CN110866558A - Multi-source data fusion analysis-based rotating equipment state early warning method - Google Patents
Multi-source data fusion analysis-based rotating equipment state early warning method Download PDFInfo
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Abstract
The invention relates to monitoring of equipment state, in particular to a rotary equipment state early warning method based on multi-source data fusion analysis, which comprises the steps of establishing a hierarchical model of monitored equipment; configuring normalization rules of various state variables of the monitored equipment; obtaining induction signals of a plurality of sensors of monitored equipment to obtain multi-source signals; confirming the multi-source signals and eliminating invalid data; carrying out state fusion quantization on the verified multi-source signal according to the hierarchical model and a state variable normalization rule; and comparing the quantized value of the state fusion quantization with a state variable normalization rule to obtain the state of the monitored equipment. The method adopts a correlation analysis and equivalent analysis method to verify the multi-source signal and eliminate invalid data, and can effectively ensure the correctness and accuracy of the data source.
Description
Technical Field
The invention relates to monitoring of equipment state, in particular to a rotary equipment state early warning method based on multi-source data fusion analysis.
Background
The common state early warning methods for the rotating equipment include a state feature analysis method, a probability model analysis method, a state classification method, a state prediction method and the like. The state feature analysis method generally extracts state features through fault mechanism modeling, and has the advantages that equipment abnormity criterion is clear, the equipment state can be accurately judged, and the state early warning accuracy rate is high; the defect is that the difficulty of feature modeling is high, and especially the effect is very poor when the monitoring equipment object has strong nonlinearity. The probability model method realizes state early warning according to probability distribution of equipment in different states, has the advantages that fault early warning threshold values do not need to be defined, and has the defect that abnormal state classification characteristics need to be obtained through a large number of tests. The state classification method realizes fault early warning by using a normal state distribution range, has the advantages that only normal state historical data is needed, data is easy to obtain, and has the defects that a common classification algorithm is complex and the result has certain randomness. The state prediction method realizes state early warning by predicting the current state or the future state, the early warning method has strong dependence on data on the prediction effect, and the calculation amount is large and is not easy to be put into practical application.
However, from the analysis on the device model, the state of one device can be characterized by the state of its child devices; the state of a sub-device may then be characterized by the state of its sub-components; the state of a sub-component can be characterized by the state of various sensors mounted on the component; thus, the state of a device can be characterized by the state of the sensors mounted on all the components of the device. From the analysis, the condition evaluation and early warning of the equipment by adopting multiple data sources (sensors) are effective and feasible. From the industrial response angle, the equipment model is simple, the operand is small, the early warning result is matched with the actual equipment operation condition, and the final target of the equipment state early warning is provided, and the equipment state data source comes from various sensors, so that the condition of how to ensure the data correctness of the sensors is the premise of the state early warning.
Disclosure of Invention
Aiming at the technical problems, the invention provides an applicable and credible equipment state quantification model and a rotary equipment state early warning method for multi-source data fusion analysis, which is used for carrying out state early warning on equipment so as to improve the accuracy of equipment state warning and ensure the operation safety of the equipment.
The technical scheme adopted by the invention is as follows: a rotating equipment state early warning method based on multi-source data fusion analysis comprises the following steps:
(1) establishing a hierarchical model of monitored equipment;
(2) configuring normalization rules of various state variables of the monitored equipment;
(3) obtaining induction signals of a plurality of sensors of monitored equipment to obtain multi-source signals;
(4) confirming the multi-source signals and eliminating invalid data;
(5) carrying out state fusion quantization on the verified multi-source signal according to the hierarchical model and a state variable normalization rule;
(6) and comparing the quantized value of the state fusion quantization with a state variable normalization rule to obtain the state of the monitored equipment.
Preferably, a model of the upper-level device, which is composed of the sub-devices b1, b2 and b3, is represented by (a) ═ b1, b2 and b3, and the following hierarchical models of the monitored devices are established:
D＝(SD1，SD2,SD3，….SDm)
SDi＝(P1,P2,P3,…Pn)
Pi＝(V1,V2,V3,..Vr)
where D denotes a device, SDi denotes the ith sub-device, Pi denotes the ith sub-component, and Vi denotes the i sensors.
Preferably, for the state variable x of the monitored equipment, the following normalization cardinalities are configured according to a threshold value set by a standard or mined:
and according to a normalization method, calculating to obtain the following quantization curves of the state variables:
1) in the normal area, the first and second regions,
2) the early-warning area is provided with a warning area,
3) a warning area is arranged in the air conditioner,
4) fault area, y ═ 0
Wherein, W is the early warning threshold of x, A is the alarm threshold of x, F is the fault threshold of x, Sx is the quantization index of x, Tx is the quantization state of x.
Preferably, when the multi-source signals are verified, the cross-correlation analysis is carried out on the sensors of the same type according to the following method:
1) configuring a correlation coefficient threshold and selecting verification signal classification for the monitored equipment in a page interaction mode;
2) generating a database verification parameter list according to the signal type form of the page;
3) and calling an authentication service program to sequentially authenticate a certain parameter pair in the parameter list.
Preferably, when the multi-source signals are verified, the following equivalent energy temperature rise methods are adopted to verify the current signals and the temperature signals for different types of current sensor signals and temperature sensor signals:
step1, traversing the database, operating each current point, and generating a new equivalent power term, wherein the calculation method is as follows:
EngCur＝I*I*T/125000；
in the above formula, the variable T is the time interval (unit: second) of two consecutive data points; the variable I is current (unit: A); the variable EngCur is equivalent power;
step2, performing block processing on the equivalent power item, and calculating the average value, the maximum value and the minimum value of each block in the following way:
EngBlockAvg＝(EngCur1+EngCur2+EngCur3+..+EngCurn)/n；
EngBlockMax＝Max(EngCur1+EngCur2+EngCur3+..+EngCurn)；
EngBlockMin＝Min(EngCur1+EngCur2+EngCur3+..+EngCurn)；
in the above formula, the variable n is the number of sample points of each block (default is 100); EngBlockAvg is the average of the blocks; EngBlockMax is the maximum value of the block; EngBlockMin is the minimum value of the block.
And after the calculation is finished, storing the results EngBlockAvg, EngBlockMax and EngBlockMin into a newly-built database table, and taking the timestamp of the first sample of the block as the timestamp of the block.
Preferably, the state fusion quantization values of the subcomponents, the sub-devices and the devices are sequentially calculated according to a minimization rule to obtain:
state fusion quantization values of subcomponents Sx (pi) ═ Minimum [ Sx (V1), Sx (V2), Sx (V3) … Sx (vn));
state fusion quantization values Sx (sdi) Minimum [ Sx (P1), Sx (P2), Sx (P3) … Sx (pn));
the state fusion quantization values of the devices Sx (d) ═ Minimum [ Sx (SD1), Sx (SD2), Sx (SD3) … Sx (sdn)).
The invention has the technical effects that:
1) the method is based on the equipment level model foundation, and each equipment consists of different parts and sensors, so the method is suitable for various electromechanical equipment.
2) The method adopts a correlation analysis and equivalent analysis method to verify the multi-source signal and eliminate invalid data, and can effectively ensure the correctness and accuracy of the data source.
3) The method normalizes any state variable, and the normalized state parameter characteristics can be compared, operated, counted and fused with each other, so that the state evaluation and early warning of equipment are facilitated.
4) The method simplifies any equipment state into four states: normal state, early warning state, alarm state, fault state, and monitoring, early warning, and diagnosis of equipment state are simpler and more intuitive.
Drawings
FIG. 1 is a diagram of a hierarchical model of a rotating device.
Fig. 2 is a flow chart of the validation of the same type of sensor.
Fig. 3 is a flow chart demonstrating the current and temperature rise data.
FIG. 4 is a diagram illustrating abnormal data and normal data.
FIG. 5 is a flow chart of invalid data culling.
Fig. 6 is a state variable normalized quantization graph.
Fig. 7 is a flow chart of a state variable quantization method.
Detailed Description
The present invention will be described in detail with reference to fig. 1 to 7, and the exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
The invention provides a rotary equipment state early warning method based on multi-source data fusion analysis, which comprises the following steps:
firstly, establishing a hierarchical model of monitored equipment, as shown in fig. 1, taking a motor rotating type device as an example, the state of one device can be represented by the state of a sub-device thereof; the state of a sub-device may then be characterized by the state of its sub-components; the state of a sub-component may be characterized by the state of a sensor mounted on the component; the state of a device can be characterized by the state of the sensors installed on all components of the device. Therefore, a simple state analysis model can be established for the monitored equipment according to the hierarchical relation;
secondly, performing correlation analysis on multi-source similar signals of the same monitoring point by adopting correlation analysis to ensure the correctness of the sensor signals, as shown in FIG. 2; and performing correlation analysis on the current signal and the temperature signal by using an equivalent temperature rise energy efficiency method to ensure the correctness of the temperature signal, as shown in fig. 3. Meanwhile, invalid data should be removed, as shown in fig. 4 and 5.
And thirdly, starting from the type of the monitored state parameter, each state variable has a respective numerical range and a respective engineering unit. And different types of parameters cannot be directly compared and operated. However, for any one state variable of the device, the state of the state variable can be divided into four state areas, including 1) a normal state 2) an early warning state 3) an alarm state 4) a fault state; in fact, the equipment state change process follows the service process of normal- > early warning- > alarming- > fault. For any state variable, its state value is quantized to the [0-1] interval. Therefore, the characteristics corresponding to all the state variables can be represented by the quantized values ([01.0 ]) and the characteristic index values, and the state normalization processing is carried out on the multi-source signals according to the rule. As shown in fig. 6 and 7.
And finally, combining the equipment level model and adopting a maximum value principle to perform multivariate data state fusion analysis on the state quantization index values of the equipment, the sub-equipment and the sub-components in a tree structure according to the following state fusion rules.
Component status quantized value min (status quantized value of sensor i)
State quantization value of sub-device ═ Minimum (state quantization value of component i)
Device state quantization value ═ Minimum (state quantization value of child device i)
The method can be operated as a pre-alarm service form and on a cloud platform, and can quickly and accurately perform pre-alarm and state tracing of the equipment state. The method comprises the following specific steps:
step1: establishing a device level model
For each monitored device, before the monitoring system and the platform operate, modeling the device, and defining the composition relationship among the device, the sub-component and the sensor in a configuration page or function, and assuming that a ═ b1, b2 and b3 indicates that the upper-layer device is composed of the sub-devices b1, b2 and b3, the model of one device can be constructed in the following way:
D＝(SD1，SD2,SD3，….SDm)
SDi＝(P1,P2,P3,…Pn)
Pi＝(V1,V2,V3,..Vr)
in the above formula: d denotes the device, SDi denotes the ith sub-device, Pi denotes the ith sub-component, and Vi denotes the i sensors.
Step2: configuring state variable normalization rules
For a certain state variable x, a threshold value set or mined according to a standard is set as a normalization base number, and a quantization curve of each state variable is calculated according to a normalization method, as shown in fig. 2.
The quantization curve relationship of the four state intervals is as follows:
1) in the normal area, the first and second regions,
2) the early-warning area is provided with a warning area,
3) a warning area is arranged in the air conditioner,
4) fault area, y ═ 0
Wherein: w is the early warning threshold of x, A is the alarm threshold of x, F is the fault threshold of x, Sx is the quantization index of x, and Tx is the quantization state of x.
And step 3: obtaining multi-source signals, validating and rejecting invalid sensor data
The correctness of the sensor data is verified whether the sensor channel is normal or not by carrying out correlation analysis and characteristic comparison on different sensor data.
For the same type of sensors (V1, V2) on the same monitoring point, the signal amplitudes of V1 and V2 may be different, but the frequency distributions of the V1 and the V2 are the same, the V1 and the V2 are subjected to cross-correlation analysis, and the correctness of the signals is judged by judging the magnitude of the correlation coefficients. If a certain device has 5 sensors A, B, C, D and E, wherein A, B and C are located at the same measuring point, D and E are located at the same measuring point, according to the rule, only the sensors at the same measuring point are corrected, and then a verification signal pair is formed as follows: AB. BC, AC, DE. The signals are periodically correlated to ensure accuracy of the multi-sensor source signals.
For different sensors, such as a current sensor and a winding temperature sensor, when the current load is increased, the temperature is increased, and when the current load is reduced, the winding temperature is reduced, so that the load parameter (current) and the temperature parameter have the same change trend, and the single temperature parameter and the load parameter cannot be directly compared and analyzed in numerical value.
Sensor data validity refers to filtering out invalid data by setting reasonable filtering rules, such as: data in which the velocity component is very high but the vibration velocity component is close to 0 may occur in the data; if the data with the speed of 0 but high current do not conform to the actual physical phenomenon, the data can be regarded as non-real data, and the method can be used for a data filtering module in a data processing process, such as the data filtering module shown in FIG. 5.
And 4, step 4: multi-source signal state data fusion
And from bottom to top, sequentially calculating the state fusion quantization values of the sensor, the part, the sub-equipment and the equipment according to a minimization rule from the sensor layer- > the part layer- > the sub-equipment layer- > the equipment layer.
State fusion quantization values of parts Sx (Pi) ═ Minimum [ Sx (V1), Sx (V2), Sx (V3) … Sx (Vn)
Status fusion quantization values Sx (SDi) ═ Minimum [ Sx (P1), Sx (P2), Sx (P3) … Sx (Pn)
Device state fusion quantization values Sx (D) ═ Minimum [ Sx (SD1), Sx (SD2), Sx (SD3) … Sx (SDn)
And 5: fusion status pre-alarm
And 4, performing real-time fusion quantization in the step 4, and corresponding to the rule according to the following formula:
and evaluating the overall state of the equipment, analyzing the degradation trend and triggering an alarm.
Claims (6)
1. A rotating equipment state early warning method based on multi-source data fusion analysis is characterized by comprising the following steps:
(1) establishing a hierarchical model of monitored equipment;
(2) configuring normalization rules of various state variables of the monitored equipment;
(3) obtaining induction signals of a plurality of sensors of monitored equipment to obtain multi-source signals;
(4) confirming the multi-source signals and eliminating invalid data;
(5) carrying out state fusion quantization on the verified multi-source signal according to the hierarchical model and a state variable normalization rule;
(6) and comparing the quantized value of the state fusion quantization with a state variable normalization rule to obtain the state of the monitored equipment.
2. The multi-source data fusion analysis-based rotating equipment state early warning method according to claim 1, wherein a (b1, b2, b3) is used for representing a model formed by sub-equipment b1, b2 and b3 of upper-layer equipment, and the following hierarchical models of monitored equipment are established:
D＝(SD1，SD2,SD3，….SDm)
SDi＝(P1,P2,P3,…Pn)
Pi＝(V1,V2,V3,..Vr)
where D denotes a device, SDi denotes the ith sub-device, Pi denotes the ith sub-component, and Vi denotes the i sensors.
3. The multi-source data fusion analysis-based rotating equipment state early warning method according to claim 2, wherein the following normalization cardinalities are configured for the state variable x of the monitored equipment according to a standard set or mined threshold value:
and according to a normalization method, calculating to obtain the following quantization curves of the state variables:
1) in the normal area, the first and second regions,
2) the early-warning area is provided with a warning area,
3) a warning area is arranged in the air conditioner,
4) fault area, y ═ 0
Wherein, W is the early warning threshold of x, A is the alarm threshold of x, F is the fault threshold of x, Sx is the quantization index of x, Tx is the quantization state of x.
4. The rotating equipment state early warning method based on multi-source data fusion analysis of claim 3, wherein when the multi-source signals are verified, cross-correlation analysis is performed on the same type of sensors according to the following method:
1) configuring a correlation coefficient threshold and selecting verification signal classification for the monitored equipment in a page interaction mode;
2) generating a database verification parameter list according to the signal type form of the page;
3) and calling an authentication service program to sequentially authenticate a certain parameter pair in the parameter list.
5. The rotating equipment state early warning method based on multi-source data fusion analysis of claim 4, wherein when the multi-source signals are verified, the following equivalent energy temperature rise method is adopted to verify the current signals and the temperature signals for different types of current sensor signals and temperature sensor signals:
step1, traversing the database, operating each current point, and generating a new equivalent power term, wherein the calculation method is as follows:
EngCur＝I*I*T/125000；
in the above formula, the variable T is the time interval (unit: second) of two consecutive data points; the variable I is current (unit: A); the variable EngCur is equivalent power;
step2, performing block processing on the equivalent power item, and calculating the average value, the maximum value and the minimum value of each block in the following way:
EngBlockAvg＝(EngCur1+EngCur2+EngCur3+..+EngCurn)/n；
EngBlockMax＝Max(EngCur1+EngCur2+EngCur3+..+EngCurn)；
EngBlockMin＝Min(EngCur1+EngCur2+EngCur3+..+EngCurn)；
in the above formula, the variable n is the number of sample points of each block (default is 100); EngBlockAvg is the average of the blocks; EngBlockMax is the maximum value of the block; EngBlockMin is the minimum value of the block;
and after the calculation is finished, storing the results EngBlockAvg, EngBlockMax and EngBlockMin into a newly-built database table, and taking the timestamp of the first sample of the block as the timestamp of the block.
6. The multi-source data fusion analysis-based rotating equipment state early warning method according to claim 5, wherein state fusion quantitative values of the sub-components, the sub-equipment and the equipment are sequentially calculated according to a minimization rule to obtain:
state fusion quantization values of subcomponents Sx (pi) ═ Minimum [ Sx (V1), Sx (V2), Sx (V3) … Sx (vn));
state fusion quantization values Sx (sdi) Minimum [ Sx (P1), Sx (P2), Sx (P3) … Sx (pn));
the state fusion quantization values of the devices Sx (d) ═ Minimum [ Sx (SD1), Sx (SD2), Sx (SD3) … Sx (sdn)).
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111818487A (en) * | 2020-05-01 | 2020-10-23 | 东风汽车集团有限公司 | Signal transmission optimization method for sensor group of electric vehicle network node |
| CN111980900A (en) * | 2020-07-15 | 2020-11-24 | 湘潭中环水务有限公司 | Water pump fault diagnosis method based on multi-source data fusion analysis |
| CN113804252A (en) * | 2021-09-10 | 2021-12-17 | 广州市吉华勘测股份有限公司 | High formwork supporting safety monitoring method, device, equipment and storage medium |
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2019
- 2019-11-13 CN CN201911108015.9A patent/CN110866558A/en not_active Withdrawn
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111818487A (en) * | 2020-05-01 | 2020-10-23 | 东风汽车集团有限公司 | Signal transmission optimization method for sensor group of electric vehicle network node |
| CN111980900A (en) * | 2020-07-15 | 2020-11-24 | 湘潭中环水务有限公司 | Water pump fault diagnosis method based on multi-source data fusion analysis |
| CN111980900B (en) * | 2020-07-15 | 2022-04-15 | 湘潭中环水务有限公司 | Water pump fault diagnosis method based on multi-source data fusion analysis |
| CN113804252A (en) * | 2021-09-10 | 2021-12-17 | 广州市吉华勘测股份有限公司 | High formwork supporting safety monitoring method, device, equipment and storage medium |
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Application publication date: 20200306 |