Mechanical equipment fault monitoring system based on data analysis

Abstract

The invention discloses a mechanical equipment fault monitoring system based on data analysis, and relates to the technical field of equipment fault monitoring, a sorting module obtains the average number of fault coefficients of forklifts in the last loading and unloading operation through a large database, the numbered forklifts are sorted again from small to large according to the average number of the fault coefficients, a sorting table is generated, and the sorting table is stored in a database; the selection module selects and displays the use sequence of the forklifts according to the sorting table, so that the use efficiency of the forklift in the workshop is improved, and after the forklifts fork objects, the monitoring module obtains the multi-source data of the forklifts, comprehensively analyzes the multi-source data, establishes fault coefficients, and improves the fault detection accuracy. Whether a fault exists in future use of the forklift or not is judged according to a comparison result of the fault coefficient and an early warning threshold value, management is carried out, and the evaluation module revises the initial safe bearing weight of the forklift through the fault coefficient. According to the method, the corrected bearing weight is obtained, so that when the performance of the forklift is reduced, the maximum bearing weight of the forklift can be reduced, and safe use of the forklift is further guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment fault monitoring, and particularly relates to a mechanical equipment fault monitoring system based on data analysis. Background Art

[0002] A forklift is a mechanical equipment used for carrying and stacking items, and is widely used in fields such as logistics, warehousing, and manufacturing. It usually consists of a vehicle frame, an engine, a running system, a lifting system, and a control system, etc. The lifting system of the forklift is one of its most important components, usually including one or more forks. The forks are lifted and lowered through a hydraulic system, can carry objects, and stack them on shelves or transport them to the destination;

[0003] The fault monitoring system is used to monitor the operating status of the forklift. The system monitors the status of the forklift through devices such as sensors, and can send an alarm prompt in time when a fault occurs in the forklift.

[0004] The prior art has the following deficiencies:

[0005] As the usage time of the forklift increases, the performance of all aspects of the forklift will decline. The existing fault monitoring system can only send an alarm prompt when a fault occurs in the forklift. There is no performance evaluation and fault prediction and processing for the forklift. When the forklift is suddenly faulty while carrying a heavy object and in the process of driving, the heavy object may fall, which not only causes the heavy object to fall and be damaged, resulting in economic losses, but also easily causes the forklift to overturn, leading to safety accidents. Summary of the Invention

[0006] The purpose of the present invention is to provide a mechanical equipment fault monitoring system based on data analysis to solve the deficiencies in the background art.

[0007] To achieve the above purpose, the present invention provides the following technical solution: A mechanical equipment fault monitoring system based on data analysis, including a number library module, a sorting module, a selection module, a monitoring module, an early warning module, a control module, an evaluation module, and a warning module;

[0008] Number library module: Used for forklift managers to input the quantity of all forklifts, and generate a unique number for the forklifts according to the input time of the forklifts and the forklift information;

[0009] Sorting module: Obtain the average fault coefficient in the previous loading and unloading operation of the forklift through a large database, and re-sort the numbered forklifts from small to large according to the average fault coefficient to generate a sorting table;

[0010] Selection module: Select and display the usage order of the forklifts according to the sorting table;

[0011] Monitoring module: After the forklift picks up an object, obtain multi-source data of the forklift and conduct comprehensive analysis to establish a fault coefficient;

[0012] Warning module: Compare the failure coefficient with the warning threshold, and determine whether to issue a warning signal according to the comparison result;

[0013] Control module: After receiving the warning signal, control the forklift to stop running;

[0014] Evaluation module: Re-correct the initial safe load capacity of the forklift through the failure coefficient to obtain the corrected load capacity;

[0015] Warning module: Used to issue an alarm prompt when the actual load weight after the forklift forks an object exceeds the corrected load weight.

[0016] In a preferred embodiment, the monitoring module includes a collection unit and an analysis unit. The collection unit is used to collect multi-source data of the forklift after the forklift forks an object. The multi-source data includes fork component parameters and vehicle parameters. The fork component parameters include the strain at the connection part between the fork and the lifting frame and the vibration frequency of the lifting component. The vehicle parameters include the increase rate of the distance between the rear wheel axle frame of the forklift and the ground. After the analysis unit performs dimensionless processing on the multi-source data, it comprehensively analyzes and establishes a failure coefficient.

[0017] In a preferred embodiment, the analysis unit performs dimensionless processing on the strain at the connection part between the fork and the lifting frame, the vibration frequency of the lifting component, and the increase rate of the distance between the rear wheel axle frame of the forklift and the ground, and comprehensively analyzes and establishes a failure coefficient. The expression is: In the formula, gz x is the failure coefficient, Δb is the strain at the connection part between the fork and the lifting frame, Δd is the vibration frequency of the lifting component, jl c is the increase rate of the distance between the rear wheel axle frame of the forklift and the ground, and α, β, and γ are the proportionality coefficients of the strain at the connection part of the lifting frame, the vibration frequency of the lifting component, and the increase rate of the distance between the rear wheel axle frame of the forklift and the ground, respectively, and α, β, and γ are all greater than 0.

[0018] In a preferred embodiment, the strain at the connection part between the fork and the lifting frame is monitored online by a pressure strain gauge arranged at the bottom of the connection part between the fork and the lifting frame, and the vibration frequency of the lifting component is monitored online by a vibration sensor arranged on the lifting component.

[0019] In a preferred embodiment, the calculation expression for the increase rate of the distance between the rear wheel axle frame of the forklift and the ground is: jl c =(j2 - j1) / j1; in the formula, j1 represents the initial distance between the rear wheel axle frame of the forklift and the ground, j2 represents the real-time distance between the rear wheel axle frame of the forklift and the ground after the forklift forks an object, and the distance between the forklift and the ground is monitored online by a laser sensor arranged at the bottom end of the rear wheel axle frame of the forklift.

[0020] In a preferred embodiment, the warning signal includes a comparison unit and a warning unit;

[0021] After the comparison unit obtains the fault coefficient gz x it compares the fault coefficient gz x with a preset warning threshold yj z and the warning unit determines whether to issue a warning signal according to the comparison result.

[0022] In a preferred embodiment, the warning unit determines whether to issue a warning signal according to the comparison result, including the following steps:

[0023] If the comparison result is that the fault coefficient gz of the forklift x > the warning threshold yj z the warning unit determines that the forklift does not support loading and unloading operations, issues a warning signal, and after the control module receives the warning signal, controls the forklift to stop running;

[0024] If the comparison result is that the fault coefficient gz of the forklift x ≤ the warning threshold yj z the warning unit determines that the forklift supports loading and unloading operations and does not issue a warning signal.

[0025] In a preferred embodiment, the evaluation module includes a storage unit and a correction unit;

[0026] The storage unit is used for the management personnel to input the initial safe load-bearing weight of the forklift. The initial safe load-bearing weight is the maximum load-bearing weight of the forklift. The correction unit re-corrects the initial safe load-bearing weight that supports loading and unloading operations through the fault coefficient to obtain the corrected load-bearing weight.

[0027] In a preferred embodiment, the correction unit re-corrects the initial safe load-bearing weight of the forklift that supports loading and unloading operations through the fault coefficient. The calculation expression of the corrected load-bearing weight is: XZ = MC - (MC / gz x k ); where XZ is the corrected load-bearing weight, MC is the initial safe load-bearing weight of the forklift that supports loading and unloading operations, and k is a constant with a value of 2.368.

[0028] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0029] 1. Before the forklift is used, the sorting module obtains the average failure coefficient in the previous loading and unloading operation of the forklift through the big database, re - sorts the numbered forklifts from small to large according to the average failure coefficient, generates a sorting table, and the selection module selects and displays the usage order of the forklifts according to the sorting table, thereby improving the usage efficiency of the forklifts in the workshop. Moreover, after the forklift forks an object, the monitoring module obtains the multi - source data of the forklift and conducts comprehensive analysis to establish a failure coefficient, and judges whether there are failures in the future use of the forklift and manages it through the comparison result between the failure coefficient and the warning threshold. The evaluation module re - corrects the initial safe load - bearing weight of the forklift through the failure coefficient to obtain the corrected load - bearing weight, so that when the performance of the forklift declines, the maximum load - bearing weight of the forklift can be reduced, further ensuring the safe use of the forklift;

[0030] 2. The present invention makes dimensionless the strain at the connection part between the fork and the lifting frame, the vibration frequency of the lifting component, and the increase rate of the distance between the rear wheel axle frame of the forklift and the ground through the analysis unit, and establishes a failure coefficient after comprehensive analysis, which not only improves the data processing efficiency, but also comprehensively processes the multi - source data, improving the accuracy of forklift failure prediction;

[0031] 3. The present invention re - corrects the initial safe load - bearing weight of the forklift supporting the loading and unloading operation through the correction unit to obtain the corrected load - bearing weight. When the weight of the object fork - lifted measured by the weighing sensor set at the bottom of the fork exceeds the corrected load - bearing weight, the warning module issues an alarm prompt, further ensuring the stability of the forklift operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to these drawings.

[0033] Figure 1 It is a system module diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0034] In order to make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0035] Embodiment 1

[0036] Please refer to Figure 1As shown in the figure, a mechanical equipment fault monitoring system based on data analysis according to this embodiment includes a number library module, a sorting module, a selection module, a monitoring module, an early warning module, a control module, an evaluation module, and a warning module;

[0037] The number library module is used for forklift managers to input the quantity of all forklifts. After that, a unique number is generated for each forklift based on the input time and forklift information of the forklift. The number information is sent to the sorting module. The sorting module obtains the average failure coefficient in the previous loading and unloading operation of the forklift through the database, and re-sorts the numbered forklifts from small to large according to the average failure coefficient to generate a sorting list. The sorting list information is sent to the selection module and the management module. The selection module selects and displays the usage order of the forklifts according to the sorting list. After the forklift picks up an object, the monitoring module obtains multi-source data of the forklift and conducts comprehensive analysis to establish a failure coefficient. The failure coefficient information is sent to the early warning module, the evaluation module, and the sorting module. The early warning module compares the failure coefficient with the early warning threshold, and judges whether to issue an early warning signal according to the comparison result. When an early warning signal is issued, the early warning signal is sent to the control module. After receiving the early warning signal, the control module controls the forklift to stop running. When the maintenance personnel receive the early warning signal, they need to find the corresponding forklift according to the number information and positioning information, and then perform maintenance on the forklift. The evaluation module re-corrects the initial safe load-bearing weight of the forklift through the failure coefficient to obtain the corrected load-bearing weight. The corrected load-bearing weight information is sent to the warning module. When the actual load-bearing weight of the forklift after picking up an object exceeds the corrected load-bearing weight, the warning module issues an alarm prompt. When the management personnel receive the alarm prompt signal, they need to manage the forklift in time. When the operator receives the alarm prompt signal, they need to stop the forklift.

[0038] In this application, the number library module, the sorting module, the selection module, and the evaluation module are all set in the remote control center; the monitoring module, the early warning module, and the control module are all set on the central console of the forklift; the warning module is set in the workshop;

[0039] The monitoring module communicates wirelessly with the sorting module and the evaluation module, and the warning module communicates wirelessly with the evaluation module.

[0040] Before the forklift is used in this application, the sorting module obtains the average failure coefficient in the previous loading and unloading operation of the forklift through the large database, re-sorts the numbered forklifts from small to large according to the average failure coefficient, generates a sorting table, and the selection module selects and displays the usage order of the forklifts according to the sorting table, thereby improving the usage efficiency of the forklifts in the workshop. Moreover, after the forklift picks up an object, the monitoring module obtains the multi-source data of the forklift and conducts a comprehensive analysis to establish a failure coefficient. Based on the comparison result between the failure coefficient and the warning threshold, it judges whether there are any faults in the future use of the forklift and conducts management. The evaluation module re-corrects the initial safe load-bearing weight of the forklift through the failure coefficient to obtain the corrected load-bearing weight. Thus, when the performance of the forklift decreases, the maximum load-bearing weight of the forklift can be reduced to further ensure the safe use of the forklift.

[0041] In this application, the numbering library module is used for forklift managers to enter the total number of all forklifts, and then generate a unique number for each forklift according to the entry time and forklift information of the forklift. Specifically:

[0042] Suppose there are n forklifts in the workshop. After entering the n forklifts into the numbering library module, the numbering library module generates a numbering table {c1, c2, c3,... c n} according to the entry time and forklift information of the forklift. The numbering table is mainly used to number the forklifts, so as to facilitate the subsequent sorting module to sort the forklifts. When numbering each forklift, the forklift information also needs to be entered. The forklift information includes the engine number, frame number, etc. of the forklift, which is convenient for subsequent querying of information such as the production date of the forklift according to the forklift number.

[0043] The sorting module obtains the average failure coefficient in the previous loading and unloading operation of the forklift through the large database, and re-sorts the numbered forklifts from small to large according to the average failure coefficient to generate a sorting table.

[0044] The current usage order of the forklift is mainly determined by the operating status of the forklift in the previous loading and unloading operation. In this application, the acquisition time of the failure coefficient is: after the forklift picks up an object, the monitoring module obtains the multi-source data of the forklift and conducts a comprehensive analysis to establish a failure coefficient. Therefore, every time the forklift picks up an object during a working day, the monitoring module obtains a failure coefficient once. Suppose the forklift picks up objects 10 times in a day, then the average failure coefficient is equal to the sum of the 10 failure coefficients divided by 10;

[0045] Suppose there are 5 forklifts in the workshop, and the numbering table is {c1, c2, c3, c4, c5}. The comparison result of the average failure coefficients of the 5 forklifts is c3 > c5 > c2 > c1 > c4. Then, the numbered forklifts are re-sorted from small to large according to the average failure coefficient, and the generated sorting table is {c3, c5, c2, c1, c4}.

[0046] The selection module selects and displays the order in which forklifts are used based on the sorting table: if the workshop needs 3 forklifts to load and unload objects, the selected forklifts are c3, c5, and c2 respectively; if the workshop needs 5 forklifts to load and unload objects, the selected forklifts are c3, c5, c2, c1, and c4 respectively.

[0047] Example 2: After a forklift picks up an object, the monitoring module obtains multi-source data of the forklift and performs a comprehensive analysis to establish a fault coefficient. The fault coefficient information is sent to the early warning module, the evaluation module, and the sorting module. The early warning module compares the fault coefficient with the early warning threshold and issues an early warning signal based on the comparison result. When the early warning signal is issued, the early warning signal is sent to the control module. After receiving the early warning signal, the control module controls the forklift to stop running.

[0048] The monitoring module includes a collection unit and an analysis unit. The collection unit is used to collect multi-source data of the forklift after the forklift picks up an object. The analysis unit performs dimensionless processing on the multi-source data, conducts comprehensive analysis, and establishes a fault coefficient.

[0049] The acquisition unit is used to collect multi-source data of the forklift after the forklift picks up an object. The multi-source data includes forklift component parameters and vehicle parameters. The forklift component parameters include the strain at the connection between the fork and the lifting frame and the vibration frequency of the lifting component. The vehicle parameters include the rate of increase in the distance between the forklift's rear wheel axle frame and the ground.

[0050] The analysis unit performs dimensionless processing on the strain at the connection between the fork and the lifting frame, the vibration frequency of the lifting components, and the rate of increase in the distance between the forklift's rear wheel axle frame and the ground. After comprehensive analysis, the failure coefficient is established, which is expressed as follows: Where gz x is the failure coefficient, Δb is the strain at the connection between the fork and the lifting frame, Δd is the vibration frequency of the lifting component, jl c is the increase rate of the distance between the forklift's rear wheel axle frame and the ground, α, β, and γ are the proportional coefficients of the strain of the lifting frame connection part, the vibration frequency of the lifting component, and the increase rate of the distance between the forklift's rear wheel axle frame and the ground, respectively, and α, β, and γ are all greater than 0.

[0051] The strain at the connection between the forks and the lift frame is monitored online using a pressure strain gauge installed at the bottom of the connection. When the forks pick up an object, the pressure strain gauge uses the strain characteristics of the material under force to measure the force and strain applied to the object. When the strain at the connection between the forks and the lift frame increases, it indicates that the deformation of the connection between the forks and the lift frame is greater, and there is a risk of the object falling from the forks.

[0052] A pressure strain gauge usually consists of a metal foil. The length, width, and thickness of the metal foil will change slightly due to external forces. When the pressure strain gauge is pasted on the surface of the object to be measured, with the action of external forces, the small deformation on the object surface will be transmitted to the pressure strain gauge, causing changes in the length, width, and thickness of the metal foil. These changes will alter the resistance value of the metal foil. Therefore, the magnitude and direction of the external force acting on the object can be calculated by measuring the change in the resistance value.

[0053] The vibration frequency of the lifting component is monitored online by setting a vibration sensor on the lifting component. After the forklift fork picks up an object, the vibration sensor collects the vibration frequency of the lifting component within 10 s. If the vibration frequency of the lifting component is too high, it will cause the object to fall off the forklift fork during the transfer process.

[0054] The acquisition logic of the increase rate of the distance between the rear-wheel axle frame of the forklift and the ground is as follows: A laser sensor is set at the bottom end of the rear-wheel axle frame of the forklift. In the situation where the forklift does not pick up an object, the initial distance between the rear-wheel axle frame of the forklift and the ground is collected through the laser sensor. After the forklift picks up an object, the real-time distance between the rear-wheel axle frame of the forklift and the ground is collected through the laser sensor. Then, the calculation expression for the increase rate of the distance between the rear-wheel axle frame of the forklift and the ground is: jl c =(j2 - j1) / j1; where j1 represents the initial distance between the rear-wheel axle frame of the forklift and the ground, and j2 represents the real-time distance between the rear-wheel axle frame of the forklift and the ground after the forklift picks up an object. The larger the increase rate of the distance between the rear-wheel axle frame of the forklift and the ground, the more serious the inclination of the forklift after picking up an object, which is likely to cause the forklift to overturn or the object to fall.

[0055] When the forklift picks up an object, if the weight of the object picked up by the forklift exceeds the counterweight at the rear of the forklift, the forklift will tilt with the front wheel as the axis.

[0056] The warning module compares the fault coefficient with the warning threshold, and judges whether to issue a warning signal according to the comparison result.

[0057] The warning signal includes a comparison unit and a warning unit;

[0058] After the comparison unit obtains the fault coefficient gz x it compares the fault coefficient gz x with the pre-set warning threshold yj z and the warning unit judges whether to issue a warning signal according to the comparison result;

[0059] Among them, the warning unit judging whether to issue a warning signal according to the comparison result includes the following steps:

[0060] If the comparison result is that the fault coefficient gz of the forklift x > warning threshold yj z, the warning unit determines that the forklift does not support loading and unloading operations, issues a warning signal, and after receiving the warning signal, the control module controls the forklift to stop running;

[0061] If the comparison result shows that the fault coefficient gz of the forklift x ≤ the warning threshold yj z , the warning unit determines that the forklift supports loading and unloading operations and does not issue a warning signal.

[0062] In this application, the analysis unit performs dimensionless processing on the strain of the connection part between the forklift fork and the lifting frame, the vibration frequency of the lifting component, and the increase rate of the distance between the rear wheel axle frame of the forklift and the ground. After comprehensive analysis, a fault coefficient is established, which not only improves the data processing efficiency, but also comprehensively processes multi-source data, improving the accuracy of forklift fault prediction.

[0063] Embodiment 3: The evaluation module re-corrects the initial safe load-bearing weight of the forklift through the fault coefficient to obtain the corrected load-bearing weight, and sends the corrected load-bearing weight information to the warning module. When the actual load-bearing weight of the forklift after picking up an object exceeds the corrected load-bearing weight, the warning module issues an alarm prompt. When the management personnel receive the alarm prompt signal, they need to manage the forklift in a timely manner. When the operator receives the alarm prompt signal, they need to stop the forklift from running.

[0064] After the forklift that does not support operation stops being used, the fault coefficients of the remaining forklifts that support operation also vary. After analyzing the fault coefficients in Embodiment 2, the larger the fault coefficient of the forklift, the worse the running stability of the forklift. Therefore, it is necessary to correct the initial safe load-bearing weight of the forklift according to the fault coefficient to further ensure the running stability of the forklift;

[0065] The evaluation module includes a storage unit and a correction unit;

[0066] The storage unit is used for the management personnel to enter the initial safe load-bearing weight of the forklift. The initial safe load-bearing weight is the maximum load-bearing weight of the forklift. The correction unit re-corrects the initial safe load-bearing weight that supports loading and unloading operations through the fault coefficient to obtain the corrected load-bearing weight;

[0067] The correction unit re-corrects the initial safe load-bearing weight of the forklift that supports loading and unloading operations through the fault coefficient. The calculation expression of the corrected load-bearing weight is: XZ = MC - (MC / gz x k ); In the formula, XZ is the corrected load-bearing weight, MC is the initial safe load-bearing weight of the forklift that supports loading and unloading operations, k is a constant, and the value is 2.368.

[0068] After obtaining the corrected load-bearing weight, when the weight of the object picked up by the weighing sensor set at the bottom of the forklift fork exceeds the corrected load-bearing weight, the warning module issues an alarm prompt.

[0069] In this application, the initial safe load-bearing weight of a forklift supporting loading and unloading operations is re-corrected by a correction unit to obtain a corrected load-bearing weight. When the weight of the object lifted by the weighing sensor set at the bottom of the forklift forks exceeds the corrected load-bearing weight, the warning module issues an alarm prompt to further ensure the stability of the forklift operation.

[0070] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0071] In the description of this specification, the description with reference to terms such as "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0072] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not elaborate on all the details, nor do they limit the present invention to only the specific implementation manners. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the present invention, so that those skilled in the art in the relevant technical field can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A mechanical equipment fault monitoring system based on data analysis, characterized in that: It includes a number library module, a sorting module, a selection module, a monitoring module, a warning module, a control module, an evaluation module, and a warning module; Number library module: Used for forklift managers to input the quantity of all forklifts, and generate a unique number for each forklift based on the input time and forklift information; Sorting module: Obtain the average failure coefficient in the previous loading and unloading operation of the forklift through the big database, and re-sort the numbered forklifts from small to large according to the average failure coefficient to generate a sorting table; Selection module: Select and display the usage order of forklifts according to the sorting table; Monitoring module: After the forklift picks up an object, obtain the multi-source data of the forklift and conduct comprehensive analysis to establish a failure coefficient; Warning module: Compare the failure coefficient with the warning threshold, and judge whether to send a warning signal according to the comparison result; Control module: After receiving the warning signal, control the forklift to stop running; Evaluation module: Re-correct the initial safe load capacity of the forklift through the failure coefficient to obtain the corrected load capacity; Warning module: Used to send an alarm prompt when the actual load capacity of the forklift exceeds the corrected load capacity after picking up an object.

2. The mechanical equipment fault monitoring system based on data analysis according to claim 1, characterized in that: The monitoring module includes a collection unit and an analysis unit. The collection unit is used to collect the multi-source data of the forklift after the forklift picks up an object. The multi-source data includes fork component parameters and vehicle parameters. The fork component parameters include the strain at the connection part between the fork and the lifting frame, and the vibration frequency of the lifting component. The vehicle parameters include the increase rate of the distance between the rear axle frame of the forklift and the ground. The analysis unit performs dimensionless processing on the multi-source data, and then conducts comprehensive analysis and establishes a failure coefficient.

3. The mechanical equipment fault monitoring system based on data analysis according to claim 2, characterized in that: The analysis unit performs dimensionless processing on the strain at the connection part between the fork and the lifting frame, the vibration frequency of the lifting component, and the increase rate of the distance between the rear axle frame of the forklift and the ground, and then conducts comprehensive analysis and establishes a failure coefficient.

4. A mechanical equipment fault monitoring system based on data analysis according to claim 3, characterized in that: The strain at the connection part between the fork and the lifting frame is monitored online through a pressure strain gauge set at the bottom of the connection part between the fork and the lifting frame, and the vibration frequency of the lifting component is monitored online through a vibration sensor set on the lifting component.

5. The mechanical equipment fault monitoring system based on data analysis according to claim 4, characterized in that: The calculation expression for the increase rate of the distance between the rear wheel axle frame of the forklift and the ground is: jl c =(j2 - j1) / j1; where j1 represents the initial distance between the rear wheel axle frame of the forklift and the ground, j2 represents the real-time distance between the rear wheel axle frame of the forklift after picking up an object and the ground, and the distance between the forklift and the ground is monitored online by a laser sensor installed at the bottom end of the rear wheel axle frame of the forklift.

6. The mechanical equipment fault monitoring system based on data analysis according to claim 3, wherein: The warning signal includes a comparison unit and a warning unit; After the comparison unit obtains the fault coefficient gz x it compares the fault coefficient gz x with a pre-set warning threshold yj z and the warning unit determines whether to issue a warning signal according to the comparison result.

7. The mechanical equipment fault monitoring system based on data analysis according to claim 6, wherein: The steps for the warning unit to judge whether to send a warning signal according to the comparison result are as follows: If the comparison result shows that the fault coefficient gz of the forklift x > the warning threshold yj z , the warning unit determines that the forklift does not support the loading and unloading operation, issues a warning signal, and after receiving the warning signal, the control module controls the forklift to stop running; If the comparison result shows that the forklift's failure coefficient gz x ≤ the warning threshold yj z , the warning unit determines that the forklift supports loading and unloading operations and does not issue a warning signal.

8. The mechanical equipment fault monitoring system based on data analysis according to claim 7, wherein: The evaluation module includes a storage unit and a correction unit; The storage unit is used for managers to input the initial safe load capacity of the forklift. The initial safe load capacity is the maximum load capacity of the forklift. The correction unit re-corrects the initial safe load capacity for supporting loading and unloading operations through the failure coefficient to obtain the corrected load capacity.

9. A mechanical equipment fault monitoring system based on data analysis according to claim 8, characterized in that: The correction unit re-corrects the initial safe load-bearing weight of the forklift supporting the loading and unloading operation through the failure coefficient. The calculation expression for the corrected load-bearing weight is: XZ = MC - (MC / gz x k ); where XZ is the corrected load-bearing weight, MC is the initial safe load-bearing weight of the forklift supporting the loading and unloading operation, and k is a constant.

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