CN104006908A - Fan energy consumption monitoring method and system - Google Patents
Fan energy consumption monitoring method and system Download PDFInfo
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Abstract
The invention discloses a fan energy consumption monitoring method and system. When a training sample in obtained, energy consumption of a fan is increased by artificially making various faults, the energy efficiency detection method and system specified by the national standard of China are adopted to test the added values of energy consumption of the fan under various faults, the values of increased energy consumption caused by different faults are classified, a vibration characteristic sample and a track characteristic sample are combined to serve as the training sample of a neural network, and a higher space multi-weight neural network is built. In all-weather energy consumption monitoring, only a low-cost three-shaft acceleration sensor and an eddy-flow sensor need to be additionally installed on the fan, three-dimensional vibration signals and shaft center orbit feature vectors are input the multi-weight neural network, and different types of network output are energy consumption addition classifications.
Description
Technical field
The present invention relates to a kind of fan energy consumption monitoring method and system.
Background technology
Blower fan, motor, water pump, compressor are referred to as " industrial motor system " by International Energy Agency (IEA).In National Development and Reform Committee's Eleventh Five-Year Plan energy conservation plan, point out, industrial motor system is the main power consumer of China, accounts for the more than 50% of whole power consumptions, and wherein the power consumption of blower fan accounts for 10.4% of national power consumption.Therefore, the raising of fan efficiency, very great to saves energy meaning.
Blower fan system has a large capacity and a wide range, and energy conservation potential is huge.The appearance of " ventilator energy efficiency limit value and efficiency grade " national standard, has stipulated efficiency grade, efficiency limit value, Energy efficiency evaluation value and the test method of ventilation blower, for the research of China's air-foil fan efficiency work provides foundation with carrying out.Also indicate the beginning of China's air-foil fan Energy Labeling work.On November 1st, 2010, blower fan was carried out the products catalogue (the 6th batch) of energy efficiency label by formally listing the People's Republic of China (PRC) in, and had taken up to implement.
When blower fan moves for a long time in commercial production, can produce a lot of faults, the vibration causing as dynamic unbalance (comprises that rotor-support-foundation system manufacture process residue is uneven; Fan blade is in rotary course, due to concentrated wear or corrosion, and local damage or stop up the reasons such as foreign matter; Fan blower is worked under High Temperature High Pressure, because thermal deformation and thermal expansion cause cambered axle phenomenon etc.); (data shows, 30%～50% equipment exists the problem that misaligns to misalign the vibration that causes.Both misaligned and can produce radial vibration, and can produce axial vibration again; Both can cause the vibration that closes on shaft coupling supporting place, and also can cause the free-ended vibration away from shaft coupling); Machinery loosening (loosening other fault of machine that both may cause also may be because other fault causes, the misaligning of the abrasion deformation of mechanical part, axle system, uneven etc. influence each other with becoming flexible); The vibration that oil whip causes; Gas impacts the vibration causing; The vibration that gaseous tension fluctuation causes; The vibration that Resonance Wave Composition causes; Blower fan drives the various faults with motor, in addition as the gear train faults such as axle, belt chain, gear, bearing, motor dust condense that poor heat radiation, the working time of causing, pollution long or dirt and water caused lubricated good etc. all can not cause Efficiency Decreasing and energy consumption raising.These faults, generally can cause motor and blower fan system heating, various loss to increase, thereby reduce system effectiveness, increase system energy consumption.Therefore, the existence of various faults and efficiency value reduce (power consumption values rising) and have cause-effect relationship, excavate the numerical relation between various fault signatures and efficiency reduction value (energy consumption lift-off value), can be used as the foundation of energy consumption monitoring.For enterprise's energy efficiency management, eliminate and change high-energy equipment in time, to realize high-energy equipment maintenance targetedly significant.
The total efficiency of ventilation blower is defined as the ratio that blower fan passes to the energy that the kinetic energy of gas and static energy sum and motor transmit.Be used in now the fan energy consumption detection system of other mechanisms such as quality testing department, adopt GB/T 1236-2000 < < industrial ventilation machine to make a service test in > > and GB-19761-2009 < < ventilator energy efficiency limit value and efficiency grade > > GB about the test method measuring system constructing of ventilation blower with standardization air channel, need be to rotating speed, pressure reduction, flow, power, temperature, the multiparameters such as torque are measured, the system price building is expensive.And existing fan energy consumption checkout equipment, for dissimilar blower fan, additionally install the structures such as air duct additional to facilitate flow and wind pressure measurement, and need to install the multiple sensors such as differential pressure pick-up, torque sensor, speed probe.After blower fan long-time running, various faults can cause energy consumption to increase, and therefore to fan energy consumption, monitoring is conducive to enterprise's energy efficiency management, eliminates and change high-energy equipment in time, realizes high-energy equipment maintenance targetedly.And above energy consumption detecting method causes existing equipment to be not suitable for blower fan application enterprise to carry out energy consumption monitoring, existing expensive device is more unsuitable for realizing round-the-clock energy efficiency monitoring for every Fans coupling.
Propelling along with energy-saving and emission-reduction fundamental state policy, the Efficiency Decreasing that the various faults of blower fan cause and energy consumption improve the attention that need cause each blower fan application enterprise, in addition external electrical network parameter changes the efficiency reduction and the energy consumption that cause increases, also can be by fan vibration signal analysis out.At equipment, have under the prerequisite of high performance-price ratio, for every high-power blower is equipped with energy-consumption monitoring device, realization is to the long-term Real-Time Monitoring of blower fan efficiency, accurately find because various mechanical faults, electric fault or power supply grid parameter change the Efficiency Decreasing phenomenon causing, for enterprise's energy efficiency management, eliminate and change high-energy equipment in time, to realize high-energy equipment maintenance targetedly significant.
Summary of the invention
The object of the present invention is to provide and be a kind ofly beneficial to enterprise's energy efficiency management, eliminate and change high-energy equipment, realize targetedly fan energy consumption monitoring method and the system of high-energy equipment trouble hunting in time.
Technical solution of the present invention is:
A fan energy consumption monitoring method, is characterized in that: comprise the following steps:
(1) first carry out off-line training sample collection:
(1) off-line training sample collection system constructing
Build energy consumption testing system, employing comprises the multiple sensors of torque sensor, differential pressure pick-up, speed probe, detect compressor flow, total pressure of fan, static pressure of fan, volume flow, fan shaft power, final fan efficiency, thus learn ventilation blower energy consumption size;
Adopt three 3-axis acceleration sensors, be placed in respectively on bearing seat shell, motor housing, ventilator housing, obtain X, Y, the Z tri-axle orthogonal vibration signals of three test points; By with rotating shaft vertical plane in two orthogonal eddy current sensors gather vibration signal simultaneously, and the figure respectively gathered data being fitted to as horizontal, ordinate, is orbit of shaft center;
(2) training sample off-line obtains during blower fan non-fault
Adopt " signal is processed and characteristic extracting module " to carry out feature extraction, through repeatedly measuring, the feature samples while obtaining many group non-fault; Efficiency value corresponding to feature samples during by non-fault orientated " energy consumption is low " as;
(3) when blower fan has fault, training sample off-line obtains
Artificial combination of manufacturing various faults and various faults, adopt three 3-axis acceleration sensors to detect the three-dimensional vibrating signal of 3, bearing fan shells, motor housing, ventilator housing, adopt two eddy current sensors to detect orbit of shaft center signal, adopt " signal is processed and characteristic extracting module " to carry out feature extraction, each fault is taken multiple measurements, obtain the feature samples under each fault; Efficiency value during by the efficiency value in different faults situation and non-fault compares, and according to difference from big to small, is divided into Four types, is defined as respectively " energy consumption is high ", " energy consumption is higher ", " energy consumption is medium ", " energy consumption is on the low side ";
(2) online energy consumption monitoring
Adopt three 3-axis acceleration sensors to detect the three-dimensional vibrating signal of 3, bearing fan shells, motor housing, ventilator housing, adopt two eddy current sensors to detect orbit of shaft center signal, adopt " signal is processed and characteristic extracting module " to carry out feature extraction to signal, obtain tested sample; Adopt multiple weighing value neural network as the core algorithm of " the Classification and Identification module based on neural network ", the training sample that adopts " energy consumption detects with training sample off-line acquisition module " to obtain is constructed the multiple degrees of freedom neural network in higher dimensional space, after completing the structure of multiple weighing value neuroid, obtain " energy consumption is high ", " energy consumption is higher ", " energy consumption is medium ", " energy consumption is on the low side ", " energy consumption is low " five sign other multiple weighing value neuron areal coverage of different energy consumption levels; Calculate sample to be identified and characterize the Euclidean distance between other multiple weighing value neuroid areal coverage of every class energy consumption level, by that the shortest class energy consumption rank of the Euclidean distance with sample to be identified, the affiliated energy consumption rank of being used as sample to be identified, and export fan energy consumption grade classification as multiple weighing value neural network.
The concrete grammar that during blower fan non-fault, training sample off-line obtains is:
During by non-fault, the time-domain signal of three 3-axis acceleration sensor outputs, carries out denoising, and adopts hypercomplex number PCA to carry out pivot analysis, is keeping under the prerequisite of three axle output signal correlativitys the vibration performance vector while obtaining non-fault;
During fan rotor failure free operation, adopt two eddy current sensors to extract orbit of shaft center, during non-fault, the time domain waveform of its vibration signal of eddy current sensor is sinusoidal curve, two orthogonal sinusoidal signals are synthesized, just obtained circle or oval, extract the geometries characteristic of orbit of shaft center image or grey level histogram feature or textural characteristics as characteristic parameter, and the vibration performance vector combination of obtaining with acceleration transducer, sample while obtaining non-fault; Adopt said method, repeatedly test the sample while obtaining many groups " energy consumption is low ".
A kind of fan energy consumption monitoring system, it is characterized in that: comprise that three are placed in respectively 3-axis acceleration sensor on bearing seat shell, motor housing, ventilator housing and two orthogonal eddy current sensors in rotating shaft vertical plane, 3-axis acceleration sensor, eddy current sensor process with signal and characteristic extracting module is connected, and signal processing and characteristic extracting module are connected with the Classification and Identification module based on neural network.
The present invention proposes the fan energy consumption monitoring method based on vibration signal and orbit of shaft center signal analysis.The method does not need to install additional the structures such as air duct in application, adopt three 3-axis acceleration sensors to detect ventilation blower multiple spot three-dimensional vibrating signal (detecting the vibration signal of motor housing, bearing seat shell, ventilator housing), adopt two eddy current sensors to detect the displacement that ventilation blower spindle eccentricity causes, and obtain orbit of shaft center.On great many of experiments basis, obtain the relation between the dissimilar fault of blower fan and fan energy consumption increase, and energy consumption different faults being caused by multiple weighing value neural network increase rank is carried out Classification and Identification.
Make a general survey of domestic existing fan energy consumption monitoring method and system, the design object unit of there is no that the present invention carries realizes.
The invention reside in a kind of novel energy-consumption monitoring system is provided, only adopt 3-axis acceleration sensor, eddy current sensor, can realize fan energy consumption increases ONLINE RECOGNITION and the monitoring of degree, avoided the cost of existing energy consumption detection system high, the problems such as difficulty have been installed, be conducive to enterprise and realize the daily energy consumption monitoring of blower fan, thereby be beneficial to enterprise's energy efficiency management, eliminate and change high-energy equipment in time, realize high-energy equipment trouble hunting targetedly.
Accompanying drawing explanation
Below in conjunction with drawings and Examples, the invention will be further described.
Fig. 1 is energy-consumption monitoring system structural drawing of the present invention.Wherein there are 3-axis acceleration sensor, eddy current sensor, energy consumption to detect by training sample off-line acquisition module, signal processing and characteristic extracting module, the Classification and Identification module based on neural network." signal is processed and characteristic extracting module " realized by software, comprises denoising, hypercomplex number PCA feature extraction, orbit of shaft center feature extraction (geometries characteristic, or grey level histogram feature, or textural characteristics)." the Classification and Identification module based on neural network " realizes energy consumption by multiple weighing value neural network increases grade classification.
Fig. 2 is the off-line acquisition methods schematic diagram of train samples of the present invention.
The experimental technique schematic diagram that the train samples that Fig. 3 is is obtained.
Fig. 4 is the schematic layout pattern of sensor.Two eddy current sensor center lines all intersect with blower fan main shaft axial line, and two eddy current sensor center lines are all perpendicular to blower fan main shaft axial line, and two eddy current sensor center lines are mutually vertical.Three acceleration transducers are fixedly mounted on respectively motor surface, bearing seat shell, ventilator housing.
Fig. 5 is multiple weighing value neural network recognization process schematic diagram.
Embodiment
A kind of fan energy consumption monitoring system, comprise that three are placed in respectively 3-axis acceleration sensor 1 on bearing seat shell, motor housing, ventilator housing and two orthogonal eddy current sensors 2 in rotating shaft vertical plane, 3-axis acceleration sensor, eddy current sensor process with signal and characteristic extracting module is connected, and signal processing and characteristic extracting module are connected with the Classification and Identification module based on neural network." signal process and characteristic extracting module ", " the Classification and Identification module based on neural network " are all relied on the hardware facilities such as PC or high performance controller (as FPGA etc.), by software, realize denoising, hypercomplex number PCA feature extraction, orbit of shaft center feature extraction, pattern-recognition based on multiple weighing value neural network.
1, first carry out off-line training sample collection, specific implementation process is:
This system is only for the off-line collection of train samples, and after having gathered, this system does not re-use in daily energy efficiency monitoring.
1.1 off-line training sample collection system constructings
According to standardization air channel make a service test > > and GB-19761-2009 < < ventilator energy efficiency limit value and efficiency grade > > national standard for GB/T 1236-2000 < < industrial ventilation machine, build energy consumption testing system, adopt torque sensor, differential pressure pick-up, the multiple sensors such as speed probe, detect compressor flow, total pressure of fan, static pressure of fan, volume flow, fan shaft power, finally can obtain fan efficiency, thereby learn ventilation blower energy consumption size.
According to layout type shown in Fig. 4, adopt three 3-axis acceleration sensors, be placed in respectively on bearing seat shell, motor housing, ventilator housing, obtain X, Y, the Z tri-axle orthogonal vibration signals of three test points.By with rotating shaft vertical plane in two orthogonal eddy current sensors gather vibration signal simultaneously, and the figure respectively gathered data being fitted to as horizontal, ordinate, is orbit of shaft center.
During 1.2 blower fan non-fault, training sample off-line obtains
Adopt GB/T 1236-2000 < < industrial ventilation machine to carry out fan efficiency detection with make a service test > > and GB-19761-2009 < < ventilator energy efficiency limit value and efficiency grade > > national standard method of standardization air channel, efficiency value while obtaining non-fault, is defined as " energy consumption is low " by this state energy consumption.During by non-fault, the time-domain signal of three 3-axis acceleration sensor outputs, carries out denoising, and adopts hypercomplex number PCA to carry out pivot analysis, is keeping under the prerequisite of three axle output signal correlativitys the vibration performance vector while obtaining non-fault.
During fan rotor failure free operation, adopt two eddy current sensors to extract orbit of shaft center, during non-fault, the time domain waveform of its vibration signal of eddy current sensor is sinusoidal curve, two orthogonal sinusoidal signals are synthesized, just obtained circle or oval, extract the geometries characteristic of orbit of shaft center image or grey level histogram feature or textural characteristics as characteristic parameter, and the vibration performance vector combination of obtaining with acceleration transducer, sample while obtaining non-fault.Adopt said method, repeatedly test the sample while obtaining many groups " energy consumption is low ".
When 1.3 blower fans have fault, training sample off-line obtains
Various faults is artificially set, as poor heat radiation, lubricated not good, rotating shaft is eccentric, line voltage reduces or frequency unstable when the new energies such as wind-powered electricity generation (especially), wheel rotation imbalance, gear train fault (as faults such as belt chain, gear, bearings), motor oil whip etc.While breaking down, vibration signal frequency domain can change.Orbit of shaft center there will be the situations such as bajiao banana figure, 8-shaped, interior ring figure, irregular component.The vibration signal characteristics of extraction and orbit of shaft center Feature Combination are got up, can characterize different faults or fault combination.During off-line training sample acquisition, the combination of every kind of fault, various faults is carried out repeatedly respectively to efficiency detects and feature extraction, obtained the many groups sample under the combined situation of every kind of fault, various faults.
Combination to every kind of fault, various faults, adopt the method for 1.2 joints to set up fan efficiency detection system, efficiency value during by the efficiency value in different faults situation and non-fault compares, according to difference from big to small, be divided into Four types, be defined as respectively " energy consumption is high ", " energy consumption is higher ", " energy consumption is medium ", " energy consumption is on the low side ".
2, online energy consumption monitoring, concrete methods of realizing is:
During online energy consumption monitoring, 1.2 and 1.3 joints " energy consumption detects with training sample off-line acquisition module " used do not re-use.Only adopt three 3-axis acceleration sensors, two eddy current sensors.Acceleration transducer and eddy current sensor are still installed according to method shown in Fig. 4.
Adopt the multiple degrees of freedom neural network in the training sample structure higher dimensional space that in 1.2 and 1.3, off-line gathers.After completing the structure of multiple weighing value neuroid, can obtain " energy consumption is high ", " energy consumption is higher ", " energy consumption is medium ", " energy consumption is on the low side ", " energy consumption is low " five characterize other multiple weighing value neuron areal coverage of different energy consumption levels.After fan operation, adopt the recognizer based on multiple weighing value neural network shown in Fig. 5, the signal gathering with 3-axis acceleration sensor and eddy current sensor, sample after feature extraction is as input, calculate sample to be identified and characterize the Euclidean distance between other multiple weighing value neuroid areal coverage of every class energy consumption level, by that the shortest class energy consumption rank of the Euclidean distance with sample to be identified, be used as the affiliated energy consumption rank of sample to be identified.And export fan energy consumption grade classification as multiple weighing value neural network.
Claims (3)
1. a fan energy consumption monitoring method, is characterized in that: comprise the following steps:
(1) first carry out off-line training sample collection:
(1) off-line training sample collection system constructing
Build energy consumption testing system, employing comprises the multiple sensors of torque sensor, differential pressure pick-up, speed probe, detect compressor flow, total pressure of fan, static pressure of fan, volume flow, fan shaft power, final fan efficiency, thus learn ventilation blower energy consumption size;
Adopt three 3-axis acceleration sensors, be placed in respectively on bearing seat shell, motor housing, ventilator housing, obtain X, Y, the Z tri-axle orthogonal vibration signals of three test points; By with rotating shaft vertical plane in two orthogonal eddy current sensors gather vibration signal simultaneously, and the figure respectively gathered data being fitted to as horizontal, ordinate, is orbit of shaft center;
(2) training sample off-line obtains during blower fan non-fault
Adopt " signal is processed and characteristic extracting module " to carry out feature extraction, through repeatedly measuring, the feature samples while obtaining many group non-fault; Efficiency value corresponding to feature samples during by non-fault orientated " energy consumption is low " as;
(3) when blower fan has fault, training sample off-line obtains
Artificial combination of manufacturing various faults and various faults, adopt three 3-axis acceleration sensors to detect the three-dimensional vibrating signal of 3, bearing fan shells, motor housing, ventilator housing, adopt two eddy current sensors to detect orbit of shaft center signal, adopt " signal is processed and characteristic extracting module " to carry out feature extraction, each fault is taken multiple measurements, obtain the feature samples under each fault; Efficiency value during by the efficiency value in different faults situation and non-fault compares, and according to difference from big to small, is divided into Four types, is defined as respectively " energy consumption is high ", " energy consumption is higher ", " energy consumption is medium ", " energy consumption is on the low side ";
(2) online energy consumption monitoring
Adopt three 3-axis acceleration sensors to detect the three-dimensional vibrating signal of 3, bearing fan shells, motor housing, ventilator housing, adopt two eddy current sensors to detect orbit of shaft center signal, adopt " signal is processed and characteristic extracting module " to carry out feature extraction to signal, obtain tested sample; Adopt multiple weighing value neural network as the core algorithm of " the Classification and Identification module based on neural network ", the training sample that adopts " energy consumption detects with training sample off-line acquisition module " to obtain is constructed the multiple degrees of freedom neural network in higher dimensional space, after completing the structure of multiple weighing value neuroid, obtain " energy consumption is high ", " energy consumption is higher ", " energy consumption is medium ", " energy consumption is on the low side ", " energy consumption is low " five sign other multiple weighing value neuron areal coverage of different energy consumption levels; Calculate sample to be identified and characterize the Euclidean distance between other multiple weighing value neuroid areal coverage of every class energy consumption level, by that the shortest class energy consumption rank of the Euclidean distance with sample to be identified, the affiliated energy consumption rank of being used as sample to be identified, and export fan energy consumption grade classification as multiple weighing value neural network.
2. fan energy consumption monitoring method according to claim 1, is characterized in that: the concrete grammar that during blower fan non-fault, training sample off-line obtains is:
During by non-fault, the time-domain signal of three 3-axis acceleration sensor outputs, carries out denoising, and adopts hypercomplex number PCA to carry out pivot analysis, is keeping under the prerequisite of three axle output signal correlativitys the vibration performance vector while obtaining non-fault;
During fan rotor failure free operation, adopt two eddy current sensors to extract orbit of shaft center, during non-fault, the time domain waveform of its vibration signal of eddy current sensor is sinusoidal curve, two orthogonal sinusoidal signals are synthesized, just obtained circle or oval, extract the geometries characteristic of orbit of shaft center image or grey level histogram feature or textural characteristics as characteristic parameter, and the vibration performance vector combination of obtaining with acceleration transducer, sample while obtaining non-fault; Adopt said method, repeatedly test the sample while obtaining many groups " energy consumption is low ".
3. a fan energy consumption monitoring system, it is characterized in that: comprise that three are placed in respectively 3-axis acceleration sensor on bearing seat shell, motor housing, ventilator housing and two orthogonal eddy current sensors in rotating shaft vertical plane, 3-axis acceleration sensor, eddy current sensor process with signal and characteristic extracting module is connected, and signal processing and characteristic extracting module are connected with the Classification and Identification module based on neural network.
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| CN201510480910.9A CN105021334B (en) | 2014-06-11 | 2014-06-11 | Fan energy consumption monitoring recognition methods based on multiple weighing value neutral net |
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Also Published As
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| CN105021334A (en) | 2015-11-04 |
| CN104006908B (en) | 2015-12-02 |
| CN105021334B (en) | 2017-07-18 |
| CN105004462A (en) | 2015-10-28 |
| CN105004462B (en) | 2017-07-04 |
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