A crane condition monitoring method, device, equipment, medium, and program product.

Abstract

This invention relates to the field of equipment monitoring technology, specifically a method, device, equipment, medium, and program product for crane condition monitoring. The method includes: acquiring a monitoring signal of the operating status of a component to be monitored, wherein the component to be monitored is a transmission component of a crane; segmenting the monitoring signal to obtain multiple monitoring signal units; determining the anomaly coefficient of each monitoring signal unit, and identifying abnormal monitoring signal units based on the anomaly coefficients; and determining whether the operating status of the component to be monitored is normal based on the abnormal monitoring signal units. This invention, by segmenting the operating status monitoring signal into multiple monitoring signal units and identifying abnormal monitoring signal units based on the degree of difference among the various monitoring signal units, thereby determining the fault status of the transmission component, can promptly detect abnormalities in the transmission component for rapid proactive early warning, improving the efficiency and accuracy of condition monitoring.

Description

Technical Field

[0001] This invention relates to the field of equipment monitoring technology, and in particular to a method, device, equipment, medium, and program product for monitoring the condition of a crane. Background Technology

[0002] With the continuous growth of international trade, port logistics has developed rapidly. Port cranes, as key loading and unloading equipment, play a vital role in port logistics, directly impacting port production efficiency and economic benefits. However, critical transmission components of cranes (such as motors, gearboxes, drums, and bearings) are prone to abnormalities or malfunctions due to long-term exposure to non-cyclic impact loads, thus affecting the safety and reliability of loading and unloading operations.

[0003] Due to the complex manufacturing process, high cost, and difficulty in replacing components, existing technologies for crane malfunctions typically employ reactive maintenance methods such as after-the-fact repairs and periodic maintenance. This means repairs are performed only after a malfunction occurs, and regular maintenance is conducted periodically. However, these methods cannot monitor the crane's fault status in real time, especially critical transmission components. Any abnormalities or malfunctions can lead to unplanned downtime, preventing normal operation and potentially impacting safe production, resulting in significant economic losses. Therefore, traditional reactive maintenance methods must be changed.

[0004] To achieve intelligent fault monitoring of cranes, some practitioners have proposed monitoring the crane's operating data and inputting it into a pre-built fault detection model for fault diagnosis. However, this approach is highly complex and relies on building sophisticated fault detection models. Cranes have complex structures, containing various transmission components, and their fault states are diverse and complex, making it difficult to build accurate fault detection models for different transmission components. Therefore, this method is not efficient or accurate in actual fault monitoring. Summary of the Invention

[0005] In view of the above-mentioned problems in the prior art, the purpose of the present invention is to provide a crane condition monitoring method, device, equipment, medium and program product, which can promptly detect abnormal conditions of crane transmission components to provide rapid proactive early warning, thereby improving the efficiency and accuracy of condition monitoring.

[0006] To address the above problems, the present invention provides a crane condition monitoring method, comprising:

[0007] Acquire the operating status monitoring signal of the component to be monitored, wherein the component to be monitored is the transmission component of the crane;

[0008] The operation status monitoring signal is segmented to obtain multiple monitoring signal units;

[0009] The abnormality coefficient of each monitoring signal unit is determined, and the abnormal monitoring signal unit with abnormality is determined based on the abnormality coefficient.

[0010] The abnormality monitoring signal unit determines whether the operating status of the monitored component is normal.

[0011] Furthermore, the transmission component includes one or more of a motor, gearbox, drum, and bearing, and the operating status monitoring signal includes a vibration acceleration signal.

[0012] Furthermore, the method also includes:

[0013] Invalid signals are removed from the operating status monitoring signals to obtain valid operating status monitoring signals;

[0014] The step of segmenting the operating status monitoring signal to obtain multiple monitoring signal units includes:

[0015] The effective operating status monitoring signal is segmented to obtain multiple monitoring signal units.

[0016] Further, the step of removing invalid signals from the operating status monitoring signals to obtain valid operating status monitoring signals includes:

[0017] Multiple intrinsic mode functions are extracted based on the aforementioned operational status monitoring signals;

[0018] Signal separation is performed based on the multiple intrinsic mode functions to obtain the effective operating status monitoring signal.

[0019] Furthermore, the extraction of multiple intrinsic mode functions based on the operating status monitoring signal includes:

[0020] The noise signal is separated from the operating status monitoring signal to obtain the first monitoring signal after noise separation;

[0021] The mean sequence is extracted from the first monitoring signal based on the data fitting method, and the mean sequence is removed from the first monitoring signal to obtain the second monitoring signal;

[0022] If the second monitoring signal satisfies the intrinsic mode function condition, an intrinsic mode function is extracted from the first monitoring signal;

[0023] The remaining signal after separating the intrinsic mode functions from the first monitoring signal is used as a new first monitoring signal, and the process returns to the step of extracting the mean sequence from the first monitoring signal based on the data fitting method, until a preset number of intrinsic mode functions are extracted.

[0024] Furthermore, the step of extracting multiple intrinsic mode functions based on the operating status monitoring signal further includes:

[0025] If the second monitoring signal does not meet the intrinsic mode function condition, the data fitting method is modified, and the process returns to the step of extracting the mean sequence from the first monitoring signal based on the data fitting method.

[0026] Furthermore, the operation status monitoring signal is segmented to obtain multiple monitoring signal units, including:

[0027] Obtain multiple pre-set segmentation thresholds;

[0028] The operating status monitoring signal is divided into multiple monitoring signal units based on the multiple segmentation thresholds.

[0029] Further, the step of determining the anomaly coefficient of each of the monitoring signal units, and determining the abnormal monitoring signal units with anomalies based on the anomaly coefficients, includes:

[0030] Feature extraction is performed on each of the monitoring signal units to obtain the corresponding feature vector;

[0031] Based on the feature vector, a set of related signal units corresponding to each monitoring signal unit is determined. The set of related signal units includes at least one monitoring signal unit whose distance from the corresponding monitoring signal unit satisfies a preset condition.

[0032] Calculate the anomaly coefficient corresponding to each of the monitoring signal units based on the relevant signal unit set;

[0033] The abnormality monitoring signal unit is determined based on the abnormality coefficient.

[0034] Another aspect of the present invention provides a crane condition monitoring device, comprising:

[0035] The monitoring signal acquisition module is used to acquire the operating status monitoring signals of the component to be monitored;

[0036] The segmentation module is used to segment the operating status monitoring signal to obtain multiple monitoring signal units;

[0037] An abnormal signal determination module is used to determine the abnormal coefficient of each of the monitoring signal units, and to determine the abnormal monitoring signal units that have an abnormality based on the abnormal coefficients.

[0038] The status monitoring module is used to determine whether the operating status of the monitored component is normal based on the abnormal monitoring signal unit.

[0039] In another aspect, the present invention provides an electronic device including a processor and a memory, wherein the memory stores at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by the processor to implement the crane status monitoring method as described above.

[0040] In another aspect, the present invention provides a computer-readable storage medium storing at least one instruction or at least one program, wherein the at least one instruction or the at least one program is loaded and executed by a processor to implement the crane status monitoring method as described above.

[0041] In another aspect, the present invention provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the crane condition monitoring method described above.

[0042] Due to the above technical solution, the present invention has the following beneficial effects:

[0043] According to the status monitoring method of the present invention, by real-time monitoring of the operating status monitoring signal of the crane transmission component, the operating status monitoring signal is divided into multiple monitoring signal units, and the abnormal monitoring signal units are found according to the degree of difference of each monitoring signal unit, thereby judging the fault status of the transmission component. It can promptly detect abnormalities in key transmission components in the crane for rapid proactive early warning, and there is no need to build complex fault detection models for different components, which can greatly reduce the complexity of status monitoring and improve the efficiency and accuracy of status monitoring. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the present invention, the accompanying drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0045] Figure 1 This is a schematic diagram of the structure of a crane condition monitoring system provided in one embodiment of the present invention;

[0046] Figure 2This is a flowchart of a crane condition monitoring method provided in one embodiment of the present invention;

[0047] Figure 3 This is a flowchart of a crane condition monitoring method provided in another embodiment of the present invention;

[0048] Figure 4 This is a flowchart of a crane condition monitoring method provided in another embodiment of the present invention;

[0049] Figure 5 This is a schematic diagram of the structure of a crane condition monitoring device provided in one embodiment of the present invention;

[0050] Figure 6 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present invention. Detailed Implementation

[0051] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0053] To make the objectives, technical solutions, and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the embodiments of the present invention and are not intended to limit the embodiments of the present invention.

[0054] The crane status monitoring method provided in this embodiment of the invention can be applied to the status monitoring of crane transmission components, and is used to monitor the operating status of transmission components such as motors, gearboxes, drums or bearings 24 hours a day.

[0055] Reference manual attached Figure 1 It shows a schematic diagram of the structure of a crane condition monitoring system provided in an embodiment of the present invention, such as... Figure 1 As shown, the crane condition monitoring system 100 may include a sensor 110, a data acquisition unit 120, a switch 130, a server 140, and a programmable logic controller (PLC) 150. The sensor 110 is communicatively connected to the data acquisition unit 120, the data acquisition unit 120 is communicatively connected to both the switch 130 and the PLC controller 150, and the switch 130 is communicatively connected to the server 140.

[0056] Specifically, the sensor 110 can be installed on crane transmission components such as motors, gearboxes, drums, and bearings, and is used to monitor the vibration, temperature, and speed of the motor; the vibration, oil temperature, and liquid level of the gearbox; and the vibration of the drum and bearings, respectively. It collects the corresponding operating status monitoring signals and sends these signals to the data acquisition unit 120. The operating status monitoring signals may include, but are not limited to, vibration acceleration signals, temperature signals, speed signals, and liquid level signals.

[0057] For example, in order to monitor motor vibration and temperature, gearbox vibration and oil temperature, drum vibration, etc., the arrangement of monitoring points corresponding to the sensor 110 can be shown in Table 1 below:

[0058] Table 1

[0059]

[0060] It is understood that all sensors in the crane condition monitoring system provided in this embodiment of the invention can collect data synchronously, which facilitates the simultaneous comparison and analysis of the operating status monitoring signals of all monitoring points.

[0061] Specifically, the data acquisition unit 120 can first filter out erroneous signals caused by external factors (such as noise, trolley braking, crane impact, etc.) from the operating status monitoring signals, obtaining effective and accurate steady-state signals for vibration data calculation (including total vibration value, envelope spectrum, FFT frequency, and long-time waveform, etc.). Then, based on the obtained vibration data, it determines the status information and alarm level of the crane transmission components. The data acquisition unit 120 can send the status information and alarm level to the PLC controller 150 for early warning display, or it can send the status information and alarm level to the server 140.

[0062] In practical applications, the acquired vibration acceleration signals include those from the correct vibration, temperature, and oil temperature of the motor, gearbox, and drum, as well as those caused by external factors such as noise, trolley braking, and crane impact. Therefore, when the vibration acceleration signal is transmitted to the acquisition unit 120, the transmitted signal can be analyzed to determine if it is invalid. If it is invalid, the signal is discarded to obtain a precise steady-state signal. Then, the acquisition unit 120 can determine the equipment status information based on the vibration acceleration signal amplitude. If the vibration acceleration signal amplitude exceeds a set first alarm threshold, it is determined that the crane equipment has a potential risk affecting stable operation, and a timely warning needs to be issued to the PLC controller 150. The name of the abnormal vibration acceleration signal and the threshold exceeding condition can be sent to the PLC controller 150 for warning display. If the vibration acceleration signal amplitude is judged to be normal, the threshold judgment of the next frame of data signal is directly performed.

[0063] The first alarm threshold can be preset according to actual conditions. For example, it can be set comprehensively according to user terminal equipment, reducer transmission parameters, bearing model, ISO vibration parameters, etc. In this embodiment of the invention, the first alarm threshold is not specifically limited.

[0064] It is understood that the data acquisition device in the crane condition monitoring system provided in this embodiment of the invention can continuously collect condition monitoring data 24 hours a day and comprehensively collect vibration data, including envelope spectrum, FFT frequency and long time domain waveform data, so as to comprehensively monitor the operating status of each transmission component of the crane, detect abnormalities in time and issue early warnings, reduce unplanned downtime caused by crane abnormalities or malfunctions, ensure safe production and reduce economic losses.

[0065] Specifically, the PLC controller 150 can store the received status information and alarm level in the PLC database, and can also send the status information and alarm level to the display device for early warning display.

[0066] Specifically, the data acquisition unit 120 can also convert the received operating status monitoring signals and the calculated vibration data (including total vibration value, envelope spectrum, FFT frequency and long time domain waveform, etc.) from electrical signals (or optical signals) into TCP / IP data signals, and transmit them to the server 140 through the switch 130.

[0067] Specifically, the server 140 can store and back up data such as received status information, alarm level, operation status monitoring signal, total vibration value, envelope spectrum, FFT frequency and long time domain waveform. It can also use the method provided in this embodiment of the invention to re-analyze the warning operation status monitoring signal for further fault diagnosis, location and other purposes.

[0068] In one possible embodiment, the collector 120 may also transmit only the warning operation status monitoring signal to the server 140 through the switch 130, so that the server 140 can re-analyze the warning operation status monitoring signal using the method provided in this embodiment of the invention, and further perform fault diagnosis, location, etc.

[0069] Optionally, based on the massive nature of vibration data, the data collector 120 can also associate equipment status information, including speed, load, forward and reverse rotation, when judging the status information and alarm level of the crane transmission components, thereby realizing equipment status storage, trigger storage, and alarm storage, thus retaining valid fault data, filtering out alarm data caused by external factors such as trolley braking and crane impact, marking and filtering data at the source, realizing automatic and intelligent diagnostic functions, and quickly warning and responding to equipment faults.

[0070] It should be noted that, Figure 1 This is merely one example; in some possible embodiments, the crane condition monitoring system may include more or fewer devices.

[0071] Reference manual attached Figure 2 This illustrates the flow chart of a crane condition monitoring method provided by an embodiment of the present invention, which can be applied to... Figure 1 In the servers, specifically such as Figure 2 As shown, the method may include the following steps:

[0072] S210: Obtain the operating status monitoring signal of the component to be monitored, wherein the component to be monitored is the transmission component of the crane.

[0073] In this embodiment of the invention, the crane condition monitoring system can acquire operating status monitoring signals obtained by sensors installed on the transmission components of the crane through a data acquisition device, and send the operating status monitoring signals to the server. The transmission components may include, but are not limited to, one or more of a motor, gearbox, drum, and bearing, and the operating status monitoring signals may include, but are not limited to, vibration acceleration signals, oil temperature signals, speed signals, or liquid level signals.

[0074] In one possible embodiment, after acquiring the operating status monitoring signals monitored and collected by the sensors, the data acquisition unit can first filter out erroneous signals caused by external factors (such as noise, trolley braking, crane impact, etc.) to obtain effective and accurate steady-state signals for vibration data calculation (including total vibration value, envelope spectrum, FFT frequency, and long-time domain waveform, etc.). Then, based on the obtained vibration data, the status information and alarm level of the crane's transmission components are determined. Only when the data acquisition unit determines that the crane has potential hazards affecting stable operation and requires an early warning will the corresponding operating status monitoring signal be sent to the server.

[0075] The specific method by which the data collector determines whether the crane has any potential hazards that could affect stable operation and require early warning can be found in [reference needed]. Figure 1 The specific details of the embodiments shown will not be repeated here.

[0076] In this embodiment of the invention, the operating status monitoring signal can be the raw data signal collected by the sensor, or it can be the effective and accurate steady-state signal obtained after the collector filters out the erroneous signals in the raw data signal caused by external factors (such as noise, truck braking, crane impact, etc.).

[0077] S230: The operating status monitoring signal is segmented to obtain multiple monitoring signal units.

[0078] In this embodiment of the invention, the operating status monitoring signal can be segmented into several interval thresholds, and each interval threshold can be regarded as a monitoring signal unit.

[0079] Specifically, the step of segmenting the operation status monitoring signal to obtain multiple monitoring signal units may include: acquiring multiple pre-set segmentation thresholds; and segmenting the operation status monitoring signal into multiple monitoring signal units according to the multiple segmentation thresholds. The segmentation thresholds can be pre-set according to actual conditions, preferably such that the interval lengths corresponding to the segmented monitoring signal units are the same. This embodiment of the invention does not impose specific limitations on this.

[0080] Optionally, several segmentation thresholds can be preset to group operation status monitoring signals located between two adjacent segmentation thresholds into the same monitoring signal unit. For example, when the operation status monitoring signal is a vibration acceleration signal, a threshold of 2 m / s² can be set. 2 4m / s 2 6m / s 2 (where m represents distance in meters and s represents time in seconds) are used as segmentation thresholds to separate items located in [0 m / s]. 2 2m / s2 ]、[2m / s 2 4m / s 2 ], [4m / s 2 6m / s 2 The operational status monitoring signals within the specified range are each divided into a monitoring signal unit.

[0081] Optionally, several segmentation threshold intervals can be preset to group operation status monitoring signals located within the same segmentation threshold interval into the same monitoring signal unit. For example, when the operation status monitoring signal is a vibration acceleration signal, [0 m / s²] can be set. 2 2m / s 2 ]、[2m / s 2 4m / s 2 ], [4m / s 2 6m / s 2 Using threshold intervals as the dividing threshold intervals, the operating status monitoring signals located in each of the aforementioned threshold intervals are divided into a monitoring signal unit.

[0082] S250: Determine the abnormality coefficient of each of the monitoring signal units respectively, and determine the abnormal monitoring signal units that have abnormalities based on the abnormality coefficients.

[0083] In this embodiment of the invention, feature vectors can be used to describe each of the monitoring signal units, and at least one monitoring signal unit whose distance from each of the monitoring signal units meets a preset condition can be determined based on the feature vectors to form a corresponding set of related signal units. Finally, the corresponding influence coefficient and anomaly coefficient are calculated based on the set of related signal units corresponding to each of the monitoring signal units.

[0084] Optionally, refer to the accompanying reference manual. Figure 3 The step of determining the anomaly coefficient of each of the monitoring signal units and determining the abnormal monitoring signal units with anomalies based on the anomaly coefficients may include:

[0085] S251: Perform feature extraction on each of the monitoring signal units to obtain the corresponding feature vector.

[0086] Specifically, existing feature extraction methods can be used to extract feature values ​​corresponding to each monitoring signal unit, thereby forming a corresponding feature vector. This embodiment of the invention will not elaborate further here. Each feature value in the feature vector can be a margin factor characterizing the impact component in the data signal, and the extracted feature vectors corresponding to each monitoring signal unit have equal lengths.

[0087] In one possible embodiment, after extracting the feature values ​​corresponding to each of the monitoring signal units, the feature values ​​can be normalized, and then a corresponding feature vector can be formed based on the normalized feature values. The specific method for normalization can be found in existing technologies, and will not be elaborated further in this embodiment.

[0088] S252: Determine the relevant signal unit set corresponding to each of the monitoring signal units based on the feature vector. The relevant signal unit set includes at least one monitoring signal unit whose distance from the corresponding monitoring signal unit meets a preset condition.

[0089] Specifically, the Euclidean distance between every two monitoring signal units can be calculated based on the feature vector to represent the distance between each pair of monitoring signal units. For each monitoring signal unit, n monitoring signal units with the smallest distance to the monitoring signal unit (i.e., closest to the monitoring signal unit) can be obtained, and these n units together with the monitoring signal unit form a corresponding set of related signal units. The value of n can be determined according to actual conditions; the n values ​​for each monitoring signal unit can be the same or different. For example, n monitoring signal units with a distance less than or equal to a preset distance threshold can be obtained, and these n units together with the monitoring signal unit form a corresponding set of related signal units. The preset distance threshold can be preset according to actual conditions, and this embodiment of the invention does not impose specific limitations on this.

[0090] S253: Calculate the anomaly coefficient corresponding to each of the monitoring signal units based on the relevant signal unit set.

[0091] Specifically, the influence coefficient of each monitoring signal unit can be calculated based on the size of the relevant signal unit set corresponding to each monitoring signal unit. Then, the size of the relevant signal unit set corresponding to each monitoring signal unit and the influence coefficient of each monitoring signal unit in the relevant signal unit set are combined to calculate the anomaly coefficient of each monitoring signal unit, which describes the degree of anomaly of each monitoring signal unit.

[0092] Specifically, the formula for calculating the influence coefficient is as follows:

[0093]

[0094] Where e represents the influence coefficient, n′=n+1, n represents the size of the relevant signal unit set corresponding to the corresponding monitoring signal unit. Since it is necessary to calculate the distance between the monitoring signal units, the count of n monitoring signal units is increased by 1 and denoted as n′; min(n′) represents the minimum size of the relevant signal unit set corresponding to each monitoring signal unit, and max(n′) represents the maximum size of the relevant signal unit set corresponding to each monitoring signal unit.

[0095] Specifically, the formula for calculating the anomaly coefficient is as follows:

[0096]

[0097] Where E represents the anomaly coefficient, m represents the number of other monitoring signal units that the corresponding monitoring signal unit can reach; ti represents the time point corresponding to the i-th monitoring signal unit that the corresponding monitoring signal unit can reach; and e ti This represents the influence coefficient corresponding to the i-th monitoring signal unit.

[0098] In practical applications, after dividing the operating status monitoring signal into multiple monitoring signal units, the feature values ​​of each monitoring signal unit can be extracted and normalized to obtain the corresponding feature vectors. The resulting feature vector set is then set as a list, and the following calculations are performed:

[0099] 1. Iterate through the feature vectors of monitoring signal units s1 to sn, calculate the distance between monitoring signal units s1 and sn, and add it to the set distances;

[0100] 2. Acquire monitoring signal units at a distance of distance (distance is the test value during the loop process) to form a set neighborhood;

[0101] 3. Iteratively obtain the feature vectors of monitoring signal units s1 to sn in the set neighbor, form the feature vector set list, and calculate the influence coefficient and the anomaly coefficient. The formula for calculating the influence coefficient is shown in equation (1) above, and the formula for calculating the anomaly coefficient is shown in equation (2) above.

[0102] S254: Determine the abnormal monitoring signal unit that has an abnormality based on the abnormality coefficient.

[0103] In this embodiment of the invention, once the number of monitoring signal units reaches a certain scale, the phase difference between each monitoring signal unit can be quickly determined, thereby finding the abnormal monitoring signal unit and then judging the operating status monitoring signal that exceeds the second alarm threshold.

[0104] Optionally, determining the abnormal monitoring signal units based on the abnormality coefficients may include: determining whether there are m consecutive monitoring signal units whose abnormality coefficients are greater than the abnormality coefficients of n monitoring signal units; if so, then identifying the monitoring signal units whose abnormality coefficients are greater than a preset second alarm threshold as abnormal monitoring signal units. The values ​​of m and n can be preset according to actual needs, and the second alarm threshold can also be preset according to actual needs; this embodiment of the invention does not impose specific limitations on this.

[0105] For example, if there are 5 consecutive monitoring signal units with abnormal coefficients greater than 3 monitoring signal units, the monitoring signal unit with an abnormal coefficient exceeding the second alarm threshold can be identified as an abnormal monitoring signal unit, thereby detecting the abnormal monitoring signal unit and its corresponding abnormal coefficient.

[0106] Alternatively, monitoring signal units with an anomaly coefficient greater than a preset second alarm threshold can be directly used as anomaly monitoring signal units. The second alarm threshold can be preset according to actual needs, and this embodiment of the invention does not impose specific limitations on it.

[0107] It is understood that by segmenting the operating status monitoring signals of the crane transmission components and identifying the abnormal intervals based on the degree of difference between each segment, the present invention can not only effectively locate the abnormal monitoring signal units by utilizing the correlation between data, but also detect abnormal states in time before a fault occurs, thereby further improving the operational reliability of the crane's key transmission components.

[0108] S270: Determine whether the operating status of the component to be monitored is normal based on the abnormality monitoring signal unit.

[0109] In this embodiment of the invention, after identifying the abnormal monitoring signal unit, the data in the abnormal monitoring signal unit can be subjected to spectrum analysis to ultimately determine the operating status of the component to be monitored. When the component to be monitored fails, the faulty component and the corresponding fault period can be further identified, wherein the fault period can include initial fault, intermediate fault, and late fault.

[0110] It should be noted that the data in the abnormal monitoring signal unit can be analyzed using existing spectrum analysis methods and fault analysis methods to determine the operating status of the monitored component, the faulty component, and the corresponding fault period, etc. The embodiments of the present invention will not be elaborated here.

[0111] In summary, the condition monitoring method according to the embodiments of the present invention, by real-time monitoring of the operating status monitoring signal of the crane transmission components, dividing the operating status monitoring signal into multiple monitoring signal units, and identifying abnormal monitoring signal units based on the degree of difference among the various monitoring signal units, thereby determining the fault state of the transmission components, can promptly detect abnormalities in key transmission components of the crane for rapid proactive early warning, and eliminates the need to construct complex fault detection models for different components, which can greatly reduce the complexity of condition monitoring and improve the efficiency and accuracy of condition monitoring.

[0112] In one possible embodiment, when the operating status monitoring signal is the raw data signal collected by the sensor, refer to the appendix to the specification. Figure 4 The method may further include:

[0113] S220: Remove invalid signals from the operation status monitoring signals to obtain valid operation status monitoring signals.

[0114] Specifically, since the sensors are affected by the external environment (noise such as wind and rain, vehicle braking, and impact of the crane) during the monitoring process, the collected operation status monitoring signals will be affected by external environmental interference. Therefore, before analyzing the operation status monitoring signals, invalid signals can be removed and useful signals can be retained.

[0115] In one possible embodiment, removing invalid signals from the operating status monitoring signal to obtain a valid operating status monitoring signal may include: extracting multiple intrinsic mode functions based on the operating status monitoring signal; and performing signal separation based on the multiple intrinsic mode functions to obtain the valid operating status monitoring signal.

[0116] Optionally, the extraction of multiple intrinsic mode functions based on the operating status monitoring signal may include the following steps S221-S224:

[0117] S221: Separate the noise signal from the operating status monitoring signal to obtain the first monitoring signal after noise separation.

[0118] Specifically, a noise signal of equal length can be separated from the operating status monitoring signal to obtain a first monitoring signal, which can be obtained by the following formula (3):

[0119] x i (t)=x(t)-a i (t) (3)

[0120] Where x(t) represents the operating status monitoring signal, a i (t) represents the noise signal, x i(t) represents the first monitoring signal after the i-th noise separation in the operation status monitoring signal x(t).

[0121] S222: Extract the mean sequence from the first monitoring signal based on the data fitting method, and remove the mean sequence from the first monitoring signal to obtain the second monitoring signal.

[0122] Specifically, the first monitoring signal after noise separation can be fitted with data, and the upper and lower envelopes of the fitted signal can be extracted. The mean sequence n(t) can then be extracted from the upper and lower envelopes, and then the first monitoring signal x after noise separation can be used as the mean sequence. i Removing the mean sequence n(t) from (t) yields a new monitoring signal, denoted as the second monitoring signal. The data fitting method for extracting the mean sequence can be preset according to actual needs; this embodiment of the invention does not impose specific limitations on this.

[0123] S223: If the second monitoring signal satisfies the intrinsic mode function condition, an intrinsic mode function is extracted from the first monitoring signal.

[0124] Specifically, after obtaining the second monitoring signal, it can be determined whether the second monitoring signal satisfies the intrinsic eigenmode function condition. If it does, the intrinsic eigenmode function can be further extracted. If it does not, the process can return to step S222 to search for a monitoring signal that satisfies the intrinsic eigenmode function condition again.

[0125] The intrinsic mode function conditions include:

[0126] 1) In a data signal, the number of poles and the number of zeros are equal or differ by less than or equal to 1, that is:

[0127] N z -1≤N p ≤N z +1 (4)

[0128] Where, N z N represents the zero-point number, which occurs when the input amplitude of the data signal is not zero and the input frequency causes the output of the data signal to be zero. p The pole number is defined as the number of poles when the input amplitude of the data signal is not zero and the input frequency makes the system output infinite (system stability is disrupted, and oscillation occurs).

[0129] 2) The data signal at any time t i The upper envelope f determined by the local maxima max (t) and the lower envelope f determined by the local minimum point. min The mean of (t) is zero, that is:

[0130]

[0131] Specifically, when it is determined that the second monitoring signal satisfies intrinsic mode function conditions 1) and 2), an intrinsic mode function can be further extracted from the first monitoring signal. When it is determined that the second monitoring signal does not satisfy the intrinsic mode function conditions, the data fitting method is modified, and the execution of step S222 is returned. Based on the modified data fitting method, the mean sequence is extracted from the first monitoring signal, and the mean sequence is removed from the first monitoring signal to obtain the second monitoring signal, until the obtained second monitoring signal satisfies the intrinsic mode function conditions.

[0132] Specifically, this can be achieved by separating the first monitoring signal x after noise. i The intrinsic mode function I is calculated from the mean sequence n(t) extracted from the first monitoring signal and n(t). i (t), the calculation formula is as follows:

[0133] I i (t)=x i (t)-n(t) (6)

[0134] The first monitoring signal x after noise separation can be used i (t) minus the intrinsic mode function I i (t), to obtain the remaining signal r i (t), i.e., the remaining signal r i (t) can be obtained by the following equation (7):

[0135] r i (t)=x i (t)-I i (t) (7)

[0136] S224: The remaining signal after separating the intrinsic mode functions from the first monitoring signal is used as a new first monitoring signal, and the process returns to the step of extracting the mean sequence from the first monitoring signal based on the data fitting method until a preset number of intrinsic mode functions are extracted.

[0137] Specifically, the intrinsic eigenmode functions (IMFs) in the first monitoring signal can be separated to obtain the remaining signal. This remaining signal is then used as the new first monitoring signal, and steps S222 and S223 are repeated to obtain the second IMF. The first IMF obtained from the first noise separation can be denoted as I. 11 (t), the intrinsic mode function I will be separated from the first monitoring signal. 11The remaining signal after (t) is used as the new first monitoring signal. The second intrinsic mode function obtained by repeating steps S222 and S223 is denoted as I. 12 (t), and so on, can be repeated multiple times to obtain multiple intrinsic eigenmode functions of the operating status monitoring signal x(t), denoted as I. 1i (t), i = 1, 2, ..., n, and the corresponding multiple residual signals are denoted as r. 1i (t), i = 1, 2, ..., n, where n is the number of extracted intrinsic mode functions.

[0138] The preset number can be preset according to actual needs. For example, it can be set to 5. That is, after each noise separation, the above steps S222 to S224 are repeated until 5 intrinsic mode functions are extracted. The present invention does not impose a specific limit on the value of the preset number.

[0139] In this embodiment of the invention, noise of different lengths can also be separated multiple times. Since multiple intrinsic mode functions can be extracted from the noise each time it is separated, multiple intrinsic mode functions can be obtained from multiple noise separations, denoted as I. ij (t), i = 1, 2, ..., N, j = 1, 2, ..., n, where N represents the number of noise separations and n represents the number of intrinsic mode functions extracted after each noise separation.

[0140] In this embodiment of the invention, the operating status monitoring signal can be decomposed, denoised, and demodulated in the above manner to obtain a new operating status monitoring signal I. j (t), the operating status monitoring signal I j (t) can be obtained by the following equation (8):

[0141]

[0142] Among them, I ij (t) represents the j-th intrinsic mode function of the i-th noise separation. Different j values ​​represent signals corresponding to different transmission components in the operation status monitoring signal, such as operation status monitoring signals corresponding to motors, gearboxes, drums, and bearings.

[0143] In this embodiment of the invention, the obtained new operating status monitoring signal can be separated to remove invalid signals and retain valid signals to obtain the valid operating status monitoring signal X(t). The valid operating status monitoring signal X(t) can be obtained by the following formula (9):

[0144]

[0145] Among them, Iij (t) represents the j-th intrinsic mode function of the noise separated in the i-th iteration, r n (t) represents the final remaining signal, n represents the number of intrinsic mode functions extracted after each noise separation, and N represents the number of noise separations.

[0146] Accordingly, the step of segmenting the operating status monitoring signal to obtain multiple monitoring signal units (i.e., step S230) can be:

[0147] S230': The effective operating status monitoring signal is segmented to obtain multiple monitoring signal units.

[0148] Specifically, after removing invalid signals from the operational status monitoring signals to obtain valid operational status monitoring signals, the operational status of the monitored component can be determined based on the obtained valid operational status monitoring signals. The specific determination process can be found in [reference needed]. Figure 2 The specific details of the illustrated embodiments will not be repeated here.

[0149] It is understood that the embodiments of the present invention continuously separate noise signals from the operating status monitoring signals, extract multiple intrinsic mode functions after removing the mean sequence, and then combine the extracted multiple intrinsic mode functions to separate invalid signals. This can efficiently eliminate invalid signals while retaining useful signals as much as possible, thereby reducing unnecessary data processing during the status monitoring process and further improving the efficiency and accuracy of status monitoring of key transmission components of cranes.

[0150] Reference manual attached Figure 5 This illustrates the structure of a crane condition monitoring device 500 provided in one embodiment of the present invention. For example... Figure 5 As shown, the device 500 may include:

[0151] The monitoring signal acquisition module 510 is used to acquire the operating status monitoring signal of the component to be monitored;

[0152] The segmentation module 520 is used to segment the operating status monitoring signal to obtain multiple monitoring signal units;

[0153] An abnormal signal determination module 530 is used to determine the abnormal coefficient of each of the monitoring signal units respectively, and to determine the abnormal monitoring signal units that have an abnormality based on the abnormal coefficients.

[0154] The status monitoring module 540 is used to determine whether the operating status of the monitored component is normal based on the abnormal monitoring signal unit.

[0155] In one possible embodiment, the device 500 may further include:

[0156] An invalid signal elimination unit is used to eliminate invalid signals from the operating status monitoring signals to obtain valid operating status monitoring signals.

[0157] The segmentation module 520 is specifically used to segment the effective operating status monitoring signal to obtain multiple monitoring signal units.

[0158] It should be noted that the apparatus provided in the above embodiments is only illustrated by the division of the above functional modules when implementing its functions. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the apparatus provided in the above embodiments and the corresponding method embodiments belong to the same concept, and the specific implementation process can be found in the corresponding method embodiments, which will not be repeated here.

[0159] One embodiment of the present invention also provides an electronic device, which includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by the processor to implement the crane status monitoring method provided in the above method embodiments.

[0160] Memory can be used to store software programs and modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory. Memory can primarily include a program storage area and a data storage area. The program storage area can store the operating system, application programs required for the functions, etc.; the data storage area can store data created based on the use of the device, etc. Furthermore, memory can include high-speed random access memory, and can also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, memory can also include a memory controller to provide the processor with access to the memory.

[0161] Refer to the attached reference manual Figure 6 The diagram shown is a block diagram of an electronic device 600 according to an embodiment of the present invention. The electronic device 600 may include one or more processors 602, system control logic 608 connected to at least one of the processors 602, system memory 604 connected to the system control logic 608, non-volatile memory (NVM) 606 connected to the system control logic 608, and network interface 610 connected to the system control logic 608.

[0162] Processor 602 may include one or more single-core or multi-core processors. Processor 602 may include any combination of general-purpose processors and special-purpose processors (e.g., graphics processors, application processors, baseband processors, etc.). In embodiments herein, processor 602 may be configured to perform operations according to... Figures 2 to 4 One or more embodiments of the various embodiments shown.

[0163] In some embodiments, system control logic 608 may include any suitable interface controller to provide any suitable interface to at least one of the processors 602 and / or any suitable device or component communicating with system control logic 608.

[0164] In some embodiments, system control logic 608 may include one or more memory controllers to provide an interface to system memory 604. System memory 604 may be used to load and store data and / or instructions. In some embodiments, memory 604 of device 600 may include any suitable volatile memory, such as suitable dynamic random access memory (DRAM).

[0165] NVM / Memory 606 may include one or more tangible, non-transitory computer-readable media for storing data and / or instructions. In some embodiments, NVM / Memory 606 may include any suitable non-volatile memory such as flash memory and / or any suitable non-volatile storage device, such as at least one of HDD (Hard Disk Drive), CD (Compact Disc) drive, and DVD (Digital Versatile Disc) drive.

[0166] NVM / Storage 606 may include a portion of storage resources mounted on the device 600, or it may be accessible by the device but is not necessarily part of the device. For example, NVM / Storage 606 may be accessed over a network via network interface 610.

[0167] Specifically, system memory 604 and NVM / memory 606 may each include a temporary copy and a permanent copy of instruction 620. Instruction 620 may include, when executed by at least one of processors 602, causing device 600 to perform, as Figures 2 to 4 The instructions for the crane condition monitoring method are shown. In some embodiments, the instructions 620, hardware, firmware, and / or their software components may additionally / alternatively be located in the system control logic 608, network interface 610, and / or processor 602.

[0168] Network interface 610 may include a transceiver for providing a radio interface to device 600, thereby enabling communication with any other suitable device (such as a front-end module, antenna, etc.) via one or more networks. In some embodiments, network interface 610 may be integrated into other components of device 600. For example, network interface 610 may be integrated into at least one of the following: a communication module of processor 602, system memory 604, NVM / memory 606, and a firmware device (not shown) with instructions, which, when at least one of processor 602 executes the instructions, enable device 600 to implement... Figures 2 to 4 One or more embodiments of the various embodiments shown.

[0169] The network interface 610 may further include any suitable hardware and / or firmware to provide a multiple-input multiple-output radio interface. For example, the network interface 610 may be a network adapter, a wireless network adapter, a telephone modem, and / or a wireless modem.

[0170] In one embodiment, at least one of the processors 602 may be packaged together with the logic of one or more controllers for system control logic 608 to form a system-in-package (SiP). In another embodiment, at least one of the processors 602 may be integrated on the same die with the logic of one or more controllers for system control logic 608 to form a system-on-a-chip (SoC).

[0171] Device 600 may further include an input / output (I / O) device 612. The I / O device 612 may include a user interface enabling a user to interact with device 600; the peripheral component interface is designed to allow peripheral components to also interact with device 600. In some embodiments, device 600 may also include sensors for determining at least one of environmental conditions and location information related to device 600.

[0172] In some embodiments, the user interface may include, but is not limited to, a display (e.g., a liquid crystal display, a touch screen display, etc.), a speaker, a microphone, one or more cameras (e.g., a still image camera and / or a video camera), a flashlight (e.g., a light-emitting diode flash), and a keyboard.

[0173] In some embodiments, the peripheral component interface may include, but is not limited to, a non-volatile memory port, an audio jack, and a power interface.

[0174] In some embodiments, the sensor may include, but is not limited to, a gyroscope sensor, an accelerometer, a proximity sensor, an ambient light sensor, and a positioning unit. The positioning unit may also be part of or interact with the network interface 610 to communicate with components of the positioning network (e.g., Global Positioning System (GPS) satellites).

[0175] It is understood that the structures illustrated in the embodiments of the present invention do not constitute a specific limitation on the electronic device 600. In other embodiments of the present invention, the electronic device 600 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0176] One embodiment of the present invention also provides a computer-readable storage medium, which can be disposed in an electronic device to store at least one instruction or at least one program related to implementing a crane condition monitoring method, wherein the at least one instruction or the at least one program is loaded and executed by the processor to implement the crane condition monitoring method provided in the above-described method embodiment.

[0177] Optionally, in embodiments of the present invention, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0178] One embodiment of the present invention also provides a computer program product comprising a computer program / instructions that, when the computer program product is run on an electronic device, are loaded and executed by a processor to implement the steps of the crane condition monitoring method provided in the various alternative embodiments described above.

[0179] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0180] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the apparatus embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0181] Those skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or by a program to instruct the relevant hardware, and the program may be stored in a computer-readable storage medium, which may be a read-only memory, a disk, or an optical disk, etc.

[0182] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring the condition of a crane, characterized in that, include: Acquire the operating status monitoring signal of the component to be monitored, wherein the component to be monitored is the transmission component of the crane; The operation status monitoring signal is segmented to obtain multiple monitoring signal units; The abnormality coefficient of each monitoring signal unit is determined, and the abnormal monitoring signal unit with abnormality is determined based on the abnormality coefficient. The abnormality monitoring signal unit determines whether the operating status of the component to be monitored is normal. The step of determining the anomaly coefficient of each of the monitoring signal units and determining the abnormal monitoring signal units with anomalies based on the anomaly coefficients includes: Feature extraction is performed on each of the monitoring signal units to obtain the corresponding feature vector; Based on the feature vector, a set of related signal units corresponding to each monitoring signal unit is determined. The set of related signal units includes at least one monitoring signal unit whose distance from the corresponding monitoring signal unit satisfies a preset condition. Based on the set of related signal units, the anomaly coefficient corresponding to each monitoring signal unit is calculated. The influence coefficient corresponding to each monitoring signal unit is calculated based on the size of the set of related signal units corresponding to each monitoring signal unit. The anomaly coefficient corresponding to each monitoring signal unit is obtained based on the size of the set of related signal units corresponding to each monitoring signal unit and the influence coefficients corresponding to each monitoring signal unit in the set of related signal units. The formula for calculating the influence coefficient is as follows: Where e represents the influence coefficient, n′=n+1, and n represents the size of the relevant signal unit set corresponding to the monitoring signal unit; min(n′) represents the minimum size of the relevant signal unit set corresponding to each monitoring signal unit, and max(n′) represents the maximum size of the relevant signal unit set corresponding to each monitoring signal unit. The formula for calculating the anomaly coefficient is as follows: Where E represents the anomaly coefficient, m represents the number of other monitoring signal units that the corresponding monitoring signal unit can reach; ti represents the time point corresponding to the i-th monitoring signal unit that the corresponding monitoring signal unit can reach, and e ti The influence coefficient corresponding to the i-th monitoring signal unit is represented; the abnormal monitoring signal unit with an abnormality is determined based on the abnormality coefficient.

2. The method according to claim 1, characterized in that, The transmission components include one or more of a motor, gearbox, drum, and bearing, and the operating status monitoring signal includes a vibration acceleration signal.

3. The method according to claim 1, characterized in that, The method further includes: Invalid signals are removed from the operating status monitoring signals to obtain valid operating status monitoring signals; The step of segmenting the operating status monitoring signal to obtain multiple monitoring signal units includes: The effective operating status monitoring signal is segmented to obtain multiple monitoring signal units.

4. The method according to claim 3, characterized in that The process of removing invalid signals from the operational status monitoring signals to obtain valid operational status monitoring signals includes: Multiple intrinsic mode functions are extracted based on the aforementioned operational status monitoring signals; Signal separation is performed based on the multiple intrinsic mode functions to obtain the effective operating status monitoring signal.

5. The method according to claim 4, characterized in that, The extraction of multiple intrinsic mode functions based on the operational status monitoring signals includes: The noise signal is separated from the operating status monitoring signal to obtain the first monitoring signal after noise separation; The mean sequence is extracted from the first monitoring signal based on the data fitting method, and the mean sequence is removed from the first monitoring signal to obtain the second monitoring signal; If the second monitoring signal satisfies the intrinsic mode function condition, an intrinsic mode function is extracted from the first monitoring signal; The remaining signal after separating the intrinsic mode functions from the first monitoring signal is used as a new first monitoring signal, and the process returns to the step of extracting the mean sequence from the first monitoring signal based on the data fitting method, until a preset number of intrinsic mode functions are extracted.

6. The method according to claim 5, characterized in that, The extraction of multiple intrinsic mode functions based on the operating status monitoring signal further includes: If the second monitoring signal does not meet the intrinsic mode function condition, the data fitting method is modified, and the process returns to the step of extracting the mean sequence from the first monitoring signal based on the data fitting method.

7. The method according to claim 1, characterized in that, The operation status monitoring signal is segmented to obtain multiple monitoring signal units, including: Obtain multiple pre-set segmentation thresholds; The operating status monitoring signal is divided into multiple monitoring signal units based on the multiple segmentation thresholds.

8. A crane condition monitoring device, characterized in that, The device includes: The monitoring signal acquisition module is used to acquire the operating status monitoring signals of the component to be monitored; The segmentation module is used to segment the operating status monitoring signal to obtain multiple monitoring signal units; An abnormal signal determination module is used to determine the abnormal coefficient of each of the monitoring signal units, and to determine the abnormal monitoring signal units that have an abnormality based on the abnormal coefficients. The status monitoring module is used to determine whether the operating status of the monitored component is normal based on the abnormal monitoring signal unit. The step of determining the anomaly coefficient of each of the monitoring signal units and determining the abnormal monitoring signal units with anomalies based on the anomaly coefficients includes: Feature extraction is performed on each of the monitoring signal units to obtain the corresponding feature vector; Based on the feature vector, a set of related signal units corresponding to each monitoring signal unit is determined. The set of related signal units includes at least one monitoring signal unit whose distance from the corresponding monitoring signal unit satisfies a preset condition. Based on the set of related signal units, the anomaly coefficient corresponding to each monitoring signal unit is calculated. The influence coefficient corresponding to each monitoring signal unit is calculated based on the size of the set of related signal units corresponding to each monitoring signal unit. The anomaly coefficient corresponding to each monitoring signal unit is obtained based on the size of the set of related signal units corresponding to each monitoring signal unit and the influence coefficients corresponding to each monitoring signal unit in the set of related signal units. The formula for calculating the influence coefficient is as follows: Where e represents the influence coefficient, n′=n+1, and n represents the size of the relevant signal unit set corresponding to the monitoring signal unit; min(n′) represents the minimum size of the relevant signal unit set corresponding to each monitoring signal unit, and max(n′) represents the maximum size of the relevant signal unit set corresponding to each monitoring signal unit. The formula for calculating the anomaly coefficient is as follows: Where E represents the anomaly coefficient, m represents the number of other monitoring signal units that the corresponding monitoring signal unit can reach; ti represents the time point corresponding to the i-th monitoring signal unit that the corresponding monitoring signal unit can reach, and e ti The influence coefficient corresponding to the i-th monitoring signal unit is represented; the abnormal monitoring signal unit with an abnormality is determined based on the abnormality coefficient.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by the processor to implement the crane condition monitoring method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction or at least one program, which is loaded and executed by a processor to implement the crane condition monitoring method as described in any one of claims 1-7.

11. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the crane condition monitoring method as described in any one of claims 1-7.

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