Hydraulic systems, fault analysis devices and systems, and construction machinery

Abstract

This disclosure provides a hydraulic system, a fault analysis device and system, and engineering machinery, relating to the field of engineering machinery technology. The hydraulic system includes: multiple components; multiple sensors, each configured to acquire multiple actual values ​​of the corresponding component's operating parameters at multiple times when the corresponding component is in a working state; and multiple controllers, each configured to determine multiple fuzzy inference results corresponding to the multiple deviations between the actual values ​​and standard values ​​of the corresponding component's operating parameters at the multiple times using a fuzzy proportional-integral-derivative (PID) control algorithm, and to send multiple fault information corresponding to the multiple fuzzy inference results, indicating whether the corresponding component has malfunctioned, to the fault analysis device of the hydraulic system manufacturer, so that the fault analysis device can perform fault analysis on the hydraulic system based on the multiple fault information.

Description

Technical Field

[0001] This disclosure relates to the field of engineering machinery technology, and in particular to a hydraulic system, a fault analysis device and system, and engineering machinery. Background Technology

[0002] As the underlying control system of construction machinery, the reliability of the hydraulic system is closely related to the overall reliability of the machinery. To improve the reliability of the hydraulic system, in the event of a malfunction, the fault should be quickly located and repaired.

[0003] In related technologies, when a hydraulic system malfunctions, operators will disassemble the hydraulic system on-site and gradually check for components that may cause the malfunction until the faulty component is found before repair. Summary of the Invention

[0004] The inventors noted that, under the methods described in the related technologies, the reliability of the hydraulic system remains low.

[0005] Through analysis, the inventors discovered that the method of disassembling the hydraulic system and troubleshooting it step by step by the operators not only consumes a lot of time, but also, because the operators rely solely on past experience and the surface phenomena of the fault to judge the cause of the hydraulic system failure, there is a possibility of misjudgment. As a result, the real cause of the hydraulic system failure cannot be resolved, thus the reliability of the hydraulic system remains low.

[0006] To address the aforementioned problems, the present disclosure proposes the following solutions.

[0007] According to one aspect of the present disclosure, a hydraulic system is provided, comprising: multiple components; multiple sensors, each sensor configured to acquire multiple actual values ​​of the operating parameters of the corresponding component at multiple times when the corresponding component is in a working state; and multiple controllers, each controller configured to determine multiple fuzzy inference results corresponding to the multiple deviations between the actual values ​​and standard values ​​of the operating parameters of the corresponding component at the multiple times using a fuzzy PID control algorithm, and to send multiple fault information corresponding to the multiple fuzzy inference results, indicating whether the corresponding component has malfunctioned, to a fault analysis device of the hydraulic system manufacturer, so that the fault analysis device can perform fault analysis on the hydraulic system based on the multiple fault information.

[0008] In some embodiments, the plurality of sensors correspond one-to-one with the plurality of components, and the plurality of controllers correspond one-to-one with the plurality of components.

[0009] In some embodiments, the plurality of components include a hydraulic motor, a hydraulic cylinder, and a solenoid valve for controlling the hydraulic motor and the hydraulic cylinder.

[0010] In some embodiments, the hydraulic system is a winch lifting system.

[0011] According to another aspect of the present disclosure, a fault analysis apparatus is provided, comprising: a memory; and a processor coupled to the memory, the processor being configured to execute, based on instructions stored in the memory: receiving a plurality of fault information sent by each controller of the hydraulic system of claim 1, indicating whether a corresponding component has malfunctioned; and performing fault analysis on the hydraulic system based on the plurality of fault information.

[0012] In some embodiments, the processor is configured to: analyze multiple sets of fault information of the plurality of components at multiple times using a decision tree to determine each component that causes the hydraulic system failure at the multiple times, wherein each set of fault information includes fault information of the plurality of components at the same time, and the decision tree is constructed based on historical fault information of the plurality of components; determine the occurrence frequency of each component that causes the hydraulic system failure at the multiple times; and identify components with an occurrence frequency greater than a preset frequency as key components causing the hydraulic system failure.

[0013] In some embodiments, the processor is further configured to: receive a risk value determined by a user for each of the plurality of components based on the plurality of fault information; and identify components whose risk values ​​exceed a preset threshold as components in the hydraulic system to be produced that require the configuration of corresponding sensors and corresponding controllers.

[0014] In some embodiments, the processor is further configured to update the decision tree based on the plurality of fault information.

[0015] According to another aspect of the present disclosure, a fault analysis system is provided, comprising: the hydraulic system described in any of the above embodiments and the fault analysis device described in any of the above embodiments.

[0016] According to another aspect of the present disclosure, an engineering machine is provided, comprising: the hydraulic system described in any of the above embodiments.

[0017] According to another aspect of the present disclosure, a fault analysis method is provided, comprising: each of a plurality of sensors in a hydraulic system acquiring multiple actual values ​​of the operating parameters of the corresponding component at multiple times when the corresponding component is in a working state, wherein the hydraulic system includes multiple components; each of a plurality of controllers in the hydraulic system determining multiple fuzzy inference results corresponding to the multiple deviations between the actual values ​​and standard values ​​of the operating parameters of the corresponding component at the multiple times using a fuzzy PID control algorithm, and sending multiple fault information corresponding to the multiple fuzzy inference results, indicating whether the corresponding component has malfunctioned, to a fault analysis device of the hydraulic system manufacturer, so that the fault analysis device performs fault analysis on the hydraulic system based on the multiple fault information.

[0018] In some embodiments, the method further includes: the fault analysis device using a decision tree to analyze multiple sets of fault information of the plurality of components at multiple times to determine the component causing the hydraulic system failure at each time, wherein each set of fault information includes the fault information of the plurality of components at the same time, and the decision tree is constructed based on the historical fault information of the plurality of components; the fault analysis device determining the occurrence frequency of each component causing the hydraulic system failure at the multiple times; and the fault analysis device identifying components with an occurrence frequency greater than a preset frequency as key components causing the hydraulic system failure.

[0019] In some embodiments, the method further includes: the fault analysis device receiving a risk value determined by a user for each of the plurality of components based on the plurality of fault information; the fault analysis device identifying components whose risk values ​​exceed a preset threshold as components in the hydraulic system to be produced that require the configuration of corresponding sensors and corresponding controllers.

[0020] In some embodiments, the method further includes: the fault analysis device updating the decision tree based on the plurality of fault information.

[0021] In some embodiments, the plurality of sensors correspond one-to-one with the plurality of components, and the plurality of controllers correspond one-to-one with the plurality of components.

[0022] In some embodiments, the plurality of components include a hydraulic motor, a hydraulic cylinder, and a solenoid valve for controlling the hydraulic motor and the hydraulic cylinder.

[0023] In some embodiments, the hydraulic system is a winch lifting system.

[0024] In this embodiment, the hydraulic system itself can diagnose whether each of its multiple components is faulty, and can also send the fault information of each component to the manufacturer's fault analysis device. This device then automatically analyzes the cause of the hydraulic system failure using the fault information from the hydraulic system's self-diagnosis. Thus, on the one hand, in the event of a hydraulic system failure, there is no need to expend significant manpower and time disassembling the system to gradually identify the faulty components, improving the efficiency of hydraulic system fault diagnosis. On the other hand, because the manufacturer's fault analysis device combines fault information determined by multiple controllers of the hydraulic system based on actual data collected by multiple sensors, it improves the accuracy of hydraulic system fault diagnosis compared to relying solely on operator experience, thereby enhancing the reliability of the hydraulic system.

[0025] The technical solutions of this disclosure will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0026] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of the structure of a hydraulic system according to some embodiments of the present disclosure;

[0028] Figure 2 This is a schematic diagram of the structure of a controller according to some embodiments of the present disclosure;

[0029] Figure 3 This is a schematic diagram of membership functions according to some embodiments of this disclosure;

[0030] Figure 4 This is a schematic diagram of the structure of a fault analysis apparatus according to some embodiments of the present disclosure;

[0031] Figure 5 This is a flowchart illustrating a method executed by a processor according to some embodiments of the present disclosure;

[0032] Figure 6 This is a schematic diagram of the structure of a hoisting system according to some embodiments of the present disclosure;

[0033] Figure 7 This is a schematic diagram of a decision tree according to some embodiments of the present disclosure;

[0034] Figure 8 This is a schematic diagram of the structure of a fault analysis system according to some embodiments of the present disclosure;

[0035] Figure 9 This is a flowchart illustrating a fault analysis method according to some embodiments of the present disclosure;

[0036] Figure 10 This is a flowchart illustrating a fault analysis method according to other embodiments of the present disclosure. Detailed Implementation

[0037] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0038] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of this disclosure.

[0039] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0040] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0041] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0042] It should be noted that similar labels and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be discussed further in subsequent figures.

[0043] According to one aspect of the embodiments of this disclosure, a hydraulic system is provided. Figure 1 This is a schematic diagram of the structure of a hydraulic system according to some embodiments of the present disclosure.

[0044] like Figure 1 As shown, the hydraulic system 100 includes multiple components 101, multiple sensors 102, and multiple controllers 103. Figure 1The diagram schematically shows four components 101a to 101d, three sensors 102a to 102c, and three controllers 103a to 103c. Component 101a corresponds to sensor 102a and controller 103a, component 101b corresponds to sensor 102b and controller 103b, and components 101c and 101d both correspond to sensor 102c and controller 103c.

[0045] Each sensor 102 can be configured to acquire multiple actual values ​​of the operating parameters of the corresponding component at multiple times when the corresponding component is in the working state.

[0046] Each controller 103 can be configured to determine multiple fuzzy inference results corresponding to multiple deviations between the actual values ​​and standard values ​​of the working parameters of the corresponding components at multiple times using a fuzzy proportional-integration-differential (PID) control algorithm. The controller then sends multiple fault information corresponding to the multiple fuzzy inference results, indicating whether the corresponding components have failed, to the fault analysis device of the hydraulic system 100 manufacturer, so that the fault analysis device can perform fault analysis on the hydraulic system 100 based on the multiple fault information.

[0047] Each controller 103 can store the correspondence between fuzzy inference results and fault information. Based on this correspondence, multiple fault information corresponding to multiple fuzzy inference results of the corresponding component can be obtained.

[0048] For example, the fault information corresponding to each fuzzy inference result can be either 0 or 1. If the fault information corresponding to the fuzzy inference result at a certain moment is 0, it means that the corresponding component has failed at that moment; if the fault information corresponding to the fuzzy inference result at a certain moment is 1, it means that the corresponding component has not failed at that moment. That is, each controller 103 can output a conclusion indicating whether the corresponding component has failed at each moment through a fuzzy PID algorithm.

[0049] It should be understood that multiple fault information corresponds one-to-one with multiple times, multiple deviations, and multiple fuzzy inference results. That is, each controller 103 outputs multiple fault information of the corresponding component at multiple times.

[0050] In the above embodiments, the hydraulic system itself can diagnose whether each of the multiple components is faulty, and can also send the fault information of each component to the manufacturer's fault analysis device. This device then automatically analyzes the cause of the hydraulic system failure using the fault information from the hydraulic system's self-diagnosis. Thus, on the one hand, in the event of a hydraulic system failure, there is no need to expend significant manpower and time disassembling the hydraulic system to gradually identify the faulty components, improving the efficiency of hydraulic system fault diagnosis. On the other hand, because the manufacturer's fault analysis device combines fault information determined by multiple controllers of the hydraulic system based on actual data collected by multiple sensors, it improves the accuracy of hydraulic system fault diagnosis compared to relying solely on operator experience, thereby enhancing the reliability of the hydraulic system.

[0051] In some embodiments, multiple sensors 102 can correspond one-to-one with multiple components 101, and multiple controllers 103 can correspond one-to-one with multiple components 101. This ensures that multiple sensors can collect the actual values ​​of the operating parameters of multiple components in a one-to-one correspondence, and that multiple controllers can determine whether multiple components have malfunctioned in a one-to-one correspondence. This improves the accuracy of fault information determined by each controller in the hydraulic system based on the actual data collected by the corresponding sensor, thereby improving the accuracy of fault diagnosis in the hydraulic system and further enhancing the reliability of the hydraulic system.

[0052] In some embodiments, the hydraulic system may be a hoisting system.

[0053] In some embodiments, the plurality of components 101 may include a hydraulic motor, a hydraulic cylinder, and a solenoid valve for controlling the hydraulic motor and the hydraulic cylinder. It should be understood that there may be one or more solenoid valves for controlling the hydraulic motor and the hydraulic cylinder.

[0054] For example, component 101a can be a hydraulic motor 101a, and sensor 102a can acquire multiple actual values ​​of the working parameters (e.g., speed) of the hydraulic motor 101a at multiple times when the hydraulic motor 101a is in working state. Then, controller 102a can send the corresponding multiple fault information to the fault analysis device of the manufacturer of hydraulic system 100.

[0055] For example, component 101b can be a hydraulic cylinder 101b. Sensor 102b can acquire multiple actual values ​​of the working parameters (such as extension and retraction speed) of hydraulic cylinder 101b at multiple times when hydraulic cylinder 101b is in working state. Then, controller 102b can send the corresponding multiple fault information to the fault analysis device of the manufacturer of hydraulic system 100.

[0056] For example, component 101c can be a solenoid valve 101c for controlling a hydraulic motor, and component 101d can be a solenoid valve 101d for controlling a hydraulic cylinder. Sensor 102c can acquire multiple actual values ​​of the operating parameters (e.g., operating pressure difference) of the two solenoid valves at multiple times when the two solenoid valves are in the working state. Then, controller 103c can send multiple fault information corresponding to solenoid valve 101c and multiple fault information corresponding to solenoid valve 101d to the fault analysis device of the hydraulic system 100 manufacturer.

[0057] Figure 2 This is a schematic diagram of the structure of a controller according to some embodiments of the present disclosure.

[0058] like Figure 2 As shown, the controller 103 may include a fuzzer 1031, a fuzzy inference engine 1032, and a defuzzer 1033.

[0059] The fuzzer 1031 can be configured to determine multiple deviations between the actual values ​​and standard values ​​of the working parameters of the corresponding component at multiple times based on multiple actual values ​​of the working parameters of the corresponding component at multiple times when the component is in working state from the sensor 102, and to fuzzify the multiple deviations and the multiple deviation change rates corresponding to the multiple deviations.

[0060] For example, the fuzzy PID control algorithm is Where e(t) is the deviation, The rate of change of deviation (hereinafter referred to as e for deviation, in the context of deviation change) K represents the rate of change of deviation. p K I K D These are the corresponding proportional control parameters, integral control parameters, and derivative control parameters, respectively.

[0061] Define the fuzzy universe as The corresponding fuzzy subset is {NB, NS, ZE, PS, PB}, where NB represents negative large, NS represents negative small, ZE represents zero, PS represents positive small, and PB represents positive large.

[0062] The transformation relation from the basic universe of discourse [a, b] to the fuzzy universe of discourse [-n, n] is as follows: Therefore, based on the above definition, according to You can use e and The precise value is mapped to a value in the fuzzy universe of discourse. It should be understood that the fundamental universe of discourse is e, K p K I K DThe actual possible range of values.

[0063] For example, the fundamental domain of discourse for the rotational speed of a hydraulic motor is [-10, 10]. The actual rotational speed of the hydraulic motor is 55 revolutions per second, and the corresponding standard value is 60 revolutions per second. Therefore, the deviation in rotational speed is 5 revolutions per second. The value of this deviation in the fuzzy domain is:

[0064] Figure 3 This is a schematic diagram of membership functions according to some embodiments of the present disclosure.

[0065] In e and After mapping the precise value to a value in the fuzzy universe, it can be determined according to... Figure 3 The membership functions and e shown are shown. The values ​​of e and e are calculated on the fuzzy universe. The membership degree of a fuzzy subset, thereby achieving the membership of e and The blurring. For example, according to Figure 3 The membership function shown can determine that the value 2 on the fuzzy universe belongs to PS, and the corresponding fuzzy value after fuzzification is PS.

[0066] The fuzzy inference engine 1032 can be configured to determine multiple fuzzy inference results corresponding to multiple biases based on the results of fuzzifying multiple biases.

[0067] In some embodiments, the fuzzy inference engine 1032 can determine the fuzzy inference results corresponding to multiple deviations based on the fuzzified results (i.e., fuzzy values) and a preset fuzzy rule table. Each fuzzy inference result may include the fuzzy values ​​corresponding to the proportional control parameter, integral control parameter, and derivative control parameter, respectively.

[0068] Table 1 shows a fuzzy rule table for some embodiments of this disclosure.

[0069] Table 1

[0070]

[0071] For example, at a certain moment, the e of the hydraulic cylinder and The corresponding fuzzy values ​​are NB and NB, respectively. According to Table 1, K at this moment can be determined. p K I K D The corresponding fuzzy values ​​are NB NB NS, that is, the fuzzy inference result corresponding to the deviation of the hydraulic cylinder at this moment is NB NB NS.

[0072] The defuzzifier 1033 can be configured to determine multiple fault information corresponding to multiple fuzzy inference results, representing whether the corresponding component has failed, and send the multiple fault information to the fault analysis device.

[0073] In some embodiments, the defuzzifier 1033 can determine multiple fault information corresponding to multiple fuzzy inference results based on multiple fuzzy inference results and a preset fault diagnosis table. The preset fault diagnosis table may include the correspondence between fuzzy inference results and fault information, which can be obtained by statistically analyzing the fuzzy inference results and the status of corresponding components during pre-market testing of the hydraulic system.

[0074] At a certain point during the test, it can be determined whether each component is faulty, thus identifying the fault information for each component. Furthermore, a fuzzy PID control algorithm can be used to obtain the fuzzy inference result for each component at that moment. Based on this, a correspondence can be established between the fuzzy inference result and the fault information for each component at that moment. Similarly, a correspondence can be established between the fuzzy inference results and the fault information for each component at multiple points in time, thereby obtaining different correspondences between fuzzy inference results and fault information.

[0075] For example, in a preset fault diagnosis table, a fault information of 0 indicates that a component has malfunctioned, and a fault information of 1 indicates that a component has not malfunctioned. The defuzzifier 1033, based on the fuzzy inference result NB NBNS of the hydraulic cylinder at a certain moment and the preset fault diagnosis table, can determine that the fault information corresponding to the fuzzy inference result NB NBNS is 0, that is, determine that the hydraulic cylinder has malfunctioned at that moment, and send this fault information to the fault analysis device.

[0076] According to another aspect of the embodiments of this disclosure, a fault analysis apparatus is provided. Figure 4 This is a schematic diagram of the structure of a fault analysis apparatus according to some embodiments of the present disclosure.

[0077] like Figure 4 As shown, the fault analysis apparatus 400 includes a memory 401 and a processor 402 coupled to the memory 401. The processor 402 is configured to execute the following method based on instructions stored in the memory 401:

[0078] The system receives multiple fault messages from each controller (e.g., controllers 103a-103c) of the hydraulic system (e.g., hydraulic system 100) of any of the above embodiments, indicating whether a corresponding component has malfunctioned; and performs fault analysis on the hydraulic system based on the multiple fault messages sent by each controller.

[0079] In some embodiments, memory 401 may include system memory, fixed non-volatile storage medium, etc. System memory may, for example, store operating system, application programs, boot loader, and other programs.

[0080] In some embodiments, the fault analysis device 400 may further include an input / output interface 403, a network interface 404, a storage interface 405, etc. These interfaces 403, 404, and 405, as well as the memory 401 and processor 402, can be connected, for example, via a bus 406. The input / output interface 403 provides a connection interface for input / output devices such as a monitor, mouse, keyboard, and touchscreen. The network interface 404 provides a connection interface for various networked devices. The storage interface 405 provides a connection interface for external storage devices such as SD cards and USB flash drives.

[0081] Figure 5 This is a flowchart illustrating a method performed by a processor according to some embodiments of the present disclosure. In some embodiments, the processor 402 may be configured to perform... Figure 5 The method shown is used to perform fault analysis on hydraulic systems.

[0082] In step 502, a decision tree is used to analyze multiple sets of fault information of multiple components at multiple times to determine the component that causes the hydraulic system failure at each time.

[0083] Here, each set of fault information includes fault information of multiple components at the same time. The decision tree is constructed based on the historical fault information of multiple components. The construction method of the decision tree will be further explained later.

[0084] It should be understood that by grouping multiple fault messages for corresponding components sent by each controller of the hydraulic system according to multiple time points, multiple sets of fault messages corresponding to different time points can be obtained. Each set of fault messages includes one fault message for each of the multiple components at the same time point. For example, if there are 3 components and the multiple time points include a first time point and a second time point, one set of fault messages can include the 3 fault messages of these 3 components at the first time point, and another set of fault messages can include the 3 fault messages of these 3 components at the second time point.

[0085] In some embodiments, the components that cause hydraulic system failure at each time point, as determined by analysis using a decision tree, can be one or more.

[0086] In step 504, the frequency of occurrence of each component that causes hydraulic system failure at multiple times is determined.

[0087] For example, by using a decision tree to analyze 100 sets of fault information for multiple components of a hydraulic system at 100 different times, it can be determined that the components causing hydraulic system failures at 100 different times include hydraulic motors, hydraulic cylinders, and solenoid valves.

[0088] Furthermore, in 80 out of 100 time points, the components causing hydraulic system failures all included hydraulic motors, thus determining that the frequency of occurrence of hydraulic motors as the cause of hydraulic system failures was 80; in 68 out of 100 time points, the components causing hydraulic system failures all included hydraulic cylinders, thus determining that the frequency of occurrence of hydraulic cylinders as the cause of hydraulic system failures was 68; and in 40 out of 100 time points, the components causing hydraulic system failures all included solenoid valves, thus determining that the frequency of occurrence of solenoid valves as the cause of hydraulic system failures was 40.

[0089] In step 506, components that appear more frequently than a preset frequency are identified as key components causing hydraulic system failure.

[0090] In some embodiments, the key components causing hydraulic system failure may be one or more. Assuming a preset frequency of 60, based on the above example, the key components causing hydraulic system failure can be identified as hydraulic motors and hydraulic cylinders.

[0091] In some embodiments, after identifying the key components that cause hydraulic system failure, the manufacturer's operators can focus on inspecting and maintaining the key components that cause hydraulic system failure, and the R&D personnel can focus on improving the key components that cause hydraulic system failure (i.e., guiding the R&D process).

[0092] In the above embodiments, the fault analysis device, based on multiple sets of fault information from multiple components at multiple times, can utilize a decision tree to analyze the correlation between the state of the components and the state of the hydraulic system, thereby identifying the key components causing the hydraulic system failure. Thus, compared to relying solely on operator experience for fault analysis, the cause of hydraulic system failure can be located quickly and accurately, improving the efficiency and accuracy of hydraulic system fault diagnosis, and ultimately enhancing the reliability of the hydraulic system.

[0093] The following uses a hydraulic system as an example of a hoisting system. Figure 5 The method shown will be further explained.

[0094] Figure 6 This is a schematic diagram of the structure of a hoisting system according to some embodiments of the present disclosure.

[0095] like Figure 6As shown, the hoisting system 600 includes a hoist support 601, a reducer 602, a drum 603, a wire rope 604, a rope clamp 605, a hydraulic cylinder 606, a hydraulic motor 607, a solenoid valve 608, and an electrical controller 609. It should be understood that... Figure 6 Only some components of the hoisting system 600 are shown schematically. In other embodiments, the hoisting system 600 may also include other possible components.

[0096] For example, the electrical controller 609 controls the extension and retraction speed of the hydraulic cylinder 606 by controlling the working pressure difference of the solenoid valve 608, so that the hydraulic cylinder 606 drives the winch support 601 to rise or fall. During the rising or falling of the winch support 601, the rope clamp 605 automatically tightens the wire rope 604 to prevent the wire rope 604 from becoming tangled or jumping. Correspondingly, the electrical controller 609 controls the speed of the hydraulic motor 607 by controlling the working pressure difference of the solenoid valve 608, so that the hydraulic motor 607 drives the reducer 602 to rotate the drum 603. During the rotation of the drum 603, the wire rope 604 is wound around it, which, in conjunction with the rising or falling of the winch support 601, enables the winch to lift or lower.

[0097] In one possible scenario, fault information from the hydraulic cylinders 606, hydraulic motors 607, solenoid valves 608, and electrical controllers 609 of the hoisting system 600 over a past period can be used as a training dataset to construct a decision tree.

[0098] Table 2 shows the training datasets for some embodiments of this disclosure.

[0099] Table 2

[0100] 1 0 1 1 0 1 2 1 1 1 0 1 3 1 0 1 1 0 4 0 1 1 1 0 5 1 1 1 0 1 6 1 0 1 0 1 7 0 1 1 1 0 8 0 1 1 0 1 9 1 1 1 0 0 10 1 0 0 0 1

[0101] As shown in Table 2, the training dataset contains 10 training samples. The hydraulic cylinder, hydraulic motor, solenoid valve, electrical controller, and hoisting system are considered as five nodes in the decision tree. Each node can take a value of either 0 or 1, where 0 indicates a fault (failure) and 1 indicates no fault (normal). Since there is a causal relationship between the values ​​of the hoisting system and the values ​​of other nodes, the values ​​of the hoisting system can also be referred to as the final state.

[0102] Define information entropy Information gain Where D represents the training dataset, p k This indicates the proportion of samples in class k.

[0103] Based on the training dataset shown in Table 2, a decision tree can be constructed using the following steps:

[0104] The first step is to calculate the information entropy of the hoisting system as the final state, and then calculate the information gain of the hydraulic cylinder, hydraulic motor, solenoid valve, and electrical controller relative to the hoisting system based on the information entropy of the hoisting system.

[0105] As shown in Table 2, among the 10 training samples, the percentage of training samples where the hoisting system had a value of 1 (i.e., no fault) is as follows: The proportion of training samples with a value of 0 (i.e., faulty) The hoisting system increases the information entropy.

[0106] Taking the calculation of the information gain of the hydraulic cylinder relative to the hoisting system as an example, Table 2 shows that in 10 training samples, 4 samples have a value of 0 for the hydraulic cylinder, and in 2 of these 4 training samples, the hoisting system has a value of 1. That is, the proportion of training samples where the hydraulic cylinder is faulty and training samples where the hoisting system is fault-free is high. There are 6 training samples where the hydraulic cylinder has a value of 1, and among these 6 training samples, the hoisting system has a value of 1 in 4 of them. This represents the proportion of training samples where the hydraulic cylinder is fault-free and the hoisting system is fault-free. achievable Therefore, the information gain of the hydraulic cylinder relative to the hoisting system can be obtained as follows: Similarly, the information gain of hydraulic motors, solenoid valves, and electrical controllers relative to the hoisting system can be calculated.

[0107] The second step is to classify the training samples according to the classification order of nodes sorted from largest to smallest information gain.

[0108] For example, if the nodes are sorted from largest to smallest information gain, and the classification order is solenoid valve, hydraulic motor, hydraulic cylinder, and electrical controller, then the solenoid valve can be taken as the root node. Based on whether the solenoid valve value is 0 or 1, the 10 training samples shown in Table 2 can be divided into two sets. One set includes training samples numbered 1 to 9, which are all training samples with a solenoid valve value of 1. The other set includes training samples numbered 10.

[0109] The third step is to repeat the second step until each classified node meets the termination condition of the recursion.

[0110] Here, the termination conditions for recursion include the following three conditions:

[0111] 1) The samples in the sample set contained in the current node belong to the same class;

[0112] 2) The sample set contained in the current node has no values ​​or the same values ​​for all samples;

[0113] 3) The current node contains an empty set of samples.

[0114] It should be understood that the recursion terminates as long as the current node satisfies any one of the above three conditions.

[0115] For example, after dividing the 10 training samples shown in Table 2 into two sets according to the solenoid valve, for the node containing the training samples numbered 1 to 9, since this node does not meet the recursion termination condition, the 9 training samples are further classified according to the hydraulic motor until each classified node meets the recursion termination condition; for the node containing only the training sample numbered 10, since this node has met the recursion termination condition, no classification is needed, and the hoisting system value of this sample is 1, so this sample is marked as a leaf node, and the category of this leaf node is set to hoisting system fault-free (i.e., hoisting is normal).

[0116] Figure 7 This is a schematic diagram of a decision tree based on some embodiments of the present disclosure.

[0117] Figure 7 The diagram shows the decision tree constructed based on the training dataset shown in Table 2, following the steps described above. It should be understood that... Figure 7 In the decision tree shown, "Winding normal" indicates that the winch hoisting system is fault-free, while "Winding failure" indicates that the winch hoisting system is faulty.

[0118] Table 3 shows a set of fault information from the hydraulic system at a certain moment.

[0119] Table 3

[0120] 0 0 1 1

[0121] based on Figure 7 The decision tree shown first determines the next node as the hydraulic motor based on the solenoid valve's value of 1. Then, it determines the next node as the hydraulic cylinder based on the hydraulic motor's value of 0. Finally, it determines the next node as the hoist failure based on the hydraulic cylinder's value of 0. At this point, since the leaf node indicating hoist failure has been reached, it can be determined that at the time corresponding to the fault information shown in Table 3, the correlation between the state of the components and the state of the hydraulic system is "solenoid valve normal - hydraulic motor failure - hydraulic cylinder failure - hoist failure". Therefore, it can be determined that the components causing the hoisting system failure at this time include the hydraulic cylinder and the hydraulic motor.

[0122] In some embodiments, the processor 402 may also be configured to update the decision tree based on multiple fault information of each of the multiple components. For example, at regular intervals, multiple fault information of each of the multiple components received during that period and historical fault information of the multiple components collected before that period may be merged into a training dataset to reconstruct the decision tree, thereby updating the decision tree.

[0123] In this way, as the hydraulic system is used for longer periods, it can continuously receive fault information from multiple components and merge the newly added fault information with historical fault information (i.e., increase the number of training samples in the training dataset). This allows the constructed decision tree to better reflect the correlation between the components and the hydraulic system, improving the accuracy of fault analysis of the hydraulic system based on the decision tree, thereby improving the reliability of the hydraulic system.

[0124] In some embodiments, the processor 402 may also be configured to: receive a risk value determined by a user for each of the multiple components based on multiple fault information of each component; and identify components whose risk values ​​exceed a preset threshold as components in the hydraulic system to be produced that require the configuration of corresponding sensors and corresponding controllers.

[0125] As one implementation method, users can determine the FMEA score for each of multiple components based on the risk coefficient evaluation criteria of Failure Mode and Effects Analysis (FMEA), and send the FMEA score of each component as its risk value to processor 402 through a human-machine interface. After processor 402 identifies components whose risk values ​​exceed a preset threshold, it can display these components to the user through the human-machine interface.

[0126] For example, a hydraulic system may contain multiple components such as wire ropes, speed reducers, hydraulic motors, hydraulic cylinders, and solenoid valves. Users can perform FMEA analysis on these components based on the FMEA risk factor assessment criteria to obtain an FMEA score K for each component. ′ (i.e., risk value), K ′ =S×O×D, where S represents the severity of the failure effect, O represents the frequency of occurrence of the failure cause, and D represents the detectability of the failure cause and / or failure mode.

[0127] Processor 402 can determine the preset threshold K according to the following formula:

[0128]

[0129] Where size(K′) refers to the number of components, cost refers to the installation cost of the sensor, and x 0.2 This refers to the 0.2 quantile of the FMEA scores for multiple components.

[0130] Processor 402 can combine a predetermined preset threshold K with the risk value K of each component from the user. ′ Compare and assign risk value K ′ Components exceeding the preset threshold K are identified as components in the hydraulic system to be manufactured that require the corresponding sensors and controllers.

[0131] In this way, manufacturers can flexibly adjust the deployment of sensors and controllers in the hydraulic system to be produced based on the components that the fault analysis device determines require the configuration of corresponding sensors and controllers. This makes the deployment of sensors and controllers in the hydraulic system to be produced more targeted, improves the accuracy of fault information determined by the controller of the subsequent hydraulic system based on the actual data collected by the sensors, and thus improves the accuracy of fault diagnosis of the hydraulic system, thereby improving the reliability of the hydraulic system.

[0132] According to another aspect of the embodiments of this disclosure, a fault analysis system is provided. Figure 8 This is a schematic diagram of the structure of a fault analysis system according to some embodiments of the present disclosure.

[0133] like Figure 8 As shown, the fault analysis system 800 includes one or more hydraulic systems 801 (e.g., hydraulic system 801 can be hydraulic system 100) in any of the above embodiments, and a fault analysis device 802 (e.g., fault analysis device 802 can be fault analysis device 400) in any of the above embodiments. Figure 8 Two hydraulic systems 801 are schematically shown.

[0134] According to another aspect of the present disclosure, a construction machine is provided, including a hydraulic system (e.g., hydraulic system 100) of any of the above embodiments. For example, the construction machine can be a vehicle (e.g., a fire truck, a crane, etc.).

[0135] For example, during the operation of construction machinery, the hydraulic system 100 may be in an active state at certain times and in a non-active state at other times. When the hydraulic system 100 is in an active state, each sensor and controller in the hydraulic system 100 may perform its respective operation as described in the embodiments of this disclosure.

[0136] According to another aspect of the embodiments of this disclosure, a fault analysis method is provided. Figure 9 This is a flowchart illustrating a fault analysis method according to some embodiments of the present disclosure.

[0137] In step 902, each of the multiple sensors in the hydraulic system acquires multiple actual values ​​of the working parameters of the corresponding component at multiple times when the corresponding component is in working condition.

[0138] Here, the hydraulic system comprises multiple components.

[0139] In some embodiments, multiple sensors of the hydraulic system correspond one-to-one with multiple components, and multiple controllers correspond one-to-one with multiple components.

[0140] In some embodiments, multiple components of the hydraulic system may include a hydraulic motor, a hydraulic cylinder, and a solenoid valve for controlling the hydraulic motor and the hydraulic cylinder.

[0141] In some embodiments, the hydraulic system may be a hoisting system.

[0142] In step 904, each of the multiple controllers in the hydraulic system determines multiple fuzzy inference results corresponding to multiple deviations between the actual values ​​and standard values ​​of the working parameters of the corresponding components at multiple times using a fuzzy PID control algorithm. The multiple fault information corresponding to the multiple fuzzy inference results, indicating whether the corresponding components have failed, is sent to the fault analysis device of the hydraulic system manufacturer so that the fault analysis device can perform fault analysis on the hydraulic system based on the multiple fault information.

[0143] Figure 10 This is a flowchart illustrating a fault analysis method according to other embodiments of the present disclosure.

[0144] and Figure 9 Compared to the embodiments shown, Figure 10 The method shown also includes steps 1002 to 1006.

[0145] In step 1002, the fault analysis device uses a decision tree to analyze multiple sets of fault information of multiple components at multiple times to determine the component that causes the hydraulic system failure at each time.

[0146] Here, each set of fault information includes fault information of multiple components at the same time, and the decision tree is constructed based on the historical fault information of multiple components.

[0147] In step 1004, the fault analysis device determines the frequency of occurrence of each component that causes a hydraulic system failure at multiple times.

[0148] In step 1006, the fault analysis device identifies components that occur more frequently than a preset frequency as key components causing hydraulic system failures.

[0149] In some embodiments, the fault analysis device can receive risk values ​​determined by the user for each of the multiple components based on multiple fault information of each component, and identify components whose risk values ​​exceed a preset threshold as components in the hydraulic system to be produced that require the configuration of corresponding sensors and corresponding controllers.

[0150] In some embodiments, the fault analysis device can update the decision tree based on multiple fault information.

[0151] In some embodiments, the fault analysis apparatus may include a receiving module and an analysis module. The receiving module may be configured to receive multiple fault messages indicating whether a corresponding component has malfunctioned, sent by each controller of the hydraulic system in any of the above embodiments. The analysis module may be configured to perform fault analysis on the hydraulic system of any of the above embodiments based on the received multiple fault messages.

[0152] It should be understood that in the fault analysis system provided in the embodiments of this disclosure, the hydraulic system can be the hydraulic system of any of the above embodiments (e.g., hydraulic system 100), and the fault analysis device can be the fault analysis device of any of the above embodiments (e.g., fault analysis device 400).

[0153] The fault analysis method provided in this disclosure can be implemented using the fault analysis system of any of the above embodiments. The beneficial effects and further embodiments of the fault analysis method provided in this disclosure can be found in the above description of the hydraulic system and fault analysis device, and will not be repeated here.

[0154] The embodiments of this disclosure have now been described in detail. To avoid obscuring the concept of this disclosure, some details known in the art have not been described. Those skilled in the art can fully understand how to implement the technical solutions disclosed herein based on the above description.

[0155] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0156] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that the functions specified in one or more flowchart illustrations and / or one or more blocks in a block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate functions for implementing the functions in the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0157] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0158] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0159] While specific embodiments of this disclosure have been described in detail by way of examples, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of this disclosure. Those skilled in the art should understand that modifications can be made to the above embodiments or equivalent substitutions can be made to some technical features without departing from the scope and spirit of this disclosure. The scope of this disclosure is defined by the appended claims.

Claims

1. A hydraulic system, comprising: Multiple components; Multiple sensors, each corresponding to one of the multiple components, are configured to acquire multiple actual values ​​of the operating parameters of the corresponding component at multiple times when the corresponding component is in the working state. Multiple controllers, each corresponding to one of the multiple components, are configured to determine multiple fuzzy inference results corresponding to the multiple deviations between the actual and standard values ​​of the corresponding component's operating parameters at multiple times using a fuzzy proportional-integral-derivative (PID) control algorithm. The controllers then send multiple fault information messages, indicating whether the corresponding component has malfunctioned, corresponding to the multiple fuzzy inference results to a fault analysis device of the hydraulic system manufacturer. The fault analysis device is configured to perform fault analysis on the hydraulic system based on the multiple fault information messages to determine the component causing the hydraulic system malfunction at each time point. Each fault information message corresponds one-to-one with the multiple times, the multiple deviations, and the multiple fuzzy inference results.

2. The system according to claim 1, wherein, The plurality of components include a hydraulic motor, a hydraulic cylinder, and a solenoid valve for controlling the hydraulic motor and the hydraulic cylinder.

3. The system according to any one of claims 1-2, wherein, The hydraulic system is a winch lifting system.

4. A fault analysis device, comprising: Memory; as well as A processor coupled to the memory is configured to execute, based on instructions stored in the memory: Receive multiple fault messages from each controller of the hydraulic system of claim 1, indicating whether a corresponding component has malfunctioned; as well as Based on the multiple fault information, a fault analysis is performed on the hydraulic system to determine the components that cause the hydraulic system to fail at each time point.

5. The apparatus according to claim 4, wherein, The processor is configured to: The decision tree is used to analyze multiple sets of fault information of the multiple components at multiple times to determine each component that causes the hydraulic system failure at multiple times. Each set of fault information includes the fault information of the multiple components at the same time. The decision tree is constructed based on the historical fault information of the multiple components. Determine the frequency of occurrence of each component that causes the hydraulic system to fail at the plurality of times; Components that occur more frequently than a preset frequency are identified as key components causing malfunctions in the hydraulic system.

6. The apparatus according to claim 4, wherein, The processor is also configured to: Receive the risk value determined by the user for each of the multiple components based on the multiple fault information; as well as Components whose risk values ​​exceed a preset threshold are identified as components in the hydraulic system to be manufactured that require the corresponding sensors and controllers.

7. The apparatus according to claim 5, wherein, The processor is also configured to: The decision tree is updated based on the multiple fault information.

8. A fault analysis system, comprising: One or more hydraulic systems according to any one of claims 1-3; as well as The fault analysis apparatus according to any one of claims 4-7.

9. An engineering machine, comprising: The hydraulic system according to any one of claims 1-3.

10. A fault analysis method, comprising: Each of the multiple sensors in the hydraulic system acquires multiple actual values ​​of the working parameters of the corresponding component at multiple times when the corresponding component is in working state. The hydraulic system includes multiple components, and the multiple sensors correspond one-to-one with the multiple components. Each controller in the hydraulic system determines multiple fuzzy inference results corresponding to the multiple deviations between the actual and standard values ​​of the working parameters of the corresponding components at multiple times using a fuzzy PID control algorithm. The controller then sends multiple fault information messages, indicating whether the corresponding components have malfunctioned, to the hydraulic system manufacturer's fault analysis device. The fault analysis device performs fault analysis on the hydraulic system based on the multiple fault information messages to identify the components causing the hydraulic system malfunction at each time point. Each controller corresponds one-to-one with each component, and each fault information message corresponds one-to-one with each time point, each deviation, and each fuzzy inference result.

11. The method of claim 10, further comprising: The fault analysis device uses a decision tree to analyze multiple sets of fault information of the multiple components at multiple times to determine the component that causes the hydraulic system to fail at each time. Each set of fault information includes the fault information of the multiple components at the same time. The decision tree is constructed based on the historical fault information of the multiple components. The fault analysis device determines the frequency of occurrence of each component that causes a fault in the hydraulic system at the plurality of times; The fault analysis device identifies components that occur more frequently than a preset frequency as key components causing the hydraulic system malfunction.

12. The method of claim 10, further comprising: The fault analysis device receives a risk value determined by the user for each of the multiple components based on the multiple fault information; The fault analysis device identifies components whose risk values ​​exceed a preset threshold as components in the hydraulic system to be manufactured that require the configuration of corresponding sensors and controllers.

13. The method of claim 11, further comprising: The fault analysis device updates the decision tree based on the multiple fault information.

14. The method of claim 10, wherein, The plurality of components include a hydraulic motor, a hydraulic cylinder, and a solenoid valve for controlling the hydraulic motor and the hydraulic cylinder.

15. The method according to any one of claims 10-14, wherein, The hydraulic system is a winch lifting system.

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