CN112053009A - Fault prediction method, device, system and storage medium - Google Patents

Fault prediction method, device, system and storage medium Download PDF

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CN112053009A
CN112053009A CN202011060978.9A CN202011060978A CN112053009A CN 112053009 A CN112053009 A CN 112053009A CN 202011060978 A CN202011060978 A CN 202011060978A CN 112053009 A CN112053009 A CN 112053009A
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李群
孙明元
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China Express Jiangsu Technology Co Ltd
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Abstract

The invention discloses a fault prediction method, which comprises the following steps: respectively acquiring vibration signals of equipment detection points of current equipment according to preset acquisition frequency; calculating skewness indexes corresponding to the vibration signals under each acquisition frequency and kurtosis indexes of the vibration signals respectively; calculating the skewness index and the kurtosis index according to a preset weight coefficient to obtain an equipment fault index under each acquisition frequency; determining the corresponding equipment fault index statistical frequency according to the service life of the current equipment; and counting the equipment fault indexes according to the equipment fault index counting frequency, determining the running state of the current equipment, and predicting the fault time point of the current equipment according to the running state. The embodiment of the invention also discloses a fault prediction device, a fault prediction system and a storage medium, which effectively solve the problem that the fault time of equipment cannot be predicted in advance in the prior art, thereby reducing the equipment shutdown risk caused by unexpected faults.

Description

Fault prediction method, device, system and storage medium
Technical Field
The present invention relates to the field of failure prediction technologies, and in particular, to a failure prediction method, apparatus, system, and storage medium.
Background
As the remaining life of the device decreases, the reliability of the device gradually decreases. The current equipment maintenance is to plan the maintenance period and frequency of the equipment by referring to the numerical relationships in the predictive maintenance. Referring to fig. 1, it can be seen that the optimal equipment maintenance stage is not the early failure warning stage or the near failure stage of the equipment, and it is not economical to receive maintenance in these two stages. Therefore, how to predict the failure time point of the device in advance is the biggest current problem.
Disclosure of Invention
Embodiments of the present invention provide a failure prediction method, apparatus, system, and storage medium, which can effectively solve the problem in the prior art that failure time of a device cannot be predicted in advance, thereby reducing a risk of device shutdown due to an unexpected failure.
An embodiment of the present invention provides a fault prediction method, including:
respectively acquiring vibration signals of equipment detection points of current equipment according to preset acquisition frequency;
calculating skewness indexes corresponding to the vibration signals under each acquisition frequency and kurtosis indexes of the vibration signals respectively;
calculating the skewness index and the kurtosis index according to a preset weight coefficient to obtain an equipment fault index under each acquisition frequency;
determining corresponding equipment fault index statistical frequency according to service life of current equipment
And counting the equipment fault indexes according to the equipment fault index counting frequency, determining the running state of the current equipment, and predicting according to the fault time point of the current equipment in the running state.
As an improvement of the above scheme, the respectively acquiring the vibration signals of the device detection points of the current device according to the preset acquisition frequency specifically includes:
the current equipment is provided with a plurality of equipment detection points;
and respectively acquiring the vibration signal of each equipment detection point of the current equipment according to a preset acquisition frequency.
As an improvement of the above scheme, after the respectively acquiring the vibration signals of each device detection point of the current device according to the preset acquisition frequency, the method further includes:
and respectively calculating the average value of the vibration signal amplitude of the equipment detection point under each acquisition frequency.
As an improvement of the above scheme, the method determines the operation state of the current device by the following steps, specifically including:
judging whether the equipment fault index counted according to the equipment fault index counting frequency exceeds a preset fluctuation range or not;
when the judgment result is that the fluctuation range does not exceed the preset fluctuation range, the current equipment is in a normal operation state, and the current equipment is maintained according to the preset maintenance date;
and when the judgment result is that the fluctuation range is exceeded, the current equipment running state is in an abnormal running state.
As an improvement of the above scheme, the method obtains the skewness index corresponding to the vibration signal through the following steps, and specifically includes:
Figure BDA0002712406450000021
wherein x isiThe vibration signal within the frequency is acquired for segment i,
Figure BDA0002712406450000022
is xiAverage value of (a).
As an improvement of the above scheme, the method obtains a kurtosis index corresponding to the vibration signal through the following steps, specifically including:
Figure BDA0002712406450000031
wherein x isiThe vibration signal within the frequency is acquired for segment i,
Figure BDA0002712406450000032
is xiAverage value of (a).
As an improvement of the above scheme, the determining the corresponding statistical frequency of the equipment fault indicator according to the service life of the current equipment specifically includes:
if the current service life of the current equipment is in a first life cycle, the equipment fault index statistical frequency of the current equipment is a first time period;
if the current service life of the current equipment is in a second life cycle, the equipment fault index statistical frequency of the current equipment is a second time period;
if the current service life of the current equipment is in a third life cycle, the equipment fault index statistical frequency of the current equipment is a third time period;
wherein the first life cycle is greater than the second life cycle, and the second life cycle is greater than the third life cycle; the first time period is greater than the second time period, and the second time period is greater than the third time period.
Another embodiment of the present invention correspondingly provides a failure prediction apparatus, including:
the acquisition module is used for respectively acquiring vibration signals of equipment detection points of the current equipment according to preset acquisition frequency;
the first calculation module is used for calculating skewness indexes corresponding to the vibration signals under each acquisition frequency and kurtosis indexes of the vibration signals respectively;
the second calculation module is used for calculating the skewness index and the kurtosis index according to a preset weight coefficient to obtain an equipment fault index under each acquisition frequency;
the statistical frequency determining module is used for determining the corresponding statistical frequency of the equipment fault indexes according to the service life of the current equipment;
and the prediction module is used for counting the equipment fault indexes according to the equipment fault index counting frequency, determining the running state of the current equipment and predicting the fault time point of the current equipment according to the running state.
Another embodiment of the present invention provides a fault prediction system, which includes a vibration sensor for acquiring a vibration signal, a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor implements the fault prediction method according to the above embodiment of the invention when executing the computer program.
Another embodiment of the present invention provides a storage medium, where the computer-readable storage medium includes a stored computer program, where when the computer program runs, the apparatus where the computer-readable storage medium is located is controlled to execute the fault prediction method according to the above-described embodiment of the present invention.
Compared with the prior art, the fault prediction method, the device, the system and the storage medium disclosed by the embodiment of the invention have the advantages that the vibration signal of the equipment detection point of the current equipment is collected through the preset collection frequency, the skewness index corresponding to the vibration signal and the kurtosis index of the vibration signal are calculated, the skewness index and the kurtosis index are calculated according to the preset weight coefficient so as to obtain the equipment fault index, the statistical frequency of the corresponding equipment fault index is determined according to the service life of the current equipment, the running state of the current equipment is determined according to the equipment fault index statistical frequency, and the fault time point of the current equipment is predicted according to the running state. Therefore, due to the fact that the service lives of the devices are different, the frequency of the statistics of the fault indexes is different, the fault time point is predicted more accurately, maintenance work of the devices is made in advance, and the risk of device shutdown caused by accidental faults is reduced.
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Fig. 1 is a schematic diagram of an equipment maintenance cycle of an equipment failure prediction method in the prior art according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a failure prediction method according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating a step S40 of a failure prediction method according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a specific example of a failure prediction method according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a device fault indicator curve according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a failure prediction apparatus according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of a failure prediction system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 2-3 are schematic flow charts of a fault prediction method according to an embodiment of the present invention.
The failure prediction method provided by the embodiment can be executed by a failure prediction system. (even a cloud server, etc.) can be implemented by software and/or hardware, and the control system can be formed by two or more physical entities or by one physical entity.
Further, the failure prediction system 100 is connected to a vibration sensor or the like (the connection may be a wired connection, a wireless connection, or the like). The fault prediction system acquires a vibration signal of the current equipment through a vibration sensor. The various information may be directly transmitted to the failure prediction system, or may be transmitted to another information processing apparatus, and after the information processing apparatus performs corresponding information processing, the information processing apparatus transmits the processed information to the failure prediction system.
An embodiment of the present invention provides a fault prediction method, including:
and S10, respectively acquiring vibration signals of the equipment detection points of the current equipment according to the preset acquisition frequency.
It should be noted that the failure detection may be any that causes a structural change and wear during use, and in the present embodiment, the tightening device in the final assembly plant is taken as an example. The preset collection frequency may be collected once every hour, or once every three hours, which is not limited herein. It can be understood that the higher the frequency of acquisition, the more accurate the predicted failure time point, and the staff can set the acquisition frequency of the equipment as required.
Specifically, a vibration signal of the current equipment is acquired according to a vibration sensor, and feature extraction is performed on the vibration signal, so that a skewness index and a kurtosis index are calculated.
And S20, respectively calculating skewness indexes corresponding to the vibration signals under each acquisition frequency and kurtosis indexes of the vibration signals.
The skewness index (skewness) is also called skewness and skewness coefficient, and is sensitive to changes in the device structure. The skewness index includes a normal distribution (skewness of 0), a right-skew distribution (also called a positive-skew distribution, whose skewness is >0), and a left-skew distribution (also called a negative-skew distribution, whose skewness is < 0). Kurtosis index (kurtosis) is sensitive to equipment wear-type failures. Therefore, the skewness index and the kurtosis index of the current equipment are calculated, so that the fault condition of the equipment is better predicted.
And S30, calculating the skewness index and the kurtosis index according to a preset weight coefficient to obtain an equipment fault index under each acquisition frequency.
It should be noted that the weight coefficient is set according to experience of equipment maintenance.
In the present embodiment, the equipment failure indicator (DEP) ═ 0.3 × SKE | +0.7 × KUR |, i.e., the skew indicator (SKE) accounts for 30% and the kurtosis indicator (KUR) accounts for 70%. It will be appreciated that a skew indicator closer to 0 indicates a structurally more stable device, and a kurtosis indicator closer to 0 indicates a lesser degree of wear of the device.
And S40, determining the corresponding equipment fault index statistical frequency according to the service life of the current equipment.
Specifically, the statistical frequency of the device failure indicators relatively changes as the service life of the device changes, i.e., the statistical frequency of the device failure indicators is more frequent when the service life of the device is close to the end of the life span. The failure prediction system may be one in which a correspondence table between service life and statistical frequency of device failure indicators is stored in advance, the correspondence may be set according to experience of maintenance personnel, and each different device has one correspondence table. It is to be understood that the prediction of the failure time point may be performed monthly or quarterly.
And S50, counting the equipment fault indexes according to the equipment fault index counting frequency, determining the running state of the current equipment, and predicting the fault time point of the current equipment according to the running state.
Specifically, after the hourly equipment failure indicator is calculated, the hourly equipment failure indicator at the frequency is counted according to the equipment failure indicator statistical frequency to obtain a failure trend, for example, when the equipment failure indicator statistical frequency is one month, the hourly equipment failure indicator in one month is plotted into a curve. The running state of the current equipment can be known according to the fault trend. It should be noted that the closer the equipment failure index is to 0, the more stable the operation state of the equipment is, and the lower the failure rate is. The larger the fluctuation amplitude of the equipment fault index is, the higher the fault rate is.
It should be noted that, since the service lives of each device are different, the operating state of the current device can be better evaluated in combination with the service lives of the devices, so that the failure time point of the device can be better predicted.
In summary, the vibration signal of the device detection point of the current device is acquired through the preset acquisition frequency, the skewness index corresponding to the vibration signal and the kurtosis index of the vibration signal are calculated, the skewness index and the kurtosis index are calculated according to the preset weight coefficient so as to obtain a device fault index, the statistical frequency of the corresponding device fault index is determined according to the service life of the current device, the device fault index is counted according to the statistical frequency of the device fault index so as to determine the running state of the current device, and the fault time point of the current device is predicted according to the running state. Therefore, due to the fact that the service lives of the devices are different, the frequency of the statistics of the fault indexes is different, the fault time point is predicted more accurately, maintenance work of the devices is made in advance, and the risk of device shutdown caused by accidental faults is reduced.
In an optional embodiment, the step S10 of respectively acquiring the vibration signals of the device detection points of the current device according to a preset acquisition frequency specifically includes:
and S100, the current equipment is provided with a plurality of equipment detection points.
S101, respectively acquiring vibration signals of each equipment detection point of the current equipment according to a preset acquisition frequency.
Specifically, when fault prediction is performed, a plurality of device detection points need to be set on a device, so that the result of the device fault index is more accurate, and prediction of a fault time point is more prepared. In this embodiment, five device detection points are provided, and the number of the device detection points may be set as required, which is not limited herein.
In an optional embodiment, after the respectively acquiring the vibration signals of each device detection point of the current device according to the preset acquisition frequency, the method further includes:
and S102, respectively calculating the average value of the vibration signal amplitude of the equipment detection point under each acquisition frequency.
Specifically, because a plurality of equipment detection points are arranged, the vibration signals collected by each equipment detection point are respectively subjected to feature extraction, so that the amplitude of the vibration signals is obtained, and in order to ensure the accuracy of calculation of the kurtosis index and the skewness index, the average value of the amplitude of the vibration signals at each time point is obtained.
In an optional embodiment, the method determines the current operating state of the device by the following steps, specifically including:
and judging whether the equipment fault index counted according to the equipment fault index counting frequency exceeds a preset fluctuation range.
Referring to fig. 3, in the present embodiment, the fluctuation range of the tightening apparatusIs enclosed as [ DEP ]1-0.06，DEP1+0.06]It should be noted that, according to the operating age of the current equipment, an equipment fault index set value can be obtained according to experience, and the equipment fault index set value changes along with the increase of the equipment operating time. DEP1The set value of the equipment fault index of the current equipment can be used as a reference according to the set value of the fault index of the starting stage of the current equipment. It is understood that the amplitude of the fluctuation (i.e. 0.06) can be set as desired, and is not limited herein.
And when the judgment result is that the fluctuation range is not more than the preset fluctuation range, the current equipment is in a normal operation state, and the current equipment is maintained according to the preset maintenance date.
In the present embodiment, the predetermined fluctuation range is not exceeded, indicating that the tightening apparatus is operating well. In the current equipment maintenance, each piece of equipment is provided with a maintenance calendar for equipment maintenance, so that when the running state of the tightening equipment is good, a worker only needs to maintain the tightening equipment according to the maintenance date on the maintenance calendar.
And when the judgment result is that the fluctuation range is exceeded, the current equipment running state is in an abnormal running state.
In this embodiment, if the fluctuation range is exceeded, it indicates that the current device operation state is in an abnormal operation state, and a device fault may occur. Then, the fault time point of the current equipment needs to be predicted by combining the service life of the current equipment, so that the maintenance date for maintaining the calendar is adjusted according to the prediction result, the advance maintenance work of the equipment is well done, and the equipment shutdown risk caused by unexpected faults is reduced.
In an optional embodiment, the method obtains the skewness index corresponding to the vibration signal through the following steps, and specifically includes:
Figure BDA0002712406450000091
wherein x isiThe vibration signal within the frequency is acquired for segment i,
Figure BDA0002712406450000092
is xiAverage value of (a).
In particular, the amount of the solvent to be used,
Figure BDA0002712406450000093
in this embodiment, if the tightening device is provided with five device detection points, an average value of the five device detection points is calculated, and then the skewness index is calculated according to the average value of the five device detection points.
In an optional embodiment, the method obtains a kurtosis index corresponding to the vibration signal by the following steps, specifically including:
Figure BDA0002712406450000101
wherein x isiThe vibration signal within the frequency is acquired for segment i,
Figure BDA0002712406450000102
is xiAverage value of (a).
In particular, the amount of the solvent to be used,
Figure BDA0002712406450000103
in this embodiment, if the tightening device is provided with five device detection points, an average value of the five device detection points is calculated, and then a kurtosis index is calculated according to the average value of the five device detection points.
In an optional embodiment, the step S40 of determining the statistical frequency of the corresponding device fault indicator according to the service life of the current device specifically includes:
s400, if the current service life of the current equipment is in a first life cycle, the equipment fault index statistical frequency of the current equipment is a first time period.
S401, if the current service life of the current equipment is in a second life cycle, the equipment fault index statistical frequency of the current equipment is a second time period.
S402, if the current service life of the current equipment is in a third life cycle, the equipment fault index statistical frequency of the current equipment is a third time period.
Wherein the first life cycle is greater than the second life cycle, and the second life cycle is greater than the third life cycle; the first time period is greater than the second time period, and the second time period is greater than the third time period.
For example, if the current device is a new device enabled stage, the device failure indicator statistical frequency may be counted according to a quarterly frequency. If the current equipment is in the middle stage of use, the equipment fault index statistical frequency can be counted according to the monthly frequency. In the end stage of the use of the equipment, the statistical frequency of the fault indexes of the equipment can be counted according to the frequency of the week degree.
It should be noted that the starting stage, the middle stage, and the end stage may be divided according to the factory specifications of the equipment, or may be divided according to the experience of the maintenance personnel, for example, the starting stage is that the remaining life of the equipment is greater than 70%, the middle stage is that the remaining life of the equipment is greater than 50%, and the remaining life of the equipment is not greater than 50% in the end stage. It is understood that the activation phase, the middle phase, the end phase and the corresponding quarter, month and week are only one example of the embodiment of the present invention, and further phases may be divided, each of the different phases corresponding to different time periods, which is not limited herein.
For ease of understanding, the following examples are set forth:
referring to fig. 4-5, selecting five tightening points of the tightening device for detection, collecting vibration signals of the device detection points of the tightening device from 7 to 11 points of the morning, performing average calculation on the vibration signal amplitudes of the five points at a certain time point, and calculating the average value of the vibration signal amplitudes of each time point; and respectively calculating a skewness index and a kurtosis index of a certain time point of the five points, performing weighted calculation on the skewness index and the kurtosis index, and finally obtaining an equipment fault index. And drawing a corresponding curve for the equipment fault index, and predicting the fault time point according to the fault trend and the service life of the tightening equipment.
Fig. 6 is a schematic structural diagram of a failure prediction apparatus according to an embodiment of the present invention.
An embodiment of the present invention correspondingly provides a failure prediction apparatus, including:
the acquisition module 10 is configured to respectively acquire vibration signals of the device detection points of the current device according to a preset acquisition frequency.
The first calculating module 20 is configured to calculate a skewness index corresponding to the vibration signal and a kurtosis index of the vibration signal at each acquisition frequency respectively.
The second calculating module 30 is configured to calculate the skewness index and the kurtosis index according to a preset weight coefficient, so as to obtain an equipment fault index at each acquisition frequency;
and the statistical frequency determining module 40 is configured to determine a corresponding device fault index statistical frequency according to the service life of the current device.
And the predicting module 50 is configured to count the device fault indicator according to the device fault indicator counting frequency, determine an operating state of the current device, and predict a fault time point of the current device according to the operating state.
The fault prediction device provided by the embodiment of the invention collects the vibration signal of the equipment detection point of the current equipment through the preset collection frequency, calculates the skewness index corresponding to the vibration signal and the kurtosis index of the vibration signal, calculates the skewness index and the kurtosis index according to the preset weight coefficient so as to obtain the equipment fault index, determines the statistical frequency of the corresponding equipment fault index according to the service life of the current equipment, determines the running state of the current equipment according to the equipment fault index statistical frequency, and predicts the fault time point of the current equipment according to the running state. Therefore, due to the fact that the service lives of the devices are different, the frequency of the statistics of the fault indexes is different, the fault time point is predicted more accurately, maintenance work of the devices is made in advance, and the risk of device shutdown caused by accidental faults is reduced.
In an alternative embodiment, the acquisition module 10 includes:
and the setting module is used for setting a plurality of equipment detection points on the current equipment.
And the signal acquisition module is used for respectively acquiring the vibration signal of each equipment detection point of the current equipment according to the preset acquisition frequency.
In an alternative embodiment, the statistical frequency determination module 40 includes:
the judging module is used for judging whether the equipment fault index exceeds a preset fluctuation range or not;
the first matching module is used for determining that the statistical frequency of the equipment fault indexes of the current equipment is a first time period if the current service life of the current equipment is in a first life cycle.
And the second matching module is used for determining that the statistical frequency of the equipment fault indexes of the current equipment is a second time period if the current service life of the current equipment is in a second life cycle.
And the third matching module is used for counting the equipment fault index frequency of the current equipment to be a third time period if the current service life of the current equipment is in a third life cycle.
Fig. 7 is a schematic diagram of a failure prediction system according to an embodiment of the present invention. The embodiment comprises a vibration sensor 13 for acquiring vibration signals, a processor 11, a memory 12, and a computer program stored in the memory 12 and configured to be executed by the processor 11, wherein the processor 11 implements the steps of the above-mentioned fault prediction method embodiments when executing the computer program. Alternatively, the processor implements the functions of the modules/units in the above device embodiments when executing the computer program.
It should be noted that the vibration sensor 13 is provided at the apparatus detection point of the present apparatus, thereby detecting
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory and executed by the processor to implement the invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program in the failure prediction system.
The fault prediction system can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing devices. The failure prediction system may include, but is not limited to, a processor 11, a memory 12. It will be appreciated by those skilled in the art that the schematic diagram is merely an example of a failure prediction system and is not intended to be limiting, and may include more or fewer components than those shown, or some components in combination, or different components, for example, the failure prediction system may also include input output devices, network access devices, buses, etc.
The Processor 11 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being the control center of the fault prediction system and connecting the various parts of the overall fault prediction system using various interfaces and lines.
The memory 12 may be used to store the computer programs and/or modules, and the processor may implement the various functions of the fault prediction system by running or executing the computer programs and/or modules stored in the memory and invoking the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Wherein, the failure prediction system integrated module/unit can be stored in a computer readable storage medium if it is implemented in the form of software functional unit and sold or used as an independent product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like.
It should be noted that the above-described device embodiments are merely illustrative, where the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. In addition, in the drawings of the embodiment of the apparatus provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, and may be specifically implemented as one or more communication buses or signal lines. One of ordinary skill in the art can understand and implement it without inventive effort.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.

Claims (10)

1. A method of fault prediction, comprising:
respectively acquiring vibration signals of equipment detection points of current equipment according to preset acquisition frequency;
calculating skewness indexes corresponding to the vibration signals under each acquisition frequency and kurtosis indexes of the vibration signals respectively;
calculating the skewness index and the kurtosis index according to a preset weight coefficient to obtain an equipment fault index under each acquisition frequency;
determining the corresponding equipment fault index statistical frequency according to the service life of the current equipment;
and counting the equipment fault indexes according to the equipment fault index counting frequency, determining the running state of the current equipment, and predicting the fault time point of the current equipment according to the running state.
2. The method for predicting faults as claimed in claim 1, wherein the step of respectively acquiring the vibration signals of the equipment detection points of the current equipment according to a preset acquisition frequency specifically comprises:
the current equipment is provided with a plurality of equipment detection points;
and respectively acquiring the vibration signal of each equipment detection point of the current equipment according to a preset acquisition frequency.
3. The failure prediction method according to claim 2, further comprising, after the separately acquiring the vibration signals at each device detection point of the current device according to a preset acquisition frequency:
and respectively calculating the average value of the vibration signal amplitude of the equipment detection point under each acquisition frequency.
4. The method of predicting a failure according to claim 1, wherein the determining the operating state of the current device according to the device failure indicator at each of the acquisition frequencies specifically includes:
judging whether the equipment fault index exceeds a preset fluctuation range or not;
when the judgment result is that the fluctuation range does not exceed the preset fluctuation range, the current equipment is in a normal operation state, and the current equipment is maintained according to the preset maintenance date;
and when the judgment result is that the fluctuation range is exceeded, the current equipment running state is in an abnormal running state.
5. The method for predicting the fault according to claim 1, wherein the method obtains the skewness index corresponding to the vibration signal through the following steps, and specifically comprises the following steps:
Figure FDA0002712406440000021
wherein x isiThe vibration signal within the frequency is acquired for segment i,
Figure FDA0002712406440000022
is xiAverage value of (a).
6. The method of predicting a fault according to claim 1, wherein the method obtains the kurtosis indicator corresponding to the vibration signal by:
Figure FDA0002712406440000023
wherein x isiThe vibration signal within the frequency is acquired for segment i,
Figure FDA0002712406440000024
is xiAverage value of (a).
7. The method of predicting a fault according to claim 1, wherein the determining a statistical frequency of a corresponding equipment fault indicator according to a service life of a current equipment specifically includes:
if the current service life of the current equipment is in a first life cycle, the equipment fault index statistical frequency of the current equipment is a first time period;
if the current service life of the current equipment is in a second life cycle, the equipment fault index statistical frequency of the current equipment is a second time period;
if the current service life of the current equipment is in a third life cycle, the equipment fault index statistical frequency of the current equipment is a third time period;
wherein the first life cycle is greater than the second life cycle, and the second life cycle is greater than the third life cycle; the first time period is greater than the second time period, and the second time period is greater than the third time period.
8. A failure prediction apparatus, comprising:
the acquisition module is used for respectively acquiring vibration signals of equipment detection points of the current equipment according to preset acquisition frequency;
the first calculation module is used for calculating skewness indexes corresponding to the vibration signals under each acquisition frequency and kurtosis indexes of the vibration signals respectively;
the second calculation module is used for calculating the skewness index and the kurtosis index according to a preset weight coefficient to obtain an equipment fault index under each acquisition frequency;
the statistical frequency determining module is used for determining the corresponding statistical frequency of the equipment fault indexes according to the service life of the current equipment;
and the prediction module is used for counting the equipment fault index according to the equipment fault index counting frequency, determining the running state of the current equipment and predicting the fault time point of the current equipment according to the running state.
9. A fault prediction system comprising a vibration sensor for acquiring vibration signals, a processor, a memory and a computer program stored in the memory and configured to be executed by the processor, the processor implementing the fault prediction method as claimed in any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, comprising a stored computer program, wherein the computer program, when executed, controls an apparatus in which the computer-readable storage medium is located to perform the fault prediction method according to any one of claims 1 to 7.
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