Drive station failure determination method, apparatus, and electronic device

Abstract

The present disclosure provides a fault determination method, device and electronic equipment of a driving station, and relates to the technical field of driving stations. The method comprises: collecting preset parameters of a driving station used to indicate faults, performing real-time calculation on the range of the preset parameters according to the collection result to obtain a real-time parameter range corresponding to the collection result; judging fault condition information of the driving station according to the real-time parameter range; if the fault condition information indicates that the driving station has a fault, generating alarm information corresponding to the fault, and outputting the alarm information. The present disclosure can dynamically determine the real-time parameter range for the latest collection result, improve the timeliness and accuracy of the threshold value used to judge the fault condition while improving the timeliness of the threshold value.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of driving station, and particularly relates to a driving station fault determination method, device and electronic equipment. BACKGROUND

[0002] The catenary conveying system is widely used in automatic production lines such as automobile manufacturing, home appliance assembly and logistics sorting, and the operation reliability of the core power unit, i.e. the driving station, directly relates to the continuity and production efficiency of the whole production line. In the long-term high-load operation process, the driving station is prone to failure due to factors such as mechanical wear, electrical aging, lubrication failure or external load mutation. If the fault source cannot be timely warned and accurately located, it may lead to unplanned downtime and rising maintenance costs, or even equipment damage and safety accidents.

[0003] Even if an alarm is issued, maintenance personnel often face the dilemma of "where is the fault" and "how to repair the fastest". Especially in large production lines, the fault point positioning is ambiguous and the path planning is missing, which further prolongs the downtime. SUMMARY

[0004] Therefore, the purpose of the present disclosure is to provide a driving station fault determination method, device and electronic equipment, which can solve the existing problems.

[0005] To achieve the above purpose, in a first aspect, the present disclosure provides a driving station fault determination method, comprising: collecting preset parameters for indicating faults of a driving station, performing real-time calculation on the range of the preset parameters according to the collection result to obtain a real-time parameter range corresponding to the collection result; judging fault condition information of the driving station according to the real-time parameter range; if the fault condition information indicates that the driving station has a fault, generating alarm information corresponding to the fault, and outputting the alarm information.

[0006] In a second aspect, a driving station fault determination device is also provided, comprising: a collection unit configured to collect preset parameters for indicating faults of a driving station, perform real-time calculation on the range of the preset parameters according to the collection result to obtain a real-time parameter range corresponding to the collection result; a judgment unit configured to judge fault condition information of the driving station according to the real-time parameter range; a generation unit configured to, if the fault condition information indicates that the driving station has a fault, generate alarm information corresponding to the fault, and output the alarm information.

[0007] In a third aspect, an electronic equipment is also provided, which comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the method of the first aspect.

[0008] In a fourth aspect, a computer readable storage medium is provided, having stored thereon a computer program, the computer program being executed by a processor to implement the method of any one of the first aspect.

[0009] In a fifth aspect, a computer program product is provided, comprising a computer program, the computer program being executed by a processor to implement the method of any one of the first aspect.

[0010] In general, the present disclosure has at least the following beneficial effects: the real-time parameter range can be dynamically determined for the latest collection result, the timeliness and accuracy of adopting the threshold to judge the fault condition are improved while the timeliness of the determined threshold is improved. BRIEF DESCRIPTION OF DRAWINGS

[0011] In the drawings, like reference numerals refer to same or similar components throughout the several views. These drawings are not necessarily to scale. It should be understood that these drawings are merely schematic representations, which are not intended to portray specific parameters of the disclosure, and that in particular, the drawings are not intended to convey the proportions of the various components.

[0012] Figure 1 A flow chart of a fault determination method of a drive station is shown according to an embodiment of the present disclosure;

[0013] Figure 2a A flow chart of fault location in a fault determination method of a drive station is shown according to an embodiment of the present disclosure;

[0014] Figure 2b Another flow chart of fault location in a fault determination method of a drive station is shown according to an embodiment of the present disclosure;

[0015] Figure 2c Another flow chart of fault location in a fault determination method of a drive station is shown according to an embodiment of the present disclosure;

[0016] Figure 2d Another flow chart of fault location in a fault determination method of a drive station is shown according to an embodiment of the present disclosure;

[0017] Figure 3 A schematic diagram of a correspondence between a fault level and an alarm level is shown according to an embodiment of the present disclosure;

[0018] Figure 4 A schematic diagram of a fault determination apparatus of a drive station is shown according to an embodiment of the present disclosure;

[0019] Figure 5 A structural schematic diagram of an electronic device is shown according to an embodiment of the present disclosure;

[0020] Figure 6A schematic diagram of a storage medium provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0021] The present disclosure will be further described below in conjunction with the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related application, and not to limit the application. In addition, it should be noted that only parts related to the application are shown in the drawings for ease of description.

[0022] It should be noted that the embodiments in the present disclosure and the features in the embodiments can be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0023] Figure 1 A fault determination method of a driving station of the present disclosure is shown. In an embodiment of the present disclosure, the method comprises:

[0024] In step S101, the preset parameters used by the driving station to indicate faults are collected, and the range of the preset parameters is calculated in real time according to the collection results, to obtain a real-time parameter range corresponding to the latest collection results.

[0025] In step S102, the fault condition information of the driving station is determined according to the real-time parameter range.

[0026] In step S103, if the fault condition information indicates that the driving station has a fault, alarm information corresponding to the fault is generated, and the alarm information is output.

[0027] In the present embodiment, the execution subject of the fault determination method of the driving station can dynamically determine the real-time parameter range for the latest collection results. The fault condition information can include various information about the fault of the driving station, such as determining whether the driving station has a fault.

[0028] The catenary in which the driving station is located is a closed-loop conveying system composed of a chain or a track, and the workpiece is hung on the chain by a sling and continuously moves under the driving of the driving station. Among them, the driving station is the power core of the catenary system, which provides the driving force of the chain movement through the motor, the speed reducer and the transmission device.

[0029] The embodiments of the present disclosure can dynamically determine the real-time parameter range for the latest collection results, which improves the timeliness of the determined threshold and helps to improve the timeliness and accuracy of the threshold in judging the fault condition.

[0030] In some optional implementations of any of the embodiments of the present disclosure, the preset parameters include temperature, current and amplitude; and the collecting, by the driving station, of the preset parameters indicating the fault includes: preferentially collecting the temperature of the driving station, and if the temperature exceeds a real-time parameter range corresponding to the temperature, then collecting the current and the amplitude.

[0031] These implementations can collect data of the temperature, current and amplitude of the driving station. Specifically, the physical entity of the driving station can be scanned by point cloud, and a twin scene of the driving station can be built. The point information obtained by scanning can be combined with picture information recognition to anchor point locate each driving station and fire-fighting equipment. A multi-level detail level (LOD) can be set to determine the marker definition, and the anchor points can be classified and processed. The category of the driving station anchor point is set as the equipment category, and the category of the fire-fighting equipment anchor point is set as the fire-fighting category. Different anchor point categories can be used for quick positioning and searching.

[0032] The temperature of the driving station can be detected by a sensor for detecting the temperature of any key component of the driving station. The key component can be a cooling system, a motor, a mechanical bearing, etc. The real-time parameter range can be a numerical range including an upper limit and a lower limit. The current can be a current with a relatively stable value, such as an input current of a power system in the driving station from a power grid. The amplitude can be the amplitude of a key mechanical component in the driving station, which can be a bearing, a rotor, etc.

[0033] Specifically, temperature abnormalities reflect problems such as equipment overload and poor heat dissipation. Current abnormalities reflect problems such as load changes and short circuits. Amplitude abnormalities reflect problems of mechanical components and transmission systems. The single-dimensional abnormal data can be used to preliminarily determine the fault type, and a comprehensive analysis model can be trained based on three-dimensional abnormal data. By analyzing the combination of abnormal data in the three dimensions, different faults can be identified, which can facilitate maintenance personnel to quickly develop a maintenance plan.

[0034] These implementations can preferentially collect the temperature directly reflecting the operation of the driving station, and when the temperature reflects a possible fault, other parameters reflecting the fault can be combined for comprehensive judgment. In this way, the accuracy of fault analysis can be ensured, the missed judgment of faults can be reduced, and the burden of collection and operation can be reduced.

[0035] In some optional implementations of any of the embodiments of the present disclosure, the number of preset parameters is a plurality, and each collected preset parameter has a corresponding real-time parameter range; the determining of the fault condition information of the drive station according to the real-time parameter range comprises: for each preset parameter, determining whether the value of the preset parameter is abnormal according to the real-time parameter range corresponding to the preset parameter to obtain abnormal condition information indicating whether the value of the preset parameter is abnormal; and determining the fault condition information including the fault location information of the drive station according to the abnormal condition information of each preset parameter.

[0036] In these implementations, the detection and analysis of whether the data is abnormal is performed every M sampling periods. Within M sampling periods, if a first-level fault such as a device load fault occurs continuously and for more than 4M sampling periods, the system pops up a pre-warning warning to prompt the security and maintenance personnel to continuously monitor the data of the drive station. If a second-level fault such as a mechanical system fault occurs continuously and for more than 3M sampling periods, the system issues a first-level alarm and stops the machine for inspection as needed. If a third-level fault such as an electrical system fault occurs continuously and for more than 2M sampling periods, the system issues a second-level alarm and immediately stops the machine. If a serious overload fault occurs continuously and for more than M sampling periods, the system issues a third-level alarm, stops the machine urgently and starts the fire-fighting measures to prepare for the hidden danger. The greater the level value of the fault level and the alarm level, the more serious the corresponding fault.

[0037] Specifically, the temperature abnormality determination formula can be: ,

[0038] is the abnormal condition information of the temperature, i.e., the dynamic abnormality determination parameter, represents the lower limit of the temperature range, represents the upper limit of the temperature range, if the condition in any bracket, i.e., or is true, 1 is returned, representing “abnormal”, and if all conditions are not true, 0 is returned, representing “normal”.

[0039] The amplitude abnormality determination formula can be:

[0040] is the abnormal condition information of the amplitude, i.e., the dynamic abnormality determination parameter, represents the lower limit of the amplitude range, represents the upper limit of the amplitude range, if the condition in any bracket is true, 1 is returned, representing “abnormal”, and if the condition is not true, 0 is returned, representing “normal”.

[0041] The current abnormality determination formula:

[0042] The abnormal condition information of the current, i.e., the dynamic abnormality determination parameter, wherein represents the lower limit of the range of the current, represents the upper limit of the range of the current, and returns 1 if the condition in any bracket is met, representing "abnormality". If the condition is not met, 0 is returned, representing "normality".

[0043] These implementation manners can comprehensively locate the fault of the driving station according to whether each preset parameter is abnormal, so as to determine the fault condition of the driving station.

[0044] In some optional application scenarios of these implementation manners, the real-time parameter range includes the upper limit and the lower limit of the range; and the determining, according to the abnormal condition information of each preset parameter, of the fault condition information of the driving station including the fault location information includes: if the collection results of at least two preset parameters are out of the corresponding real-time parameter range, for each preset parameter, determining a dynamic deviation degree of the preset parameter according to the collection result, the upper limit and the lower limit of the range of the preset parameter, determining the sum of the dynamic deviation degrees of each preset parameter; for each preset parameter, determining a deviation proportion of the preset parameter according to the weight coefficient of the dynamic deviation degree of the preset parameter; and determining, according to the deviation proportions of the numerically abnormal preset parameters, the fault condition information of the driving station including the fault location information.

[0045] In these application scenarios, if the result of the abnormality determination indicates that at least two preset parameters are numerically abnormal, the step of locating through the dynamic deviation degree is entered. Specifically, the deviation degree can be determined from the collection result, the upper limit and the lower limit of the range, as the dynamic deviation degree. The above execution subject can determine the deviation degree in various ways. For example, the collection result, the upper limit and the lower limit of the range are input into a preset deviation degree formula or model to obtain the deviation degree output from the formula or model.

[0046] For example, the mean value of the upper limit and the lower limit of the range can be determined as follows:

[0047] , wherein, represents the lower limit of the range of the preset parameter X, represents the upper limit of the range of the preset parameter X, X is the temperature T, the amplitude A or the current I.

[0048] The dynamic deviation degree of each preset parameter is as follows:

[0049]

[0050]

[0051]

[0052] wherein is a dynamic deviation degree of the temperature, is a dynamic deviation degree of the amplitude, is a dynamic deviation degree of the current, is a mean of the upper limit and the lower limit of the range of the temperature, is a mean of the upper limit and the lower limit of the range of the amplitude, is a mean of the upper limit and the lower limit of the range of the current.

[0053] Therefore, the sum of the dynamic deviation degrees can be calculated as , and the deviation degree proportion is as follows:

[0054]

[0055]

[0056]

[0057] is a weight coefficient of the dynamic deviation degree of the temperature, is a weight coefficient of the dynamic deviation degree of the amplitude, is a weight coefficient of the dynamic deviation degree of the current.

[0058] These application scenarios can indicate the difference between different parameters and normal values through the deviation degrees of various parameters, so as to determine the contribution of the parameter corresponding component to the fault, so as to realize fault positioning.

[0059] In some cases of these application scenarios, the fault condition information including the fault positioning information of the driving station is determined according to the deviation degree proportion of each preset parameter of the numerical anomaly, including: for the deviation degree proportion of each preset parameter of the numerical anomaly, determining the deviation degree interval where the deviation degree proportion is located, the deviation degree interval includes a maximum numerical interval indicating a dominant role in the fault, a middle numerical interval indicating a synergistic role in the fault, or a minimum numerical interval indicating a secondary role in the fault; according to the deviation degree interval corresponding to each preset parameter of the numerical anomaly, generating the fault positioning information of the driving station, to obtain the fault condition information including the fault positioning information.

[0060] In these cases, maximum, intermediate, and minimum value ranges can be defined. For example, the maximum value range could refer to a deviation percentage > 50%, indicating that the preset parameter corresponding to this deviation percentage plays a dominant role in the fault. The intermediate value range could refer to a deviation percentage between 30% and 50%, indicating that the preset parameter corresponding to this deviation percentage has a non-dominant but still significant contribution to the fault, i.e., a synergistic effect. The minimum value range could refer to a deviation percentage < 30%, indicating that the preset parameter corresponding to this deviation percentage has a minor effect on the fault, serving as a secondary result of the core fault.

[0061] like Figure 2a As shown in the figure, the abnormal situation information is determined based on the deviation percentage when there are abnormal temperatures, abnormal currents, and no abnormal amplitudes. If the temperature deviation percentage is >50% and the current deviation percentage is <30%, the fault information includes a cooling system fault. If the temperature deviation percentage is <30% and the current deviation percentage is >50%, the fault information includes a motor overload fault. If 30% ≤ temperature deviation percentage ≤ 50% and 30% ≤ current deviation percentage ≤ 30%, the fault information includes poor electrical contact. The location information includes the cooling system, motor, and electrical components, respectively.

[0062] like Figure 2b As shown in the figure, the abnormality information is determined based on the deviation percentage when temperature, current, and amplitude are abnormal. If the temperature deviation percentage is >50% and the amplitude deviation percentage is <30%, the fault information includes a cooling system fault. If the temperature deviation percentage is <30% and the amplitude deviation percentage is >50%, the fault information includes a bearing wear fault. If 30% ≤ temperature deviation percentage ≤ 50% and 30% ≤ amplitude deviation percentage ≤ 30%, the fault information includes a loose mechanical component fault.

[0063] like Figure 2c As shown in the figure, the abnormal situation information is determined based on the deviation percentage when there are no abnormalities in temperature, current, and amplitude. If the current deviation percentage is >50% and the amplitude deviation percentage is <30%, the fault information includes abnormal inverter output. If the current deviation percentage is <30% and the amplitude deviation percentage is >50%, the fault information includes motor rotor eccentricity. If 30% ≤ current deviation percentage ≤ 50% and 30% ≤ amplitude deviation percentage ≤ 30%, the fault information includes load shedding or no-load operation.

[0064] like Figure 2dAs shown, the figure shows that the abnormal situation information indicates temperature abnormality, current abnormality and amplitude abnormality, and the determination is based on the deviation degree proportion. If the temperature weight proportion is > 50%, the current weight proportion is < 30%, and the amplitude weight proportion is < 30%, the fault condition information includes motor jam. If the temperature weight proportion is < 30%, the current weight proportion is > 50%, and the amplitude weight proportion is < 30%, the fault condition information includes electrical short circuit fault. If the temperature weight proportion is < 30%, the current weight proportion is < 30%, and the amplitude weight proportion is > 50%, the fault condition information includes transmission system fracture or jam. If 30%≤temperature weight proportion≤50%, 30%≤current weight proportion≤30%, and 30%≤amplitude weight proportion≤30%, the fault condition information includes systematic failure.

[0065] These cases can dynamically determine the deviation degree proportion of the collection results of the preset parameters, further focus on the fault clues, reduce the diagnosis range to specific subsystems or components, and construct a hierarchical fault diagnosis process.

[0066] Optionally, if the collection results of at least two preset parameters exceed the corresponding real-time parameter range, the fault level corresponding to the fault is associated with the number of preset parameters exceeding the real-time parameter range and the deviation degree interval corresponding to the preset parameters; if there is a single preset parameter in each preset parameter that exceeds the real-time parameter range, the fault level corresponding to the current fault is a first-level fault; if there is a single preset parameter in each preset parameter whose deviation degree interval is the maximum numerical interval, the fault level corresponding to the current fault is a second-level fault; if there are two preset parameters in each preset parameter whose deviation degree interval is the middle numerical interval, the fault level corresponding to the current fault is a third-level fault; if there are three preset parameters in each preset parameter whose deviation degree interval is the middle numerical interval, the fault level corresponding to the current fault is a fourth-level fault.

[0067] These implementations can accurately determine the current fault level through the number of parameters exceeding the real-time parameter range and the corresponding deviation degree interval.

[0068] Optionally, the fault corresponding to the fault condition information has a corresponding fault level, and the alarm information includes an alarm level; if the fault condition information indicates that the driving station has a fault, generating alarm information corresponding to the fault includes: for the fault level corresponding to the fault, if the duration of the fault level reaches the preset time threshold corresponding to the fault level, generating an alarm level corresponding to the fault level, and the higher the fault level, the smaller the corresponding preset time threshold.

[0069] Specifically, abnormal analysis can be performed on a plurality of preset parameters, and alarm level comprehensive judgment can be performed according to the data abnormality duration in the sampling period.

[0070] like Figure 3 As shown in the diagram, the system analyzes abnormal values ​​of preset parameters every M sampling periods. If a Level 1 fault occurs for more than 4M consecutive sampling periods within M sampling periods (a single preset parameter fault), the system will issue a warning, prompting maintenance personnel to continuously monitor the data at the warning-driven station. If a Level 2 fault occurs for more than 3M consecutive sampling periods (e.g., a single preset parameter deviation percentage within the maximum value range), the system will issue a Level 1 alarm and initiate a shutdown inspection as needed. If a Level 3 fault occurs for more than 2M consecutive sampling periods (e.g., two preset parameters deviation percentages within the middle value range), the system will issue a Level 2 alarm and immediately shut down. If a Level 4 fault occurs for more than M consecutive sampling periods (e.g., three preset parameters deviation percentages within the middle value range), the system will issue a Level 3 alarm, initiate an emergency shutdown, and activate fire-fighting measures to eliminate potential hazards.

[0071] These implementation methods can accurately determine the matching alarm level based on the fault level and duration, thereby achieving reasonable alarms.

[0072] In some optional implementations of any embodiment of this disclosure, the real-time calculation of the range of preset parameters based on the acquisition results includes: for the drive station, determining the current continuous running time and load rate, wherein the load rate is determined by the real-time current of the motor in the drive station; and determining the corresponding real-time parameter range based on the latest acquisition results according to the continuous running time and the load rate.

[0073] In these implementation methods, data cleaning and analysis are performed on the data information of three preset parameters, specifically for dynamic threshold calculation. The core of the real-time parameter range calculation is to dynamically adjust the normal range of each preset parameter based on real-time operating conditions (such as drive station load rate and runtime) to calculate the range of data anomaly alarms, thus solving the problem of false alarms or missed alarms under different operating conditions with fixed parameter ranges.

[0074] When determining the real-time temperature parameter range, the higher the load rate, the greater the heat dissipation pressure on the equipment. Therefore, the upper limit should be appropriately increased, and the lower limit slightly decreased (to avoid misjudging low temperatures). As the operating time increases, the equipment accumulates heat, so the upper limit should be lowered to prevent overheating, and the lower limit should be fine-tuned accordingly.

[0075] Formula for the normal range of real-time parameter range:

[0076]

[0077]

[0078] in Represents the lower limit of the temperature range. upper limit of the range of temperature, lower limit of the range of temperature, upper limit of the range of temperature, real-time current, rated current, continuous running time, settable empirical adjustment coefficient.

[0079] Constraint: ≥ 35℃, the minimum guarantee threshold, to avoid excessive reduction, ≤ 65℃ is the absolute safety upper limit.

[0080] Dynamic early warning or alarm threshold formula: , .

[0081] early warning threshold, the early warning threshold is 1.1 times the upper limit of the range, alarm threshold, the alarm threshold is 1.2 times the upper limit of the range or 0.8 times the lower limit of the range.

[0082] If the collection result of each preset parameter reaches the above dynamic alarm threshold, an alarm is given. As can be seen, the alarm threshold in the present disclosure is also dynamically adjusted, making the triggering of the alarm more accurate. The collection result reaching the alarm threshold can not only serve as a prerequisite for generating alarm information, but also as a prerequisite for determining fault condition information.

[0083] In the real-time parameter range judgment of amplitude, the higher the load rate, the greater the mechanical stress, resulting in an elevated amplitude baseline, and the range upper limit of the amplitude must be tightened, i.e., reduced to prevent mechanical damage, and the range lower limit is slightly raised. The longer the running time, the less the amplitude is affected, only slightly corrected, and the amplitude baseline is slightly raised due to long-term running of the components.

[0084] Normal range formula of real-time parameter range:

[0085]

[0086]

[0087] wherein lower limit of the range of amplitude, upper limit of the range of amplitude, lower limit of the range of amplitude, upper limit of the range of amplitude, real-time current, rated current, continuous running time, settable empirical adjustment coefficient.

[0088] Dynamic early warning or alarm threshold formula:

[0089] Early warning threshold, early warning threshold is 1.2 times the upper limit of the range, Alarm threshold, alarm threshold is 1.6 times the upper limit of the range or 0.5 times the lower limit of the range.

[0090] In the real-time parameter range judgment of the current, the load rate is a direct determinant of the current. When the load rate rises, the upper limit of the dynamic threshold of the current significantly increases to match the load demand, and the dynamic lower limit synchronously increases slightly. The running time has less effect on the current, only because the heating of the components causes the resistance to increase slightly, and the dynamic upper limit is slightly adjusted upward.

[0091] Dynamic normal range formula:

[0092]

[0093] Among them, represents the lower limit of the current range, represents the upper limit of the current range, is the lower limit of the reference fixed range, is the upper limit of the reference fixed range, is the real-time current, is the rated current, is the continuous running time, is a settable empirical adjustment coefficient.

[0094] Dynamic early warning / alarm threshold formula:

[0095] Early warning threshold, early warning threshold is 1.1 times the dynamic upper limit, Alarm threshold, alarm threshold is 1.3 times the upper limit of the range or 0.7 times the lower limit of the range.

[0096] These implementation manners can accurately control the current actual working condition through the load rate and the continuous running time, so as to obtain an accurate real-time parameter range.

[0097] In some optional implementation manners of any embodiment of the present disclosure, a fault determination system is provided, which includes a fault analysis module, a data acquisition module, a positioning module and an alarm module.

[0098] ​​​​The fault analysis layer can perform real-time calculation on the range of the preset parameters according to the collection result, obtain a real-time parameter range corresponding to the collection result, and determine the fault level in the fault condition information of the driving station according to the real-time parameter range. The temperature is taken as the first monitoring point, and the current and the amplitude are taken as the second monitoring point. When the temperature exceeds the real-time parameter range, the data collection layer collects the current and the amplitude data.

[0099] The data collection layer is configured to collect the temperature, the current and the amplitude of the driving station in real time, and generate driving station and fire-fighting equipment anchor points.

[0100] The alarm module is configured to generate alarm information corresponding to the fault if the fault condition information indicates that the driving station has a fault, and output the alarm information. Specifically, the alarm module can analyze the continuous sampling period of the abnormal fault signal, determine the fault level of the driving station, and generate an alarm level.

[0101] Specifically, the alarm module can also provide maintenance prompts, issue different warnings for different levels of faults, and pop up related operation prompts. The alarm module can perform yellow marking processing on the pre-alarm driving station, pop up pre-alarm abnormal data analysis, and remind maintenance personnel to pay attention to key continuous monitoring. The alarm module can perform orange marking processing on the first-level alarm driving station, pop up fault source and possible information, output the cause of the fault, and form a maintenance best route with the maintenance personnel as the starting point and the orange alarm driving station as the end point. The maintenance personnel can reach the fault point according to the indication. The alarm module can perform orange-red marking processing on the second-level alarm driving station, pop up whether to perform shutdown inspection measures, and perform maintenance inspection according to the position of the nearest maintenance personnel. The alarm module can perform red marking processing on the third-level alarm driving station, prompt the maintenance personnel to immediately shut down, trigger the system fire-fighting plan, detect the position of the nearest fire-fighting equipment, and display a production line fire-fighting evacuation route schematic diagram. The alarm module can generate a best maintenance path with the maintenance personnel position as the starting point, the fire-fighting equipment as the intermediate way point, and the third-level alarm marking position as the end point, so that the maintenance personnel can quickly reach the third-level alarm point and perform safety protection.

[0102] The positioning module can determine positioning information in the fault condition information of the driving station according to the real-time parameter range. Specifically, the positioning module can activate fault positioning according to the alarm level of the driving station, generate positioning information to distinguish different degrees of fault driving stations, and obtain a best maintenance route indication to improve the maintenance efficiency of the driving station.

[0103] The fault determination device 400 of the driving station is configured to execute the fault determination method of the driving station described in the above embodiments. Figure 4As shown, the device comprises: a collection unit 401 configured to collect preset parameters for indicating faults of a driving station, perform real-time calculation on the range of the preset parameters according to the collection result, and obtain a real-time parameter range corresponding to the collection result; a judgment unit 402 configured to judge fault condition information of the driving station according to the real-time parameter range; and a generation unit 403 configured to generate alarm information corresponding to the fault if the fault condition information indicates that the driving station has a fault, and output the alarm information.

[0104] The driving station fault determination device provided by the above embodiments of the present disclosure has the same beneficial effects as the method adopted, run or implemented by the application program stored therein, based on the same inventive concept as the driving station fault determination method provided by the embodiments of the present disclosure.

[0105] The present disclosure also provides an electronic device corresponding to the driving station fault determination method provided by the above embodiments, to execute the driving station fault determination method. The embodiments of the present disclosure are not limited.

[0106] Please refer to Figure 5 which shows a schematic diagram of an electronic device provided by some embodiments of the present disclosure. As shown in Figure 5 The electronic device 50 comprises a processor 500, a memory 501, a bus 502 and a communication interface 503, the processor 500, the communication interface 503 and the memory 501 are connected through the bus 502; the memory 501 stores a computer program executable on the processor 500, and the processor 500 executes the computer program to perform the method provided by any of the preceding embodiments of the present disclosure.

[0107] The memory 501 can include a high-speed random access memory (RAM) and can also include a non-volatile memory such as at least one disk memory. The communication between the system network element and at least one other network element is realized through at least one communication interface 503 (which can be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. can be used.

[0108] The bus 502 can be an ISA bus, a PCI bus or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 501 is used to store programs, and the processor 500 executes the programs after receiving execution instructions. The driving station fault determination method disclosed in any of the preceding embodiments of the present disclosure can be applied to the processor 500 or implemented by the processor 500.

[0109] The processor 500 can be an integrated circuit chip with signal processing capability. In implementation, each step of the above method can be completed by integrated logic circuits or instructions in the form of software in the processor 500. The processor 500 described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a ready-to-program gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. Each method, step and logic block disclosed in the embodiments of the present disclosure can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in combination with the embodiments of the present disclosure can be directly embodied as a hardware coding processor for execution, or a combination of hardware and software modules in the coding processor for execution. The software module can be located in a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium in the art. The storage medium is located in the memory 501, and the processor 500 reads the information in the memory 501, and combines the hardware to complete the steps of the above method.

[0110] The electronic device provided by the embodiments of the present disclosure and the fault determination method of the driving station provided by the embodiments of the present disclosure have the same beneficial effects as the method adopted, run or implemented.

[0111] The present disclosure also provides a computer readable storage medium corresponding to the fault determination method of the driving station provided by the preceding embodiments. Please refer to Figure 6 The computer readable storage medium shown is an optical disc 60, and a computer program (i.e. program product) is stored on the optical disc 60. When the processor runs the computer program, the fault determination method of the driving station provided by any of the preceding embodiments is executed.

[0112] It should be noted that examples of the computer readable storage medium can also include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical, magnetic storage medium, which will not be described one by one here.

[0113] The computer readable storage medium provided by the above embodiments of the present disclosure has the same inventive concept as the method for determining the failure of the driving station provided by the embodiments of the present disclosure, and has the same beneficial effects as the method adopted, run or implemented by the application stored therein.

[0114] It should be noted that:

[0115] It should be noted that:

[0116] From the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be realized by means of software and a necessary general hardware platform, of course, they can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present disclosure can be embodied in the form of a software product in essence or in the form of a contribution to the prior art. The computer software product is stored in a storage medium (such as a ROM / RAM, a magnetic disk, or an optical disk), and includes a plurality of instructions for causing a terminal (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods described in the various embodiments of the present disclosure.

[0117] The embodiments of the present disclosure are described above in conjunction with the accompanying drawings, which are merely specific embodiments of the present disclosure, but the present disclosure is not limited to the above specific embodiments. The above specific embodiments are merely illustrative, not restrictive, and those skilled in the art can make many forms without departing from the scope of the present disclosure and the protection scope of the claims under the inspiration of the present disclosure.

Claims

1. A method for determining a failure of a drive station, characterized in that The method comprises the following steps: Collecting preset parameters of the driving station for indicating faults, the preset parameters including temperature, current and amplitude; The collecting of the preset parameters of the driving station for indicating faults comprises: collecting the temperature of the driving station preferentially, and collecting the current and the amplitude if the temperature exceeds the real-time parameter range corresponding to the temperature; Real-time calculation of the range of the preset parameters according to the collection results to obtain the real-time parameter range corresponding to the collection results; Judging the fault condition information of the driving station according to the real-time parameter range; If the fault condition information indicates that the driving station has a fault, generating alarm information corresponding to the fault and outputting the alarm information; The judging of the fault condition information of the driving station according to the real-time parameter range comprises: For each preset parameter, determining whether the value of the preset parameter is abnormal according to the real-time parameter range corresponding to the preset parameter to obtain abnormal condition information indicating whether the value of the preset parameter is abnormal; and determining the fault condition information of the driving station including fault positioning information according to the abnormal condition information of each preset parameter; The determining of the fault condition information of the driving station including fault positioning information according to the abnormal condition information of each preset parameter comprises: If the collection results of at least two preset parameters exceed the corresponding real-time parameter range, for each preset parameter, determining a dynamic deviation degree of the preset parameter according to the collection result, the upper limit and the lower limit of the range of the preset parameter, determining the sum of the dynamic deviation degrees of each preset parameter, for each preset parameter, determining a deviation degree proportion of the preset parameter according to a weight coefficient of the dynamic deviation degree of the preset parameter, determining a deviation degree interval in which the deviation degree proportion is located, the deviation degree interval including a maximum value interval indicating a dominant role in the fault, an intermediate value interval indicating a cooperative role in the fault or a minimum value interval indicating a secondary role in the fault, and generating the fault positioning information of the driving station according to the deviation degree interval corresponding to each preset parameter with a value abnormality to obtain the fault condition information including the fault positioning information; The dynamic deviation degree is used to determine the contribution of the component corresponding to the parameter to the fault and to locate the fault; The method further comprises: Judging the positioning information in the fault condition information of the driving station according to the real-time parameter range, which comprises: activating fault positioning according to the alarm level of the driving station to generate positioning information to distinguish different degrees of fault driving stations to obtain the best route indication for maintenance; The maintenance personnel arrive at the fault point according to the best route indication for maintenance.

2. The method of claim 1, wherein, If the collection results of at least two preset parameters exceed the corresponding real-time parameter range, the fault level corresponding to the fault is associated with the number of preset parameters exceeding the real-time parameter range and the deviation degree interval corresponding to the preset parameters; If there is a single preset parameter exceeding the real-time parameter range among each preset parameter, the fault level corresponding to the current fault is a first-level fault; If the deviation degree interval corresponding to a single preset parameter among each preset parameter is the maximum value interval, the fault level corresponding to the current fault is a second-level fault. If there are two preset parameters corresponding to the intermediate value interval in each preset parameter, the fault level corresponding to the current fault is a third-level fault; If there are three preset parameters corresponding to the intermediate value interval in each preset parameter, the fault level corresponding to the current fault is a fourth-level fault.

3. The method of claim 1, wherein, The fault corresponding to the fault condition information has a corresponding fault level, and the alarm information includes an alarm level; If the fault condition information indicates that the drive station has a fault, the alarm information corresponding to the fault is generated, including: For the fault level corresponding to the fault, if the duration of the fault level reaches the preset duration threshold corresponding to the fault level, the alarm level corresponding to the fault level is generated, and the higher the fault level, the smaller the corresponding preset duration threshold.

4. The method according to any one of claims 1 to 3, characterized in that, The real-time calculation of the range of the preset parameter according to the acquisition result includes: For the drive station, the current continuous running time and the load rate are determined, and the load rate is determined by the real-time current of the motor in the drive station; According to the continuous running time and the load rate, the corresponding real-time parameter range of the latest acquisition result is determined.

5. A failure determination device of a drive station, characterized by comprising: Including: The acquisition unit is configured to acquire the preset parameters of the drive station for indicating faults, including temperature, current and amplitude; The acquisition unit is further configured to acquire the temperature of the drive station first, and if the temperature exceeds the real-time parameter range corresponding to the temperature, the current and amplitude are acquired; According to the acquisition result, the range of the preset parameter is calculated in real time to obtain the real-time parameter range corresponding to the acquisition result; The judgment unit is configured to determine the fault condition information of the drive station according to the real-time parameter range; The generation unit is configured to generate alarm information corresponding to the fault if the fault condition information indicates that the drive station has a fault, and output the alarm information; The judgment unit is further configured to determine the fault condition information of the drive station according to the real-time parameter range in the following way: For each preset parameter, determine whether the value of the preset parameter is abnormal according to the real-time parameter range corresponding to the preset parameter, to obtain abnormal condition information indicating whether the value of the preset parameter is abnormal; According to the abnormal condition information of each preset parameter, the fault condition information of the drive station including the fault positioning information is determined; The determination of the fault condition information of the drive station including the fault positioning information according to the abnormal condition information of each preset parameter includes: If the collection results of at least two preset parameters exceed the corresponding real-time parameter range, for each preset parameter, a dynamic deviation degree of the preset parameter is determined according to the collection result of the preset parameter, the upper limit of the range and the lower limit of the range, and a sum of the dynamic deviation degrees of the respective preset parameters is determined; for each preset parameter, a deviation degree proportion of the preset parameter is determined according to a weight coefficient of the dynamic deviation degree of the preset parameter; a deviation degree interval in which the deviation degree proportion is located is determined, the deviation degree interval including a maximum value interval indicating a dominant effect on a fault, a middle value interval indicating a synergistic effect on the fault or a minimum value interval indicating a secondary effect on the fault; according to the deviation degree intervals corresponding to the respective preset parameters of the numerical anomaly, fault positioning information of the driving station is generated, and fault condition information including the fault positioning information is obtained; The dynamic deviation degree is used to determine the contribution of the parameter corresponding component to the fault and to perform fault positioning; The apparatus is further configured to: According to the real-time parameter range, the positioning information in the fault condition information of the driving station is determined, including: according to the driving station alarm level, activating the fault positioning, generating the positioning information to distinguish different degree fault driving station to get the best maintenance route indication; The maintenance personnel arrive at the fault point according to the best maintenance route indication.

6. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor runs the computer program to implement the method of any one of claims 1-4.

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