CN113984185A - Mechanical equipment working hour calculation system and method - Google Patents
Mechanical equipment working hour calculation system and method Download PDFInfo
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Abstract
The invention belongs to the technical field of mechanical equipment working hour calculation, and particularly relates to a mechanical equipment working hour calculation system and a method, wherein the mechanical equipment working hour calculation system comprises a vibration sensor and a cloud server, the vibration sensor is installed on mechanical equipment to be detected for vibration, and the output end of the vibration sensor is in wireless communication connection with the cloud server; software for calculating man-hour according to the vibration condition of the mechanical equipment is arranged on the cloud server; compared with the prior art, the invention has the advantages and positive effects that: (1) the mechanical equipment working hour calculation system and the method are used for judging the state of the mechanical equipment, do not need to extract the multidimensional characteristics of the acceleration signal of the mechanical equipment and do not need to carry out a large amount of supervised model learning training, so that the mechanical equipment working hour calculation is simpler and more convenient, and the operability is stronger; (2) the system and the method for calculating the working hours of the mechanical equipment have strong applicability and can be used for calculating the working hours of different mechanical equipment.
Description
Technical Field
The invention belongs to the technical field of mechanical equipment working hour calculation, and particularly relates to a mechanical equipment working hour calculation system and method.
Background
The real on-site actual working condition of the engineering machinery equipment is reflected by the operating state of the engineering machinery equipment, the problems of virtual reporting of working hours and difficulty in supervision easily occur in the current settlement mode based on the shift, and once the phenomena of no output of work, delayed time or idle dragging occur, the bill settlement can be increased accordingly, the work is accumulated for a long time, and the economic benefits of enterprises are damaged.
The existing method mainly comprises the steps of collecting a large number of equipment acceleration signals, manually marking corresponding working state labels, extracting multi-dimensional features (time domain, frequency domain or time-frequency domain), adopting a supervised learning method to identify three states of static state, idling state and working state of mechanical equipment, and counting the working hours of the equipment based on the three states.
The method needs a large amount of collected and labeled data at the early stage to meet the data requirement of supervised learning, meanwhile, the trained model has poor generalization capability due to strong relevance with the collected data, after equipment is replaced, if classification accuracy is guaranteed, the model needs to be collected again and manually labeled again to be trained, the current engineering equipment has different functions and various quantities, and each mechanical equipment has extremely high cost for training a high-precision model, and almost no feasibility exists.
Disclosure of Invention
The invention aims at the problems and provides a mechanical equipment working hour calculation system and a mechanical equipment working hour calculation method.
In order to achieve the purpose, the invention adopts the technical scheme that: a mechanical equipment working hour computing system comprises a vibration sensor and a cloud server, wherein the vibration sensor is installed on mechanical equipment to be detected for vibration, and the output end of the vibration sensor is in wireless communication connection with the cloud server; and the cloud server is provided with software for calculating the working hours according to the vibration condition of the mechanical equipment.
Preferably, the mobile device of the user side is further included, the mobile device of the user side includes a mobile phone and/or a notebook computer, and the mobile device of the user side is in wireless communication connection with the cloud server.
Preferably, the vibration sensor is a wireless internet of things acceleration sensor.
Preferably, the mechanical equipment to be detected for vibration comprises an excavator, a scraper, a dump truck and a machine tool.
A mechanical equipment man-hour calculating method based on the mechanical equipment man-hour calculating system comprises the following steps:
step one, the vibration sensor uninterruptedly collects vibration signals of mechanical equipment according to a preset sampling period and sampling frequency and sends the collected vibration signals to a remote cloud server;
secondly, the cloud server identifies a time period when the mechanical equipment is in a static state and a time period when the mechanical equipment is in a non-static state according to the energy of the vibration signal, wherein the non-static state comprises an idle state and a working state;
thirdly, the cloud server carries out outlier counting on the time domain waveform of the vibration signal energy in the non-static state time period of the mechanical equipment; when the number of outliers of the vibration signal energy time domain waveform in a sampling period is greater than the threshold value T of the number of outliers2When the sampling period is over, the mechanical equipment is in a working state in a time period corresponding to the sampling period; when the outlier of the time domain waveform of the vibration signal energy in a sampling period is not more than the threshold T of the outlier2Then, the mechanical equipment is in an idle state in a time period corresponding to the sampling period;
fourthly, assigning the mechanical equipment state value of each static state time period in the second step to be-1; assigning the mechanical equipment state value of each working state time period in the third step to be 1; assigning the mechanical equipment state value of each idle state time period in the third step to be 0 to obtain a preliminary mechanical equipment state value time domain graph;
step five, performing time smoothing state correction on the preliminary mechanical equipment state value time domain graph in the step four; in the time smooth state correction, correcting a short idle time period in a non-static state time period into a working state time period, wherein a mechanical equipment state value in the short idle time period is corrected from 0 to 1;
step six, after the time smooth state is corrected, a final mechanical equipment state value time domain graph is obtained; and according to the final mechanical equipment state value time domain diagram, performing mechanical equipment working hour data segmentation and warehousing.
Preferably, the sampling period in the first step includes a sampling time and an interval time.
Preferably, the vibration energy threshold T is set in the second step1When the vibration signal energy in a sampling period is larger than the vibration energy threshold T1Then the mechanical equipment is in a non-static state in the sampling period; when the vibration signal energy in a sampling period is not more than the vibration energy threshold T1Then the mechanical device is in a quiescent state during that sampling period.
Preferably, the third step adopts a quartile method to count the outliers.
Preferably, in the fifth step, the time-smooth state correction of the preliminary time-domain diagram of the state values of the mechanical equipment is completed by using an array mean function, and the time-smooth state correction comprises setting the maximum idle speed duration T3And a modified threshold value Ts, determination of an array of calculated averages and comparison of the average of the array to the modified threshold value Ts.
Preferably, the calculating the array of averages includes respectively extending T forward and backward around any point on the preliminary time domain plot of the plant condition values3The mechanical equipment state values corresponding to all sampling periods in the range of/2;
when the average value of the array is not less than the correction threshold Ts, the preliminary mechanical equipment state value time domain graph T corresponding to the array is used3Correcting the state value of the mechanical equipment at the middle point of the range to be 1; when the average value of the array is smaller than a correction threshold value Ts, a preliminary mechanical equipment state value time domain graph T corresponding to the array is obtained3The machine state value at the midpoint of the range is corrected to 0.
Compared with the prior art, the invention has the advantages and positive effects that:
(1) the mechanical equipment working hour calculation system and the method are used for judging the state of the mechanical equipment, do not need to extract the multidimensional characteristics of the acceleration signal of the mechanical equipment and do not need to carry out a large amount of supervised model learning training, so that the mechanical equipment working hour calculation is simpler and more convenient, and the operability is stronger;
(2) the system and the method for calculating the working hours of the mechanical equipment have strong applicability and can be used for calculating the working hours of different mechanical equipment;
(3) the cloud server identifies the time period of the mechanical equipment in the static state and the time period of the mechanical equipment in the non-static state according to the energy of the vibration signal; the idle speed state and the working state of the mechanical equipment are identified by counting the outliers of the time domain waveform of the vibration signal energy in the non-static state time period of the mechanical equipment; the identification method grasps the characteristics of the vibration signal of the mechanical equipment, so that the state identification is accurate and reliable;
(4) the time smooth state correction of the preliminary mechanical equipment state value time domain graph is completed by using the array mean function, and the idle state in a short time is corrected to be a working state, so that the working time calculation is more in line with the actual situation and more reasonable.
Drawings
In order to more clearly illustrate the technical solution of the embodiment of the present invention, the drawings used in the description of the embodiment will be briefly introduced below, and fig. 1 is a schematic diagram of a method for calculating the man-hour of a mechanical device;
FIG. 2 is a diagram illustrating two steps of a mechanical equipment man-hour calculation method, in which an original vibration signal is identified according to the energy of a vibration signal in a time period when a mechanical equipment is in a stationary state and a time period when the mechanical equipment is not in a stationary state;
fig. 3 is a schematic diagram illustrating an idling state and a working state of a mechanical device by performing outlier statistical analysis on a time-domain waveform of vibration signal energy in a non-stationary time period of the mechanical device in a mechanical device working hour calculation method;
FIG. 4 is a time domain diagram of the state values of the mechanical device obtained in step four of the method for calculating the man-hour of the mechanical device;
FIG. 5 is a time domain diagram of the final mechanical equipment state values obtained in step six of the mechanical equipment man-hour calculation method;
FIG. 6 is a schematic diagram of a vibration sensor sampling cycle;
FIG. 7 is a view of a quartile case.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, the present invention will be further described with reference to the accompanying drawings and examples.
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 thus the present invention is not limited to the specific embodiments of the present disclosure.
Example 1
The invention is further described with reference to fig. 1 to 7, and a mechanical equipment working hour computing system comprises a vibration sensor and a cloud server, wherein the vibration sensor is mounted on a mechanical equipment to be detected for vibration, and the output end of the vibration sensor is in wireless communication connection with the cloud server; the cloud server is provided with software for calculating the working hours according to the vibration condition of the mechanical equipment.
The mobile equipment at the user side comprises a mobile phone and/or a notebook computer. And the mobile equipment at the user side is in wireless communication connection with the cloud server.
The vibration sensor is a wireless internet of things acceleration sensor (model DT-WS 127).
The mechanical equipment to be detected to vibrate comprises an excavator, and the wireless internet of things acceleration sensor is installed at the top of an excavator cab.
Example 2
The difference between this example and example 1 is: the mechanical device to be tested for vibration is a scraper.
As shown in fig. 1, a method for calculating man-hour of mechanical equipment based on the above system for calculating man-hour of mechanical equipment includes the following steps:
in the first step, a vibration sensor uninterruptedly acquires vibration signals of mechanical equipment according to a preset sampling period and sampling frequency (as shown in fig. 6, one sampling period in fig. 6 includes sampling time + interval time, S represents sampling time, and F represents interval time; for example, each sampling period is 1min, the sampling time in the sampling period is 1S, and the sampling frequency is 256HZ, that is, 256 pieces of data are acquired in the sampling time S), and transmits the acquired vibration signals to a remote cloud server.
Secondly, the cloud server identifies a time period when the mechanical equipment is in a static state and a time period when the mechanical equipment is in a non-static state according to the energy of the vibration signal, wherein the non-static state comprises an idle state and a working state, and the idle state and the working state are shown in fig. 2.
As can be seen from fig. 2, the time domain waveforms of the typical vibration signals in the three operating states are greatly different. In a static state, data are disordered and the fluctuation range is very small; the fluctuation range is enlarged during idling, a certain periodicity (related to the vibration frequency of the engine) is reflected, and outliers rarely appear in the vibration signal energy data during idling; during work, the fluctuation range of vibration signal energy data is large, vibration signals are more disordered compared with idling, and some outliers are generated.
The basic observation results of the above three data are consistent with the actual situation. The vibration signal is weak when the device is static, the vibration signal becomes strong when the device is idling and works, and a proper vibration energy threshold T is set based on the energy of the vibration signal1The stationary state can be recognized.
Thirdly, the cloud server performs outlier point counting on the time domain waveform of the vibration signal energy in the non-static state time period of the mechanical equipment, as shown in fig. 3; when the number of outliers of the vibration signal energy time domain waveform in a sampling period is greater than the threshold value T of the number of outliers2When the sampling period is over, the mechanical equipment is in a working state in a time period corresponding to the sampling period; when the outlier of the time domain waveform of the vibration signal energy in a sampling period is not more than the threshold T of the outlier2And then, the mechanical equipment is in an idle state in a time period corresponding to the sampling period.
Drawing a box body diagram by using a quartile, as shown in fig. 7, and judging the number of vibration signal energy data outliers in each sampling period; the vibration signal energy data is above the upper edge or below the lower edge of the quartile bin plot (corresponding to the upper and lower horizontal lines in fig. 7), which is referred to as outliers. The upper edge of the quartile bin plot is the upper quartile Q3 plus 1.5 times the bin length; the lower edge is the box length subtracted by 1.5 times from the lower quartile Q1; the upper box body is an upper quartile Q3; the lower box body is a lower quartile Q1; the length of the box body is the upper quartile minus the lower quartile Q3-Q1.
Fourthly, assigning the mechanical equipment state value of each static state time period in the second step to be-1; assigning the mechanical equipment state value of each working state time period in the third step to be 1; and assigning the mechanical equipment state value of each idle state time period in the third step to be 0 to obtain a preliminary mechanical equipment state value time domain diagram, as shown in fig. 4.
Step five, performing time smoothing state correction on the preliminary mechanical equipment state value time domain graph in the step four; in the time smoothing state correction, a short idle time period in a non-static state time period is corrected to an operating state time period, and a mechanical equipment state value in the short idle time period is corrected from 0 to 1.
Step six, step five, after finishing the time smooth state correction, obtaining a final mechanical equipment state value time domain diagram, as shown in fig. 5; and (4) dividing and warehousing the mechanical equipment working hour data according to the final mechanical equipment state value time domain diagram (completing the mechanical equipment working hour calculation of the current day every day).
The sampling period in step one includes a sampling time S and an interval time F, as shown in fig. 6.
Setting a vibration energy threshold T in the second step1When the vibration signal energy in a sampling period is larger than the vibration energy threshold T1Then the mechanical equipment is in a non-static state in the sampling period; when the vibration signal energy in a sampling period is not more than the vibration energy threshold T1Then the mechanical device is in a quiescent state during that sampling period.
In step three, the number of outliers is counted by using a quartile method, as shown in fig. 7.
In the fifth step, the time smooth state correction of the preliminary mechanical equipment state value time domain graph is finished by utilizing an array mean function (mean function), and the time smooth state correction comprises the step of setting the maximum idle speed duration T3And modifying the threshold Ts, calculating an array of averagesDetermination and comparison of the average of the array with the modified threshold Ts.
The calculation of the array of mean values comprises a forward and backward extension T, respectively, centered on any point on the time-domain diagram of the preliminary machine state values3And the mechanical equipment state values corresponding to all sampling periods in the range of/2.
When the average value of the array is not less than the correction threshold Ts, the preliminary mechanical equipment state value time domain graph T corresponding to the array is used3Correcting the state value of the mechanical equipment at the middle point of the range to be 1; when the average value of the array is smaller than the correction threshold Ts, the preliminary mechanical equipment state value time domain graph T corresponding to the array is used3The machine state value at the midpoint of the range is corrected to 0.
For the state value Si of any point on the preliminary mechanical equipment state value time domain diagram, finding out the T before Si3T after/2 and Si3(ii) ({ Si-n., Si-2, Si-1, Si, Si +1, Si + 2.. Si + m } over time,. Si, then the current state value Si is smoothed to S'i＝mean{Si-n,...,Si-2,Si-1,Si,Si+1,Si+2,...,Si+m}。
And comparing the time-smoothed state with a set threshold Ts, and taking the time-smoothed state as a final work hour calculation basis, wherein the correction formula is as follows:
the above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in other forms, and any person skilled in the art may apply the above-mentioned technical details to other fields by using the equivalent embodiments with equivalent changes, which may be changed or modified by the technical details disclosed in the above description, but any simple modification and equivalent changes made to the above embodiments according to the technical essence of the present invention may still fall within the protection scope of the technical solution of the present invention.
Claims (10)
1. The mechanical equipment working hour computing system is characterized by comprising a vibration sensor and a cloud server, wherein the vibration sensor is installed on mechanical equipment to be detected for vibration, and the output end of the vibration sensor is in wireless communication connection with the cloud server; and the cloud server is provided with software for calculating the working hours according to the vibration condition of the mechanical equipment.
2. The mechanical equipment working hour computing system according to claim 1, further comprising a user-side mobile device, wherein the user-side mobile device comprises a mobile phone and/or a notebook computer, and the user-side mobile device is in wireless communication connection with a cloud server.
3. The mechanical equipment man-hour computing system according to claim 1 or 2, wherein the vibration sensor is a wireless internet of things acceleration sensor.
4. The machine-equipment man-hour calculation system according to claim 3, wherein the machine equipment to be detected for vibration includes an excavator, a scraper, a dump truck, and a machine tool.
5. The mechanical equipment man-hour calculating method based on the mechanical equipment man-hour calculating system according to claim 4, characterized by comprising the steps of:
step one, the vibration sensor uninterruptedly collects vibration signals of mechanical equipment according to a preset sampling period and sampling frequency and sends the collected vibration signals to a remote cloud server;
secondly, the cloud server identifies a time period when the mechanical equipment is in a static state and a time period when the mechanical equipment is in a non-static state according to the energy of the vibration signal, wherein the non-static state comprises an idle state and a working state;
thirdly, the cloud server carries out outlier counting on the time domain waveform of the vibration signal energy in the non-static state time period of the mechanical equipment; when the number of outliers of the vibration signal energy time domain waveform in a sampling period is greater than the threshold value T of the number of outliers2When the sampling period is over, the mechanical equipment is in a working state in a time period corresponding to the sampling period; when one is presentThe outlier number of the time domain waveform of the vibration signal energy in the sampling period is not more than the outlier threshold T2Then, the mechanical equipment is in an idle state in a time period corresponding to the sampling period;
fourthly, assigning the mechanical equipment state value of each static state time period in the second step to be-1; assigning the mechanical equipment state value of each working state time period in the third step to be 1; assigning the mechanical equipment state value of each idle state time period in the third step to be 0 to obtain a preliminary mechanical equipment state value time domain graph;
step five, performing time smoothing state correction on the preliminary mechanical equipment state value time domain graph in the step four; in the time smooth state correction, correcting a short idle time period in a non-static state time period into a working state time period, wherein a mechanical equipment state value in the short idle time period is corrected from 0 to 1;
step six, after the time smooth state is corrected, a final mechanical equipment state value time domain graph is obtained; and according to the final mechanical equipment state value time domain diagram, performing mechanical equipment working hour data segmentation and warehousing.
6. The mechanical equipment man-hour calculation method according to claim 5, wherein the sampling period in the first step includes a sampling time and an interval time.
7. The method for calculating man-hour of mechanical equipment according to claim 6, wherein the vibration energy threshold T is set in the second step1When the vibration signal energy in a sampling period is larger than the vibration energy threshold T1Then the mechanical equipment is in a non-static state in the sampling period; when the vibration signal energy in a sampling period is not more than the vibration energy threshold T1Then the mechanical device is in a quiescent state during that sampling period.
8. The method for calculating the working hours of mechanical equipment according to claim 5 or 7, wherein the third step is a step of counting the outliers by a quartile method.
9. The mechanical equipment man-hour calculation method according to claim 8, wherein the time-smoothing state correction of the preliminary mechanical equipment state value time-domain graph is performed using an array mean function in the fifth step, and the time-smoothing state correction includes setting a maximum idle duration T3And a modified threshold value Ts, determination of an array of calculated averages and comparison of the average of the array to the modified threshold value Ts.
10. The method of claim 9, wherein computing the array of averages includes extending T forward and backward, respectively, centered at any point on the preliminary time-domain plot of machine state values3The mechanical equipment state values corresponding to all sampling periods in the range of/2;
when the average value of the array is not less than the correction threshold Ts, the preliminary mechanical equipment state value time domain graph T corresponding to the array is used3Correcting the state value of the mechanical equipment at the middle point of the range to be 1; when the average value of the array is smaller than a correction threshold value Ts, a preliminary mechanical equipment state value time domain graph T corresponding to the array is obtained3The machine state value at the midpoint of the range is corrected to 0.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106596162A (en) * | 2016-12-29 | 2017-04-26 | 上海威惠智能科技有限公司 | Intelligent vibration detecting method, device and system |
| CN107270956A (en) * | 2017-06-05 | 2017-10-20 | 浙江聚励云机械科技有限公司 | A kind of mechanically moving equipment task time computational methods based on vibrating sensor |
| WO2018119845A1 (en) * | 2016-12-29 | 2018-07-05 | 深圳配天智能技术研究院有限公司 | State detection method and system for numerical control machine tool |
| CN109186696A (en) * | 2018-10-11 | 2019-01-11 | 北京中位科技有限公司 | Monitoring method when a kind of novel work |
| CN110221563A (en) * | 2018-11-25 | 2019-09-10 | 董志强 | Engineering machinery current working status remotely judges and telework time set |
| CN110702173A (en) * | 2019-11-08 | 2020-01-17 | 杭州慧工科技有限公司 | Self-adaptive engineering machinery monitoring equipment and use method thereof |
| CN111272456A (en) * | 2020-02-14 | 2020-06-12 | 南京智鹤电子科技有限公司 | Mechanical state detection method based on position change data and electronic equipment |
| CN111324863A (en) * | 2020-02-14 | 2020-06-23 | 南京智鹤电子科技有限公司 | Mechanical state detection method and electronic device |
| CN111504385A (en) * | 2020-05-13 | 2020-08-07 | 兰州工业学院 | Multi-parameter monitoring device and method suitable for abnormal state of mechanical equipment |
| CN112146749A (en) * | 2020-09-08 | 2020-12-29 | 成都安尔法智控科技有限公司 | Method and system for analyzing starting and stopping states of equipment based on vibration signals |
-
2021
- 2021-10-28 CN CN202111263246.4A patent/CN113984185A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106596162A (en) * | 2016-12-29 | 2017-04-26 | 上海威惠智能科技有限公司 | Intelligent vibration detecting method, device and system |
| WO2018119845A1 (en) * | 2016-12-29 | 2018-07-05 | 深圳配天智能技术研究院有限公司 | State detection method and system for numerical control machine tool |
| CN109496285A (en) * | 2016-12-29 | 2019-03-19 | 深圳配天智能技术研究院有限公司 | The condition detection method and system of numerically-controlled machine tool |
| CN107270956A (en) * | 2017-06-05 | 2017-10-20 | 浙江聚励云机械科技有限公司 | A kind of mechanically moving equipment task time computational methods based on vibrating sensor |
| CN109186696A (en) * | 2018-10-11 | 2019-01-11 | 北京中位科技有限公司 | Monitoring method when a kind of novel work |
| CN110221563A (en) * | 2018-11-25 | 2019-09-10 | 董志强 | Engineering machinery current working status remotely judges and telework time set |
| CN110702173A (en) * | 2019-11-08 | 2020-01-17 | 杭州慧工科技有限公司 | Self-adaptive engineering machinery monitoring equipment and use method thereof |
| CN111272456A (en) * | 2020-02-14 | 2020-06-12 | 南京智鹤电子科技有限公司 | Mechanical state detection method based on position change data and electronic equipment |
| CN111324863A (en) * | 2020-02-14 | 2020-06-23 | 南京智鹤电子科技有限公司 | Mechanical state detection method and electronic device |
| CN111504385A (en) * | 2020-05-13 | 2020-08-07 | 兰州工业学院 | Multi-parameter monitoring device and method suitable for abnormal state of mechanical equipment |
| CN112146749A (en) * | 2020-09-08 | 2020-12-29 | 成都安尔法智控科技有限公司 | Method and system for analyzing starting and stopping states of equipment based on vibration signals |
Non-Patent Citations (1)
| Title |
|---|
| 范文超;: "基于Qt和Matlab的数控机床主轴振动监测系统设计", 北京信息科技大学学报(自然科学版), no. 01, 15 February 2020 (2020-02-15), pages 81 - 84 * |
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