CN109668588A - Air cooling refrigeration unit sensor fault diagnosis method based on virtual-sensor - Google Patents
Air cooling refrigeration unit sensor fault diagnosis method based on virtual-sensor Download PDFInfo
- Publication number
- CN109668588A CN109668588A CN201910144270.2A CN201910144270A CN109668588A CN 109668588 A CN109668588 A CN 109668588A CN 201910144270 A CN201910144270 A CN 201910144270A CN 109668588 A CN109668588 A CN 109668588A
- Authority
- CN
- China
- Prior art keywords
- sensor
- virtual
- refrigeration unit
- air cooling
- cooling refrigeration
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D18/00—Testing or calibrating apparatus or arrangements provided for in groups G01D1/00 - G01D15/00
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/049—Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The present invention relates to a kind of air cooling refrigeration unit sensor fault diagnosis method based on virtual-sensor, comprising the following steps: correlation analysis is carried out to any two sensor in air cooling refrigeration unit;According to correlation analysis as a result, being grouped to refrigeration unit sensor and obtaining different groups;Virtual-sensor is constructed in each group;By virtual-sensor reading compared with respective sensor actual read number, if virtual-sensor reading mutates, there are failures for respective sensor.The present invention has rational design, it uses correlation analysis to analyze existing correlation between air cooling refrigeration unit sensor, the strong sensor of correlation is divided into one group, and virtual-sensor is constructed using long short-term memory Recognition with Recurrent Neural Network in group, by judging whether virtual-sensor reading mutates, to accurately and rapidly be positioned to refrigeration unit multisensor failure, energy waste is avoided, the service life of equipment is improved.
Description
Technical field
The invention belongs to air cooling refrigeration unit multi-sensor technology field, especially a kind of wind based on virtual-sensor
Cold type refrigeration unit sensor fault diagnosis method.
Background technique
With increasingly sharpening for world energy sources shortage problem, energy conservation and environmental protection has become current mostly important project, and builds
Industry is built as high-energy source and consumes industry, the influence to ecological environment can not be ignored.Energy consumption in construction industry is very big by one
It is derived partly from the energy consumption of building air conditioning after coming into operation, when air conditioning sensor breaks down, can lead to the energy of air-conditioning system
Loss-rate increases by 50% under normal circumstances.Therefore, energy saving reliability service of the refrigeration unit multiple sensor faults diagnosis to air-conditioning system
It plays a crucial role, it is in the urgent need to address at present for how detecting refrigeration unit multisensor failure accurately and timely
Problem.
Summary of the invention
It is an object of the invention to overcome the deficiencies in the prior art, propose that a kind of design is reasonable and can sufficiently excavate refrigeration machine
The air cooling refrigeration unit sensor fault diagnosis method based on virtual-sensor of group multisensor behavioral characteristics.
The present invention solves its technical problem and adopts the following technical solutions to achieve:
A kind of air cooling refrigeration unit sensor fault diagnosis method based on virtual-sensor, comprising the following steps:
Step 1 carries out correlation analysis to any two sensor in air cooling refrigeration unit;
Step 2, according to correlation analysis as a result, being grouped to refrigeration unit sensor and obtaining different groups;
Step 3 constructs virtual-sensor in each group;
Step 4 reads virtual-sensor compared with respective sensor actual read number, if virtual-sensor reading occurs to dash forward
Become, then there are failures for respective sensor.
The sensor includes temperature sensor, flow sensor and the pressure sensor of air cooling refrigeration unit.
The step 1 carries out correlation analysis using pearson related coefficient method.
When the step 2 is grouped refrigeration unit sensor, the sensor by correlation greater than 0.5 is grouped into the same group
Not;If a sensor is both less than 0.5 with other all the sensors correlations, the maximum sensor of property associated therewith is chosen
Place group is group where the sensor.
The concrete methods of realizing of the step 3 are as follows: in different groups, recycle nerve net using long short-term memory respectively
Network constructs virtual-sensor.
The method of the long short-term memory Recognition with Recurrent Neural Network construction virtual-sensor are as follows: if containing n sensing in group
Device then constructs n long short-term memory Recognition with Recurrent Neural Network respectively, wherein neural network input is all sensings in same group
The measured value at T moment before device, neural network output are the corresponding virtual-sensor of different sensors, and T is any greater than 0
Number.
The judgment method that the step 4 virtual-sensor reading mutates are as follows: if current time virtual-sensor and reality
The difference of last moment virtual-sensor of the difference of border sensor reading greater than twice and real sensor reading, then it is assumed that virtual to pass
Sensor reading mutates.
The advantages and positive effects of the present invention are:
The present invention has rational design, uses correlation analysis to existing phase between air cooling refrigeration unit sensor
Closing property is analyzed, and the strong sensor of correlation is divided into one group, and long short-term memory Recognition with Recurrent Neural Network structure is utilized in group
Virtual-sensor is built out, by judging whether virtual-sensor reading mutates, thus accurately and rapidly more to refrigeration unit
Sensor fault is positioned, and energy waste is avoided, and improves the service life of equipment.
Detailed description of the invention
Fig. 1 is process flow diagram of the invention.
Specific embodiment
The embodiment of the present invention is further described below in conjunction with attached drawing.
A kind of air cooling refrigeration unit sensor fault diagnosis method based on virtual-sensor, as shown in Figure 1, include with
Lower step:
Step 1 carries out correlation point to any two sensor in air cooling refrigeration unit by pearson related coefficient
Analysis.
Sensor of the invention includes all the sensors such as temperature, flow, the pressure of air cooling refrigeration unit.For difference
The fault diagnosis of seed type sensor must train different long short-term memory Recognition with Recurrent Neural Network models, only deposit for synchronization
The sensor failure the case where.
Step 2, according to correlation analysis as a result, be grouped to refrigeration unit sensor, wherein correlation is greater than 0.5
Sensor is same group.
In this step, according to correlation analysis as a result, being grouped to refrigeration unit sensor, wherein correlation is greater than
0.5 sensor is same group, if a certain sensor C and other all the sensors correlations are both less than 0.5, selection and its
Group where the sensor of correlation maximum is group where C.
Step 3 constructs virtual-sensor in each group.
In this step, virtual-sensor is constructed using long short-term memory Recognition with Recurrent Neural Network in each group, in difference
Group in, if containing n sensor, be respectively necessary for a long short-term memory Recognition with Recurrent Neural Network of construction n, wherein neural network
Input is the measured value at T moment before all the sensors in same group, and T is the arbitrary number greater than 0, long short-term memory circulation mind
It is the corresponding virtual-sensor of different sensors through network output.
Step 4 reads virtual-sensor compared with respective sensor actual read number, if virtual-sensor reading occurs to dash forward
Become, then there are failures for respective sensor.
In this step, if the difference of current time virtual-sensor and real sensor reading is greater than twice of last moment
The difference of virtual-sensor and real sensor reading, then it is assumed that virtual-sensor reading mutates.
The present invention does not address place and is suitable for the prior art.
It is emphasized that embodiment of the present invention be it is illustrative, without being restrictive, therefore packet of the present invention
Include and be not limited to embodiment described in specific embodiment, it is all by those skilled in the art according to the technique and scheme of the present invention
The other embodiments obtained, also belong to the scope of protection of the invention.
Claims (7)
1. a kind of air cooling refrigeration unit sensor fault diagnosis method based on virtual-sensor, it is characterised in that including following
Step:
Step 1 carries out correlation analysis to any two sensor in air cooling refrigeration unit;
Step 2, according to correlation analysis as a result, being grouped to refrigeration unit sensor and obtaining different groups;
Step 3 constructs virtual-sensor in each group;
Step 4 reads virtual-sensor compared with respective sensor actual read number, if virtual-sensor reading mutates,
Then there are failures for respective sensor.
2. the air cooling refrigeration unit sensor fault diagnosis method according to claim 1 based on virtual-sensor,
Be characterized in that: the sensor includes temperature sensor, flow sensor and the pressure sensor of air cooling refrigeration unit.
3. the air cooling refrigeration unit sensor fault diagnosis method according to claim 1 based on virtual-sensor,
Be characterized in that: the step 1 carries out correlation analysis using pearson related coefficient method.
4. the air cooling refrigeration unit sensor fault diagnosis method according to claim 1 based on virtual-sensor,
It is characterized in that: when the step 2 is grouped refrigeration unit sensor, the sensor that correlation is greater than 0.5 being divided into same
Group;If a sensor is both less than 0.5 with other all the sensors correlations, the maximum sensing of property associated therewith is chosen
Group where device is group where the sensor.
5. the air cooling refrigeration unit sensor fault diagnosis method according to claim 1 based on virtual-sensor,
It is characterized in that: the concrete methods of realizing of the step 3 are as follows: in different groups, recycle nerve using long short-term memory respectively
Net structure virtual-sensor.
6. the air cooling refrigeration unit sensor fault diagnosis method according to claim 5 based on virtual-sensor,
It is characterized in that: the method for the long short-term memory Recognition with Recurrent Neural Network construction virtual-sensor are as follows: if containing n sensing in group
Device then constructs n long short-term memory Recognition with Recurrent Neural Network respectively, wherein neural network input is all sensings in same group
The measured value at T moment before device, neural network output are the corresponding virtual-sensor of different sensors, and T is any greater than 0
Number.
7. the air cooling refrigeration unit sensor fault diagnosis method according to claim 1 based on virtual-sensor,
It is characterized in that: the judgment method that the step 4 virtual-sensor reading mutates are as follows: if current time virtual-sensor and reality
The difference of last moment virtual-sensor of the difference of border sensor reading greater than twice and real sensor reading, then it is assumed that virtual to pass
Sensor reading mutates.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910144270.2A CN109668588A (en) | 2019-02-27 | 2019-02-27 | Air cooling refrigeration unit sensor fault diagnosis method based on virtual-sensor |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910144270.2A CN109668588A (en) | 2019-02-27 | 2019-02-27 | Air cooling refrigeration unit sensor fault diagnosis method based on virtual-sensor |
Publications (1)
Publication Number | Publication Date |
---|---|
CN109668588A true CN109668588A (en) | 2019-04-23 |
Family
ID=66151856
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201910144270.2A Pending CN109668588A (en) | 2019-02-27 | 2019-02-27 | Air cooling refrigeration unit sensor fault diagnosis method based on virtual-sensor |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN109668588A (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111652375A (en) * | 2020-06-02 | 2020-09-11 | 中南大学 | Intelligent detection and diagnosis method and device for cooling coil faults based on Bayesian inference and virtual sensing |
WO2022079686A1 (en) * | 2020-10-15 | 2022-04-21 | Abb Schweiz Ag | Online frequently derived measurements for process monitoring, control and optimization |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101331504A (en) * | 2005-11-18 | 2008-12-24 | 卡特彼勒公司 | Process model based virtual sensor system and method |
CN101681155A (en) * | 2007-06-15 | 2010-03-24 | 卡特彼勒公司 | Virtual sensor system and method |
EP2413205A1 (en) * | 2009-03-27 | 2012-02-01 | Honda Motor Co., Ltd. | Controller for plant |
CN103646179A (en) * | 2013-12-19 | 2014-03-19 | 宜春市脉恩多能科技有限公司 | Method for measuring refrigerating capacity of air conditioner by virtual sensor |
CN105446821A (en) * | 2015-11-11 | 2016-03-30 | 哈尔滨工程大学 | Improved neural network based fault diagnosis method for intelligent underwater robot propeller |
CN106640548A (en) * | 2016-12-19 | 2017-05-10 | 北京金风科创风电设备有限公司 | State monitoring method and device for wind generating set |
-
2019
- 2019-02-27 CN CN201910144270.2A patent/CN109668588A/en active Pending
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101331504A (en) * | 2005-11-18 | 2008-12-24 | 卡特彼勒公司 | Process model based virtual sensor system and method |
CN101681155A (en) * | 2007-06-15 | 2010-03-24 | 卡特彼勒公司 | Virtual sensor system and method |
EP2413205A1 (en) * | 2009-03-27 | 2012-02-01 | Honda Motor Co., Ltd. | Controller for plant |
CN103646179A (en) * | 2013-12-19 | 2014-03-19 | 宜春市脉恩多能科技有限公司 | Method for measuring refrigerating capacity of air conditioner by virtual sensor |
CN105446821A (en) * | 2015-11-11 | 2016-03-30 | 哈尔滨工程大学 | Improved neural network based fault diagnosis method for intelligent underwater robot propeller |
CN106640548A (en) * | 2016-12-19 | 2017-05-10 | 北京金风科创风电设备有限公司 | State monitoring method and device for wind generating set |
Non-Patent Citations (1)
Title |
---|
王路瑶 等: "基于长短期记忆神经网络的数据中心空调系统传感器故障诊断", 《化工学报》 * |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111652375A (en) * | 2020-06-02 | 2020-09-11 | 中南大学 | Intelligent detection and diagnosis method and device for cooling coil faults based on Bayesian inference and virtual sensing |
CN111652375B (en) * | 2020-06-02 | 2023-06-06 | 中南大学 | Intelligent detection and diagnosis method and device for cooling coil faults based on Bayesian reasoning and virtual sensing |
WO2022079686A1 (en) * | 2020-10-15 | 2022-04-21 | Abb Schweiz Ag | Online frequently derived measurements for process monitoring, control and optimization |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN106662072B (en) | Wind-driven generator method for monitoring state and system | |
CN109781399A (en) | A kind of air cooling refrigeration unit sensor fault diagnosis method neural network based | |
CN104483575A (en) | Self-adaptive load event detection method for noninvasive power monitoring | |
Sun et al. | A hybrid ICA-BPNN-based FDD strategy for refrigerant charge faults in variable refrigerant flow system | |
CN105846780A (en) | Decision tree model-based photovoltaic assembly fault diagnosis method | |
CN102034344B (en) | On-line detection and diagnosis device and method for voltage signal of photoelectric isolating type excess channel | |
CN109660206A (en) | A kind of diagnosing failure of photovoltaic array method based on Wasserstein GAN | |
CN110889841A (en) | YOLOv 3-based bird detection algorithm for power transmission line | |
CN103103570B (en) | Based on the aluminium cell condition diagnostic method of pivot similarity measure | |
CN111143438A (en) | Workshop field data real-time monitoring and anomaly detection method based on stream processing | |
CN109668588A (en) | Air cooling refrigeration unit sensor fault diagnosis method based on virtual-sensor | |
CN109543743A (en) | A kind of refrigeration unit multiple sensor faults diagnosis method based on reconstruction prediction residual | |
CN103792486B (en) | Based on circuit board testing design and the correlation matrix method for building up of fault effects data in FMEA | |
CN104506137A (en) | Equipment fault diagnosis method and apparatus | |
CN103529337B (en) | The recognition methods of nonlinear correlation relation between equipment failure and electric quantity information | |
CN102636998B (en) | Automatic control method for air exhaust of spatial environment simulator and automatic control system | |
CN204989367U (en) | Low pressure user transmission line detecting system that visits one house after another | |
CN117518939A (en) | Industrial control system based on big data | |
CN106227186A (en) | A kind of test system and method for electric automobile heat management pipeline | |
CN116756505B (en) | Photovoltaic equipment intelligent management system and method based on big data | |
CN116221037A (en) | Wind turbine generator monitoring method and device | |
CN107085934B (en) | Performance detection method and system for electricity consumption information acquisition equipment | |
CN106249070A (en) | A kind of test system and method for electric automobile heat management pipeline | |
CN107703913A (en) | A kind of UPFC method for diagnosing faults | |
CN207817139U (en) | Insulator breakdown diagnostic device based on Study On Reliability Estimation Method For Cold Standby Systems |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190423 |
|
RJ01 | Rejection of invention patent application after publication |