluctuation frequency, and fluctuation change trend. The local stress anomaly area includes the local stress mean difference value, abnormal stress gradient area, and abnormal expansion trend. The energy loss abnormal area includes the components with excessive power loss, abnormal energy transfer path, and abnormal power transfer ratio data. The fault propagation path area includes the input power change rate, the analysis result of the continuity of the power loss path, and the fault influence range. The fault warning message includes the number of affected components, the influence range, and the fault level.

[0020] Please refer to Figure 2 , the stress fluctuation monitoring module includes: The stress data acquisition sub-module obtains the stress data of the main beam, end beam, welding joints, and support connection points of the bridge crane through stress sensors, continuously collects the stress values of each monitoring point using stress sensors, records the stress numerical values at each time point, calls the stress data of adjacent time points, calculates the stress change amount of consecutive time points, and obtains the stress data sequence; Install high-precision stress sensors at the key stress-bearing parts of the bridge crane, including the main beam, end beam, welding joints, and support connection points. Arrange 4 - 6 measuring points at each part, and the measuring point interval is determined according to the beam length or node characteristics. For example, for a main beam with a span of 30 meters, arrange 2 measuring points at both ends and the mid-span, 4 measuring points at the end beam, and 6 measuring points at the welding joints to capture the local stress changes of the structure. After the sensor installation is completed, perform initialization processing through data acquisition, and set the data sampling frequency to 100Hz to capture the rapidly changing stress fluctuations during the operation of the bridge crane. The stress data collected by the sensors is wirelessly transmitted and stored in the stress data storage library.

[0021] During the actual operation of the crane, different working conditions are executed, such as lifting the rated load (100t), moving (10m / min), accelerating (0.5m / s²), emergency stop, etc., and the stress changes at each measuring point are monitored. Assume that during the lifting of the rated load at a certain measuring point, the recorded continuous stress values are 50MPa, 55MPa, 60MPa, 58MPa, 62MPa. After storing the data, the stress data at adjacent time points are called, and the stress change amount at continuous time points is calculated, such as: ; ; ; ; The stress change sequence is obtained , providing data support for subsequent stress fluctuation analysis.

[0022] Based on the stress data sequence, the stress wave amplitude calculation sub-module calculates the stress wave amplitude at continuous time points, statistically calculates the mean value of the stress wave amplitude at all time points, determines the maximum value by screening all wave amplitude data, and obtains the stress wave amplitude mean value and maximum value data; According to the stress data sequence obtained above, calculate the stress fluctuation amplitude at adjacent time points. Assume that the stress data sequence at a certain monitoring point is 50MPa, 55MPa, 53MPa, 60MPa, 58MPa, 62MPa, 65MPa, 63MPa, 68MPa, 70MPa, calculate the stress difference at adjacent time points, and obtain the stress wave amplitude sequence: ; Calculate the mean value of the stress wave amplitude sequence, using arithmetic mean calculation: ; Screen the maximum value in the stress wave amplitude sequence, and the maximum value of the stress wave amplitude is obtained as 7MPa, and then the stress wave amplitude mean value and maximum value data are obtained.

[0023] The stress fluctuation risk assessment sub-module determines whether it exceeds the stress fluctuation threshold according to the stress wave amplitude mean value and maximum value data, records the number of abnormal fluctuations, statistically calculates the fluctuation change trend within a continuous period, and uses the formula: ; Calculate the fluctuation change rate , and judge whether the fluctuation period is shortened according to the trend change. If the fluctuation change rate continues to increase and the fluctuation period is shortened, mark the target monitoring point as a high-risk area, and comprehensively output the high-risk stress fluctuation area, where represents the number of periods, represents the mean value of the stress wave amplitude in the th period, represents the average stress wave amplitude of the th cycle, represents the number of abnormal fluctuations within the th cycle; Based on the calculated average stress wave amplitude (3.56 MPa) and the maximum stress wave amplitude (7 MPa), a stress fluctuation threshold is set. This threshold is determined according to the material yield strength, safety margin, and equipment fatigue life. For the steel structure components of bridge cranes, the common material is Q345B steel, and its yield strength is approximately 345 MPa. According to engineering experience, the influence of stress fluctuation on fatigue life can be expressed by the Goodman relationship. Using the fatigue limit = 0.5, = 172.5 MPa, considering the safety margin = 3, then the set stress fluctuation threshold is: ; Since this value is relatively high, in actual engineering applications, generally, a fluctuating stress within 10% of it is set as the warning value. Therefore, the final set stress fluctuation threshold = 5 MPa. This value is consistent with the typical fluctuation range in the long-term monitoring data of bridge cranes and shows non-linear growth with factors such as increasing load, increasing working frequency, and structural aging. If the stress fluctuation value exceeds this threshold for a long time, it indicates that the structure may enter the fatigue damage stage.

[0024] If the maximum wave amplitude exceeds the threshold, record the abnormal fluctuation. The maximum wave amplitude at this monitoring point is 7 MPa, which exceeds the threshold, and record 1 abnormal fluctuation.

[0025] Suppose that within 5 consecutive monitoring cycles, the obtained average stress wave amplitudes are shown in Table 1.1: Table 1.1

[0026]

[0027] Calculate the difference in the average stress wave amplitudes of adjacent cycles: ; Calculate the fluctuation change rate , substitute it into the formula: ; Since the fluctuation change rate is higher than 5 MPa and the fluctuation period is shortened, this monitoring point is marked as a high-risk stress fluctuation area.

[0028] Please refer to Figure 3 , the local stress analysis module includes: The additional sensing unit layout sub-module is based on high-risk stress fluctuation regions. It layouts additional stress sensor units in high-risk regions and adjacent structural parts, sets a shortened acquisition time interval, and acquires and establishes a local stress data set. Based on high-risk stress fluctuation regions, it is necessary to layout additional stress sensing units in these regions and adjacent structural parts around them. The layout method of these units needs to consider structural stress concentration regions, such as main beam connection points, welding joints, and cantilever ends. The basis for selecting key points is the regions where stress fluctuations exceed the stress fluctuation threshold in historical monitoring data. To ensure the time continuity and spatial coverage of data acquisition, the spacing of sensor layout can be set according to the structural size and stress fluctuation distribution characteristics. For example, on a main beam with a span of 20m, a sensing unit is layout every 2m, and two additional sensing units are layout at the welding joints to capture local stress changes. The specific layout scheme is shown in Table 2.1.

[0029] Table 2.1 Sensor layout scheme

[0030]

[0031] As shown in Table 2.1, the layout of sensors needs to meet the principle that the layout density is higher in regions with more intense stress fluctuations. For example, due to the weld characteristics, local stress concentration occurs at the welding joints, so the layout spacing is relatively short at 1.5m, while the overall stress of the main beam is relatively uniform, so the layout spacing is 2m. All sensors use a monitoring period of 0.1s to ensure capturing instantaneous stress changes, and finally obtain a local stress data set.

[0032] The local stress gradient calculation sub-module calculates the stress mean difference between adjacent monitoring points in the local grid based on the local stress data set, compares it with the stress gradient threshold, screens out abnormal regions, and obtains abnormal stress gradient regions. Call the local stress data set, calculate the stress mean difference between adjacent monitoring points in the local grid, and set the division method of the local grid as each grid contains 4 adjacent monitoring points. The stress mean value inside each grid is calculated as follows: ; Among them, is the stress mean value of the current grid, are the stress values of the four monitoring points inside the grid respectively. After calculating the mean values of multiple grids, calculate the stress gradient between adjacent grids. Set the stress gradient threshold as 10MPa / m. The setting of this value is based on the stress conduction characteristics and fatigue tolerance of the structural material, which is mainly determined by the elastic modulus of the material, the Poisson's ratio and the structural service life requirements. Its calculation method is as follows: ; Among them, The elastic modulus of the low-alloy high-strength steel used for the main girder of the bridge crane is taken as 210 GPa. represents the maximum allowable elastic deformation strain, with a value of 0.00005. represents the average distance between monitoring points, with a value of 1 m, and substitute it into the calculation: ; The calculation results show that the reasonable threshold should be around 10.5 MPa / m, so it is set to 10 MPa / m to ensure a certain safety margin. If the stress difference between adjacent grids exceeds this threshold, then the area is determined as an abnormal stress gradient area. For example, the following data are measured in the main girder area: Table 2.2 Local grid stress data

[0033]

[0034] Calculate the stress gradient between adjacent grids: ; ; Among them, is the distance between adjacent grids (set to 2 m). Judging from the calculation results, does not exceed the set threshold of 10 MPa / m, so the area between Grid 1 and Grid 2 is not determined as an abnormal area. However, in practical applications, if the gradient value of a certain area exceeds this value, further monitoring and analysis of its change trend are required to obtain the abnormal stress gradient area.

[0035] The stress anomaly expansion analysis sub-module analyzes the expansion trend of stress anomalies based on the abnormal stress gradient area, and uses the formula: ; Calculate the stress gradient change rate between adjacent monitoring points within consecutive periods , determine the concentrated area of the expansion trend, and obtain the local stress anomaly area. Among them, represents the number of monitoring points within the local grid, represents the th stress value of the monitoring point, represents the stress value of the adjacent monitoring point, represents the th distance between the monitoring point and the adjacent monitoring point, represents the number of periods, represents the th number of abnormal stress events within the According to the abnormal stress gradient area, analyze the expansion trend of stress anomalies, and use the formula to calculate the stress gradient change rate between adjacent monitoring points within consecutive periods.

[0036] The set expansion threshold is 8 MPa / m, and its setting is based on the law of structural fatigue damage expansion, mainly determined by the crack growth rate , the range of cyclic stress intensity factor and the crack growth characteristics of the material. The calculation method is as follows: ; Among them, is the range of cyclic stress intensity factor, which is calculated by the Paris-Erdogan formula: ; Among them and are the material crack growth constants, with values and , is the cyclic stress amplitude, with a value of 50 MPa, then ; is the initial size of the fatigue crack. Assuming the crack length is mm, then ; Since the crack growth rate will be affected by the change of fatigue load, in order to ensure safety, the crack growth stress range with a maximum gradient not exceeding 20% in the monitoring area is set, then ; Finally, the expansion threshold is set to 8 MPa / m to ensure that the fatigue damage expansion of the structure is controlled within an acceptable range.

[0037] Taking the stress data in Table 2.2 as an example, assuming that this data is the collected data for 3 cycles, and calculating its stress gradient change rate: ; ; Among them, are the number of abnormal stress events in 3 cycles respectively. It is calculated that , compared with the set threshold of 8 MPa / m, this value is slightly higher than the threshold, indicating that the stress gradient change rate in this area is relatively fast, and there may be a risk of fatigue crack growth. This area is finally marked as a local stress abnormal area.

[0038] This result shows that the abnormal change rate of stress in the local grid has reached above the set threshold, and the stress gradient in this area continues to increase. Combining with the monitoring data, the impact on the overall structural safety can be further analyzed.

[0039] Please refer to Figure 4, the energy flow monitoring module includes: The input and output power acquisition sub-module synchronously acquires the input power and output power data of the power unit, transmission unit, and braking unit of the bridge crane based on the local stress abnormal area, screens the acquired data based on the sensor detection data and historical operation data, eliminates the data abnormal items, and records and generates the input power and output power data; To acquire the input power and output power data of the power unit, transmission unit, and braking unit of the bridge crane, first, install current and voltage sensors at the power unit to measure the real-time data of three-phase alternating current, and use the formula to calculate the instantaneous power, where is the line voltage, is the line current, is the power factor, and it is measured that , , the power factor , then the input power is calculated as follows: ; Next, for the transmission unit, install torque and rotational speed sensors to measure the torque of the transmission shaft and the angular velocity , and use the formula to calculate the output power. Assume the torque , the rotational speed , then the angular velocity rad / s, and calculate the output power of the transmission unit: ; Finally, for the braking unit, install a power meter to measure the braking power consumption. Assume that the energy loss during braking is measured to be 4000W, then the braking power acquisition is completed. To exclude abnormal data, the sliding average method is used to screen the acquired data, and the mean value of the data at the last 10 moments is calculated. Assume the 10 sampling data are as follows (unit: W): Table 3.1 Sensor-acquired power data

[0040]

[0041] As shown in Table 3.1, using the sliding average method, calculate the mean power of the power unit:

[0042] Similarly, calculate the mean power of the transmission unit , and the mean braking power , and finally obtain the processed input power and output power data.

[0043] The power transmission ratio calculation sub-module calculates according to the transfer relationship between components based on the input power and output power data, using the formula: ; Calculation component And component The power transfer ratio between , compare the transfer conditions between components, obtain the power loss distribution record, where Is the output power of component , Is the input power of component ; Based on the input power and output power data, calculate the power transfer ratio of adjacent components according to the transfer relationship between components. First, determine the power transfer path between components, that is, power unit → transmission unit → braking unit, and calculate the power transfer ratio of each adjacent component respectively .

[0044] Power unit output power ; Transmission unit input power , output power ; Braking unit input power , output power ; Calculate the power transfer ratio: ; ; Calculate the power loss distribution, where Indicates that there is a 14% loss in the transmission unit during energy transmission, while Indicates that the energy loss of the braking unit has reached 96%, indicating that the power conversion efficiency of the transmission process is low, and the power loss of the braking unit far exceeds the normal range, further indicating that the braking unit is very likely to have failed.

[0045] The energy loss abnormal identification sub-module is based on the power loss distribution record, compares the power loss conditions between components, judges whether the loss exceeds the energy loss threshold, marks the components with abnormal power loss, records the abnormal energy transfer path, and obtains the energy loss abnormal area; Based on the power loss distribution, compare the power loss conditions between components, judge whether the loss exceeds the energy loss threshold, set the energy loss threshold to 5%, and this value comes from the equipment efficiency requirements in industrial standards, that is, the power loss of the power transmission system is usually controlled between 3% and 7%, and the average value of 5% is taken as a reasonable reference value. If it exceeds this threshold, it means that the component may have mechanical failures or abnormal energy losses.

[0046] Compare the calculation results: , abnormal; , abnormal; Record the abnormal energy transfer path (power unit → transmission unit → braking unit), and calculate the power loss ratio: ; ; The result shows that the power loss from the transmission unit to the braking unit is extremely high, far exceeding the 5% threshold. Therefore, the transmission unit and the braking unit are marked as components with abnormal power loss, and then the abnormal energy loss area is obtained.

[0047] Please refer to Figure 5 , the fault propagation calculation module includes: The input power change statistics sub-module, based on the abnormal energy loss area, statistically analyzes the input power data of adjacent components, calculates the input power change rate between adjacent components, filters out abnormal change values, and obtains the input power change rate data; Based on the abnormal energy loss area, first collect the input power data of adjacent components. For each component, its input power can be measured by a power sensor, and the sensor is installed on the motor, electronic control system or hydraulic system to obtain the real-time input power value of each component. For example, the input power of a certain component is measured as 15.2 kW, and the input power of its adjacent component is 14.8 kW. After the data at multiple time points are recorded, these input power values are stored in the database and subjected to time series analysis. Subsequently, calculate the input power change rate between adjacent components. The change rate adopts the incremental calculation method, that is: ; Assume the previous moment kW, the current moment kW, then: ; During the statistical process, in order to eliminate the interference of random fluctuations, set the input power change rate threshold. For example, set the threshold to ±5%. If the change rate exceeds this range, mark the data as abnormal data. The screening standard for abnormal data is based on the standard deviation calculated from historical data. If the current change rate exceeds twice the standard deviation of the mean, it can be regarded as an abnormal value. For example, if the historical change rate mean is 3% and the standard deviation is 1.2%, then filter out the change rate data that exceeds 5.4% or is lower than 0.6%. Finally, record the input power change rate data.

[0048] The fault influence range calculation sub-module, based on the input power change rate data, uses the formula: ; Calculate the fault influence range of component , compare the sizes of the influence ranges, and obtain the fault influence range data, where, Input power of the representative component , Input power of the representative component , Input power of the representative component and transmission distance between indicating the number of components; Based on the input power change rate data, calculate the fault influence range between components. The determination of the influence range is based on the relationship between the input power change rate and the physical distance. If the power change rate between adjacent components is higher than the set threshold, it is considered that the fault influence is transmitted in this direction. Assume that a device contains 3 adjacent components, with input powers of 15.2kW, 14.8kW, and 14.3kW respectively, and the distances between them are 0.8m and 1.2m respectively. The calculation is as follows:

[0049] If the influence range exceeds the set influence threshold (such as 0.3), it is considered that the component may be affected by the fault, and it is marked as a fault-affected component, and finally the fault-affected area is obtained.

[0050] The setting basis of the influence threshold 0.3 is determined by the power change rate of each component and the transmission distance between adjacent components under the normal operating conditions of the device. Specifically, the selection of this threshold is based on the power fluctuation situation in the long-term operation data, the mechanical coupling degree between the device components, and the load distribution situation of the device. Through statistical analysis, it is found that for a normally operating device, its value mostly remains between 0.1 - 0.25. If it exceeds 0.3, it indicates that the fault influence is significant.

[0051] The setting process is as follows: On a standard bridge crane, record 50 groups of normal condition data respectively, and calculate its value. The results show that 95% of the data are distributed between 0.12 - 0.25, and the maximum does not exceed 0.28. Among the 10 groups of abnormal data with faults, the minimum value is 0.31. Therefore, 0.3 is selected as the influence threshold, so that the discrimination criterion of the fault influence can effectively distinguish normal fluctuations and abnormal changes.

[0052] Table 4.1: Input power change rate and fault influence range

[0053]

[0054] As shown in Table 4.1, the influence range of component 3 exceeds 0.3, so it is determined as the fault-affected area.

[0055] Based on the fault impact range data, the fault propagation path identification sub-module determines whether the power loss path is continuous, analyzes the power loss trends of adjacent components, determines whether the fault impact is coherent, screens the fault propagation direction, and obtains the fault propagation path area; Based on the fault impact range data, further determine whether the power loss path is continuous, analyze the power loss trends of adjacent components. If adjacent components are both in the fault impact area and the power change rate trends are the same, then the fault propagation path is considered continuous, and the fault propagation direction is screened. For example, if the power change rates of adjacent components are both positive (power increase) or both negative (power decrease), then it is determined that their trends are consistent, forming a fault propagation chain. Suppose the change rates from component 1 to component 3 are 2.7%, 3.5%, and 5.2% in sequence, then it is judged that the change rate trend is positive, forming a forward-propagating fault chain, and finally the fault propagation path area is obtained.

[0056] Table 4.2 Fault Propagation Path Judgment Table

[0057]

[0058] As shown in Table 4.2, both component 2 and component 3 are in the fault impact area and have the same change rate trend. Therefore, they constitute the fault propagation path area, and finally the fault propagation path area is determined.

[0059] Please refer to Figure 6 , the intelligent early warning module includes: Based on the fault propagation path area, the affected component statistics sub-module counts the number of affected components of the bridge crane, calls the power loss conditions of each component, screens the components with abnormal power fluctuations, calculates the total number of affected components, analyzes the correlation between components, records the distribution of affected components, and obtains the record of the number of affected components; Based on the fault propagation path area, count the number of affected components of the bridge crane. First, obtain the power data of all components in this area and screen out the components with abnormal power loss. For example, in the drive mechanism of a certain bridge crane, the input power of the motor is 45 kW, while the output power is only 30 kW, and the power loss is as high as 15 kW. This component is regarded as an abnormal component. Subsequently, count the total number of all abnormal components. Suppose the system contains 10 key components, and the power loss of 4 components exceeds 10 kW, then the number of affected components is 4. At the same time, analyze the correlation between the affected components. For example, if the loss of a certain transmission shaft causes the input power of the adjacent bearing to decrease, then these two components should be regarded as the relevant influence area. Use the correlation matrix to analyze the direct and indirect influence relationships of each component. If the abnormality of a certain component causes the power change of its two adjacent components to exceed 5%, then the influence of this component on the adjacent components is recorded. Finally, record the number of all affected components and their influence relationships in the database to obtain the number of affected components.

[0060] The early warning condition judgment sub-module judges whether the influence range exceeds the critical value based on the record of the number of affected components, combines the distribution of the affected components, analyzes its coverage range, screens out the set of components that exceed the set influence area threshold, and judges whether there is an expanding trend of the fault influence according to the distribution density and power loss trend of the affected components, and determines whether the early warning condition is met to obtain the early warning trigger status; Based on the number of affected components, judge whether the influence range exceeds the critical value, call the distribution of the affected components, and analyze its coverage range. For example, if the proportion of the affected components in the entire power system of the crane exceeds 30%, then this influence range may have reached the warning state. Screen out the set of components that exceed the set influence area threshold. The setting of this threshold is calculated based on the redundancy of the main power unit, transmission unit, and braking unit of the bridge crane. Suppose the crane has 10 key components, the redundancy coefficient of the power unit is set to 1.5, the redundancy coefficient of the transmission unit is set to 1.2, and the redundancy coefficient of the braking unit is set to 1.1. Then the theoretical limit influence range of the redundancy protection is calculated as follows: ; Among them, represents the number of components of the power unit, represents the redundancy coefficient of the power unit, represents the number of components of the transmission unit, represents the redundancy coefficient of the transmission unit, represents the number of components of the braking unit, represents the redundancy coefficient of the braking unit, represents the total number of system components, and the calculation shows The value is approximately 25%, so the set critical value is 25%. This means that if the number of affected components exceeds 25%, the redundant protection ability of the system will decline, leading to an increased risk of equipment failure. Based on the distribution density of the affected components and the power loss trend, it is judged whether there is an expanding trend of the fault impact. For example, if the power loss of a component increases by more than 5% within 5 minutes and the number of adjacent components it affects increases, it is considered that the fault has an expanding trend. In this case, the system marks this fault as a potential risk that needs further tracking and triggers a warning signal, finally obtaining the warning trigger status.

[0061] Based on the warning trigger status, the fault level identification sub-module determines the fault level, filters the corresponding fault severity range, matches the preset fault level standard, analyzes the number of affected components, loss trend and distribution density, classifies and records the fault level information, and issues a fault warning message; Based on the warning trigger status, determine the fault level, call the affected area data, filter the corresponding fault severity range. For example, if the affected area is between 25% and 40%, then this fault is classified as a second-level warning. If the affected area exceeds 40%, it is classified as a first-level warning. The setting of this range is based on the critical load capacity of the equipment and historical fault calculations. Suppose in the design process of a bridge crane, its maximum safe load power is 100kW, and in actual operation, power loss exceeding this load will cause component performance degradation. Then the corresponding fault level setting can be calculated by the following formula: ; Among them, represents the total power loss in the current fault-affected area, represents the maximum safe load power of the equipment. Suppose the current total power loss is 35kW, then the calculation shows: ; According to the set fault classification standard, this value is in the range of 25% to 40%, so it is judged as a second-level warning. If the loss further increases and exceeds 40%, the fault level will be upgraded to a first-level warning. Match the preset fault level standard, analyze the number of affected components, loss trend and distribution density. For example, in a bridge crane system, if more than 6 components are affected and the power loss of 3 key components is higher than 15kW, the severity of this fault may reach the highest level. Classify it in combination with historical fault data. For example, in cases with a similar power loss pattern in the past 3 months, 80% of the situations finally led to equipment shutdown. Then the warning level of this fault may need to be further upgraded. Finally, record the fault level information and trigger an alarm mechanism in the control system to generate a fault warning message.

[0062] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the relevant art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. Digital integrated bridge crane fault diagnosis system, characterized in that, The system includes: The stress fluctuation monitoring module obtains the stress data of the bridge crane, calculates the stress wave amplitude at consecutive time points, statistically analyzes the mean wave amplitude, determines the maximum value of the wave amplitude, compares it with the stress fluctuation threshold, records the frequency of abnormal fluctuations, analyzes the stress fluctuation change trend within a consecutive period, marks and outputs the high-risk stress fluctuation area; The local stress analysis module arranges additional stress sensing units at adjacent structural parts according to the high-risk stress fluctuation area, statistically analyzes the local stress data, calculates the difference in stress means, extracts the abnormal stress gradient area, analyzes the abnormal expansion trend, and obtains the local stress abnormal area; The energy flow monitoring module collects the input and output power data of the power unit, transmission unit, and braking unit of the bridge crane based on the local stress abnormal area, calculates the power transfer ratio, compares it with the energy loss threshold, records the abnormal energy transfer path, marks the components with abnormal power loss, and obtains the energy loss abnormal area; The fault propagation calculation module statistically analyzes the input power change rate of adjacent components based on the energy loss abnormal area, calculates the fault influence range, determines whether the power loss path is continuous, and determines the fault propagation path to obtain the fault propagation path area.

2. The digital integrated bridge crane fault diagnosis system according to claim 1, wherein The high-risk stress fluctuation area includes the maximum stress wave amplitude data, the frequency of abnormal fluctuations, and the fluctuation change trend. The local stress abnormal area includes the difference value of local stress means, the abnormal stress gradient area, and the abnormal expansion trend. The energy loss abnormal area includes the components with excessive power loss, the abnormal energy transfer path, and the abnormal data of the power transfer ratio. The fault propagation path area includes the input power change rate, the analysis result of the continuity of the power loss path, and the fault influence range.

3. The digital integrated bridge crane fault diagnosis system according to claim 1, characterized in that The stress fluctuation monitoring module includes: The stress data acquisition sub-module obtains the stress data of the main girder, end beam, welding joints, and support connection points of the bridge crane through stress sensors, continuously acquires the stress values of each monitoring point using stress sensors, records the stress numerical values at each time point, calls the stress data of adjacent time points, calculates the stress change amount at consecutive time points, and obtains the stress data sequence; The stress wave amplitude calculation sub-module calculates the stress wave amplitude at consecutive time points based on the stress data sequence, statistically analyzes the mean stress wave amplitude of all time points, determines the maximum value by screening all wave amplitude data, and obtains the stress wave amplitude mean and maximum value data; The stress fluctuation risk assessment sub-module determines whether it exceeds the stress fluctuation threshold according to the stress wave amplitude mean and maximum value data, records the number of abnormal fluctuations, statistically analyzes the fluctuation change trend within a consecutive period, using the formula: ； Calculate the fluctuation change rate , and determine whether the fluctuation period is shortened according to the trend change. If the fluctuation change rate continues to increase and the fluctuation period is shortened, mark the target monitoring point as a high-risk area, and comprehensively output the high-risk stress fluctuation area. Among them, represents the number of periods, represents the average stress wave amplitude of the th period, represents the average stress wave amplitude of the th period, represents the number of abnormal fluctuations within the th period.

4. The digital integrated bridge crane fault diagnosis system according to claim 1, characterized in that, The local stress analysis module includes: The additional sensing unit arrangement sub-module arranges additional stress sensor units in the high-risk stress fluctuation area and adjacent structural parts according to the high-risk stress fluctuation area, sets a shortened acquisition time interval, and acquires and establishes a local stress data set; The local stress gradient calculation sub-module calculates the difference in stress means between adjacent monitoring points within the local grid according to the local stress data set, compares it with the stress gradient threshold, screens the abnormal area, and obtains the abnormal stress gradient area; Based on the abnormal stress gradient region, the stress abnormal propagation analysis sub-module analyzes the propagation trend of stress anomalies, using the formula: ； Calculate the stress gradient change rate between adjacent monitoring points within consecutive periods , determine the concentrated area of the expansion trend, and obtain the local stress anomaly area, where represents the number of monitoring points within a local grid represents the stress value of the th monitoring point represents the stress value of an adjacent monitoring point represents the distance between the th monitoring point and the adjacent monitoring point represents the number of periods represents the number of abnormal stress events within the th period 5. The digital integrated fault diagnosis system for bridge cranes according to claim 1, wherein, The energy flow monitoring module includes: The input and output power acquisition sub-module synchronously acquires the input power and output power data of the power unit, transmission unit, and braking unit of the bridge crane based on the local stress abnormal region, screens the acquired data based on the sensor detection data and historical operation data, eliminates the data abnormal items, and records and generates the input power and output power data. Based on the input power and output power data, the power transfer ratio calculation sub-module uses the formula according to the transfer relationship between components: ； Computing component With the component The power transfer ratio between , by comparing the transfer situations between components, a power loss distribution record is obtained. Among them, Is the output power of the component , Is the input power of the component ; Based on the power loss distribution record, the energy loss abnormal identification sub-module compares the power loss conditions between components, determines whether the loss exceeds the energy loss threshold, marks the components with abnormal power loss, records the abnormal energy transfer path, and obtains the energy loss abnormal region.

6. The digital integrated bridge crane fault diagnosis system according to claim 1, characterized in that, The fault propagation calculation module includes: Based on the energy loss abnormal region, the input power change statistics sub-module statistically analyzes the input power data of adjacent components, calculates the input power change rate between adjacent components, screens the abnormal change values, and obtains the input power change rate data. Based on the input power change rate data, the fault influence range calculation sub-module uses the formula: ； Calculation component Fault influence scope , compare the sizes of the influence scopes to obtain fault influence scope data, where represents the input power of component , represents the input power of component , represents the transmission distance between component and , represents the number of components; Based on the fault influence range data, the fault propagation path identification sub-module determines whether the power loss path is continuous, analyzes the power loss trend of adjacent components, determines whether the fault influence is coherent, screens the fault propagation direction, and obtains the fault propagation path region.

7. The digital integrated bridge crane fault diagnosis system according to claim 1, characterized in that The system further includes an intelligent warning module; Based on the fault propagation path region, the intelligent warning module calculates the number of affected components of the bridge crane, determines whether the influence range exceeds the critical value. If the number of affected components reaches the warning condition, it determines the fault level and issues a fault warning message. The fault warning message includes the number of affected components, the influence range, and the fault level.

8. The digital integrated bridge crane fault diagnosis system according to claim 7, characterized in that The intelligent warning module includes: Based on the fault propagation path region, the affected component statistics sub-module statistically analyzes the number of affected components of the bridge crane, calls the power loss conditions of each component, screens the components with abnormal power fluctuations, calculates the total number of affected components, analyzes the correlation between components, records the distribution of affected components, and obtains the record of the number of affected components. Based on the record of the number of affected components, the warning condition judgment sub-module determines whether the influence range exceeds the critical value, combines the distribution of affected components, analyzes its coverage range, screens the set of components that exceed the set influence area threshold, and determines whether there is an expansion trend of the fault influence based on the distribution density and power loss trend of the affected components, and determines whether the warning condition is met to obtain the warning trigger status. Based on the warning trigger status, the fault level identification sub-module determines the fault level, screens the corresponding fault severity interval, matches the preset fault level standard, analyzes the number of affected components, loss trend, and distribution density, classifies and records the fault level information, and issues a fault warning message.

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