CN110082078A - Trend prediction method, device, computer equipment and the storage medium of ship tail axis - Google Patents
Trend prediction method, device, computer equipment and the storage medium of ship tail axis Download PDFInfo
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- CN110082078A CN110082078A CN201910189409.5A CN201910189409A CN110082078A CN 110082078 A CN110082078 A CN 110082078A CN 201910189409 A CN201910189409 A CN 201910189409A CN 110082078 A CN110082078 A CN 110082078A
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- 238000000034 method Methods 0.000 title claims abstract description 52
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- 238000012544 monitoring process Methods 0.000 claims abstract description 78
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- 238000004590 computer program Methods 0.000 claims description 28
- 238000001228 spectrum Methods 0.000 claims description 26
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B63—SHIPS OR OTHER WATERBORNE VESSELS; RELATED EQUIPMENT
- B63B—SHIPS OR OTHER WATERBORNE VESSELS; EQUIPMENT FOR SHIPPING
- B63B71/00—Designing vessels; Predicting their performance
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
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Abstract
The present invention relates to the trend prediction method of ship tail axis, device, computer equipment and storage mediums, belong to ship machine monitoring technical field.Method includes: to obtain tailing axle monitoring data；The acquisition data for the ship tail axis that tailing axle monitoring data are sent according to acquisition equipment determine；Obtain the state evaluation data of ship tail axis；State evaluation data are determined according to the fault signature of ship tail axis；Tailing axle monitoring data are compared with state evaluation data, the status predication result of ship tail axis is determined according to comparison result.Above-mentioned technical proposal, the trend prediction method for solving ship tail axis are easy to be influenced by external environment and subjective factor, as a result often not accurate enough problem.The status predication of ship tail axis can be automatically determined as a result, effectively improving the status predication accuracy rate of ship tail axis.
Description
Technical field
The present invention relates to ship machine monitoring technical fields, more particularly to the trend prediction method, device, calculating of ship tail axis
Machine equipment and storage medium.
Background technique
Engineering ship has the characteristics that offshore, mobile operational process and reliability requirement are high.Meanwhile the dynamic power machine of engineering ship
It further include special plant, such as tailing axle.Structure is complicated for engineering ship, and operating condition multiplicity, working environment compare evil
It is bad；Therefore the requirement to the dynamic power machine performance of engineering ship is relatively high.Therefore it is true for actively developing the status monitoring of working ship
Protect the premise of engineering ship safe and reliable operation.In realizing process of the present invention, inventor's discovery at least exists in the prior art
Following problem: the monitoring of engineering ship mainly inspects periodically the working condition of ship tail axis by staff to realize at present.
The trend prediction method of such ship tail axis is easy to be influenced by external environment and subjective factor, as a result often not accurate enough.
Summary of the invention
Based on this, the embodiment of the invention provides the trend prediction method of ship tail axis, device, computer equipment and storages
Medium can carry out status predication to ship tail axis automatically, effectively improve the monitoring accuracy of tailing axle.
The content of the embodiment of the present invention is as follows:
A kind of trend prediction method of ship tail axis, comprising the following steps: obtain tailing axle monitoring data；The tailing axle monitoring
The acquisition data for the ship tail axis that data are sent according to acquisition equipment determine；Obtain the state evaluation data of the ship tail axis；
The state evaluation data are determined according to the fault signature of the ship tail axis；The tailing axle monitoring data are commented with the state
Valence mumber determines the status predication result of the ship tail axis according to comparison result according to being compared.
In one embodiment, the step of state evaluation data for obtaining the ship tail axis, comprising: described in acquisition
The fault signature of ship tail axis；According to the fault signature and predetermined corresponding relationship, the event of the ship tail axis is determined
Hinder vibration frequency, obtains the state evaluation data of the ship tail axis；Wherein, the corresponding relationship includes fault signature and event
Hinder the mapping relations of vibration frequency.
In one embodiment, the step of acquisition tailing axle monitoring data, comprising: receive the acquisition that acquisition equipment is sent
Data；The acquisition data are obtained by sensor and are sent to the acquisition equipment；The sensor is set to the ship tail
Axis；It determines the corresponding frequency spectrum data of the acquisition data, obtains the tailing axle monitoring data.
In one embodiment, the sensor includes acceleration transducer；The acquisition for receiving acquisition equipment and sending
The step of data, comprising: receive the vibration signal that acquisition equipment is sent；Wherein, the vibration signal is passed by the acceleration
Sensor measurement obtains.
In one embodiment, described that the tailing axle monitoring data are compared with the state evaluation data, according to
Comparison result determines the step of status predication result of the ship tail axis, comprising: the frequency spectrum data and the failure shake
Dynamic frequency is compared；If the frequency spectrum data matches with the fault vibration frequency, the state of the ship tail axis is determined
Prediction result is tailing axle failure.
In one embodiment, the status predication result of the determination ship tail axis be tailing axle failure the step of it
Afterwards, further includes: export fault message to alarm terminal；The fault message formulates the dimension of ship tail axis for guidance management personnel
The plan of repairing.
In one embodiment, the step of status predication result that the ship tail axis is determined according to comparison result it
Afterwards, further includes: obtain the spare parts logistics information in the acquisition data, the ship is determined according to the spare parts logistics information
The components to break down in tail axis.
Correspondingly, the embodiment of the present invention provides a kind of status predication device of ship tail axis, comprising: monitoring data obtain mould
Block, for obtaining tailing axle monitoring data；The acquisition data for the ship tail axis that the tailing axle monitoring data are sent according to acquisition equipment
It determines；Data acquisition module, for obtaining the state evaluation data of the ship tail axis；The state evaluation data are according to
The fault signature of ship tail axis determines；And prediction result determining module, it is used for the tailing axle monitoring data and the state
Evaluation data are compared, and the status predication result of the ship tail axis is determined according to comparison result.
The trend prediction method and device of above-mentioned ship tail axis determine that tailing axle monitors number according to the acquisition data of acquisition equipment
According to tailing axle monitoring data being compared with state evaluation data, the status predication result of available ship tail axis.It can be automatic
The status predication of ship tail axis is determined as a result, effectively improving the status predication accuracy rate of ship tail axis.
A kind of computer equipment can be run on a memory and on a processor including memory, processor and storage
Computer program, the processor performs the steps of when executing the computer program obtains tailing axle monitoring data；The tail
The acquisition data for the ship tail axis that shaft monitoring data are sent according to acquisition equipment determine；Obtain the state evaluation of the ship tail axis
Data；The state evaluation data are determined according to the fault signature of the ship tail axis；By the tailing axle monitoring data with it is described
State evaluation data are compared, and the status predication result of the ship tail axis is determined according to comparison result.
Above-mentioned computer equipment can automatically determine the status predication of ship tail axis as a result, effectively improving the shape of ship tail axis
State predictablity rate.
A kind of computer readable storage medium, is stored thereon with computer program, and the computer program is held by processor
It is performed the steps of when row and obtains tailing axle monitoring data；The ship tail axis that the tailing axle monitoring data are sent according to acquisition equipment
Acquisition data determine；Obtain the state evaluation data of the ship tail axis；The state evaluation data are according to the ship tail
The fault signature of axis determines；The tailing axle monitoring data are compared with the state evaluation data, it is true according to comparison result
The status predication result of the fixed ship tail axis.
Above-mentioned computer readable storage medium can automatically determine the status predication of ship tail axis as a result, effectively improving ship machine
The status predication accuracy rate of tailing axle.
Detailed description of the invention
Fig. 1 is the applied environment figure of the trend prediction method of ship tail axis in one embodiment；
Fig. 2 is the flow diagram of the trend prediction method of ship tail axis in one embodiment；
Fig. 3 is the flow diagram of the trend prediction method of ship tail axis in another embodiment；
Fig. 4 is the structural block diagram of the status predication device of ship tail axis in one embodiment；
Fig. 5 is the internal structure of computer equipment in one embodiment.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
The trend prediction method of ship tail axis provided by the present application can be applied in application environment as shown in Figure 1.It should
Application environment includes ship tail axis 101, acquisition equipment 102 and server 103；Wherein, tailing axle 101, acquisition equipment 102 and service
Device 103 is able to carry out network communication by network connection.The acquisition Data Concurrent that acquisition equipment 102 obtains ship tail axis 101 is sent
To server 103, server 103 determines therefrom that the status predication result of ship tail axis 101.Wherein, ship tail axis 101 can be
Various types of tailing axles on engineering ship；Acquisition equipment 102 can be the various devices with Signals collecting function, such as: it passes
Sensor etc.；Server 103 can realize with the server cluster of independent server either multiple servers composition, when
So, server also could alternatively be other devices having data processing function, such as: processor, smart phone etc., it can be with
It is virtual equipment, such as: Cloud Server etc..
The embodiment of the present invention provides trend prediction method, device, computer equipment and the storage medium of a kind of ship tail axis.
It is described in detail separately below.
In one embodiment, as shown in Fig. 2, providing a kind of trend prediction method of ship tail axis.It answers in this way
For being illustrated for the server end in Fig. 1, comprising the following steps:
S201, tailing axle monitoring data are obtained；The acquisition number for the ship tail axis that tailing axle monitoring data are sent according to acquisition equipment
According to determination.
Ship tail axis (also abbreviation tailing axle below) is one section of axis of most end in shafting.Engineering ship refers at harbour, boat
The ship of engineer operation is engaged in the waters such as road, shipyard, may include dredger, crane ship, pile driving barge, pier, concrete
Mixer ship and the ship of auxiliary activities etc..Specifically, engineering ship, which can inhale ship and be twisted for rake, inhales ship.
The acquisition data of ship tail axis refer to the operation of the ship tail axis obtained by acquisition equipment during the work time
Status data can largely reflect that ship tail axis is in normal condition or malfunction.Acquiring data can be with
It is the data such as the vibration signal (frequency, amplitude etc.) of ship tail axis, operating time, running temperature, revolving speed, displacement.
Acquisition equipment can be the equipment with data acquisition and integration function；The acquisition equipment can connect multiple types
Sensor, by sensor come the data of monitoring ship tail axis.Acquisition equipment can obtain the monitoring data of sensor and to monitoring
Data are integrated, and the acquisition data that integration obtains are sent to server by interchanger.Server is according to acquisition equipment
Acquisition data can obtain tailing axle monitoring data.
S202, the state evaluation data for obtaining ship tail axis；State evaluation data are true according to the fault signature of ship tail axis
It is fixed.
State evaluation data refer to the data that can be evaluated the operating status of ship tail axis, can be ship tail axis
The corresponding feature operation data of different operating statuses.
State evaluation data can determine that the fault signature of ship tail axis refers to ship when failure according to fault signature
The feature that tail axis shows, such as: be corroded, axis not just, insufficient lubrication, misalign, bending shaft, tail-rotor abrasion etc.；
It also may include work information, such as failure, the tailing axle maintenance record information of burst etc..It certainly, can be in addition to fault signature
State evaluation data are determined according to other information, such as: historical failure number, historical failure frequency etc..
When acquiring data is vibration signal, state evaluation data may be considered vibration limit value, i.e., when vibration signal is super
It crosses or when lower than the vibration limit value (be specifically more than again below can be determines according to actual conditions), it is believed that ship tail axis
Malfunction in malfunction or a certain grade of arrival.
Further, state evaluation data can be according to the Historical Monitoring data determination for setting historical time section.Example
Such as, the case where breaking down in setting historical time section and fault signature are determined, and determines corresponding Historical Monitoring data, it is right
These data are integrated, and state evaluation data are obtained.
S203, tailing axle monitoring data are compared with state evaluation data, ship tail axis is determined according to comparison result
Status predication result.
State evaluation data can characterize the different operating statuses of ship tail axis, therefore, by gear-box monitoring data and state
Evaluation data are compared, and just can determine that out operating status of the ship tail axis within following a period of time, obtain status predication knot
Fruit.
Further, state evaluation data may include multiple types, by tailing axle prediction data and various states review number
When according to being compared, if tailing axle prediction data matches with one of state evaluation data, shape can be pointedly determined
State prediction result.Corresponding, status predication result also may include multiple types, such as: normal operation, vibration frequency be abnormal,
The information such as temperature is excessively high, abnormal components.
It further, also may include multiple grades for the failure of a certain type；For example, working as status predication result
When state evaluation data corresponding to B grade with A failure match, determine that the status predication result of ship tail axis is B grade
A failure.
The trend prediction method of ship tail axis provided in this embodiment can automatically determine out the status predication knot of ship tail axis
Fruit effectively improves the status predication efficiency of ship tail axis, without the working condition of manual periodic inspection ship tail axis；Separately
Outside, also it is not readily susceptible to the influence of external environment, it can be deduced that accurate status predication result.
In one embodiment, the step of obtaining the state evaluation data of ship tail axis, comprising: obtain the event of ship tail axis
Hinder feature；According to fault signature and predetermined corresponding relationship, determines the fault vibration frequency of ship tail axis, obtain ship tail
The state evaluation data of axis；Wherein, corresponding relationship includes the mapping relations of fault signature Yu fault vibration frequency.
Under different faults feature, ship tail axis is corresponding with different fault vibration frequencies；Such as: assuming that operating normally shape
The vibration frequency of state is 200-300Hz, and the just not corresponding vibration frequency of axis is 0-5Hz, and the corresponding vibration of when insufficient lubrication is frequently
Rate is 100-150Hz；5Hz, 100Hz and 150Hz can be then determined as to fault vibration frequency.It can be directly by fault vibration
Frequency is determined as state evaluation data.It is of course also possible to the range that fault vibration frequency is constituted is determined as state evaluation data,
For example, 0-5Hz and 100-150Hz are directly determined as state evaluation data.
Continue above-mentioned example, include in corresponding relationship can be following mapping table: 1, axis not just ← → vibration frequency
For 0Hz and 5Hz；2, insufficient lubrication ← → vibration frequency is 100Hz and 150Hz.When determining the fault signature of ship tail axis, lead to
Crossing the mapping table searched in corresponding relationship just can determine that out corresponding fault vibration frequency, and then obtain state evaluation data.When
So, this mapping table can be stored in advance in the database；When determining the fault signature of ship tail axis, character can be passed through
With etc. modes corresponding fault vibration frequency is searched from database.
The present embodiment determines fault vibration frequency, determination process according to fault signature and predetermined corresponding relationship
It can carry out automatically, the accuracy rate and convenience of status predication result can be effectively improved.
In one embodiment, the step of obtaining tailing axle monitoring data, comprising: receive the acquisition number that acquisition equipment is sent
According to；Acquisition data are obtained by sensor and are sent to acquisition equipment；Sensor is set to ship tail axis；Determine that acquisition data are corresponding
Frequency spectrum data, obtain tailing axle monitoring data.Sensor can be the industrial sensor of armouring.
Wherein it is determined that the process of frequency spectrum data can be with are as follows: carry out the processing such as Fourier transformation to acquisition data, corresponded to
Frequency domain data；The information such as frequency are extracted from frequency domain data, as frequency spectrum data.
The present embodiment determines corresponding frequency spectrum data according to acquisition data, as tailing axle monitoring data；Identified tailing axle
Monitoring data can characterize the vibration frequency of tailing axle well, convenient for being compared with fault vibration frequency, and then determine accurately
Tailing axle status predication result.
In one embodiment, sensor includes acceleration transducer.Specifically, acceleration transducer can be piezoelectric type
Acceleration transducer.
Sensor can be set (can be set in the position close to ship machine gear-box in multiple positions of ship machine gear-box
On, to get more accurate vibration signal) on.It is possible to further be arranged on some components of ship machine gear-box；
For example, sensor can be arranged on tailing axle blade, using the vibration signal of blade as the vibration signal of ship machine gear-box.
Acceleration transducer can be multiple acceleration transducers for being only capable of monitoring a direction, such as: monitoring is hung down respectively
Straight and horizontal direction two acceleration transducers.Certainly, if a sensor can measure the vibration of multiple directions simultaneously
Signal can also carry out the vibration signal of monitoring ship machine gear-box only with a sensor.
Certainly, in some embodiments, sensor can also be other types, such as: speed probe etc..Wherein, turn
Fast sensor can monitor the transmission speed and frequency of tailing axle.Wherein, speed probe can be magnetoelectric tachometric transducer.
In some embodiments, sensor connect with acquisition equipment (sensor and acquire the frame that is constituted of equipment can be with
Referred to as acquisition system), equipment, which is acquired, by cable connects server.Server passes through again in mobile data network and the monitoring of land bank
The heart carries out data transmission, and Lu An monitoring center and server constitute wide local area network, may be implemented on multiple engineering ships
The synchronization monitoring of not shipmate machine equipment.
Further, the step of receiving the acquisition data that acquisition equipment is sent, comprising: receive the vibration that acquisition equipment is sent
Signal；Wherein, vibration signal is obtained by acceleration transducer measurement.I.e. vibration signal is being set according to the acceleration transducer
The monitoring data determined on direction (this direction can be determines according to actual conditions) obtain.It simultaneously can be round-the-clockly to ship tail axis
Status monitoring is carried out, can find the failure of ship tail axis, at the first time to be handled in time.
The load of different operating condition lower tooth roller boxs is different, for example, the load of gear-box can be because of factors such as density, the flows of silt
Change and fluctuates；And the variation of vibration signal can be caused by loading variation, pass through the pass between research vibration signal and failure
System can judge fault condition by vibration signal.Above-described embodiment obtains the vibration signal of ship machine gear-box by sensor,
Ship machine gear-box most directly reliable information, therefore the status predication of the ship machine gear-box determined according to vibration signal can be got
As a result accuracy with higher.
In one embodiment, tailing axle monitoring data are compared with state evaluation data, are determined according to comparison result
The step of status predication result of ship tail axis, comprising: frequency spectrum data is compared with fault vibration frequency；If frequency spectrum data
Match with fault vibration frequency, determines that the status predication result of ship tail axis is tailing axle failure.
Wherein, frequency spectrum data matches with fault vibration frequency can refer to the consistent or difference of the two less than preset
Threshold value；, can be when frequency spectrum data fall into the corresponding range of fault vibration frequency when state evaluation data refer to range, it will
The status predication result of ship tail axis is determined as tailing axle failure.
Frequency spectrum data is compared the present embodiment with fault vibration frequency, and then determines the status predication of ship tail axis
As a result；The determination process of status predication result is simple, can effectively improve the status predication accuracy rate of ship tail axis.
In one embodiment, after the step of status predication result for determining ship tail axis is tailing axle failure, further includes:
Fault message is exported to alarm terminal；Fault message formulates the maintenance project of ship tail axis for guidance management personnel.
Wherein, alarm terminal can be various types of terminals, such as: mobile phone, intercom, central station of floating dock display etc..Pipe
Reason personnel can be diagnosed and be repaired to ship tail axis, the dimension of ship tail axis can be effectively reduced after receiving fault message
Protect cost.As shown in figure 3, after determining status predication result fault message can be exported to alarm terminal, server may be used also
To carry out data analysis to fault message and provide fault diagnosis suggestion.Administrative staff carry out fault diagnosis later, arrange maintenance
Plan, and carry out troubleshooting.
In addition, fault message can refer to warning message when ship tail axis breaks down；It can also refer to fault pre-alarming information,
Such as: in 2 hours following, vibration frequency failure will occur in tailing axle.Administrative staff can refer to the maintenance personal of engineering ship, collection
Control the monitoring personnel etc. of room.
Status predication result is output to alarm terminal by above-described embodiment, and administrative staff can be made to get ship tail axis in time
Fault message, it is ensured that the normal operation of ship tail axis.
In one embodiment, however, it is determined that the status predication result of ship tail axis be it is normal, can also be in real time to alarm
Terminal exports the operating status of ship tail axis, and administrative staff is enabled to grasp the operating status of ship tail axis in real time.
In one embodiment, it after the step of determining the status predication result of ship tail axis according to comparison result, also wraps
It includes: obtaining the spare parts logistics information in acquisition data, determined according to spare parts logistics information and to be broken down in ship tail axis
Components.
Wherein, spare parts logistics information refers to the running state information of components, can be with the corresponding vibration of nulling component
Signal.When being broken down due to different components, the corresponding vibration signal of ship tail axis has difference, for example, vibration frequency
Center or frequency peak etc. can change.Therefore spare parts logistics therein can be analyzed according to acquisition data to believe
Breath, and then the components to break down can be analyzed.
It is of course also possible to obtain the bearing information carried in acquisition data, determine whether bearing occurs according to bearing information
Failure.
The present embodiment further determines that the components to break down, energy after the status predication result for determining ship tail axis
So that the status predication result of ship tail axis is more accurate, so that administrative staff targetedly safeguard ship tail axis.
In one embodiment, it after the step of determining the status predication result of ship tail axis according to comparison result, also wraps
It includes: according to the service life of status predication prediction of result ship tail axis.
Under different operating statuses, the remaining service life of ship tail axis is different, therefore, can be pre- according to state
Result is surveyed to determine the service life of ship tail axis.It is of course also possible in conjunction with degradation speed, (degradation speed can be according to ship tail
The state trend curves of the Historical Monitoring data of axis determines) predict the service life of ship tail axis.Use to ship tail axis
Service life is predicted, administrative staff can be made sufficiently to know the operating status and service life of tailing axle, and then preferably controlled
System and management.
In one embodiment, server is also able to achieve following functions: vibration signal real-time monitoring, vibration signal transfinite report
Police, vibration signal historical data management, vibration signal trend analysis, ship machine fault diagnosis, rotor/gear/shaft hold accident analysis,
Bearing data base administration, data remote detecting, and ship machine monitoring report, ship machine SCADA (Supervisory can be generated
Control And Data Acquisition, i.e. data acquisition and supervisor control) data & plant maintenance record.
The above method in order to better understand, the trend prediction method of a ship tail axis of the present invention detailed below
Application example.
1, the vibration signal of tailing axle is obtained for a long time by the acceleration transducer being arranged on tailing axle.The vibration that will constantly accumulate
Dynamic signal is handled, and determines the Historical Monitoring data of tailing axle.
2, these Historical Monitoring data are handled, obtains fault vibration frequency.
3, tailing axle monitoring data are obtained by acceleration transducer, determines the corresponding frequency spectrum data of tailing axle monitoring data；
Frequency spectrum data is compared with fault vibration frequency, obtains the status predication result of ship tail axis.
4, it if it is determined that tailing axle can break down or damage within this week, is then exported before tailing axle reaches malfunction
Fault pre-alarming information realizes the predictive maintenance of tailing axle to prompt administrative staff to take tailing axle maintenance game appropriate.
The trend prediction method of ship tail axis provided in this embodiment can automatically determine the status predication knot of ship tail axis
The determination of fruit, status predication result does not need the participation of manpower, effectively improves the status predication accuracy rate of ship tail axis；Energy simultaneously
Make the operating status of administrative staff's timely learning ship machine.
It should be noted that for the various method embodiments described above, describing for simplicity, it is all expressed as a series of
Combination of actions, but those skilled in the art should understand that, the present invention is not limited by the sequence of acts described, because according to
According to the present invention, certain steps can use other sequences or carry out simultaneously.
Based on thought identical with the trend prediction method of ship tail axis in above-described embodiment, the present invention also provides ship machines
The status predication device of tailing axle, the device can be used for executing the trend prediction method of above-mentioned ship tail axis.For ease of description, ship
In the structural schematic diagram of the status predication Installation practice of tail axis, part related to the embodiment of the present invention illustrate only,
It will be understood by those skilled in the art that the restriction of schematic structure not structure twin installation, may include more more or less than illustrating
Component, perhaps combine certain components or different component layouts.
Such as Fig. 4, the status predication device of ship tail axis includes that monitoring data obtain module 401,402 and of data acquisition module
Prediction result determining module 403, detailed description are as follows:
Monitoring data obtain module 401, for obtaining tailing axle monitoring data；Tailing axle monitoring data are sent according to acquisition equipment
Ship tail axis acquisition data determine.
Data acquisition module 402, for obtaining the state evaluation data of ship tail axis；State evaluation data are according to ship tail
The fault signature of axis determines.
And prediction result determining module 403, for tailing axle monitoring data to be compared with state evaluation data, root
The status predication result of ship tail axis is determined according to comparison result.
The present embodiment can automatically determine the status predication of ship tail axis as a result, effectively improving the status predication of ship tail axis
Accuracy rate.
In one embodiment, data acquisition module 402, comprising: fault signature acquisition submodule, for obtaining ship tail
The fault signature of axis；Data acquisition submodule is evaluated, for determining ship machine according to fault signature and predetermined corresponding relationship
The fault vibration frequency of tailing axle obtains the state evaluation data of ship tail axis；Wherein, corresponding relationship includes fault signature and event
Hinder the mapping relations of vibration frequency.
In one embodiment, monitoring data obtain module 401, comprising: acquisition data receiver submodule is adopted for receiving
Collect the acquisition data that equipment is sent；Acquisition data are obtained by sensor and are sent to acquisition equipment；Sensor is set to ship tail
Axis；Monitoring data determine submodule, for determining the corresponding frequency spectrum data of acquisition data, obtain tailing axle monitoring data.
In one embodiment, sensor includes acceleration transducer.
In one embodiment, data receiver submodule is acquired, is also used to receive the vibration signal that acquisition equipment is sent；Its
In, vibration signal is obtained by acceleration transducer measurement.
In one embodiment, prediction result determining module 403, comprising: comparison result determines submodule, is used for frequency spectrum
Data are compared with fault vibration frequency；Prediction result determines submodule, if being used for frequency spectrum data and fault vibration frequency phase
Matching determines that the status predication result of ship tail axis is tailing axle failure.
In one embodiment, prediction result determining module 403, comprising: maintenance submodule, for being exported to alarm terminal
Fault message；Fault message formulates the maintenance project of ship tail axis for guidance management personnel.
In one embodiment, it after the step of determining the status predication result of ship tail axis according to comparison result, also wraps
It includes: obtaining the spare parts logistics information in acquisition data, determined according to spare parts logistics information and to be broken down in ship tail axis
Components.
In one embodiment, further includes: life prediction module, for according to status predication prediction of result ship tail axis
Service life.
It should be noted that the status predication device of ship tail axis of the invention and the state of ship tail axis of the invention are pre-
Survey method corresponds, in the technical characteristic and its advantages of the embodiment elaboration of the trend prediction method of above-mentioned ship tail axis
Suitable for the embodiment of the status predication device of ship tail axis, particular content can be found in chatting in embodiment of the present invention method
It states, details are not described herein again, hereby give notice that.
In addition, the logic of each program module is drawn in the embodiment of the status predication device of the ship tail axis of above-mentioned example
Divide and be merely illustrative of, can according to need in practical application, such as the configuration requirement of corresponding hardware or the reality of software
Above-mentioned function distribution is completed by different program modules, i.e., filled the status predication of the ship tail axis by existing convenient consideration
The internal structure set is divided into different program modules, to complete all or part of the functions described above.
The trend prediction method of ship tail axis provided by the present application can be applied in computer equipment as shown in Figure 5.
The computer equipment can be server, be also possible to terminal device, and internal structure chart can be as shown in Figure 5.The computer
Equipment includes processor, memory, network interface and the database connected by system bus.Wherein, processor is for providing
Calculating and control ability；Memory includes non-volatile memory medium, built-in storage, which is stored with behaviour
Make system, computer program (a kind of computer program realizes ship tail axis trend prediction method when being executed by processor) and
Database, the built-in storage provide environment for the operation of operating system and computer program in non-volatile memory medium；Number
According to library for storing data needed for the trend prediction method implementation procedure of ship tail axis；Network interface is used for and external end
End passes through network connection communication.
It will be understood by those skilled in the art that structure shown in Fig. 5, only part relevant to application scheme is tied
The block diagram of structure does not constitute the restriction for the computer equipment being applied thereon to application scheme, specific computer equipment
It may include perhaps combining certain components or with different component layouts than more or fewer components as shown in the figure.
In one embodiment, a kind of computer equipment is provided, including memory, processor and storage are on a memory
And the computer program that can be run on a processor, processor performs the steps of when executing computer program obtains tailing axle prison
Measured data；The acquisition data for the ship tail axis that tailing axle monitoring data are sent according to acquisition equipment determine；Obtain the shape of ship tail axis
State evaluates data；State evaluation data are determined according to the fault signature of ship tail axis；By tailing axle monitoring data and state evaluation number
According to being compared, the status predication result of ship tail axis is determined according to comparison result.
In one embodiment, the event for obtaining ship tail axis is also performed the steps of when processor executes computer program
Hinder feature；According to fault signature and predetermined corresponding relationship, determines the fault vibration frequency of ship tail axis, obtain ship tail
The state evaluation data of axis；Wherein, corresponding relationship includes the mapping relations of fault signature Yu fault vibration frequency.
In one embodiment, reception acquisition equipment is also performed the steps of when processor executes computer program to send
Acquisition data；Acquisition data are obtained by sensor and are sent to acquisition equipment；Sensor is set to ship tail axis；Determine acquisition
The corresponding frequency spectrum data of data, obtains tailing axle monitoring data.
In one embodiment, sensor includes acceleration transducer.
In one embodiment, reception acquisition equipment is also performed the steps of when processor executes computer program to send
Vibration signal；Wherein, vibration signal is obtained by acceleration transducer measurement.
In one embodiment, it also performs the steps of when processor executes computer program by frequency spectrum data and failure
Vibration frequency is compared；If frequency spectrum data matches with fault vibration frequency, determine that the status predication result of ship tail axis is
Tailing axle failure.
In one embodiment, it is also performed the steps of when processor executes computer program to alarm terminal and exports event
Hinder information；Fault message formulates the maintenance project of ship tail axis for guidance management personnel.
In one embodiment, it is also performed the steps of when processor executes computer program in acquisition acquisition data
Spare parts logistics information determines the components to break down in ship tail axis according to spare parts logistics information.
In one embodiment, it also performs the steps of when processor executes computer program according to status predication result
Predict the service life of ship tail axis.
In one embodiment, a kind of computer readable storage medium is provided, computer program is stored thereon with, is calculated
Machine program performs the steps of when being executed by processor obtains tailing axle monitoring data；Tailing axle monitoring data are according to acquisition equipment hair
The acquisition data of the ship tail axis sent determine；Obtain the state evaluation data of ship tail axis；State evaluation data are according to ship tail
The fault signature of axis determines；Tailing axle monitoring data are compared with state evaluation data, ship tail is determined according to comparison result
The status predication result of axis.
In one embodiment, it is also performed the steps of when computer program is executed by processor and obtains ship tail axis
Fault signature；According to fault signature and predetermined corresponding relationship, determines the fault vibration frequency of ship tail axis, obtain ship machine
The state evaluation data of tailing axle；Wherein, corresponding relationship includes the mapping relations of fault signature Yu fault vibration frequency.
In one embodiment, reception acquisition equipment hair is also performed the steps of when computer program is executed by processor
The acquisition data sent；Acquisition data are obtained by sensor and are sent to acquisition equipment；Sensor is set to ship tail axis；Determination is adopted
Collect the corresponding frequency spectrum data of data, obtains tailing axle monitoring data.
In one embodiment, sensor includes acceleration transducer.
In one embodiment, reception acquisition equipment hair is also performed the steps of when computer program is executed by processor
The vibration signal sent；Wherein, vibration signal is obtained by acceleration transducer measurement.
In one embodiment, it is also performed the steps of when computer program is executed by processor by frequency spectrum data and event
Barrier vibration frequency is compared；If frequency spectrum data matches with fault vibration frequency, the status predication result of ship tail axis is determined
For tailing axle failure.
In one embodiment, it also performs the steps of when computer program is executed by processor and is exported to alarm terminal
Fault message；Fault message formulates the maintenance project of ship tail axis for guidance management personnel.
In one embodiment, it is also performed the steps of when computer program is executed by processor in acquisition acquisition data
Spare parts logistics information, the components to break down in ship tail axis are determined according to spare parts logistics information.
In one embodiment, it also performs the steps of when computer program is executed by processor according to status predication knot
The service life of fruit prediction ship tail axis.
It will appreciated by the skilled person that realizing all or part of the process in above-described embodiment method, being can
It is completed with instructing relevant hardware by computer program, the program can be stored in a computer-readable storage and be situated between
In matter, sells or use as independent product.The more specific example (non-exhaustive list) of computer-readable medium includes
Below: there is the electrical connection section (electronic device) of one or more wirings, portable computer diskette box (magnetic device), arbitrary access
Memory (RAM), read-only memory (ROM), erasable edit read-only storage (EPROM or flash memory), optical fiber dress
It sets and portable optic disk read-only storage (CDROM).In addition, computer-readable medium, which can even is that, to be printed on it
The paper of described program or other suitable media, because can be for example by carrying out optical scanner to paper or other media, then
It edited, interpreted or is handled when necessary with other suitable methods electronically to obtain described program, then by it
Storage is in computer storage.
It should be appreciated that each section of the invention can be realized with hardware, software, firmware or their combination.Above-mentioned
In embodiment, software that multiple steps or method can be executed in memory and by suitable instruction execution system with storage
Or firmware is realized.It, and in another embodiment, can be under well known in the art for example, if realized with hardware
Any one of column technology or their combination are realized: having a logic gates for realizing logic function to data-signal
Discrete logic, with suitable combinational logic gate circuit specific integrated circuit, programmable gate array (PGA), scene
Programmable gate array (FPGA) etc..
The term " includes " of the embodiment of the present invention and " having " and their any deformations, it is intended that cover non-exclusive
Include.Such as contain series of steps or the process, method, system, product or equipment of (module) unit are not limited to
The step of listing or unit, but optionally further comprising the step of not listing or unit, or optionally further comprising for these
The intrinsic other step or units of process, method, product or equipment.
Each technical characteristic of embodiment described above can be combined arbitrarily, for simplicity of description, not to above-mentioned reality
It applies all possible combination of each technical characteristic in example to be all described, as long as however, the combination of these technical characteristics is not deposited
In contradiction, all should be considered as described in this specification.
The embodiments described above only express several embodiments of the present invention, should not be understood as to the invention patent range
Limitation.It should be pointed out that for those of ordinary skill in the art, without departing from the inventive concept of the premise,
Various modifications and improvements can be made, and these are all within the scope of protection of the present invention.Therefore, the scope of protection of the patent of the present invention
It should be determined by the appended claims.
Claims (10)
1. a kind of trend prediction method of ship tail axis, which comprises the following steps:
Obtain tailing axle monitoring data；The tailing axle monitoring data are true according to the acquisition data for the ship tail axis that acquisition equipment is sent
It is fixed；
Obtain the state evaluation data of the ship tail axis；The state evaluation data are according to the fault signature of the ship tail axis
It determines；
The tailing axle monitoring data are compared with the state evaluation data, the ship tail axis is determined according to comparison result
Status predication result.
2. the trend prediction method of ship tail axis according to claim 1, which is characterized in that described to obtain the ship tail
The step of state evaluation data of axis, comprising:
Obtain the fault signature of the ship tail axis；
According to the fault signature and predetermined corresponding relationship, determines the fault vibration frequency of the ship tail axis, obtain
The state evaluation data of the ship tail axis；Wherein, the corresponding relationship includes fault signature and fault vibration frequency is reflected
Penetrate relationship.
3. the trend prediction method of ship tail axis according to claim 2, which is characterized in that the acquisition tailing axle monitors number
According to the step of, comprising:
Receive the acquisition data that acquisition equipment is sent；The acquisition data are obtained by sensor and are sent to the acquisition equipment；
The sensor is set to the ship tail axis；
It determines the corresponding frequency spectrum data of the acquisition data, obtains the tailing axle monitoring data.
4. the trend prediction method of ship tail axis according to claim 3, which is characterized in that the sensor includes accelerating
Spend sensor；
Described the step of receiving the acquisition data that acquisition equipment is sent, comprising:
Receive the vibration signal that acquisition equipment is sent；Wherein, the vibration signal is obtained by the acceleration transducer measurement.
5. the trend prediction method of ship tail axis according to claim 3, which is characterized in that described to monitor the tailing axle
Data are compared with the state evaluation data, and the step of the status predication result of the ship tail axis is determined according to comparison result
Suddenly, comprising:
The frequency spectrum data is compared with the fault vibration frequency；
If the frequency spectrum data matches with the fault vibration frequency, determine that the status predication result of the ship tail axis is tail
Axis failure.
6. the trend prediction method of ship tail axis according to claim 5, which is characterized in that the determination ship tail
After the step of status predication result of axis is tailing axle failure, further includes:
Fault message is exported to alarm terminal；The fault message is based on the maintenance that guidance management personnel formulate ship tail axis
It draws.
7. the trend prediction method of ship tail axis according to any one of claims 1 to 6, which is characterized in that the basis
Comparison result determined after the step of status predication result of the ship tail axis, further includes:
The spare parts logistics information in the acquisition data is obtained, the ship tail axis is determined according to the spare parts logistics information
In the components that break down.
8. a kind of status predication device of ship tail axis characterized by comprising
Monitoring data obtain module, for obtaining tailing axle monitoring data；The tailing axle monitoring data are sent according to acquisition equipment
The acquisition data of ship tail axis determine；
Data acquisition module, for obtaining the state evaluation data of the ship tail axis；The state evaluation data are according to
The fault signature of ship tail axis determines；
And prediction result determining module, for the tailing axle monitoring data to be compared with the state evaluation data, root
The status predication result of the ship tail axis is determined according to comparison result.
9. a kind of computer equipment including memory, processor and stores the meter that can be run on a memory and on a processor
Calculation machine program, which is characterized in that the processor is realized described in any one of claim 1 to 7 when executing the computer program
Method the step of.
10. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
The step of claim 1 to 7 described in any item methods are realized when being executed by processor.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110979592A (en) * | 2019-12-13 | 2020-04-10 | 智慧航海（青岛）科技有限公司 | System and method for detecting rotary vibration of propeller rudder of intelligent ship |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6058502A (en) * | 1997-09-03 | 2000-05-02 | Nec Corporation | Diagnostic system analyzing frequency spectrum of electric power for diagnosing integrated circuit, method and information storage medium storing computer program for the method |
| CN101819093A (en) * | 2010-03-31 | 2010-09-01 | 桂林电子科技大学 | Fault diagnosis device for spindle bearing of roadheader cutting head and use method thereof |
| CN101858778A (en) * | 2010-05-28 | 2010-10-13 | 浙江大学 | Vibration monitoring-based wind generator set automatic fault diagnosis method |
| CN102564568A (en) * | 2011-12-29 | 2012-07-11 | 华北电力大学 | Early fault search method for large rotary machinery under complicated working conditions |
| CN103808405A (en) * | 2012-11-08 | 2014-05-21 | 极晨智道信息技术(北京)有限公司 | Automatic diagnostic method of gas power generator group vibration fault of device thereof |
| US20140214363A1 (en) * | 2013-01-25 | 2014-07-31 | General Electric Company | Fault frequency set detection system and method |
| CN204165751U (en) * | 2014-11-11 | 2015-02-18 | 沈阳黎明航空发动机(集团)有限责任公司 | A kind of Aero-engine Bearing failure diagnosis data collection device |
| CN105203346A (en) * | 2015-10-23 | 2015-12-30 | 珠海格力电器股份有限公司 | Fault diagnosis method and system for range hood based on EMD (Empirical Mode Decomposition) noise reduction |
| CN106949056A (en) * | 2017-05-02 | 2017-07-14 | 辽宁工程技术大学 | A kind of pressure fan running status is perceived and failure prediction system and method |
| CN109100150A (en) * | 2018-08-09 | 2018-12-28 | 中国船舶重工集团柴油机有限公司 | Low-speed diesel engine remote condition monitoring and fault diagnosis system |
-
2019
- 2019-03-13 CN CN201910189409.5A patent/CN110082078A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6058502A (en) * | 1997-09-03 | 2000-05-02 | Nec Corporation | Diagnostic system analyzing frequency spectrum of electric power for diagnosing integrated circuit, method and information storage medium storing computer program for the method |
| CN101819093A (en) * | 2010-03-31 | 2010-09-01 | 桂林电子科技大学 | Fault diagnosis device for spindle bearing of roadheader cutting head and use method thereof |
| CN101858778A (en) * | 2010-05-28 | 2010-10-13 | 浙江大学 | Vibration monitoring-based wind generator set automatic fault diagnosis method |
| CN102564568A (en) * | 2011-12-29 | 2012-07-11 | 华北电力大学 | Early fault search method for large rotary machinery under complicated working conditions |
| CN103808405A (en) * | 2012-11-08 | 2014-05-21 | 极晨智道信息技术(北京)有限公司 | Automatic diagnostic method of gas power generator group vibration fault of device thereof |
| US20140214363A1 (en) * | 2013-01-25 | 2014-07-31 | General Electric Company | Fault frequency set detection system and method |
| CN204165751U (en) * | 2014-11-11 | 2015-02-18 | 沈阳黎明航空发动机(集团)有限责任公司 | A kind of Aero-engine Bearing failure diagnosis data collection device |
| CN105203346A (en) * | 2015-10-23 | 2015-12-30 | 珠海格力电器股份有限公司 | Fault diagnosis method and system for range hood based on EMD (Empirical Mode Decomposition) noise reduction |
| CN106949056A (en) * | 2017-05-02 | 2017-07-14 | 辽宁工程技术大学 | A kind of pressure fan running status is perceived and failure prediction system and method |
| CN109100150A (en) * | 2018-08-09 | 2018-12-28 | 中国船舶重工集团柴油机有限公司 | Low-speed diesel engine remote condition monitoring and fault diagnosis system |
Non-Patent Citations (1)
| Title |
|---|
| 陈新轩 等: "《现代测试信号处理理论与实践》", 机械工业出版社, pages: 14 - 16 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110979592A (en) * | 2019-12-13 | 2020-04-10 | 智慧航海（青岛）科技有限公司 | System and method for detecting rotary vibration of propeller rudder of intelligent ship |
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Application publication date: 20190802 |