CN112232569A - Mechanical equipment fault early warning method and system and readable storage medium - Google Patents
Mechanical equipment fault early warning method and system and readable storage medium Download PDFInfo
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Abstract
The invention relates to a mechanical equipment fault early warning method, a system and a readable storage medium, wherein the method comprises the following steps: acquiring historical operating data of equipment, and establishing a big data early warning platform; setting a sampling interval, acquiring the running state of equipment according to the same sampling interval, and establishing a model; predicting the running state change information of the equipment according to the model, and fitting a curve graph; predicting equipment fault information according to the graph; feeding back equipment fault information to an early warning platform for analysis to obtain result information; and generating a maintenance strategy according to the result information.
Description
Technical Field
The invention relates to a mechanical equipment fault early warning method, in particular to a mechanical equipment fault early warning method, a mechanical equipment fault early warning system and a readable storage medium.
Background
The failure prediction is the research frontier and hotspot of the life prediction technology, is originally derived from experience knowledge in actual work, the specific implementation of the failure prediction is to obtain the health state of equipment by means of hearing, touching, seeing and other senses of field experts, and a plurality of innovative technologies and methods emerge along with the rapid array of information science and control theory, so that the rapid development of the equipment life prediction and health management technology is further promoted, the failure prediction and health management is widely applied to the industrial production fields of electric power, chemical engineering, building materials and the like, the equipment life prediction and health management is taken as a new subject and plays an increasingly important role in national economic construction, and more enterprises adopt the equipment life prediction technology to ensure the safe and efficient operation of equipment and large-scale machinery.
The existing fault prediction is mainly used for predicting the safe operation of equipment in the aspects of the health service life, the fault severity, the vibration intensity and the energy of the equipment, only a specific single fault is predicted, the fault type and the fault part cannot be predicted and diagnosed, in addition, the vibration information acquired by a vibration sensor is only installed at a certain position or a certain direction of mechanical equipment to judge the operation state of the equipment, the obtained result has one-sidedness, the connection between vibration signals in two directions of the same section is neglected, the fault analysis of the equipment is difficult to approach an actual value, and the deviation is large.
In order to be able to carry out accurate fault diagnosis on rotating equipment, a system matched with the rotating equipment needs to be developed for control, a time sequence is established through vibration characteristics acquired through total-vector fusion, a prediction model is established according to acquired total-vector characteristic historical data, the change trend of the characteristic data is predicted, a vibration one-dimensional amplitude and a two-dimensional frequency spectrum structure are established for equipment state monitoring, equipment fault early warning is realized, how to realize accurate control on fault diagnosis of rotating machinery equipment is urgent to be solved.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a mechanical equipment fault early warning method, a mechanical equipment fault early warning system and a readable storage medium.
In order to achieve the purpose, the invention adopts the technical scheme that: a mechanical equipment fault early warning method comprises the following steps:
acquiring historical operating data of equipment, and establishing a big data early warning platform;
setting a sampling interval, acquiring the running state of equipment according to the same sampling interval, and establishing a model;
predicting the running state change information of the equipment according to the model, and fitting a curve graph;
predicting equipment fault information according to the graph;
feeding back equipment fault information to an early warning platform for analysis to obtain result information;
and generating a maintenance strategy according to the result information.
In a preferred embodiment of the invention, a sampling interval is set, the running state of the equipment is obtained according to the same sampling interval, and a model is established; the method specifically comprises the following steps:
acquiring a device vibration signal, acquiring a vibration vector under vibration harmonic waves, and extracting a characteristic value;
sorting the characteristic values according to a sampling time sequence to obtain a plurality of groups of time sequences to obtain full-vector time sequence characteristic data;
establishing a prediction model through the holovector time series characteristic data,
and early warning the fault state of the equipment through a prediction model.
In a preferred embodiment of the present invention, the predictive model expression is as follows:
γ＝λ1x1+λ2x2+λ3x3…+λjxj+ξ
where gamma denotes the prediction model, lambda1Representing a model parameter, λ, at a first sampling interval2Representing the model parameter, λ, at the second sampling intervaljRepresenting the model parameter, x, at the jth sampling interval1Representing the amplitude of vibration, x, at a first sampling intervaljRepresenting the vibration amplitude at the jth sampling interval, and ξ represents random noise.
In a preferred embodiment of the invention, the a-measuring point is constructed, a coordinate system is established,
under the first sampling interval time, extracting the signal characteristics of the point a, decomposing the signal characteristics of the point a to obtain the signal characteristics of the point a in the X directionx1Signal and a in Y directiony1A signal;
under the second sampling interval time, extracting the signal characteristics of the point a, decomposing the signal characteristics of the point a to obtain the signal characteristics of the point a in the X directionx2Signal and a in Y directiony2A signal;
extracting the signal characteristics of the point a at the nth sampling interval, and decomposing the signal characteristics of the point a to obtain the point a in the X directionxnSignal and a in Y directionynA signal;
carrying out homologous information fusion on n signals in the X direction at different sampling time intervals to obtain a signal discrete sequence in the X direction,
carrying out homologous information fusion on n signals in the Y direction at different sampling time intervals to obtain a signal discrete sequence in the Y direction;
and acquiring the running state information of the equipment according to the signal discrete sequences in the X direction and the Y direction.
In a preferred embodiment of the present invention, the discrete sequence in the X direction is denoted as { XnThe discrete sequence in the Y direction is denoted as { Y }nWhere n is 0, 1, 2 …
For { xnAnd { y }nCarrying out discrete Fourier transform to obtain a data sequence;
construction of a complex sequence z from a data sequencen}，{zn}＝{xn}+{yn}；
Calculating the holo-vector spectrum information under the vibration harmonic wave according to the complex sequence,
and analyzing the equipment fault state according to the full vector spectrum information.
In a preferred embodiment of the invention, equipment vibration signals are collected, and full vector feature extraction is carried out to form a historical database;
establishing a prediction model according to a historical database;
setting a sampling interval, and performing predictive calculation on the sampling data through a predictive model when next sampling is performed to form a vibration characteristic value;
establishing vibration frequency spectrum information according to the vibration characteristic value;
and predicting the vibration intensity variation according to the vibration spectrum information to obtain result information and generate equipment operation state information.
The second aspect of the present invention also provides a mechanical equipment fault early warning system, including: the mechanical equipment fault early warning method comprises a memory and a processor, wherein the memory comprises a mechanical equipment fault early warning method program, and the mechanical equipment fault early warning method program realizes the following steps when being executed by the processor:
acquiring historical operating data of equipment, and establishing a big data early warning platform;
setting a sampling interval, acquiring the running state of equipment according to the same sampling interval, and establishing a model;
predicting the running state change information of the equipment according to the model, and fitting a curve graph;
predicting equipment fault information according to the graph;
feeding back equipment fault information to an early warning platform for analysis to obtain result information;
and generating a maintenance strategy according to the result information.
In a preferred embodiment of the invention, the a-measuring point is constructed, a coordinate system is established,
under the first sampling interval time, extracting the signal characteristics of the point a, decomposing the signal characteristics of the point a to obtain the signal characteristics of the point a in the X directionx1Signal and a in Y directiony1A signal;
under the second sampling interval time, extracting the signal characteristics of the point a, decomposing the signal characteristics of the point a to obtain the signal characteristics of the point a in the X directionx2Signal and a in Y directiony2A signal;
extracting the signal characteristics of the point a at the nth sampling interval, and decomposing the signal characteristics of the point a to obtain the point a in the X directionxnSignal and a in Y directionynA signal;
carrying out homologous information fusion on n signals in the X direction at different sampling time intervals to obtain a signal discrete sequence in the X direction,
carrying out homologous information fusion on n signals in the Y direction at different sampling time intervals to obtain a signal discrete sequence in the Y direction;
and acquiring the running state information of the equipment according to the signal discrete sequences in the X direction and the Y direction.
In a preferred embodiment of the invention, equipment vibration signals are collected, and full vector feature extraction is carried out to form a historical database;
establishing a prediction model according to a historical database;
setting a sampling interval, and performing predictive calculation on the sampling data through a predictive model when next sampling is performed to form a vibration characteristic value;
establishing vibration frequency spectrum information according to the vibration characteristic value;
and predicting the vibration intensity variation according to the vibration spectrum information to obtain result information and generate equipment operation state information.
A third aspect of the present invention provides a computer-readable storage medium, where the computer-readable storage medium includes a program for a method for early warning a failure of a mechanical device, and when the program is executed by a processor, the method for early warning a failure of a mechanical device implements any one of the steps of the method for early warning a failure of a mechanical device.
The invention solves the defects in the background technology, and has the following beneficial effects:
(1) modeling and predictive analysis are carried out through the full-vector vibration time series characteristic data, the full-vector vibration time series characteristic data can be well used for trend prediction of time series data, vibration strength, fault property and equipment state are monitored through a predictive model, performance changes of the rotary equipment in the operation process are comprehensively reflected, and fault diagnosis is more accurate.
(2) The vibration value and the frequency spectrum structure change condition are predicted by establishing a prediction model, state evaluation is carried out according to alarm threshold value setting at a device measuring point, the running health state of the device in a future time period is judged, a fault part is judged, real-time evaluation and early warning of the health state of the device can be realized, and decision reference is provided for device management and maintenance better.
(3) Time tear is constructed through vibration characteristics acquired through holovector fusion, a prediction model is constructed according to obtained holovector characteristic historical data, the change trend of the characteristic data is predicted, a vibration one-dimensional amplitude and a two-dimensional frequency spectrum structure are established for monitoring the state of equipment, the early warning of equipment faults is realized, and the early warning result is closer to an actual value.
Drawings
The invention is further illustrated with reference to the following figures and examples.
FIG. 1 is a flow chart illustrating a method for early warning of a fault in a mechanical device according to the present invention;
FIG. 2 shows a flow chart of a method for early warning of a fault condition of a device through a predictive model;
FIG. 3 illustrates a flow diagram of a method of homologous information fusion;
FIG. 4 is a flow chart of a method for monitoring the operating condition of a device through vibration spectrum;
FIG. 5 shows a block diagram of a mechanical device fault warning system.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
Fig. 1 shows a flow chart of a mechanical equipment fault early warning method according to the present invention.
As shown in fig. 1, a first aspect of the present invention provides a method for early warning of a fault of a mechanical device, including:
s102, obtaining historical operation data of the equipment, and establishing a big data early warning platform;
s104, setting a sampling interval, acquiring the running state of the equipment according to the same sampling interval, and establishing a model;
s106, predicting the running state change information of the equipment according to the model, and fitting a curve graph;
s108, predicting equipment fault information according to the graph;
s110, feeding back the equipment fault information to an early warning platform for analysis to obtain result information;
and S112, generating a maintenance strategy according to the result information.
It should be noted that modeling and predictive analysis are performed through the full-vector vibration time series characteristic data, the trend prediction for the time series data can be well performed, vibration intensity, fault property and equipment state are monitored through the predictive model, performance changes in the operation process of the rotary equipment are comprehensively reflected, fault diagnosis is more accurate, maintenance strategies include maintenance sequence and maintenance time, when a plurality of fault points occur, damage force to the fault is evaluated according to fault degrees of different fault points and the degree of influence on the mechanical equipment, and when the damage force to the fault is large, priority processing is performed.
As shown in fig. 2, the present invention discloses a flow chart of a method for early warning a failure state of a device through a prediction model;
according to the embodiment of the invention, a sampling interval is set, the running state of equipment is obtained according to the same sampling interval, and a model is established; the method specifically comprises the following steps:
s202, collecting equipment vibration signals, obtaining vibration vectors under vibration harmonics, and extracting characteristic values;
s204, sequencing the characteristic values according to a sampling time sequence to obtain a plurality of groups of time sequences to obtain full-vector time sequence characteristic data;
s206, establishing a prediction model through the holo-vector time series characteristic data,
and S208, early warning the equipment fault state through the prediction model.
It should be noted that the vibration value and the frequency spectrum structure change condition are predicted by establishing a prediction model, state evaluation is performed according to alarm threshold setting at a device measuring point, the running health state of the device in a future time period is judged, and a fault part is judged, so that real-time evaluation and early warning of the health state of the device can be realized, and decision reference is provided for device management and maintenance better.
According to the embodiment of the invention, the prediction model expression is as follows:
γ＝λ1x1+λ2x2+λ3x3…+λjxj+ξ
where gamma denotes the prediction model, lambda1Representing a model parameter, λ, at a first sampling interval2Representing the model parameter, λ, at the second sampling intervaljRepresenting the model parameter, x, at the jth sampling interval1Representing the amplitude of vibration, x, at a first sampling intervaljRepresenting the vibration amplitude at the jth sampling interval, and ξ represents random noise.
As shown in FIG. 3, the present invention discloses a flow chart of a method for fusing homologous information;
according to the embodiment of the present invention, S302, the a measuring point is constructed, the coordinate system is established,
s304, under the first sampling interval time, extracting the signal characteristics of the point a, decomposing the signal characteristics of the point a to obtain the signal characteristics of the point a in the X directionx1Signal and a in Y directiony1A signal;
s306, under the second sampling interval time, extracting the signal characteristics of the point a, decomposing the signal characteristics of the point a to obtain the point a in the X directionx2Signal and a in Y directiony2A signal;
s308, extracting the signal characteristics of the point a at the nth sampling interval, and decomposing the signal characteristics of the point a to obtain the signal characteristics of the point a in the X directionxnSignal and a in Y directionynA signal;
s310, carrying out homologous information fusion on n signals in the X direction at different sampling time intervals to obtain a signal discrete sequence in the X direction,
s312, performing homologous information fusion on n signals in the Y direction at different sampling time intervals to obtain a signal discrete sequence in the Y direction;
and S314, acquiring the running state information of the equipment according to the signal discrete sequences in the X direction and the Y direction.
It should be noted that, the homologous information fusion is to process and reuse acquired data according to a certain criterion, the same type of sensors are adopted, vibration signals of equipment are measured in different directions, during signal acquisition, a plurality of sensors keep synchronously measuring data information of a unified vibration source, and integrity of measured data characteristics is ensured.
According to the embodiment of the invention, the discrete sequence in the X direction is marked as { XnThe discrete sequence in the Y direction is denoted as { Y }nWhere n is 0, 1, 2 …
For { xnAnd { y }nCarrying out discrete Fourier transform to obtain a data sequence;
construction of a complex sequence z from a data sequencen}，{zn}＝{xn}+{yn}；
Calculating the holo-vector spectrum information under the vibration harmonic wave according to the complex sequence,
and analyzing the equipment fault state according to the full vector spectrum information.
It should be noted that the full-vector time sequence data is subjected to stabilization processing, equalization processing and then abnormal data elimination, real-time data capable of accurately reflecting the operation state of the equipment is reserved, the real-time data is subjected to standardization processing to obtain a set of new time sequence data, the time sequence characteristics can be more accurately and comprehensively reflected through the set of time sequence data, and equipment faults are rapidly analyzed.
As shown in fig. 4, the present invention discloses a flow chart of a method for monitoring the operation status of a device through a vibration spectrum;
according to the embodiment of the invention, S402, equipment vibration signals are collected, and full vector feature extraction is carried out to form a historical database;
s404, establishing a prediction model according to the historical database;
s406, setting a sampling interval, and performing predictive calculation on the sampling data through a predictive model when next sampling is performed to form a vibration characteristic value;
s408, establishing vibration frequency spectrum information according to the vibration characteristic value;
and S410, predicting the vibration intensity variation according to the vibration spectrum information to obtain result information, and generating equipment operation state information.
It should be noted that a time sequence is constructed through vibration characteristics acquired through holovector fusion, a prediction model is constructed according to obtained holovector characteristic historical data, the variation trend of the characteristic data is predicted, a vibration one-dimensional amplitude and a two-dimensional frequency spectrum structure are established for monitoring the state of the equipment, the fault early warning of the equipment is realized, and the early warning result is closer to an actual value.
As shown in fig. 5, the invention discloses a mechanical equipment fault early warning system block diagram;
the second aspect of the present invention also provides a mechanical equipment fault early warning system, where the system 5 includes: a memory 51 and a processor 52, wherein the memory includes a program of a mechanical equipment failure warning method, and the program of the mechanical equipment failure warning method when executed by the processor implements the following steps:
acquiring historical operating data of equipment, and establishing a big data early warning platform;
setting a sampling interval, acquiring the running state of equipment according to the same sampling interval, and establishing a model;
predicting the running state change information of the equipment according to the model, and fitting a curve graph;
predicting equipment fault information according to the graph;
feeding back equipment fault information to an early warning platform for analysis to obtain result information;
and generating a maintenance strategy according to the result information.
It should be noted that the maintenance strategy includes a maintenance sequence and a maintenance time, when a plurality of fault points occur, the damage force to the fault is evaluated according to the fault degrees of different fault points and the degree of influence on the mechanical equipment, and when the damage force to the fault is large, priority processing is performed.
According to the embodiment of the invention, the measuring point a is constructed, a coordinate system is established,
under the first sampling interval time, extracting the signal characteristics of the point a, decomposing the signal characteristics of the point a to obtain the signal characteristics of the point a in the X directionx1SignalA in Y directiony1A signal;
under the second sampling interval time, extracting the signal characteristics of the point a, decomposing the signal characteristics of the point a to obtain the signal characteristics of the point a in the X directionx2Signal and a in Y directiony2A signal;
extracting the signal characteristics of the point a at the nth sampling interval, and decomposing the signal characteristics of the point a to obtain the point a in the X directionxnSignal and a in Y directionynA signal;
carrying out homologous information fusion on n signals in the X direction at different sampling time intervals to obtain a signal discrete sequence in the X direction,
carrying out homologous information fusion on n signals in the Y direction at different sampling time intervals to obtain a signal discrete sequence in the Y direction;
and acquiring the running state information of the equipment according to the signal discrete sequences in the X direction and the Y direction.
It should be noted that, the homologous information fusion is to process and reuse acquired data according to a certain criterion, the same type of sensors are adopted, vibration signals of equipment are measured in different directions, during signal acquisition, a plurality of sensors keep synchronously measuring data information of a unified vibration source, and integrity of measured data characteristics is ensured.
According to the embodiment of the invention, equipment vibration signals are collected, and full vector feature extraction is carried out to form a historical database;
establishing a prediction model according to a historical database;
setting a sampling interval, and performing predictive calculation on the sampling data through a predictive model when next sampling is performed to form a vibration characteristic value;
establishing vibration frequency spectrum information according to the vibration characteristic value;
and predicting the vibration intensity variation according to the vibration spectrum information to obtain result information and generate equipment operation state information.
It should be noted that a time sequence is constructed through vibration characteristics acquired through holovector fusion, a prediction model is constructed according to obtained holovector characteristic historical data, the variation trend of the characteristic data is predicted, a vibration one-dimensional amplitude and a two-dimensional frequency spectrum structure are established for monitoring the state of the equipment, the fault early warning of the equipment is realized, and the early warning result is closer to an actual value.
According to the embodiment of the invention, a sampling interval is set, the running state of equipment is obtained according to the same sampling interval, and a model is established; the method specifically comprises the following steps:
acquiring a device vibration signal, acquiring a vibration vector under vibration harmonic waves, and extracting a characteristic value;
sorting the characteristic values according to a sampling time sequence to obtain a plurality of groups of time sequences to obtain full-vector time sequence characteristic data;
establishing a prediction model through the holovector time series characteristic data,
and early warning the fault state of the equipment through a prediction model.
It should be noted that the vibration value and the frequency spectrum structure change condition are predicted by establishing a prediction model, state evaluation is performed according to alarm threshold setting at a device measuring point, the running health state of the device in a future time period is judged, and a fault part is judged, so that real-time evaluation and early warning of the health state of the device can be realized, and decision reference is provided for device management and maintenance better.
According to the embodiment of the invention, the prediction model expression is as follows:
γ＝λ1x1+λ2x2+λ3x3…+λjxj+ξ
where gamma denotes the prediction model, lambda1Representing a model parameter, λ, at a first sampling interval2Representing the model parameter, λ, at the second sampling intervaljRepresenting the model parameter, x, at the jth sampling interval1Representing amplitude of vibration at first sampling intervalValue, xjRepresenting the vibration amplitude at the jth sampling interval, and ξ represents random noise.
According to the embodiment of the invention, the discrete sequence in the X direction is marked as { XnThe discrete sequence in the Y direction is denoted as { Y }nWhere n is 0, 1, 2 …
For { xnAnd { y }nCarrying out discrete Fourier transform to obtain a data sequence;
construction of a complex sequence z from a data sequencen}，{zn}＝{xn}+{yn}；
Calculating the holo-vector spectrum information under the vibration harmonic wave according to the complex sequence,
and analyzing the equipment fault state according to the full vector spectrum information.
It should be noted that the full-vector time sequence data is subjected to stabilization processing, equalization processing and then abnormal data elimination, real-time data capable of accurately reflecting the operation state of the equipment is reserved, the real-time data is subjected to standardization processing to obtain a set of new time sequence data, the time sequence characteristics can be more accurately and comprehensively reflected through the set of time sequence data, and equipment faults are rapidly analyzed.
A third aspect of the present invention provides a computer-readable storage medium, where the computer-readable storage medium includes a program for a method for early warning a failure of a mechanical device, and when the program is executed by a processor, the method for early warning a failure of a mechanical device implements any one of the steps of the method for early warning a failure of a mechanical device.
Modeling and predictive analysis are carried out through the full-vector vibration time series characteristic data, the full-vector vibration time series characteristic data can be well used for trend prediction of time series data, vibration strength, fault property and equipment state are monitored through a predictive model, performance changes of the rotary equipment in the operation process are comprehensively reflected, and fault diagnosis is more accurate.
The vibration value and the frequency spectrum structure change condition are predicted by establishing a prediction model, state evaluation is carried out according to alarm threshold value setting at a device measuring point, the running health state of the device in a future time period is judged, a fault part is judged, real-time evaluation and early warning of the health state of the device can be realized, and decision reference is provided for device management and maintenance better.
The vibration characteristic construction time sequence acquired through the whole-vector fusion, the prediction model is constructed according to the obtained whole-vector characteristic historical data, the change trend of the characteristic data is predicted, the vibration one-dimensional amplitude and the two-dimensional frequency spectrum structure are established for monitoring the equipment state, the equipment fault early warning is realized, and the early warning result is closer to the actual value.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of a unit is only one logical function division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods of the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A mechanical equipment fault early warning method is characterized by comprising the following steps:
acquiring historical operating data of equipment, and establishing a big data early warning platform;
setting a sampling interval, acquiring the running state of equipment according to the same sampling interval, and establishing a model;
predicting the running state change information of the equipment according to the model, and fitting a curve graph;
predicting equipment fault information according to the graph;
feeding back equipment fault information to an early warning platform for analysis to obtain result information;
and generating a maintenance strategy according to the result information.
2. The mechanical equipment fault pre-warning method according to claim 1,
setting a sampling interval, acquiring the running state of equipment according to the same sampling interval, and establishing a model; the method specifically comprises the following steps:
acquiring a device vibration signal, acquiring a vibration vector under vibration harmonic waves, and extracting a characteristic value;
sorting the characteristic values according to a sampling time sequence to obtain a plurality of groups of time sequences to obtain full-vector time sequence characteristic data;
establishing a prediction model through the holovector time series characteristic data,
and early warning the fault state of the equipment through a prediction model.
3. The mechanical equipment fault early warning method according to claim 2, characterized in that:
the prediction model expression is as follows:
γ＝λ1x1+λ2x2+λ3x3…+λjxj+ξ
where gamma denotes the prediction model, lambda1Representing a model parameter, λ, at a first sampling interval2Representing the model parameter, λ, at the second sampling intervaljRepresenting the model parameter, x, at the jth sampling interval1Representing the amplitude of vibration, x, at a first sampling intervaljRepresenting the vibration amplitude at the jth sampling interval, and ξ represents random noise.
4. The mechanical equipment fault early warning method according to claim 1, characterized in that: constructing a measuring point a, establishing a coordinate system,
under the first sampling interval time, extracting the signal characteristics of the point a, decomposing the signal characteristics of the point a to obtain the signal characteristics of the point a in the X directionx1SignalA in Y directiony1A signal;
under the second sampling interval time, extracting the signal characteristics of the point a, decomposing the signal characteristics of the point a to obtain the signal characteristics of the point a in the X directionx2Signal and a in Y directiony2A signal;
extracting the signal characteristics of the point a at the nth sampling interval, and decomposing the signal characteristics of the point a to obtain the point a in the X directionxnSignal and a in Y directionynA signal;
carrying out homologous information fusion on n signals in the X direction at different sampling time intervals to obtain a signal discrete sequence in the X direction,
carrying out homologous information fusion on n signals in the Y direction at different sampling time intervals to obtain a signal discrete sequence in the Y direction;
and acquiring the running state information of the equipment according to the signal discrete sequences in the X direction and the Y direction.
5. The mechanical equipment fault early warning method according to claim 4, characterized in that: the discrete sequence in the X direction is denoted as { XnThe discrete sequence in the Y direction is denoted as { Y }nWhere n is 0, 1, 2 …
For { xnAnd { y }nCarrying out discrete Fourier transform to obtain a data sequence;
construction of a complex sequence z from a data sequencen}，{zn}＝{xn}+{yn}；
Calculating the holo-vector spectrum information under the vibration harmonic wave according to the complex sequence,
and analyzing the equipment fault state according to the full vector spectrum information.
6. The mechanical equipment fault early warning method according to claim 1, characterized in that: further comprising: collecting equipment vibration signals, and performing holovector feature extraction to form a historical database;
establishing a prediction model according to a historical database;
setting a sampling interval, and performing predictive calculation on the sampling data through a predictive model when next sampling is performed to form a vibration characteristic value;
establishing vibration frequency spectrum information according to the vibration characteristic value;
and predicting the vibration intensity variation according to the vibration spectrum information to obtain result information and generate equipment operation state information.
7. A mechanical device fault early warning system, the system comprising: the mechanical equipment fault early warning method comprises a memory and a processor, wherein the memory comprises a mechanical equipment fault early warning method program, and the mechanical equipment fault early warning method program realizes the following steps when being executed by the processor:
acquiring historical operating data of equipment, and establishing a big data early warning platform;
setting a sampling interval, acquiring the running state of equipment according to the same sampling interval, and establishing a model;
predicting the running state change information of the equipment according to the model, and fitting a curve graph;
predicting equipment fault information according to the graph;
feeding back equipment fault information to an early warning platform for analysis to obtain result information;
and generating a maintenance strategy according to the result information.
8. The mechanical equipment fault early warning system of claim 7, wherein:
constructing a measuring point a, establishing a coordinate system,
under the first sampling interval time, extracting the signal characteristics of the point a, decomposing the signal characteristics of the point a to obtain the signal characteristics of the point a in the X directionx1Signal and a in Y directiony1A signal;
under the second sampling interval time, extracting the signal characteristics of the point a, decomposing the signal characteristics of the point a to obtain the signal characteristics of the point a in the X directionx2Signal and a in Y directiony2A signal;
extracting the signal characteristics of the point a at the nth sampling interval, and decomposing the signal characteristics of the point a to obtain the point a in the X directionxnSignal and a in Y directionynSignal；
Carrying out homologous information fusion on n signals in the X direction at different sampling time intervals to obtain a signal discrete sequence in the X direction,
carrying out homologous information fusion on n signals in the Y direction at different sampling time intervals to obtain a signal discrete sequence in the Y direction;
and acquiring the running state information of the equipment according to the signal discrete sequences in the X direction and the Y direction.
9. The mechanical equipment fault early warning system of claim 7, wherein: collecting equipment vibration signals, and performing holovector feature extraction to form a historical database;
establishing a prediction model according to a historical database;
setting a sampling interval, and performing predictive calculation on the sampling data through a predictive model when next sampling is performed to form a vibration characteristic value;
establishing vibration frequency spectrum information according to the vibration characteristic value;
and predicting the vibration intensity variation according to the vibration spectrum information to obtain result information and generate equipment operation state information.
10. A computer-readable storage medium characterized by: the computer-readable storage medium includes therein a mechanical equipment failure warning method program which, when executed by a processor, implements the steps of the mechanical equipment failure warning method according to any one of claims 1 to 6.
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