Method and device for diagnosing faults of an engineering vehicle, and storage medium

Abstract

The present disclosure provides a fault diagnosis method, device and storage medium of an engineering vehicle. The fault diagnosis method of the engineering vehicle comprises: collecting a plurality of signals of a plurality of system branches of the engineering vehicle by using a plurality of sensors of the engineering vehicle; processing the plurality of signals to obtain a plurality of working condition parameters and a plurality of characteristic parameters; determining a working condition of the engineering vehicle according to the plurality of working condition parameters and the plurality of characteristic parameters; and determining, for each system branch, fault information of the system branch in the working condition according to the plurality of signals corresponding to the system branch, the fault information including at least one of a fault position, a fault type and a fault degree.

Description

Technical Field

[0001] This disclosure relates to the field of mechanical fault diagnosis technology, and in particular to a fault diagnosis method, device and storage medium for engineering vehicles. Background Technology

[0002] Electric-drive engineering vehicles inevitably experience various malfunctions during use. Failure to address these malfunctions promptly can lead to deviations in system performance from requirements, reducing the vehicle's reliability and stability. Therefore, timely identification and resolution of malfunctions in electric-drive engineering vehicles are of paramount importance.

[0003] Traditional methods of routine inspection and regular maintenance of electric-drive engineering vehicles can reduce the occurrence of malfunctions, but they require experienced professionals and rely on a large amount of manpower and resources. Summary of the Invention

[0004] The inventors noted that the safe operation of components such as the power battery, motor controller, motor, and actuators is crucial to maintaining the reliability and stability of electric-drive engineering vehicles. Currently, electric-drive engineering vehicles suffer from low levels of intelligence, lack fault diagnosis systems, and struggle to detect the status of critical components.

[0005] Accordingly, this disclosure provides a fault diagnosis method for engineering vehicles, which utilizes multiple signals from multiple system branches of the engineering vehicle to perform fault diagnosis and can accurately identify fault information of the engineering vehicle.

[0006] According to a first aspect of the present disclosure, a fault diagnosis method for an engineering vehicle is provided, comprising: using multiple sensors of the engineering vehicle to collect multiple signals from multiple system branches of the engineering vehicle; processing the multiple signals to obtain multiple operating condition parameters and multiple characteristic parameters; determining the operating condition of the engineering vehicle based on the multiple operating condition parameters and multiple characteristic parameters; and for each system branch, determining fault information of the system branch under the operating condition based on the multiple signals corresponding to the system branch, wherein the fault information includes at least one of fault location, fault type, and fault severity.

[0007] According to a second aspect of the present disclosure, a fault diagnosis device for engineering vehicles is provided, comprising: a memory; and a processor coupled to the memory, the processor being configured to execute instructions stored in the memory to implement a fault diagnosis method for engineering vehicles as described in any of the above embodiments.

[0008] According to a third aspect of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions that, when executed by a processor, implement the fault diagnosis method for engineering vehicles as described in any of the above embodiments.

[0009] According to a fourth aspect of the present disclosure, a computer program product is provided, including computer instructions, wherein when executed by a processor, the computer instructions implement the fault diagnosis method for engineering vehicles as described in any of the above embodiments.

[0010] Other features and advantages of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0011] 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.

[0012] Figure 1 This is a schematic diagram of the structure of an engineering vehicle according to an embodiment of the present disclosure.

[0013] Figure 2 This is a schematic flowchart of a fault diagnosis method for engineering vehicles according to an embodiment of the present disclosure.

[0014] Figure 3 This is a schematic diagram of sensor distribution according to an embodiment of the present disclosure.

[0015] Figure 4 This is a flowchart illustrating a method for determining operating conditions according to an embodiment of this disclosure.

[0016] Figure 5 This is a schematic diagram of the structure of a fault diagnosis device for an engineering vehicle according to an embodiment of the present disclosure.

[0017] Figure 6 This is a schematic diagram of the structure of a fault diagnosis device for an engineering vehicle according to another embodiment of the present disclosure. Detailed Implementation

[0018] 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. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit this disclosure or its application or use. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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.

[0024] The inventors noted that the operating conditions of electric-driven engineering vehicles account for a large proportion of actual working conditions. Electric-driven engineering vehicles typically adopt a distributed multi-motor configuration, with different motors and power transmission systems applicable to driving and operating conditions. The fault diagnosis solutions in related technologies are not applicable to electric-driven engineering vehicles.

[0025] Figure 1 This is a schematic diagram of the structure of an engineering vehicle according to an embodiment of this disclosure. Based on the system architecture of the engineering vehicle 10, multiple components in the engineering vehicle 10 can be divided into multiple system branches. For example... Figure 1 As shown, the engineering vehicle 10 includes a first system branch, a second system branch, a third system branch, a fourth system branch, and a power battery 15. Each system branch is independently encapsulated within the engineering vehicle.

[0026] The first system branch is the driving system branch, which includes a power distribution box 111, a driving motor controller 112, a driving motor 113, a drive shaft 114, and a drive axle hub 115.

[0027] Distribution box 111 can be a high-voltage distribution box.

[0028] The second system branch is the working system branch, which includes a power distribution box 111, a hydraulic motor controller 121, a hydraulic motor 122, a hydraulic pump 123, a hydraulic valve block 124, and a working cylinder 125.

[0029] The third system branch is the vehicle low-voltage system branch, which includes a power distribution box 111, a DC / DC converter 131, a battery 132, and a low-voltage power consumption component 133.

[0030] Battery 132 can be a 24V battery.

[0031] The fourth system branch is the vehicle high-voltage system branch, including the power distribution box 111, the air conditioning compressor 141, and the heating device 142.

[0032] The heating device 142 can be a PTC (Positive Temperature Coefficient) heating system.

[0033] Figure 2 This is a schematic flowchart illustrating a fault diagnosis method for an engineering vehicle according to an embodiment of the present disclosure. In some embodiments, the following fault diagnosis method for engineering vehicles is executed by a fault diagnosis device for the engineering vehicle, including steps S21 to S24.

[0034] In step S21, multiple signals from multiple system branches of the engineering vehicle are collected using multiple sensors of the engineering vehicle.

[0035] Figure 3 This is a schematic diagram of sensor distribution according to an embodiment of this disclosure. Figure 3 As shown, a current sensor, a voltage sensor, and a temperature sensor are installed at the output terminals of the power battery 15 and the power distribution box 111 to collect the current signal, voltage signal, and temperature signal of the power battery 15 and the power distribution box 111.

[0036] Current sensors are installed at the input terminals of the drive motor controller 112 and the hydraulic motor controller 121 to collect the current signals of the drive motor controller 112 and the hydraulic motor controller 121.

[0037] Encoders are installed on the motor shaft end of the travel motor 113, the load shaft end of the travel motor 113, the motor shaft end of the hydraulic motor 122, and the load shaft end of the hydraulic motor 122 to collect the speed and angle signals of the travel motor 113 and the hydraulic motor 122.

[0038] A vibration sensor is installed at the input end of the drive axle hub 115, and a speed sensor is installed at the output end of the drive axle hub 115, so as to collect the vibration signal and speed signal of the drive axle hub 115.

[0039] A speed sensor is installed at the inlet of the hydraulic pump 123, and a pressure sensor is installed at the outlet of the hydraulic pump 123, so as to collect the speed and pressure signals of the hydraulic pump 123.

[0040] A displacement sensor and a pressure sensor are installed at the working cylinder 125 to collect the displacement and pressure signals of the working cylinder 125.

[0041] Figure 3 The diagram primarily illustrates the sensor distribution for the first and second system branches. For the third and fourth system branches, current and voltage sensors can be installed at the input terminals of the DC / DC converter 131, the battery 132, the air conditioning compressor 141, and the heating device 142 to collect current and voltage signals from these components. This sensor distribution is merely an example; sensors can be configured according to the system architecture and fault diagnosis requirements of the engineering vehicle.

[0042] In step S22, multiple signals are processed to obtain multiple operating condition parameters and multiple characteristic parameters.

[0043] Multiple operating parameters may include the power battery output current. Drive axle hub speed and hydraulic pump pressure .

[0044] Several characteristic parameters may include the input current of the drive motor controller. Input current of hydraulic motor controller The rotational speed of the motor shaft of the travel motor Speed ​​at the load shaft end of the drive motor The rotational speed of the motor shaft of the hydraulic motor Speed ​​at the load shaft end of the hydraulic motor Hydraulic pump speed Working cylinder pressure and displacement of the working cylinder Multiple items in the list.

[0045] Each operating condition parameter or characteristic parameter can be determined based on the value of the signal corresponding to that parameter within a preset time range. The preset time range can be set to 10 seconds. For example, each operating condition parameter or characteristic parameter can be the average value of the signal corresponding to that parameter over the previous 10 seconds. Alternatively, each operating condition parameter or characteristic parameter can also be the value of the signal corresponding to that parameter at the current moment.

[0046] In step S23, the working condition of the engineering vehicle is determined based on multiple working condition parameters and multiple characteristic parameters.

[0047] In some embodiments, the primary operating condition of the engineering vehicle is determined based on multiple operating parameters.

[0048] Level 1 operating conditions can include no start, idling, unloaded driving, and working conditions.

[0049] In some embodiments, in response to the power battery output current indicating that the power battery of the engineering vehicle is not outputting current, the first operating condition is determined to be not started.

[0050] For example, in the output current of the power battery If the value is 0, the first-level operating condition of the engineering vehicle is determined to be "not started".

[0051] In some embodiments, in response to the power battery output current indicating the presence of output current in the power battery, the first-level operating condition of the engineering vehicle is determined based on the drive axle hub speed and the hydraulic pump pressure.

[0052] For example, in the output current of the power battery If the value is greater than 0, the first-level working condition of the engineering vehicle is further determined based on the drive axle hub speed and hydraulic pump pressure.

[0053] In some embodiments, in response to the drive axle hub speed indicating that the drive axle of the engineering vehicle is not operating and the hydraulic pump pressure indicating that the hydraulic pump of the engineering vehicle is not operating, the first operating condition is determined to be the idling condition.

[0054] For example, at the drive axle hub speed The pressure is 0, and the hydraulic pump pressure is... When the value is 0, the first-level operating condition of the engineering vehicle is determined to be the idling condition.

[0055] In some embodiments, in response to the drive axle hub speed indicating that the drive axle is in operation and the hydraulic pump pressure indicating that the hydraulic pump is not in operation, the first operating condition is determined to be the no-load driving condition.

[0056] For example, at the drive axle hub speed The value is not zero, and the hydraulic pump pressure is... When the value is 0, the first-level operating condition of the engineering vehicle is determined to be the no-load driving condition.

[0057] In some embodiments, in response to the drive axle hub speed indicating that the drive axle is in operation and the hydraulic pump pressure indicating that the hydraulic pump is in operation, a first-level operating condition is determined as the working condition.

[0058] For example, at the drive axle hub speed The value is not zero, and the hydraulic pump pressure is... If the value is greater than 0, the first-level working condition of the engineering vehicle is determined as the working condition.

[0059] In some embodiments, in response to the primary working condition of the engineering vehicle being a working condition, the secondary working condition of the engineering vehicle is determined based on multiple characteristic parameters.

[0060] In some embodiments, the typical operating condition database includes a set of characteristic parameters for each of multiple secondary operating conditions, and each set of characteristic parameters for a secondary operating condition includes a preset range for each of the multiple characteristic parameters. Based on the multiple characteristic parameters and the set of characteristic parameters for the multiple secondary operating conditions, the secondary operating condition in which the engineering vehicle is located is determined.

[0061] Multiple secondary operating conditions can include low-speed operation, heavy-load reverse, forward operation, and power recovery. For example, a typical operating sequence for an engineering vehicle during normal operation is from unloaded forward to low-speed operation, from low-speed operation to heavy-load reverse, from heavy-load reverse to forward operation, and from forward operation to power recovery, with a high degree of continuity between operating conditions. The engineering vehicle can complete multiple work cycles in the above sequence.

[0062] Test data of multiple characteristic parameters of engineering vehicles under different secondary working conditions can be collected in advance, the numerical range of each characteristic parameter corresponding to each secondary working condition can be divided, and a characteristic parameter set for each secondary working condition can be formed, thereby constructing a database of typical working conditions.

[0063] If each of the multiple characteristic parameters of an engineering vehicle matches a preset range in the characteristic parameter set of a certain secondary working condition, then the secondary working condition in which the engineering vehicle is located is determined to be that secondary working condition.

[0064] Figure 4 This is a flowchart illustrating a method for determining operating conditions according to an embodiment of this disclosure. Figure 4 As shown, the method for determining the operating conditions includes steps S41 to S46.

[0065] In step S41, signal data from multiple sensors are acquired.

[0066] In step S42, multiple signals are processed to determine multiple operating parameters.

[0067] In step S43, the first-level working condition of the engineering vehicle is determined based on multiple working condition parameters.

[0068] Level 1 operating conditions include not started, idling, working, and unloaded driving.

[0069] When the engineering vehicle is in operation, proceed to step S44.

[0070] In step S44, multiple signals are processed to determine multiple characteristic parameters.

[0071] It should be noted that multiple operating parameters and multiple characteristic parameters can be determined in the same step or in different steps.

[0072] In step S45, the characteristic parameter sets of multiple secondary working conditions are obtained from the typical working condition database.

[0073] In step S46, the secondary working condition of the engineering vehicle is determined based on multiple characteristic parameters and multiple sets of characteristic parameters of secondary working conditions.

[0074] By using the methods described in the above embodiments, the working conditions of engineering vehicles are subdivided into multiple primary working conditions and multiple secondary working conditions, and the working conditions of engineering vehicles are determined using multiple working condition parameters and multiple feature parameters, which can improve the accuracy of working condition identification results.

[0075] In step S24, for each system branch, fault information of the system branch under operating conditions is determined based on multiple signals corresponding to the system branch. The fault information includes at least one of fault location, fault type, and fault severity.

[0076] In some embodiments, for each target component in a system branch, fault information of the target component is determined based on one or more signals corresponding to the target component.

[0077] For each target component's target signal, the typical fault database can include a preset range for the target signal. For example, by comparing the target component's target signal with the corresponding preset range in the typical fault database, it can be determined whether the target component has malfunctioned.

[0078] When a target component malfunctions, its fault information can be determined based on the fault frequency appearing in the spectrum of the target component's signal. For example, the fault location can be determined based on the fault frequency appearing in the target signal's spectrum, the fault severity can be determined based on the amplitude of the fault frequency, and the fault type can be determined based on both the fault frequency and its amplitude.

[0079] The typical fault database can also include a preset range of fault frequency amplitudes. For example, by comparing the amplitude of fault frequencies appearing in the target signal spectrum with the corresponding preset ranges in the typical fault database, fault information of the target component can be determined.

[0080] In some embodiments, the target component is a power battery, a power distribution box, a DC / DC converter, a storage battery, an air conditioning compressor, or a heating device. In response to a target signal from the target component exceeding a preset range, a fault is determined in the target component. The target signal includes at least one of a voltage signal, a current signal, and a temperature signal. Based on the target signal of the target component, fault information of the target component is determined.

[0081] For example, if the voltage signal of the power battery exceeds the preset range of the power battery voltage in the typical fault database, a fault is determined to have occurred in the power battery. By comparing the voltage signal of the power battery with the reference value of the power battery voltage in the typical fault database, it can be further determined that the power battery has faults such as internal short circuits or open circuits within the cells.

[0082] If the voltage signal of the aforementioned target component exceeds the preset range, the fault in the target component may be an internal short circuit or open circuit within the battery cell. If the current signal of the aforementioned target component exceeds the preset range, the fault in the target component may be an external short circuit or a loose connection. If the temperature signal of the aforementioned target component exceeds the preset range, the fault in the target component may be localized high temperature or thermal runaway.

[0083] In some embodiments, the target component is a drive motor or a hydraulic motor. In response to a target parameter of the target component exceeding a target parameter threshold, a fault is determined in the target component. The target parameter includes at least one of the following: the difference in rotational speed between the two ends, the difference in rotational angle between the two ends, or the difference in angular acceleration between the two ends. Fault information of the target component is determined based on its target parameters.

[0084] The encoders installed on the motor shaft end and load shaft end of the travel motor and hydraulic motor can detect the speed signals at both ends of the travel motor and hydraulic motor, and can also convert them into the angle signals and angular acceleration signals at both ends.

[0085] The speed difference between the two ends of the target component is the difference between the speed of the first end and the speed of the second end of the target component; the angle difference between the two ends of the target component is the difference between the angle of rotation of the first end and the angle of rotation of the second end of the target component; the angular acceleration difference between the two ends of the target component is the difference between the angular acceleration of the first end and the angular acceleration of the second end of the target component. The first end can be the motor shaft end, and the second end can be the load shaft end.

[0086] Taking a travel motor as an example, assuming the actual rotational speed of the motor shaft is... The actual rotational speed of the load shaft end of the driving motor is Then the speed difference between the two ends of the driving motor is The system determines a fault in the drive motor when the speed difference between its two ends exceeds a threshold value. By comparing this speed difference with a reference value in a typical fault database, the system can pinpoint the fault information. For example, if the speed difference between the motor's two ends exceeds the threshold value, the fault might be motor slippage.

[0087] Assume the rotation angle of the motor shaft end of the driving motor is... The rotation angle of the load shaft end of the driving motor is Then the angle difference between the two ends of the driving motor is The system determines a fault in the drive motor when the angle difference between its two ends exceeds a threshold value. By comparing this angle difference with a reference value in a typical fault database, the system can identify the specific fault. For example, if the angle difference exceeds the threshold value, the fault might be due to coupling wear or excessive gear backlash.

[0088] Assume the angular acceleration at the end of the motor shaft of the driving motor is The angular acceleration at the load shaft end of the driving motor is The difference in angular acceleration between the two ends of the driving motor is The system determines a fault in the travel motor if the difference in angular acceleration between its two ends exceeds a threshold value. By comparing this difference with reference values ​​in a typical fault database, the system can identify the specific fault. For example, a difference in angular acceleration between the motor's two ends exceeding the threshold value might indicate a broken gear or a rolling element failure.

[0089] In some embodiments, the target component is a drive motor or a hydraulic motor. A fault is determined in the target component in response to a motor fault frequency appearing in a target signal. The target signal includes at least one of a current signal, a speed signal, a rotation angle signal, and an angular acceleration signal. Fault information of the target component is determined based on the motor fault frequency in the target signal.

[0090] In some embodiments, the motor failure frequency is determined based on the electrical frequency and the pole passing frequency, which in turn is determined based on the slip and the electrical frequency.

[0091] For the drive motor, the current signal of the drive motor can be acquired from the signal interface of the drive motor controller. From the current signal of the driving motor The extracted electrical frequency is The actual rotational speed of the motor shaft is obtained from encoders located at the motor shaft end and the load shaft end of the travel motor. and the actual speed of the load shaft end Motor failure frequency is usually calculated using the speed at the load shaft end.

[0092] Assume the number of pole pairs of the driving motor is Then the synchronous speed of the driving motor As shown in formula (1).

[0093] (1)

[0094] slip of the drive motor As shown in formula (2).

[0095] (2)

[0096] Pole pass frequency of the driving motor As shown in formula (3).

[0097] (3)

[0098] The frequency of motor failures of the driving motor is shown in formula (4).

[0099] (4)

[0100] Where k and m are coefficients, taking values ​​greater than 0. In other words, the motor fault frequency includes the fundamental frequency and its harmonics.

[0101] For hydraulic motors, the current signal of the hydraulic motor can be acquired from the signal interface of the hydraulic motor controller. From the current signal of the hydraulic motor The extracted electrical frequency is The actual rotational speed of the hydraulic motor shaft is obtained from encoders located at the motor shaft end and the load shaft end of the hydraulic motor. and the actual speed of the load shaft end .

[0102] Assume the number of pole pairs of the hydraulic motor is Then the synchronous speed of the hydraulic motor As shown in formula (5).

[0103] (5)

[0104] Slip of hydraulic motor As shown in formula (6).

[0105] (6)

[0106] The pole passing frequency of the hydraulic motor As shown in formula (7).

[0107] (7)

[0108] The frequency of motor failure of hydraulic motor is shown in formula (8).

[0109] (8)

[0110] For drive motors and hydraulic motors, when a motor malfunctions, the frequency of the motor malfunction will be reflected in the spectrum of the current signal collected by the current sensor and the spectrum of the speed signal, angle signal and angular acceleration signal collected by the encoder. The motor malfunction information can be determined by combining multiple motor signals.

[0111] If the motor fault frequency appears in the current signal spectrum, while the motor speed difference, angle difference, and angular acceleration difference remain stable, the motor fault is likely an electrical fault in the motor rotor or stator. If the motor speed difference, angle difference, or angular acceleration difference exceeds a preset range, but the motor fault frequency does not appear in the current signal spectrum, the motor fault is likely a mechanical transmission fault.

[0112] When the same motor fault frequency appears simultaneously in the spectrum of the current signal acquired by the current sensor and the spectrum of the signal acquired by the encoder, the motor fault may be torsional resonance or a loose connection. Comparing the observed motor fault frequency with the corresponding parameter values ​​in a typical fault database can determine the motor fault information. For example, the instantaneous angular acceleration characteristic frequency can be obtained by converting the signal acquired by the encoder; the Hilbert envelope spectrum of the current can be obtained by processing the current signal; and the instantaneous angular acceleration characteristic frequency and the Hilbert envelope spectrum of the current can be compared in the same spectrum to determine the motor fault information.

[0113] The motor's current signal can also be compared with the corresponding current parameter values ​​in a typical fault database to determine the motor's fault information. If the motor's current signal exceeds the preset range, the motor fault may be an inter-turn short circuit or a broken rotor bar.

[0114] In some embodiments, the target component is a drive axle hub. A drive axle fault frequency is detected in the vibration signal or rotational speed signal of the target component, indicating that the target component has malfunctioned. Fault information of the target component is determined based on the drive axle fault frequency, vibration frequency, vibration amplitude in the vibration signal, and the drive axle fault frequency and rotational speed fluctuation value in the rotational speed signal.

[0115] Mechanical failures in drive axles are typically caused by components such as bearings or gears. Bearing components include the outer ring, inner ring, rolling elements, and cage.

[0116] In some embodiments, the drive axle failure frequencies include bearing failure frequencies and gear failure frequencies. Bearing failure frequencies include the outer bearing ring failure frequency, the inner bearing ring failure frequency, the rolling element failure frequency, and the bearing cage failure frequency. Gear failure frequencies are determined based on the gear meshing frequency.

[0117] The failure frequency of the bearing outer ring is shown in formula (9).

[0118] (9)

[0119] The failure frequency of the bearing inner ring is shown in formula (10).

[0120] (10)

[0121] The failure frequency of the bearing rolling elements is shown in formula (11).

[0122] (11)

[0123] The failure frequency of the bearing cage is shown in formula (12).

[0124] (12)

[0125] in, For bearing speed, Where d is the number of rolling elements, d is the diameter of the rolling elements, and D is the pitch circle diameter. It represents the contact angle.

[0126] There are multiple pairs of meshing gears inside the drive axle, and the meshing frequency of each pair of meshing gears is shown in formula (13).

[0127] (13)

[0128] in, The rotational speed of the shaft containing the gear can be determined by the rotational speed of the drive axle. It is obtained by converting the transmission ratio with the output speed of the drive axle. This represents the number of teeth on the gear.

[0129] Based on the meshing frequency of a pair of meshing gears and its multipliers The frequency difference between adjacent peak values ​​on both sides determines the rotational frequency of the shaft where the faulty gear in this pair of meshing gears is located, thereby determining which shaft the faulty gear in this pair of meshing gears is located on.

[0130] If the vibration or speed signal of the drive axle hub contains a drive axle fault frequency, the fault in the drive axle hub may be a bearing fault or a gear fault. By comparing the drive axle fault frequency, vibration frequency, vibration amplitude in the vibration signal of the drive axle hub, and the drive axle fault frequency and speed fluctuation value in the speed signal with the corresponding parameter values ​​in the typical fault database, the fault location, fault type, and fault severity of the drive axle hub can be determined.

[0131] In some embodiments, the target component is a hydraulic pump. A fault is determined in the target component based on the occurrence of a hydraulic pump failure frequency in the pressure signal, a pressure pulsation exceeding a pressure pulsation threshold, or a hydraulic pump failure frequency in the speed signal. Fault information of the target component is determined based on the hydraulic pump failure frequency and pressure pulsation amplitude in the pressure signal, and the hydraulic pump failure frequency and speed fluctuation value in the speed signal.

[0132] In some embodiments, the hydraulic pump failure frequency includes the gear failure frequency or the plunger failure frequency.

[0133] Mechanical failures in hydraulic pumps are typically caused by components such as gears or pistons. For gear pumps, the failure frequency includes gear failure frequency, which can be referenced to the gear failure frequency of the drive axle hub. For piston pumps, the failure frequency includes piston failure frequency.

[0134] The plunger failure frequency is shown in formula (14).

[0135] (14)

[0136] in, The speed of the hydraulic pump. This refers to the number of plungers in the hydraulic pump.

[0137] Combining pressure and speed signals of the hydraulic pump for fault diagnosis can improve the accuracy of hydraulic pump fault diagnosis. When a hydraulic pump fault is determined, the fault frequency and pressure pulsation amplitude in the pressure signal, and the fault frequency and speed fluctuation value in the speed signal, are compared with corresponding parameter values ​​in a typical fault database to determine the fault location, fault type, and fault severity.

[0138] In some embodiments, the target component is a hydraulic cylinder. A fault is determined in the target component when its pressure or displacement signal exceeds a preset range. Fault information of the target component is determined based on its pressure and displacement signals.

[0139] If the pressure or displacement signal of the working cylinder exceeds the preset range, the fault in the working cylinder may be mechanical jamming or internal leakage. By comparing the pressure and displacement signals of the working cylinder with the pressure and displacement parameter values ​​in a typical fault database, the location, type, and severity of the fault in the working cylinder can be determined.

[0140] Based on the fault information of each target component in the system branch, the fault information of the system branch under operating conditions is obtained. The typical fault database can be updated using the fault information of each system branch under operating conditions to improve the accuracy of fault diagnosis for engineering vehicles.

[0141] Furthermore, the fault information of each system branch obtained through the above fault diagnosis method under the operating conditions reflects the faults that engineering vehicles are prone to under the operating conditions, and can be used to make targeted improvements to engineering vehicles.

[0142] The fault diagnosis method for engineering vehicles described in the above embodiments determines the operating condition of the engineering vehicle based on multiple operating condition parameters and multiple characteristic parameters, and determines the fault information of each system branch under the operating condition based on multiple signals corresponding to each system branch. This can improve the accuracy of fault diagnosis for engineering vehicles, realize the intelligent and automated fault diagnosis, and also uncover the correlation between operating conditions and fault information.

[0143] Figure 5 This is a schematic diagram of the structure of a fault diagnosis device for an engineering vehicle according to an embodiment of this disclosure. Figure 5 As shown, the fault diagnosis device 50 includes a recording unit 51, a comparison unit 52, and an analysis unit 53.

[0144] The recording unit 51 is configured to receive and store data collected by multiple sensors sent by the sensing unit 54.

[0145] The comparison unit 52 is configured to process signals from multiple sensors to obtain multiple operating condition parameters and multiple characteristic parameters, and to determine the operating condition of the engineering vehicle based on the multiple operating condition parameters and multiple characteristic parameters.

[0146] The analysis unit 53 is configured to determine the fault information of each system branch in the engineering vehicle under operating conditions based on the data stored in the recording unit 51.

[0147] Figure 6 This is a schematic diagram of the structure of a fault diagnosis device for an engineering vehicle according to another embodiment of this disclosure. Figure 6 As shown, the fault diagnosis device 60 for engineering vehicles includes a memory 61, a processor 62, and a bus 63 connecting different system components.

[0148] The memory 61 may include, for example, system memory, non-volatile storage media, etc. The system memory may store, for example, an operating system, application programs, a boot loader, and other programs. The system memory may include volatile storage media, such as random access memory (RAM) and / or cache memory. The non-volatile storage media may store, for example, instructions for a corresponding embodiment of at least one fault diagnosis method for an engineering vehicle being executed. Non-volatile storage media include, but are not limited to, disk storage, optical storage, flash memory, etc.

[0149] Processor 62 can be implemented using a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA) or other programmable logic devices, discrete hardware components such as discrete gates or transistors. Accordingly, the method in any of the above embodiments can be implemented by instructions in the central processing unit (CPU) running memory to execute the corresponding steps, or by dedicated circuitry to execute the corresponding steps.

[0150] For example, processor 62 is configured for memory-based instruction execution implementation such as Figure 2 The fault diagnosis method for engineering vehicles involved in any of the embodiments.

[0151] Bus 63 can use any of the various bus architectures. For example, bus architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MCA) bus, and the Peripheral Component Interconnect (PCI) bus.

[0152] The input / output interface 64, network interface 65, storage interface 66, memory 61, and processor 62 of the fault diagnosis device 60 for engineering vehicles can be connected via bus 63. Input / output interface 64 provides a connection interface for input / output devices such as monitors, mice, and keyboards. Network interface 65 provides a connection interface for various networked devices. Storage interface 66 provides a connection interface for external storage devices such as floppy disks, USB flash drives, and SD cards.

[0153] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations thereof, can be implemented by computer-readable program instructions.

[0154] These computer-readable program instructions are provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable device to produce a machine, such that execution of the instructions by the processor produces means for implementing the functions specified in one or more boxes of the flowchart and / or block diagram.

[0155] These computer-readable program instructions may also be stored in a computer-readable storage medium. These instructions cause a computer to work in a particular manner to produce an article of manufacture, including instructions that implement the functions specified in one or more boxes in a flowchart and / or block diagram.

[0156] This disclosure may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects.

[0157] This disclosure also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement... Figure 2 The fault diagnosis method for engineering vehicles involved in any of the embodiments.

[0158] This disclosure also provides a computer program product, including computer instructions, wherein the computer instructions, when executed by a processor, implement as follows: Figure 2 The fault diagnosis method for engineering vehicles involved in any of the embodiments.

[0159] In some embodiments, the functional units described above may be implemented as general-purpose processors, programmable logic controllers (PLCs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or any suitable combination thereof for performing the functions described herein.

[0160] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0161] The description in this disclosure is provided for illustrative and descriptive purposes only and is not intended to be exhaustive or to limit the disclosure to its forms. Many modifications and variations will be apparent to those skilled in the art. The embodiments were chosen and described in order to better illustrate the principles and practical application of this disclosure and to enable those skilled in the art to understand this disclosure and to design various embodiments with various modifications suitable for a particular purpose.

Claims

1. A fault diagnosis method for engineering vehicles, comprising: Using multiple sensors of the engineering vehicle, multiple signals from multiple system branches of the engineering vehicle are collected; The multiple signals are processed to obtain multiple operating condition parameters and multiple characteristic parameters; The working condition of the engineering vehicle is determined based on the multiple working condition parameters and the multiple characteristic parameters. For each system branch, fault information of the system branch under the operating condition is determined based on multiple signals corresponding to the system branch. The fault information includes at least one of fault location, fault type, and fault severity.

2. The fault diagnosis method for engineering vehicles according to claim 1, wherein, The step of determining the fault information of the system branch under the operating condition based on multiple signals corresponding to the system branch includes: For each target component in the system branch, fault information of the target component is determined based on one or more signals corresponding to the target component.

3. The fault diagnosis method for engineering vehicles according to claim 2, wherein, The target component is a power battery, power distribution box, DC / DC converter, storage battery, air conditioning compressor, or heating device. Determining the fault information of the target component based on one or more signals corresponding to the target component includes: In response to the target component's target signal exceeding a preset range, it is determined that the target component has malfunctioned, wherein the target signal includes at least one of a voltage signal, a current signal, and a temperature signal; Based on the target signal of the target component, the fault information of the target component is determined.

4. The fault diagnosis method for engineering vehicles according to claim 2, wherein, The target component is a drive motor or a hydraulic motor, and determining the fault information of the target component based on one or more signals corresponding to the target component includes: In response to the target parameter of the target component being greater than the target parameter threshold, it is determined that the target component has malfunctioned. The target parameter includes at least one of the following: the difference in rotational speed at both ends, the difference in rotational angle at both ends, or the difference in angular acceleration at both ends. The difference in rotational speed at both ends of the target component is the difference between the rotational speed at the first end of the target component and the rotational speed at the second end of the target component. The difference in rotational angle at both ends of the target component is the difference between the rotational angle at the first end of the target component and the rotational angle at the second end of the target component. The difference in angular acceleration at both ends of the target component is the difference between the angular acceleration at the first end of the target component and the angular acceleration at the second end of the target component. Based on the target parameters of the target component, the fault information of the target component is determined.

5. The fault diagnosis method for engineering vehicles according to claim 2, wherein, The target component is a drive motor or a hydraulic motor, and determining the fault information of the target component based on one or more signals corresponding to the target component includes: In response to the occurrence of motor failure frequency of the target signal of the target component, it is determined that the target component has failed, wherein the target signal includes at least one of current signal, speed signal, rotation angle signal and angular acceleration signal; The fault information of the target component is determined based on the motor fault frequency in the target signal of the target component.

6. The fault diagnosis method for engineering vehicles according to claim 5, wherein, The motor failure frequency is determined based on the electrical frequency and the pole passing frequency, and the pole passing frequency is determined based on the slip rate and the electrical frequency.

7. The fault diagnosis method for engineering vehicles according to claim 2, wherein, The target component is a drive axle wheel hub, and determining the fault information of the target component based on one or more signals corresponding to the target component includes: In response to the occurrence of a drive axle fault frequency in the vibration signal or rotation speed signal of the target component, it is determined that the target component has malfunctioned; The fault information of the target component is determined based on the drive axle fault frequency, vibration frequency, vibration amplitude in the vibration signal of the target component, and the drive axle fault frequency and speed fluctuation value in the speed signal.

8. The fault diagnosis method for engineering vehicles according to claim 7, wherein, The drive axle failure frequencies include bearing failure frequencies and gear failure frequencies. The bearing failure frequencies include the bearing outer ring failure frequency, the bearing inner ring failure frequency, the bearing rolling element failure frequency, and the bearing cage failure frequency. The gear failure frequencies are determined based on the gear meshing frequency.

9. The fault diagnosis method for engineering vehicles according to claim 2, wherein, The target component is a hydraulic pump, and determining the fault information of the target component based on one or more signals corresponding to the target component includes: In response to the occurrence of hydraulic pump failure frequency in the pressure signal of the target component, pressure pulsation exceeding the pressure pulsation threshold, or hydraulic pump failure frequency in the speed signal, it is determined that the target component has failed. The fault information of the target component is determined based on the hydraulic pump failure frequency and pressure pulsation amplitude in the pressure signal of the target component, and the hydraulic pump failure frequency and speed fluctuation value in the speed signal of the target component.

10. The fault diagnosis method for engineering vehicles according to claim 9, wherein, The hydraulic pump failure frequency includes gear failure frequency or plunger failure frequency.

11. The fault diagnosis method for engineering vehicles according to claim 2, wherein, The target component is a hydraulic cylinder, and determining the fault information of the target component based on one or more signals corresponding to the target component includes: If the pressure signal or displacement signal of the target component exceeds a preset range, it is determined that the target component has malfunctioned. Based on the pressure and displacement signals of the target component, the fault information of the target component is determined.

12. The fault diagnosis method for engineering vehicles according to claim 1, wherein, Determining the operating condition of the engineering vehicle based on the plurality of operating condition parameters and the plurality of characteristic parameters includes: Based on the multiple operating parameters, the first-level operating condition of the engineering vehicle is determined; In response to the fact that the engineering vehicle is in the primary working condition, which is the working condition, the secondary working condition of the engineering vehicle is determined based on the multiple characteristic parameters.

13. The fault diagnosis method for engineering vehicles according to claim 12, wherein, The multiple operating parameters include the power battery output current, the drive axle wheel hub speed, and the hydraulic pump pressure. Determining the primary operating condition of the engineering vehicle based on these multiple operating parameters includes: In response to the power battery output current indicating that the power battery of the engineering vehicle is not outputting current, the first-level operating condition is determined to be not started; In response to the power battery output current indicating that the power battery has an output current, the first-level working condition of the engineering vehicle is determined based on the drive axle hub speed and the hydraulic pump pressure.

14. The fault diagnosis method for engineering vehicles according to claim 13, wherein, The step of determining the first-level operating condition of the engineering vehicle based on the drive axle hub speed and the hydraulic pump pressure includes: In response to the drive axle hub speed indicating that the drive axle of the engineering vehicle is not running, and the hydraulic pump pressure indicating that the hydraulic pump of the engineering vehicle is not running, the first-level operating condition is determined to be the idling condition; In response to the drive axle hub speed indicating that the drive axle is in operation and the hydraulic pump pressure indicating that the hydraulic pump is not running, the first-level operating condition is determined to be an unloaded driving condition; In response to the drive axle hub speed indicating that the drive axle is in operation and the hydraulic pump pressure indicating that the hydraulic pump is in operation, the first-level working condition is determined as the operating condition.

15. The fault diagnosis method for engineering vehicles according to claim 12, wherein, The typical operating condition database includes a set of feature parameters for each of the multiple secondary operating conditions. Each set of feature parameters for a secondary operating condition includes a preset range for each of the multiple feature parameters. Determining the secondary operating condition of the engineering vehicle based on these multiple feature parameters includes: Based on the multiple characteristic parameters and the characteristic parameter set of the multiple secondary working conditions, the secondary working condition of the engineering vehicle is determined.

16. A fault diagnosis device for engineering vehicles, comprising: Memory; A processor, coupled to a memory, is configured to implement the fault diagnosis method for engineering vehicles as described in any one of claims 1-15 based on the execution of instructions stored in the memory.

17. A computer-readable storage medium, wherein, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the fault diagnosis method for the engineering vehicle as described in any one of claims 1-15.

18. A computer program product comprising computer instructions, wherein the computer instructions, when executed by a processor, implement the fault diagnosis method for an engineering vehicle as described in any one of claims 1-15.

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