Digital Integrated Fault Diagnosis System for Bridge Cranes

Abstract

The present invention relates to the field of intelligent monitoring technology, specifically a fault diagnosis system for digital integrated bridge cranes. The system includes 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. In the present invention, by analyzing the stress fluctuation data in continuous cycles, the change trend of the structural health state is identified, enhancing the real-time control ability of the structural safety of the bridge crane. Based on high-risk areas, local grid stress monitoring is implemented to capture the abnormal expansion trend and improve the accuracy of fault prediction. By identifying the components with excessive power loss and abnormal energy transfer paths during the power transfer process, the in-depth analysis of the equipment operation state is ensured. Combining the calculation of the input power change rate to calculate the fault influence range, identifying the continuity of the power loss path, analyzing the fault propagation trend, optimizing the fault tracing and diffusion prediction ability, and improving the comprehensiveness of the operation state evaluation of the bridge crane.

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 includes real-time monitoring and data analysis of the operating states 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 in combination with historical data to achieve accurate perception of the equipment state 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, a fault diagnosis system for digital integrated bridge cranes refers to a system that based on sensor network and data processing technology, monitors the operating state of bridge cranes in real time and identifies potential faults. This system aims at key components such as the structural stress, vibration characteristics, motor state, 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 state 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 the local abnormal expansion trend and making it difficult to early warn of potential hazards 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. 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 disadvantages 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:

[0007] The stress fluctuation monitoring module acquires the stress data of the bridge crane, calculates the stress wave amplitude at consecutive time points, statistically analyzes the mean value of the wave amplitude, determines the maximum value of the wave amplitude, compares it with the stress fluctuation threshold, records the abnormal fluctuation frequency, analyzes the stress fluctuation change trend within a consecutive period, marks and outputs the high-risk stress fluctuation area;

[0008] 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;

[0009] The energy flow monitoring module, based on the local stress abnormal area, collects the input and output power data of the power unit, transmission unit, and braking unit of the bridge crane, 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;

[0010] The fault propagation calculation module, based on the energy loss abnormal area, statistically analyzes the input power change rate of adjacent components, 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.

[0011] As a further solution of the present invention, the high-risk stress fluctuation area includes the maximum stress wave amplitude data, abnormal fluctuation frequency, and fluctuation change trend; the local stress abnormal 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.

[0012] As a further solution of the present invention, the stress fluctuation monitoring module includes:

[0013] The stress data acquisition sub-module acquires 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;

[0014] The stress wave amplitude calculation sub-module calculates the stress wave amplitudes at consecutive time points based on the stress data sequence, statistically calculates the mean value of the stress wave amplitudes at all time points, determines the maximum value by screening all the wave amplitude data, and obtains the mean value and maximum value data of the stress wave amplitude;

[0015] The stress fluctuation risk assessment sub-module determines whether it exceeds the stress fluctuation threshold according to the mean value and maximum value data of the stress wave amplitude, records the number of abnormal fluctuations, statistically analyzes the fluctuation change trend within a continuous period, and uses the formula:

[0016] ;

[0017] Calculate the fluctuation change rate , and determine whether the fluctuation period is shortened according to the trend change situation. 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 mean value of the stress wave amplitude in the th period, represents the mean value of the stress wave amplitude in the th period, represents the number of abnormal fluctuations within the th period.

[0018] As a further solution of the present invention, the local stress analysis module includes:

[0019] 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;

[0020] The local stress gradient calculation sub-module calculates the difference in the mean stress values of adjacent monitoring points within a local grid according to the local stress data set, compares it with the stress gradient threshold, screens out the abnormal area, and obtains the abnormal stress gradient area;

[0021] The stress anomaly expansion analysis sub-module analyzes the expansion trend of the stress anomaly based on the abnormal stress gradient area, and uses the formula:

[0022] ;

[0023] Calculate the stress gradient change rate of adjacent monitoring points within a continuous period , 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 stress value of the th monitoring point, represent the stress values of adjacent monitoring points, represent the distance between the th monitoring point and its adjacent monitoring point, represent the number of cycles, represent the number of abnormal stress events within the

[0024] As a further solution of the present invention, the energy flow monitoring module includes:

[0025] The input-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 anomaly area, screens the acquired data based on the sensor detection data and historical operation data, eliminates the data anomaly items, and records and generates the input power and output power data;

[0026] The power transfer ratio calculation sub-module calculates the power transfer ratio

[0027] ;

[0028] between component and component according to the transfer relationship between components, using the formula: , compares the transfer situations between components, and obtains the power loss distribution record, where is the output power of component , and is the input power of component ;

[0029] The energy loss anomaly identification sub-module compares the power loss situations 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, and records the abnormal energy transfer path to obtain the energy loss anomaly area.

[0030] As a further solution of the present invention, the fault propagation calculation module includes:

[0031] The input power change statistics sub-module statistically analyzes the input power data of adjacent components based on the energy loss anomaly area, calculates the input power change rate between adjacent components, screens the abnormal change values, and obtains the input power change rate data;

[0032] The fault influence range calculation sub-module calculates the fault influence range of component

[0033] ;

[0034] using the formula: of component , by comparing the sizes of the influence ranges, fault influence range data is obtained, 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;

[0035] Based on the fault influence range data, the fault propagation path recognition sub-module determines whether the power loss path is continuous, analyzes the power loss trends of adjacent components, determines whether the fault influence is coherent, screens the fault propagation direction, and obtains the fault propagation path area.

[0036] As a further solution of the present invention, the system further includes an intelligent early warning module;

[0037] Based on the fault propagation path area, the intelligent early 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 early warning condition, it determines the fault level and issues a fault early warning message;

[0038] The fault early warning message includes the number of affected components, the influence range, and the fault level.

[0039] As a further solution of the present invention, the intelligent early warning module includes:

[0040] 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;

[0041] 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;

[0042] Based on the warning trigger status, the fault level recognition sub-module determines the fault level, screens 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 early warning message.

[0043] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0044] In the present invention, through the data analysis of continuous periodic stress fluctuations, 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 safety of the bridge crane structure 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, and 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, the components with excessive loss and abnormal energy transfer paths during the power transfer process are identified, the in-depth analysis of the equipment operation state is ensured, the fault influence range is calculated in combination with the input power change rate, the continuity of the power loss path is identified, the propagation trend of the fault is analyzed, the fault tracing and diffusion prediction ability is optimized, the comprehensiveness of the operation state evaluation of the bridge crane is improved. 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 early warning ability, and ensures the timeliness and pertinence of maintenance decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is the system flow chart of the present invention;

[0046] Figure 2 is the flow chart of the stress fluctuation monitoring module of the present invention;

[0047] Figure 3 is the flow chart of the local stress analysis module of the present invention;

[0048] Figure 4 is the flow chart of the energy flow monitoring module of the present invention;

[0049] Figure 5 is the flow chart of the fault propagation calculation module of the present invention;

[0050] Figure 6 is the flow chart of the intelligent early warning module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0051] In order to make the objectives, technical solutions and advantages of the present invention clearer, 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.

[0052] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is 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 thus cannot be construed as a limitation on 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.

[0053] Please refer to Figure 1 , the digital integrated bridge crane fault diagnosis system includes:

[0054] 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 wave amplitudes within a consecutive period, determines the maximum value of the wave amplitude, judges whether it exceeds the stress fluctuation threshold, records the frequency of abnormal fluctuations, analyzes the stress fluctuation change trend within a 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;

[0055] 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;

[0056] The energy flow monitoring module, based on the local stress abnormal area, 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;

[0057] The fault propagation calculation module, based on the energy loss abnormal area, statistically calculates 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;

[0058] The intelligent warning module, based on the fault propagation path area, 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, determines the fault level, and issues a fault warning message.

[0059] 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 abnormal energy loss area includes components with excessive power loss, abnormal energy transfer paths, 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 information includes the number of affected components, the influence range, and the fault level.

[0060] Please refer to Figure 2 , the stress fluctuation monitoring module includes:

[0061] 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 collects 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 of consecutive time points, and obtains the stress data sequence;

[0062] Install high-precision stress sensors at the key stressed parts of the bridge crane, including the main girder, end beam, welding joints, and support connection points. Arrange 4 - 6 measuring points at each part, and determine the measuring point interval according to the beam length or node characteristics. For example, for a main girder with a span of 30 meters, arrange 2 measuring points at each end and the mid-span, 4 measuring points on the end beam, and 6 measuring points at the welding joints to capture the local stress changes of the structure. After the sensors are installed, 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.

[0063] During the actual operation of the crane, perform different working conditions operations, such as lifting the rated load (100t), moving (10m / min), accelerating (0.5m / s²), emergency stop, etc., and monitor the stress changes of each measuring point. Suppose a certain measuring point records consecutive stress values of 50MPa, 55MPa, 60MPa, 58MPa, 62MPa during the lifting of the rated load. After storing the data, call the stress data of adjacent time points and calculate the stress change amount of consecutive time points, such as:

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] Obtain the stress change sequence , providing data support for subsequent stress fluctuation analysis.

[0069] Based on the stress data sequence, the stress wave amplitude calculation sub-module calculates the stress wave amplitudes at consecutive time points, statistically calculates the mean value of the stress wave amplitudes at all time points, determines the maximum value by screening all the amplitude data, and obtains the mean value and maximum value data of the stress wave amplitudes;

[0070] According to the stress data sequence obtained above, calculate the stress fluctuation amplitude between adjacent time points. Assume that the stress data sequence of a certain monitoring point is 50MPa, 55MPa, 53MPa, 60MPa, 58MPa, 62MPa, 65MPa, 63MPa, 68MPa, 70MPa. Calculate the stress differences between adjacent time points to obtain the stress wave amplitude sequence:

[0071] ;

[0072] Calculate the mean value of the stress wave amplitude sequence using arithmetic mean:

[0073] ;

[0074] Screen the maximum value in the stress wave amplitude sequence to obtain the maximum stress wave amplitude of 7MPa, and further obtain the mean value and maximum value data of the stress wave amplitude.

[0075] Based on the mean value and maximum value data of the stress wave amplitude, the stress fluctuation risk assessment sub-module determines whether it exceeds the stress fluctuation threshold, records the number of abnormal fluctuations, statistically calculates the fluctuation change trend within a continuous period, and uses the formula:

[0076] ;

[0077] Calculate the fluctuation change rate , and based on the trend change situation, 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, where represents the number of periods, represents the mean value of the stress wave amplitude in the th period, represents the mean value of the stress wave amplitude in the th period, represents the number of abnormal fluctuations in the th period;

[0078] 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 about 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:

[0079] ;

[0080] Since this value is on the high side, in practical 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.

[0081] 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 the abnormal fluctuation is recorded 1 time.

[0082] Suppose that within 5 consecutive monitoring periods, the average stress wave amplitudes obtained are shown in Table 1.1:

[0083] Table 1.1

[0084]

[0085] Calculate the difference in the average stress wave amplitude between adjacent periods:

[0086] ;

[0087] Calculate the fluctuation change rate , substitute it into the formula:

[0088] ;

[0089] 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.

[0090] Please refer to Figure 3 , the local stress analysis module includes:

[0091] The additional sensing unit layout sub-module is based on high-risk stress fluctuation regions. Additional stress sensor units are laid out in high-risk regions and adjacent structural parts, and the shortened acquisition time interval is set to collect and establish a local stress data set.

[0092] Based on high-risk stress fluctuation regions, additional stress sensing units need to be laid out in these regions and adjacent structural parts around them. The layout 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 laid out every 2m, and two additional sensing units are laid out at the welding joints to capture local stress changes. The specific layout scheme is shown in Table 2.1.

[0093] Table 2.1 Sensor Layout Scheme

[0094]

[0095] As shown in Table 2.1, the layout of sensors needs to meet the principle of higher layout density 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 shorter 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 a local stress data set is obtained.

[0096] 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, filters out abnormal regions, and obtains abnormal stress gradient regions.

[0097] 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 to that each grid contains 4 adjacent monitoring points. The stress mean value inside each grid is calculated as follows:

[0098] ;

[0099] Among them, is the stress mean value of the current grid, are the stress values of the four monitoring points in the grid respectively. After calculating the mean values of multiple grids, calculate the stress gradient between adjacent grids. Set the stress gradient threshold to 10MPa / m. The setting basis of this value lies in the stress conduction characteristics and fatigue tolerance of the structural material, mainly determined by the elastic modulus of the material, Poisson's ratio and the structural service life requirements, and its calculation method is as follows:

[0100] ;

[0101] Among them, is the elastic modulus of the low-alloy high-strength steel used for the main girder of the bridge crane, with a value of 210 GPa, represents the maximum allowable elastic deformation strain, with a value of 0.00005, represents the average spacing between monitoring points, with a value of 1 m, and substitute it into the calculation:

[0102] ;

[0103] 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:

[0104] Table 2.2 Local grid stress data

[0105]

[0106] Calculate the stress gradient between adjacent grids:

[0107] ;

[0108] ;

[0109] Among them, is the adjacent grid spacing (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. In actual 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.

[0110] Based on the abnormal stress gradient area, the stress anomaly expansion analysis sub-module analyzes the expansion trend of the stress anomaly and uses the formula:

[0111] ;

[0112] Calculate the stress gradient change rate between adjacent monitoring points within a continuous period , 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 values of adjacent monitoring points, represents the distance between the th monitoring point and its adjacent monitoring point, represents the number of cycles, represents the number of abnormal stress events within the

[0113] According to the abnormal stress gradient region, analyze the expansion trend of stress anomalies, and use the formula to calculate the stress gradient change rate of adjacent monitoring points within consecutive cycles.

[0114] The set expansion threshold is 8 MPa / m, and its setting basis lies in the structural fatigue damage expansion law, which is mainly determined by the crack growth rate , the cyclic stress intensity factor range and the crack growth characteristics of the material. The calculation method is as follows:

[0115] ;

[0116] Among them, is the cyclic stress intensity factor range, and is calculated using the Paris-Erdogan formula:

[0117] ;

[0118] Among them and are the material crack growth constants, with values and , is the cyclic stress amplitude, with a value of 50 MPa, then

[0119] ;

[0120] is the initial size of the fatigue crack. Assuming the crack length mm, then

[0121] ;

[0122] Since the crack growth rate will be affected by the change of fatigue load, to ensure safety, set the maximum gradient within the monitoring area not to exceed 20% of the crack growth stress range, then

[0123] ;

[0124] Finally, set the expansion threshold to 8 MPa / m to ensure that the fatigue damage expansion of the structure is controlled within an acceptable range.

[0125] Taking the stress data in Table 2.2 as an example, assuming that this data is the acquisition data for 3 cycles, and calculating the stress gradient change rate:

[0126] ;

[0127] ;

[0128] Among them, are the number of abnormal stress events within 3 cycles respectively. It is calculated that , comparing 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 propagation. This area is finally marked as a local stress abnormal area.

[0129] This result shows that the stress abnormal change rate within 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.

[0130] Please refer to Figure 4 , the energy flow monitoring module includes:

[0131] The input and output power acquisition sub-module, based on the local stress abnormal area, synchronously acquires the input power and output power data of the power unit, transmission unit, and braking unit of the bridge crane, 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;

[0132] To acquire the input power and output power data of the power unit, transmission unit, and braking unit of the bridge crane, first, at the power unit, install current and voltage sensors 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. It is measured that , , the power factor , then the input power is calculated as follows:

[0133] ;

[0134] 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. Assuming the torque , the rotational speed , then the angular velocity rad / s, calculate the output power of the transmission unit:

[0135] ;

[0136] Finally, for the braking unit, install a power meter to measure the braking power consumption. Assuming that the energy loss during braking is measured to be 4000 W, the braking power acquisition is completed. To exclude abnormal data, the sliding average method is used to screen the acquired data, and the average value of the data at the last 10 moments is calculated. Assuming that the data of 10 samplings are as follows (unit: W): Table 3.1 Sensor-acquired power data

[0137]

[0138] As shown in Table 3.1, use the sliding average method to calculate the average power of the power unit:

[0139]

[0140] Similarly, calculate the average power of the transmission unit , the average braking power , and finally obtain the processed input power and output power data.

[0141] The power transmission ratio calculation sub-module is based on the input power and output power data. According to the transmission relationship between components, the formula:

[0142] ;

[0143] Calculate the component and the component The power transmission ratio between them, compare the transmission situations between components, and obtain a record of power loss distribution, where is the output power of the component , is the input power of the component ;

[0144] Based on the input power and output power data, according to the transmission relationship between components, calculate the power transmission ratio of adjacent components. First, determine the power transmission path between components, that is, power unit → transmission unit → braking unit, and calculate the power transmission ratio of each adjacent component .

[0145] Output power of the power unit ;

[0146] Input power of the transmission unit , output power ;

[0147] Input power of the braking unit , output power ;

[0148] Calculate the power transfer ratio:

[0149] ;

[0150] ;

[0151] 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 reaches 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.

[0152] The energy loss anomaly identification sub-module, based on the power loss distribution record, compares the power loss situations among 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 area;

[0153] Based on the power loss distribution, compare the power loss situations among components, determine whether the loss exceeds the energy loss threshold. Set the energy loss threshold to 5%, which is derived 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 take the average value of 5% as a reasonable reference value. If it exceeds this threshold, it indicates that the component may have mechanical failures or abnormal energy losses.

[0154] Compare the calculation results:

[0155] , abnormal;

[0156] , abnormal;

[0157] Record the abnormal energy transfer path (power unit → transmission unit → braking unit), and calculate the power loss ratio:

[0158] ;

[0159] ;

[0160] This result shows that the power loss from the transmission unit to the braking unit is extremely high, far exceeding the 5% threshold. Therefore, mark the transmission unit and the braking unit as components with abnormal power loss, and then obtain the energy loss abnormal area.

[0161] Please refer to Figure 5 , the fault propagation calculation module includes:

[0162] The input power change statistics sub-module, based on the energy loss abnormal 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;

[0163] Based on the energy loss abnormal area, first collect the input power data of adjacent components. For each component, its input power can be measured by a power sensor 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 is calculated using the incremental calculation method, that is:

[0164] ;

[0165] Assume the previous moment kW, and the current moment kW, then:

[0166] ;

[0167] During the statistical process, in order to eliminate random fluctuation interference, set the input power change rate threshold. For example, set the threshold to ±5%. If the change rate exceeds this range, mark this 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.

[0168] The fault influence range calculation sub-module, based on the input power change rate data, uses the formula:

[0169] ;

[0170] Calculate the fault influence range of component , compare the sizes of the influence ranges, and obtain the fault influence range data. Among them, represents the input power of component , represents the input power of component , represents the transfer distance between component and , represents the number of components;

[0171] Based on the input power change rate data, calculate the fault influence range among 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. Suppose a device contains 3 adjacent components, with input powers of 15.2 kW, 14.8 kW, and 14.3 kW respectively, and the distances between them are 0.8 m and 1.2 m respectively. The calculation is as follows:

[0172]

[0173] If the influence range exceeds the set influence threshold (such as 0.3), it is considered that this component may be affected by the fault, and mark it as a fault-affected component, and finally obtain the fault influence area.

[0174] 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 device components, and the load distribution situation of the device. Through statistical analysis, it is found that for a device in normal operation, its value mostly remains between 0.1 - 0.25. If it exceeds 0.3, it indicates that the fault influence is significant.

[0175] 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 is 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, select 0.3 as the influence threshold, so that the discrimination criterion of fault influence can effectively distinguish normal fluctuations and abnormal changes.

[0176] Table 4.1: Input power change rate and fault influence range

[0177]

[0178] As shown in Table 4.1, the influence range of Component 3 exceeds 0.3, so it is determined as the fault influence area.

[0179] The fault propagation path identification sub-module, based on the fault influence range data, judges 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 area;

[0180] 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 have the same power change rate trend, then the fault propagation path is considered continuous, and the fault propagation direction is screened out. For example, if the power change rates of adjacent components are both positive (power increases) or both negative (power decreases), then it is determined that their trends are the same, 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.

[0181] Table 4.2 Fault Propagation Path Judgment Table

[0182]

[0183] 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.

[0184] Please refer to Figure 6 , the intelligent early warning module includes:

[0185] The affected component statistics sub-module, based on the fault propagation path area, counts the number of affected components of the bridge crane, calls the power loss situation of each component, screens out 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;

[0186] 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 impact area. Use the correlation matrix to analyze the direct and indirect impact relationships between components. For example, if the abnormality of a certain component causes the power change of its adjacent two components to exceed 5%, then the impact of this component on the adjacent components is recorded. Finally, record the number of all affected components and their impact relationships in the database to obtain the number of affected components.

[0187] Based on the record of the number of affected components, the warning condition judgment sub-module determines whether the affected scope exceeds the critical value, analyzes its coverage in combination with the distribution of the affected components, filters out the set of components that exceed the threshold of the affected area, and determines whether there is an expanding trend in the fault impact based on the distribution density and power loss trend of the affected components, determines whether the warning condition is met, and obtains the warning trigger status;

[0188] Based on the number of affected components, determine whether the affected scope exceeds the critical value, call the distribution of the affected components, and analyze its coverage. For example, if the proportion of affected components in the entire power system of the crane exceeds 30%, then the affected scope may have reached the warning state. Filter out the set of components that exceed the set threshold of the affected area. 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. Assume that 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 affected scope of the redundancy protection is calculated as follows:

[0189] ;

[0190] 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 components in the system, and the calculated value is about 25%. Therefore, the set critical value is 25%, which means that if the number of affected components exceeds 25%, the redundancy protection ability of the system will decline, leading to an increased risk of equipment failure. Based on the distribution density and power loss trend of the affected components, determine whether there is an expanding trend in the fault impact. For example, if the power loss of a certain component increases by more than 5% within 5 minutes and the number of adjacent components it affects increases, then it is considered that the fault has an expanding trend. In this case, the system marks this fault as a potential risk that needs to be further tracked and triggers a warning signal, and finally obtains the warning trigger status.

[0191] Based on the warning trigger status, the fault level identification sub-module determines the fault level, filters out 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;

[0192] Based on the warning trigger status, determine the fault level, call the scope of influence data, and screen the corresponding fault severity interval. For example, if the scope of influence is between 25% and 40%, the fault is classified as a secondary warning, while if the scope of influence exceeds 40%, it is classified as a primary warning. The setting of this interval is based on the critical load capacity of the equipment and historical fault calculations. Suppose a bridge crane has a maximum safe load power of 100kW during the design process, and during actual operation, power loss exceeding this load will cause component performance degradation. Then the corresponding fault level setting can be calculated using the following formula:

[0193] ;

[0194] Wherein, represents the total power loss within the current fault impact area, represents the maximum safe load power of the equipment. Suppose the current total power loss is 35kW, then the calculation shows:

[0195] ;

[0196] According to the set fault classification standard, this value is within the range of 25% to 40%, so it is determined as a secondary warning. If the loss further increases and exceeds 40%, the fault level will be upgraded to a primary 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. Combine historical fault data for classification. For example, in cases with a similar power loss pattern in the past 3 months, 80% of the situations eventually 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.

[0197] The above is only a preferred embodiment of the present invention, and does not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content 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 based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

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

2. The digital integrated fault diagnosis system for bridge cranes according to claim 1, characterized in that, The high-risk stress fluctuation areas include the maximum stress wave amplitude data, the frequency of abnormal fluctuations, and the fluctuation change trend. The local stress abnormal areas include the difference value of local stress mean values, the abnormal stress gradient areas, and the abnormal expansion trend. The energy loss abnormal areas include the components with excessive power loss, the abnormal energy transfer paths, and the abnormal power transfer ratio data. The fault propagation path areas include 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 local stress analysis module includes: The additional sensing unit arrangement sub-module, based on the high-risk stress fluctuation areas, arranges additional stress sensor units in the high-risk areas and adjacent structural parts, sets a shortened acquisition time interval, and acquires and establishes the local stress data set; The local stress gradient calculation sub-module, according to the local stress data set, calculates the difference in stress mean values between adjacent monitoring points within the local grid, compares with the stress gradient threshold, screens the abnormal areas, and obtains the abnormal stress gradient areas; 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 the local grid represents the stress value of the th monitoring point represents the stress value of the 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 4. The digital integrated fault diagnosis system for bridge cranes according to claim 1, characterized in that, 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 abnormal data 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 calculates according to the transfer relationship between components using the formula: ； Computing component With component The power transfer ratio between , compare the transfer conditions between components to obtain a power loss distribution record, where Is the output power of component , Is the input power of component . Based on the power loss distribution record, the energy loss abnormal identification sub-module compares the power loss situations 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.

5. The digital integrated fault diagnosis system for bridge cranes according to claim 1, wherein 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 range , compare the size of the influence range to obtain the fault influence range data, where represents the input power of component , represents the input power of component , represents the transfer distance between component and , indicates 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.

6. 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.

7. The digital integrated bridge crane fault diagnosis system according to claim 6, 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 situations 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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