CN112053009B - Fault prediction method, device, system and storage medium - Google Patents

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

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Publication number
CN112053009B
CN112053009B CN202011060978.9A CN202011060978A CN112053009B CN 112053009 B CN112053009 B CN 112053009B CN 202011060978 A CN202011060978 A CN 202011060978A CN 112053009 B CN112053009 B CN 112053009B
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equipment
fault
index
current
frequency
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CN112053009A (en
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李群
孙明元
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China Express Jiangsu Technology Co Ltd
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China Express Jiangsu Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/20—Administration of product repair or maintenance
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

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 frequencies; respectively calculating a skewness index corresponding to the vibration signal at each acquisition frequency and a kurtosis index of the vibration signal; calculating the skewness index and the kurtosis index according to a preset weight coefficient to obtain equipment fault indexes under each acquisition frequency; determining corresponding equipment fault index statistical frequency according to the service life of the current equipment; and 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. The embodiment of the invention also discloses a fault prediction device, a 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 fault prediction technologies, and in particular, to a fault prediction method, device, system, and storage medium.
Background
As the remaining lifetime of the device decreases, the reliability of the device gradually decreases. Current equipment maintenance is to plan the maintenance period and frequency of equipment with reference to each numerical relationship in predictive maintenance. Referring to fig. 1, it can be seen that the best equipment maintenance stage is not the early warning stage or the near failure stage when the equipment is just started, 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 problem facing the current.
Disclosure of Invention
The embodiment of the invention provides a fault prediction method, a device, a system and a storage medium, which can 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.
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 frequencies;
respectively calculating a skewness index corresponding to the vibration signal at each acquisition frequency and a kurtosis index of the vibration signal;
calculating the skewness index and the kurtosis index according to a preset weight coefficient to obtain equipment fault indexes under each acquisition frequency;
determining corresponding equipment fault index statistical frequency according to service life of current equipment
And 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.
As an improvement of the above solution, the respectively collecting vibration signals of the device detection points of the current device according to the preset collection frequency specifically includes:
the current equipment is provided with a plurality of equipment detection points;
and respectively acquiring vibration signals of each equipment detection point of the current equipment according to a preset acquisition frequency.
As an improvement of the above solution, 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 at each acquisition frequency.
As an improvement of the above solution, the method determines the current operating state of the 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;
when the judgment result is that the fluctuation range does not exceed the preset fluctuation range, the current equipment is in a normal running state, and the current equipment is maintained according to a preset maintenance date;
and when the judgment result is that the preset 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 deflection index corresponding to the vibration signal through the following steps:
wherein x is i Vibration signals in the frequency are acquired for the i segments,is x i Average value of (2).
As an improvement of the scheme, the method acquires kurtosis indexes corresponding to vibration signals through the following steps:
wherein x is i Vibration signals in the frequency are acquired for the i segments,is x i Average value of (2).
As an improvement of the above solution, the determining the corresponding equipment failure index statistical frequency according to the service life of the current equipment specifically includes:
if the current service life of the current equipment is in a first service life period, 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 service life period, 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 service life period, 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 fault 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 the skewness index corresponding to the vibration signal and the kurtosis index of the vibration signal under each acquisition frequency respectively;
the second calculation module is used for calculating the skewness index and the kurtosis index according to preset weight coefficients to obtain equipment fault indexes under each acquisition frequency;
the statistical frequency determining module is used for determining the corresponding equipment fault index statistical frequency 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.
Another embodiment of the present invention provides a fault prediction system, including 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, where the processor executes the computer program to implement the fault prediction method according to the embodiment of the present invention.
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, a device where the computer readable storage medium is controlled to execute the fault prediction method described in the foregoing 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 acquire the vibration signal of the equipment detection point of the current equipment through the preset acquisition frequency, calculate the skewness index corresponding to the vibration signal and the kurtosis index of the vibration signal, calculate the skewness index and the kurtosis index according to the preset weight coefficient to obtain the equipment fault index, determine the corresponding equipment fault index statistical frequency according to the service life of the current equipment, and calculate the running state of the current equipment according to the equipment fault index statistical frequency, and predict the fault time point of the current equipment according to the running state. Therefore, due to the fact that the service lives of the equipment are different, the frequency of statistics of fault indexes is different, so that fault time points are predicted more accurately, maintenance work of the equipment is finished in advance, and equipment shutdown risk caused by unexpected faults is reduced.
Drawings
FIG. 1 is a schematic diagram of a maintenance cycle of a device according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating a fault prediction method according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating a fault prediction method according to an embodiment of the present invention in step S40;
FIG. 4 is a schematic diagram of a specific example of a fault prediction method according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a fault indicator of an apparatus 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 fault prediction system according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 2-3, a flow chart of a fault prediction method according to an embodiment of the present invention is shown.
The fault prediction method provided by the present embodiment may be executed by a fault prediction system. And (even a cloud server, etc.), the control system may be implemented by software and/or hardware, and may be formed by two or more physical entities or may be formed by one physical entity.
Further, the fault prediction system 100 is connected to a vibration sensor or the like (the connection may be a wired connection or a wireless connection). The fault prediction system acquires a vibration signal of the current equipment through a vibration sensor. The above-mentioned various information may be directly transmitted to the failure prediction system, or may be transmitted to another information processing apparatus after being processed by the corresponding information processing apparatus, and then the processed information may be transmitted to the failure prediction system by the information processing apparatus.
An embodiment of the present invention provides a fault prediction method, including:
s10, respectively acquiring vibration signals of equipment detection points of current equipment according to preset acquisition frequencies.
The fault detection may be performed by any method that changes the structure and causes wear during use, and in this embodiment, the tightening device in the assembly shop is taken as an example. The preset collection frequency may be collected once every hour, or may be collected once every three hours, which is not limited herein. It will be appreciated that the higher the frequency of acquisition, the more accurate the predicted failure time point, and that the operator can set the acquisition frequency of the device as desired.
Specifically, a vibration signal of the current equipment is obtained according to a vibration sensor, and feature extraction is carried out on the vibration signal, so that a skewness index and a kurtosis index are calculated.
S20, calculating a skewness index corresponding to the vibration signal and a kurtosis index of the vibration signal under each acquisition frequency respectively.
It should be noted that, the skewness index (skewness) is also called a skewness, a skewness coefficient, and is relatively sensitive to changes in the equipment configuration. The skewness index includes a normal distribution (skewness=0), a right-hand distribution (also called a positive-hand distribution, its skewness > 0), and a left-hand distribution (also called a negative-hand distribution, its skewness < 0). Kurtosis index (kurtosis) is relatively 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.
S30, calculating the skewness index and the kurtosis index according to a preset weight coefficient to obtain the equipment fault index under each acquisition frequency.
The weight coefficient is set according to the experience of equipment maintenance.
In this embodiment, the equipment failure index (DEP) =0.3×|ske|+0.7×|kur|, that is, the skewness index (SKE) accounts for 30% and the kurtosis index (KUR) accounts for 70%. It is understood that a deviation index closer to 0 indicates that the device is more structurally stable, and a kurtosis index closer to 0 indicates that the device has less wear.
S40, determining corresponding equipment fault index statistical frequency according to the service life of the current equipment.
Specifically, the frequency of the statistics of the equipment failure indicators changes relatively as the service life of the equipment changes, i.e., the more frequently the equipment failure indicators count as the service life of the equipment approaches the end of the life span. The corresponding relation table of the service life and the equipment fault index statistical frequency can be prestored in the fault prediction system, the corresponding relation can be set according to the experience of maintenance personnel, and each different equipment has a corresponding relation table. It will be appreciated that the failure time point may be predicted by month or by quarter.
S50, 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.
Specifically, after the equipment failure index of each hour is calculated, the equipment failure index of each hour under the frequency is counted according to the equipment failure index counting frequency to obtain a failure trend, for example, when the equipment failure index counting frequency is one month, the equipment failure index of each hour in one month is drawn into a curve. And the running state of the current equipment can be known according to the fault trend. 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 failure index is, the higher the failure rate is.
It should be noted that, because the service lives of the devices are different, the service lives of the devices can be combined to better evaluate the operation state of the current device, so as to better predict the failure time point of the device.
In summary, 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, then the skewness index and the kurtosis index are calculated according to the preset weight coefficient, so that the equipment fault index is obtained, the corresponding equipment fault index statistical frequency 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 equipment are different, the frequency of statistics of fault indexes is different, so that fault time points are predicted more accurately, maintenance work of the equipment is finished in advance, and equipment shutdown risk caused by unexpected faults is reduced.
In an optional embodiment, the step S10 specifically includes:
s100, the current device is provided with a plurality of device detection points.
S101, respectively acquiring vibration signals of each equipment detection point of the current equipment according to a preset acquisition frequency.
Specifically, when performing fault prediction, a plurality of device detection points need to be set on the device, so that the result of the device fault index is more accurate, and the prediction of the fault time point is more prepared. In this embodiment, five device detection points are provided, and the number of device detection points may be set as required, which is not limited herein.
In an alternative embodiment, after the vibration signals of each device detection point of the current device are respectively acquired according to the preset acquisition frequency, the method further includes:
s102, respectively calculating the average value of the vibration signal amplitude of the equipment detection point at each acquisition frequency.
Specifically, as a plurality of equipment detection points are arranged, the characteristic extraction is carried out on the vibration signals collected by each equipment detection point, so that the vibration signal amplitude is obtained, and in order to ensure the accuracy of kurtosis index and skewness index calculation, the average value of the vibration signal amplitude at each time point is obtained.
In an alternative embodiment, the method determines the current operating state of the device by the steps of:
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 apparatus is [ DEP 1 -0.06，DEP 1 +0.06]It should be noted that, according to the operation period of the current device, a device failure index set value may be obtained empirically, and as the operation time of the device increases, the device failure index set value changes. DEP (DEP) 1 The fault index set value of the current equipment can be used as a reference according to the fault index set value of the starting stage of the current equipment. It will be appreciated that the amplitude of the ripple (i.e., 0.06) may be set as desired and is not limited herein.
And when the judgment result is that the fluctuation range does not exceed the preset fluctuation range, the current equipment is in a normal running state, and the current equipment is maintained according to a preset maintenance date.
In this embodiment, if the preset fluctuation range is not exceeded, it is indicated that the tightening device is in good operation. In the prior equipment maintenance, each equipment is provided with a maintenance calendar for equipment maintenance, so when the operation 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 preset 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 is indicated that the current device operation state is in an abnormal operation state, and a device failure may occur. And then the service life of the current equipment is combined, the fault time point of the current equipment is predicted, and the maintenance date of the maintenance calendar is adjusted according to the prediction result, so that the advanced maintenance work of the equipment is finished, and the equipment shutdown risk caused by unexpected faults is reduced.
In an optional embodiment, the method obtains the deviation index corresponding to the vibration signal through the following steps:
wherein x is i Vibration signals in the frequency are acquired for the i segments,is x i Average value of (2).
In particular, the method comprises the steps of,in this embodiment, the tightening device is provided with five device detection points, and then calculates an average value of the five device detection points, and then calculates the skewness index according to the average value of the five device detection points.
In an alternative embodiment, the method obtains the kurtosis index corresponding to the vibration signal through the following steps:
wherein x is i Vibration signals in the frequency are acquired for the i segments,is x i Average value of (2).
In particular, the method comprises the steps of,in this embodiment, the tightening apparatus is provided with five apparatus detection points, and then calculates an average value of the five apparatus detection points, and then calculates a kurtosis index according to the average value of the five apparatus detection points.
In an optional embodiment, the determining the corresponding equipment failure index statistical frequency according to the service life of the current equipment, step S40 specifically includes:
s400, if the current service life of the current equipment is in a first service life period, 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 service life period, the equipment fault index statistical frequency of the current equipment is a second time period.
S402, if the current service life of the current device is in a third service life period, the statistical frequency of the device fault index of the current device 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 phase, the device failure index statistics frequency may be counted at a quarterly frequency. If the current equipment is used in the middle stage, the equipment fault index statistical frequency can be counted according to the frequency of the month. In the final stage of equipment use, the equipment fault index statistical frequency can be counted according to the frequency of the circumference.
The starting stage, the middle stage and the final stage may be classified according to factory specifications of the apparatus, or may be classified according to experience of maintenance personnel, for example, the starting stage is that the remaining life of the apparatus is greater than 70%, the middle stage is that the remaining life of the apparatus is greater than 50%, and the final stage is that the remaining life of the apparatus is not greater than 50%. It will be appreciated that the enabling phase, mid-phase, end-phase and corresponding quarters, months, zhou Du are just one example of an embodiment of the present invention, and that more phases may be divided, each of which corresponds to a different time period, and are not limited herein.
For ease of understanding, the following examples are presented:
referring to figures, 4-5, selecting five tightening points of tightening equipment for detection, collecting vibration signals of equipment detection points of the tightening equipment from 7 to 11 am points, carrying out 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 at each time point; and respectively calculating the skewness index and the kurtosis index of a certain time point of the five points, carrying out weighted calculation on the skewness index and the kurtosis index, and finally obtaining the equipment fault index. Drawing a corresponding curve for the equipment fault index, and predicting a fault time point according to the fault trend and the service life of the tightening equipment.
Referring to fig. 6, a schematic structural diagram of a fault prediction device according to an embodiment of the present invention is shown.
An embodiment of the present invention correspondingly provides a fault prediction apparatus, including:
and the acquisition module 10 is used for respectively acquiring vibration signals of the equipment detection points of the current equipment according to the 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.
A second calculation module 30, configured to calculate the skewness index and the kurtosis index according to a preset weight coefficient, so as to obtain an equipment failure index at each acquisition frequency;
the statistical frequency determining module 40 is configured to determine a corresponding statistical frequency of the equipment failure index according to the service life of the current equipment.
And the prediction module 50 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.
According to the fault prediction device provided by the embodiment of the invention, the vibration signal of the equipment detection point of the current equipment 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 that the equipment fault index is obtained, the corresponding equipment fault index statistical frequency 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 equipment are different, the frequency of statistics of fault indexes is different, so that fault time points are predicted more accurately, maintenance work of the equipment is finished in advance, and equipment shutdown risk caused by unexpected faults is reduced.
In an alternative embodiment, the acquisition module 10 comprises:
the setting module is used for setting a plurality of device detection points for the current device.
The signal acquisition module is used for respectively acquiring vibration signals of each equipment detection point of the current equipment according to the preset acquisition frequency.
In an alternative embodiment, the statistical frequency determining module 40 includes:
the judging module is used for judging whether the equipment fault index exceeds a preset fluctuation range;
and the first matching module is used for setting the equipment fault index statistical frequency of the current equipment as 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 setting the equipment fault index statistical frequency of the current equipment as a second time period if the current service life of the current equipment is in a second service life period.
And the third matching module is used for setting the equipment fault index statistical frequency of the current equipment to be a third time period if the current service life of the current equipment is in a third service life period.
Referring to fig. 7, a schematic diagram of a fault prediction system according to an embodiment of the present invention is provided. 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, which processor 11 implements the steps of the various fault prediction method embodiments described above when executing the computer program. Alternatively, the processor may implement the functions of the modules/units in the above-described device embodiments when executing the computer program.
The vibration sensor 13 is disposed at a device detection point of the current device to detect
The computer program may be divided into one or more modules/units, which are stored in the memory and executed by the processor to accomplish the present invention, for example. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions for describing the execution of the computer program in the fault prediction system.
The fault prediction system can be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The fault prediction system may include, but is not limited to, a processor 11, a memory 12. Those skilled in the art will appreciate that the schematic diagram is merely an example of a failure prediction system and is not limiting of the failure prediction system, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the failure prediction system may also include input-output devices, network access devices, buses, etc.
The processor 11 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like that is the control center of the fault prediction system, with various interfaces and lines connecting the various parts of the overall fault prediction system.
The memory 12 may be used to store the computer programs and/or modules, and the processor implements the various functions of the fault prediction system by running or executing the computer programs and/or modules stored in the memory, and invoking 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 (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Wherein the integrated modules/units of the failure prediction system may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth.
It should be noted that the above-described apparatus embodiments are merely illustrative, and the units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. In addition, in the drawings of the embodiment of the device provided by the invention, the connection relation between the modules represents that the modules have communication connection, and can be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that changes and modifications may be made without departing from the principles of the invention, such changes and modifications are also intended to be within the 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 frequencies;
respectively calculating a skewness index corresponding to the vibration signal at each acquisition frequency and a kurtosis index of the vibration signal;
calculating the skewness index and the kurtosis index according to a preset weight coefficient to obtain equipment fault indexes under each acquisition frequency;
determining corresponding equipment fault index statistical frequency according to the service life of the current equipment;
and 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.
2. The fault prediction method according to claim 1, wherein the step of 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 vibration signals of each equipment detection point of the current equipment according to a preset acquisition frequency.
3. The fault prediction method as claimed in claim 2, further comprising, after the separately acquiring the vibration signal of each device detection point of the current device according to the preset acquisition frequency:
and respectively calculating the average value of the vibration signal amplitude of the equipment detection point at each acquisition frequency.
4. The method for predicting faults as claimed in claim 1, wherein said determining the current operating state of the device according to the device fault indicator at each acquisition frequency comprises:
judging whether the equipment failure index exceeds a preset fluctuation range;
when the judgment result is that the fluctuation range does not exceed the preset fluctuation range, the current equipment is in a normal running state, and the current equipment is maintained according to a preset maintenance date;
and when the judgment result is that the preset fluctuation range is exceeded, the current equipment running state is in an abnormal running state.
5. The fault prediction method as claimed in claim 1, wherein the method includes the steps of obtaining a bias index corresponding to the vibration signal, specifically including:
wherein x is i Collecting vibration signals in frequency for the section i, < >>Is x i Average value of (2).
6. The fault prediction method of claim 1, wherein the method obtains the kurtosis index corresponding to the vibration signal by:
wherein x is i Collecting vibration signals in frequency for the section i, < >>Is x i Average value of (2).
7. The fault prediction method as claimed in claim 1, wherein said determining the corresponding statistical frequency of the equipment fault indicators according to the service life of the current equipment specifically includes:
if the current service life of the current equipment is in a first service life period, 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 service life period, 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 service life period, 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 the skewness index corresponding to the vibration signal and the kurtosis index of the vibration signal under each acquisition frequency respectively;
the second calculation module is used for calculating the skewness index and the kurtosis index according to preset weight coefficients to obtain equipment fault indexes under each acquisition frequency;
the statistical frequency determining module is used for determining the corresponding equipment fault index statistical frequency according to the service life of the current equipment;
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 a vibration signal, 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 according to any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored computer program, wherein the computer program, when run, controls a device 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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