CN103671190A - Intelligent early stage on-line fault diagnosis system of mine fan - Google Patents
Intelligent early stage on-line fault diagnosis system of mine fan Download PDFInfo
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- CN103671190A CN103671190A CN201310426466.3A CN201310426466A CN103671190A CN 103671190 A CN103671190 A CN 103671190A CN 201310426466 A CN201310426466 A CN 201310426466A CN 103671190 A CN103671190 A CN 103671190A
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Abstract
The invention discloses an intelligent early stage on-line fault diagnosis system of a mine fan, and belongs to the field of equipment early stage fault diagnosis. According to the importance of the mine fan on coal mine safety and the complexity of early stage fault diagnosis, accurate extraction of operating state features of the mine fan is achieved, and tiny change of the operating state of the mine fan can also be recognized. Due to the fact that only fault early warning can be achieved for a fault diagnosis device of an existing mine fan, early faults can not be diagnosed. According to the intelligent early stage on-line fault diagnosis system of the mine fan, the state information extracted by a sensor and a transmitter is preprocessed through a cluster and singular value decomposition method, the state features of the mine fan are extracted, and noise interference is avoided; on the aspect of fault identification and classification, a support vector machine model is utilized, meanwhile, data of each diagnosis are used for continuously enriching and updating a training and learning sample of a support vector machine, so that the model includes more information, and thus the purposes of accuracy, rapidness and intelligence of the early faults of the mine fan are achieved.
Description
Technical field
The present invention relates to the early stage mine fan online system failure diagnosis of a kind of intelligence, belong to field of diagnosis about equipment fault, by the state signal of analysis and computing mine fan, diagnose and identify state and the change of state of mine fan, especially for the fault diagnosis of the mine fan in rugged environment, belong to mine fan Incipient Fault Diagnosis field.
Background technique
Mine fan is to mine, constantly to carry the visual plant of fresh air, dilution dust, toxic and harmful, the normal work of mine fan is the prerequisite of safety of coal mines, the fault of mine fan will cause huge economic loss, even serious casualties.In order to guarantee mine fan safety, reliably and reposefully to move, need to carry out Incipient Fault Diagnosis to mine fan.
Due to mine fan work under bad environment, long-play, very easily breaks down, fault type is many, characteristic signal is faint and background noise is strong, for accurately, diagnose out fault type and the abort situation of mine fan as soon as possible, need to select suitable method for diagnosing faults and hardware platform.The monitoring system of traditional mine fan can only be found the middle and advanced stage fault of mine fan, and can not diagnose out initial failure, and wrong diagnosis rate is high, poor reliability.Fault discovery more early, less to the maintenance cost of mine fan.Mine fan needs accurately objectively fault diagnosis system, realizes mine fan Incipient Fault Diagnosis reliably, guarantees mine fan safety.The current domestic ripe mine fan Incipient Fault Diagnosis system that also do not have, therefore researchs and develops the early stage mine fan online system failure diagnosis of a kind of intelligence and has important practical significance.
Summary of the invention
The object of the invention is to: the middle and advanced stage fault that can only find mine fan for the monitoring system of current mine fan, and can not diagnose out the deficiency of the initial failure of mine fan mine fan, researched and developed the early stage mine fan online system failure diagnosis of a kind of intelligence, this system is for the Incipient Fault Diagnosis of mine fan.By the state signal of sensor measurement mine fan, the state signal of equipment is transferred in upper-position unit by lower-position unit PLC, by clustering algorithm, state signal is done to pretreatment, cancelling noise signal, utilize singular value decomposition to extract the status flag of mine fan, signal characteristic is input in supporting vector machine model to the initial failure of identification and diagnosis mine fan.
The early stage mine fan online system failure diagnosis of intelligence, usings cluster and singular value decomposition as the extracting method of signal characteristic, utilizes support vector machine identification and tracing trouble, and signal characteristic extracting methods wherein comprises the following steps:
S1. utilize (the k) (k=1 of data x (θ) of method processing data acquisition module (known) collection of cluster, 2 ... N), N sampled point, θ is signal code, representation temperature, vibration, negative pressure, carbonomonoxide concentration, methane concentration, voltage, electric current, θ=1,2 ... ss is signal sum, set number of clusters n, after analysis, comprise signal maximum bunch for required signal, remaining bunch concentrates the signal comprising as being abnormity point, remove the abnormity point in signal, obtain comprising bunch collection K that data are maximum
i(θ) (x), K
i(θ) (x)=[x
i1, x
i2... x
im], i is the ordinal number of divided bunch collection, i=1,2 ... n, m is that the signal that comprises bunch collection that data are maximum is counted, m≤N;
S2. to the K in S1
i(θ) (x) structure Hankel matrix A,
S3. the matrix A in S2 is done to singular value decomposition, getting first singular value λ (θ) is signal characteristic;
S4. with the λ in S3 (θ) structure X={ λ (1), λ (2) ... λ (s) } be the input of support vector machine, output Y={y
1, y
2y
s, y wherein
θequal 0 or θ, breaking down in certain position, exports corresponding code θ, normally exports 0, according to code, determines location of fault.
S5. with the form of form, record the training input and output of supporting vector machine model, form walk crosswise the output that expresses support for vector machine, the sequence number of perpendicular line display sample, support vector machine after having trained diagnosis output all to compare with this form, if export identically with form, the state of mine fan is identical with sample;
The early stage mine fan online system failure diagnosis of a kind of intelligence, comprise upper-position unit, lower-position unit, signal acquisition module, temperature transducer, vibration transducer, B/P EGR Back Pressure Transducer EGR, methane transducer, carbon monoxide transducer, ACR series network multifunctional power meter; Two sensors of same measured point are connected to analog-to-digital conversion module SM331 by shielding wire, and SM331 is converted into digital quantity analog amount, is input in CPU314; The voltage and current of mine fan motor is measured by ACR series network multifunctional power meter, by RS485, is input in CPU314; The hardware of every mine fan Incipient Fault Diagnosis system is two covers, each other redundancy; CPU314 is input to upper-position unit by measured signal, and upper-position unit calculates the mean value of two sensors of same measured point as the signal of this measuring point; Two PLC monitor mutually, and the PLC of working fan periodically sends instruction to another PLC, and reset timer, if can not reset, sends malfunction alarm; There is the state of abnormal or fault simulation blower fan and make corresponding control strategy in mine fan, start and stop, automatic pouring machine, the air door of controlling blower fan open and close; Upper-position unit utilizes WinCC as configuration software, finishing man-machine interaction, both can show the status parameter of blower fan, the performance curve of the electric current and voltage of showing temperature, motor, vibration, negative pressure, air quantity, carbon monoxide content, methane content, mine fan, air door state and petrol station state, can realize control inputs again, the alarm threshold value of input temp, vibration, negative pressure, air quantity, methane content, carbon monoxide content; Can, by keyboard input control order on upper station of WinCC, control the switching of air door, the start and stop of blower fan; Temperature transducer Pt100 is arranged on respectively the both sides of bearing support and the stator coil side of motor, and all bearing supports are all installed integral type vibration transmitters in the horizontal and vertical directions; CPU314 output point connects relay, Control damper motor, fan motor and petrol station electric power switch.
Native system is integrated in fault diagnosis algorithm in WinCC, and only configuration software can complete fault diagnosis and the control of mine fan.
The early stage mine fan on-line fault diagnosis of the intelligence system that the present invention proposes, its advantage is:
1, realize the mine fan Incipient Fault Diagnosis of online real-time intelligent, can accurately extract the status flag of mine fan, can diagnose out mine fan whether to have type and the abort situation of fault and fault as soon as possible.
2,, for the work under bad environment of mine fan and high to reliability requirement, all hardware of fault diagnosis system all adopts two redundant measure, the Incipient Fault Diagnosis system of guaranteeing mine fan reliably, accurately operation.
3, all fault diagnosis algorithms are integrated in upper-position unit configuration software WinCC, and a software completes diagnosis and the control of mine fan fault, have increased the reliability of system.
Accompanying drawing explanation
Fig. 1 native system hardware schematic diagram;
Fig. 2 native system Troubleshooting Flowchart;
Embodiment
Below in conjunction with accompanying drawing and example, the present invention is described in detail:
The hardware configuration of this system as shown in Figure 1, mainly by upper-position unit, lower-position unit, data acquisition module, control inputs module, control output module and sensor forms, all hardware is all two covers, each other without cross-reference, redundancy each other.Between upper-position unit and lower-position unit, adopt Profibus agreement to be connected between lower-position unit and lower-position unit.
This system fault diagnosis flow process as shown in Figure 2, Cluster Classification bunch collection number n is set, the length of window l of singular value is set, the signal of data collecting module collected is input in lower-position unit PLC, and then be transferred in upper-position unit, the mean value of two signals of same node is as the signal of this measuring point, by cluster, remove abnormity point, utilize singular value decomposition to extract the status flag of signal, training using the result of singular value decomposition as supporting vector machine model and diagnosis input, the state of output mine fan, if any fault, reports to the police and automatically takes corresponding control.The mine fan of take is example, and its elementary process is as follows:
1. respectively No. 1, No. 2 PLC being set to address is that the address of 1,2, No. 1 upper-position unit, No. 2 upper-position units is made as 3,4;
2. on each bearing of mine fan and motor stator, two Pt100 temperature transducers are installed, in the substantially horizontal of each bearing support and Vertical direction, two integrated vibration transmitters are installed respectively as vibration transducer respectively, at exhaust outlet, two carbon monoxide transducers and two methane transducers are installed, at intake grill and the exhaust outlet of blower fan, two B0300 type technical grade micro-pressure transmitters are installed as B/P EGR Back Pressure Transducer EGR respectively, the voltage and current that two ACR series network multifunctional power meters are measured mine fan is installed on the PT of mine fan electric closet;
3. except two ACR series network multifunctional power meters, all sensors are connected to the modulus dress die change piece SM331 of Siemens by shielding wire, the address of setting two ACR series network multifunctional power meters is respectively 10,11, by RS485 communications protocol, be input to the P0 mouth of Siemens CPU314;
4. by DP line, connect No. 1 PLC and 1 upper-position unit, No. 2 PLC and No. 2 upper-position units, No. 1 PLC and No. 2 PLC;
5. on upper-position unit, the threshold value of inputting mine fan temperature, vibration, negative pressure, air quantity, carbon monoxide content, methane content, electric moter voltage and current of electric by keyboard, in WinCC, exceeds threshold value and reports to the police immediately;
6. setting and drawing number of categories is 3, the signal collecting be x (θ) (k), θ is signal code, the θ of bearing 1 temperature is 1, the θ of motor stator temperature is 2, the θ of bearing 3 temperature is 3, the θ of bearing 4 temperature is 4, the θ of bearing 5 temperature is 5, the θ of the horizontal and vertical vibration of bearing 1 is 6 and 7, the θ of the horizontal and vertical vibration of bearing 2 is 8 and 9, the θ of the horizontal and vertical vibration of bearing 3 is 10 and 11, the θ of the horizontal and vertical vibration of bearing 3 is 12 and 13, the θ of the horizontal and vertical vibration of bearing 4 is 14 and 15, , wind inlet negative pressure and exhaust outlet negative pressure θ are respectively 16 and 17, the θ of carbonomonoxide concentration is 18, the θ of methane concentration is 19, the θ of voltage is 20, the θ of electric current is 21, , sampling number k=1, 2 ... 512, utilize cluster analysis x (θ) (k), obtain a bunch collection K
i, K
i=[x
i1, x
i2... x
im], i=1,2,3, m≤N,
7. utilize SVD analysis package containing the maximum bunch collection K of data
i, structure bunch collection K
ihankel matrix A:
Wherein, l is length of window, and 1<l<m, the exponent number of matrix A (k=N-l+1).After obtaining track matrix A, need to ask the singular value of A.X=AA
t, X is l * l matrix, the eigenvalue of trying to achieve X is: λ
1, λ
2, λ
3λ
d, get its square root
be the singular value (i≤d) of track matrix A, as signal characteristic.If the eigenvalue of matrix X is all non-vanishing, d=l.
8. by first singular value λ (θ)
1as the training input of supporting vector machine model, input vector is X={ λ (1)
1, λ (2)
1... λ (21)
1, breaking down in certain position, exports corresponding signal code, normally exports 0 and set up supporting vector machine model;
9. with the form of form, record the input and output of support vector machine, when diagnosis, Output rusults and this form are compared, guarantee the reliability of diagnostic result.The y coordinate of form is training sample numbering, the output that abscissa is supporting vector machine model.For support vector machine, only have one not to be 0 output, determine fault.If occur that two or more are not 0 output, with form contrast, the state that the sample of identical output represents in form the state of take is mine fan.Test output 100400000000000000000 identical with the output 100400000000000000000 of form middle (center) bearing 4 faults, is defined as bearing 4 faults;
10. according to the result of diagnosis, on upper-position unit, show the state of mine fan, if any abnormal state or fault, report to the police immediately and take control measure.
Claims (3)
1. the early stage mine fan online system failure diagnosis of intelligence, usings cluster and singular value decomposition as the extracting method of signal characteristic, utilizes support vector machine identification and tracing trouble, it is characterized in that: signal characteristic extracting methods wherein comprises the following steps:
S1. utilize data x (θ) that the method processing data acquisition module of cluster gathers (k) (k=1,2 ... N), N sampled point, θ is signal code, representation temperature, vibration, negative pressure, carbonomonoxide concentration, methane concentration, voltage, electric current, θ=1,2 ... s s is signal sum, set number of clusters n, after analysis, comprise signal maximum bunch for required signal, remaining bunch concentrates the signal comprising as being abnormity point, remove the abnormity point in signal, obtain comprising bunch collection K that data are maximum
i(θ) (x), K
i(θ) (x)=[x
i1, x
i2... x
im], i is the ordinal number of divided bunch collection, i=1,2 ... n, m is that the signal that comprises bunch collection that data are maximum is counted, m≤N;
S2. to the K in S1
i(θ) (x) structure Hankel matrix A,
S3. the matrix A in S2 is done to singular value decomposition, getting first singular value λ (θ) is signal characteristic;
S4. with the λ in S3 (θ) structure X={ λ (1), λ (2) ... λ (s) } be the input of support vector machine, output Y={y
1, y
2y
s, y wherein
θequal 0 or θ, breaking down in certain position, exports corresponding code θ, normally exports 0, according to code, determines location of fault.
S5. with the form of form, record the training input and output of supporting vector machine model, form walk crosswise the output that expresses support for vector machine, the sequence number of perpendicular line display sample, support vector machine after having trained diagnosis output all to compare with this form, if export identically with form, the state of mine fan is identical with sample.
2. the early stage mine fan online system failure diagnosis of intelligence, its feature: comprise upper-position unit, lower-position unit, signal acquisition module, temperature transducer, vibration transducer, B/P EGR Back Pressure Transducer EGR, methane transducer, carbon monoxide transducer, ACR series network multifunctional power meter; Two sensors of same measured point are connected to analog-to-digital conversion module SM331 by shielding wire, and SM331 is converted into digital quantity analog amount, is input in CPU314; The voltage and current of mine fan motor is measured by ACR series network multifunctional power meter, by RS485, is input in CPU314; The hardware of every mine fan Incipient Fault Diagnosis system is two covers, each other redundancy; CPU314 is input to upper-position unit by measured signal, and upper-position unit calculates the mean value of two sensors of same measured point as the signal of this measuring point; Two PLC monitor mutually, and the PLC of working fan periodically sends instruction to another PLC, and reset timer, if can not reset, sends malfunction alarm; There is the state of abnormal or fault simulation blower fan and make corresponding control strategy in mine fan, start and stop, automatic pouring machine, the air door of controlling blower fan open and close; Upper-position unit utilizes WinCC as configuration software, finishing man-machine interaction, both can show the status parameter of blower fan, the performance curve of the electric current and voltage of showing temperature, motor, vibration, negative pressure, air quantity, carbon monoxide content, methane content, mine fan, air door state and petrol station state, can realize control inputs again, the alarm threshold value of input temp, vibration, negative pressure, air quantity, methane content, carbon monoxide content; By keyboard input control order on upper station of WinCC, control the switching of air door, the start and stop of blower fan; Temperature transducer Pt100 is arranged on respectively the both sides of bearing support and the stator coil side of motor, and all bearing supports are all installed integral type vibration transmitters in the horizontal and vertical directions; CPU314 output point connects relay, Control damper motor, fan motor and petrol station electric power switch.
3. the early stage mine fan online system failure diagnosis of intelligence, is characterized in that: fault diagnosis algorithm is integrated in WinCC, and only configuration software can complete fault diagnosis and the control of mine fan.
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| CN105403422A (en) * | 2015-11-30 | 2016-03-16 | 惠州学院 | Blind-separation-technology-based detection system for diagnosing CO2 air conditioner fault of rescue capsule |
| CN106527169A (en) * | 2017-01-20 | 2017-03-22 | 深圳大图科创技术开发有限公司 | Intelligent home control system based on Bluetooth |
| CN108074197A (en) * | 2016-11-11 | 2018-05-25 | 河北新天科创新能源技术有限公司 | The control method of fan trouble data analysis system |
| CN108802040A (en) * | 2017-05-04 | 2018-11-13 | 南京市特种设备安全监督检验研究院 | A kind of unmanned plane device and detection method for crane surface defects detection |
| CN110107359A (en) * | 2019-05-14 | 2019-08-09 | 安徽理工大学 | A kind of ventilation monitoring device applied in Safety of Coal Mine Production |
| CN110374907A (en) * | 2019-07-15 | 2019-10-25 | 山东浪潮人工智能研究院有限公司 | A kind of coal mine blower time series data method of sampling and tool based on concept drift |
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| CN108074197A (en) * | 2016-11-11 | 2018-05-25 | 河北新天科创新能源技术有限公司 | The control method of fan trouble data analysis system |
| CN108074197B (en) * | 2016-11-11 | 2021-11-09 | 河北新天科创新能源技术有限公司 | Control method of fan fault data analysis system |
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| CN111578446A (en) * | 2020-05-06 | 2020-08-25 | 济南浪潮高新科技投资发展有限公司 | Coal mine ventilation equipment detection method, equipment and medium |
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| CN118395216B (en) * | 2024-06-27 | 2024-08-27 | 中核四川环保工程有限责任公司 | Fan fault monitoring method and system based on mathematical coupling under nuclear facility environment |
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