CN115480556A - Intelligent online fault diagnosis system for heating and ventilation electromechanical equipment - Google Patents
Intelligent online fault diagnosis system for heating and ventilation electromechanical equipment Download PDFInfo
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- CN115480556A CN115480556A CN202211132084.5A CN202211132084A CN115480556A CN 115480556 A CN115480556 A CN 115480556A CN 202211132084 A CN202211132084 A CN 202211132084A CN 115480556 A CN115480556 A CN 115480556A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
Abstract
The invention discloses an intelligent online fault diagnosis system of heating and ventilation electromechanical equipment, which belongs to the technical field of equipment fault diagnosis and comprises a client, an information collector, a simulation module, a fault analysis module, a maintenance feedback module, a compression recovery module and a data server, wherein the client is used for receiving the analysis result of each submodule for a user to check; according to the invention, by constructing the detection neural network and the fault diagnosis tree, the fault detection efficiency can be improved, meanwhile, data analysis can be carried out aiming at unknown operation information, the use limitation is small, the use by a user is convenient, old data stored in the data server can be automatically recovered, the abnormal data transmission caused by excessive stored data is avoided, and the data transmission stability is ensured.
Description
Technical Field
The invention relates to the technical field of equipment fault diagnosis, in particular to an intelligent online fault diagnosis system for heating and ventilating electromechanical equipment.
Background
The heating and ventilation electromechanical device is an integral part of the building. In particular to a system or related equipment which is used for indoor or vehicle heating, ventilation and air conditioning. The design of the heating, ventilating and air conditioning system is applied to thermodynamics, hydrodynamics and fluid machinery, and is an important branch subject in the field of mechanical engineering. Meanwhile, the intelligent building facility management system also cultivates and engages in the technical fields of building environment control, building energy conservation and building facility intelligence, has the professions of high-level engineering technical talents and management talents of public facility systems such as air conditioning, heat supply, ventilation, building water supply and drainage, fuel gas supply and the like, building heat energy supply system and building energy conservation design, construction, debugging, operation management capability and building automation system scheme making capability, and has the advantages of controlling the temperature and humidity of air, improving indoor comfort level and the like, so that the heating ventilation electromechanical equipment becomes an important ring in medium and large industrial buildings or office buildings;
through retrieval, the Chinese patent No. CN109443812A discloses a method and a system for diagnosing faults of heating, ventilation and air conditioning equipment based on image data, the method and the system overcome the current situation that the running parameters of the equipment are difficult to obtain, are independent of the existing automatic control system, do not need to update and transform the existing monitoring platform, can be directly applied to the actual monitoring platform of the heating, ventilation and air conditioning system, but have underground fault detection efficiency, can not judge unknown running information and have large use limitation; in addition, the existing intelligent online fault diagnosis system for the heating and ventilation electromechanical device cannot recover old data, so that data transmission is abnormal easily, and the stability of data transmission is reduced.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides an intelligent online fault diagnosis system for heating and ventilating electromechanical equipment.
In order to achieve the purpose, the invention adopts the following technical scheme:
an intelligent online fault diagnosis system of heating and ventilation electromechanical equipment comprises a client, an information collector, a simulation module, a fault analysis module, a maintenance feedback module, a compression recovery module and a data server;
the client is used for receiving the analysis results of the sub-modules for the user to view;
the information collector is used for collecting operation information of each group of heating and ventilating electromechanical equipment;
the simulation module is used for acquiring parameter information of related heating and ventilating electrical equipment from the data server to construct an equipment simulation model;
the fault analysis module is used for receiving the collected operation information of each group and carrying out matching analysis on the operation information and the simulation model;
the maintenance feedback module is used for receiving the analysis result and selectively feeding back related maintenance personnel;
the compression recovery module is used for compressing and recovering the internal space of the data server;
the data server is used for receiving and storing data files related to all groups of heating and ventilating electric equipment.
As a further scheme of the present invention, the client specifically includes a smart phone, a tablet computer, a notebook computer, and a desktop computer.
As a further scheme of the invention, the specific steps of the simulation module for constructing the equipment simulation model are as follows:
the method comprises the following steps: the simulation module collects the models of the heating and ventilating electrical equipment, then searches from the data server according to the models, extracts the parameter information of the equipment if the equipment with the same model exists, and then constructs an equipment simulation model according to the extracted parameter information of the equipment;
step two: if the data server does not have the equipment with the model, the simulation model receives equipment information uploaded by the client and equipment pictures acquired by an external camera, and then performs mutual transformation of an image space and a frequency space on the acquired equipment pictures through Fourier forward-inverse transformation;
step three: filtering high-frequency components in the equipment picture in the frequency space to filter noise in the equipment picture, then constructing an equipment simulation model according to the equipment information, and optimizing the generated simulation equipment through the processed equipment picture.
As a further scheme of the present invention, the specific calculation formula of the forward and reverse fourier transform in step two is as follows:
u and v are frequency variables, x and y are coordinates of each pixel point of the picture of the equipment, a formula (1) is Fourier forward transformation, and a formula (2) is Fourier inverse transformation.
As a further scheme of the present invention, the fault analysis model matching analysis specifically comprises the following steps:
step (1): constructing and training a group of detection neural networks by using a fault analysis model, and extracting relevant expert knowledge, engineer experience and knowledge from a data server by using the detection neural networks to construct a basic fault diagnosis tree;
step (2): then, importing the operation information collected by the information collector into a detection neural network, extracting characteristic data in each group of operation information, then removing unimportant operation information through feature dimensionality reduction, and then carrying out normalization processing on each group of characteristic data to convert the characteristic data into a specified detection interval;
and (3): inputting the processed data into a detection neural network, comparing the data with a basic fault diagnosis tree by the detection neural network, and if consistent fault information exists, feeding the consistent fault information back to a maintenance feedback module and a client;
and (4): if no consistent fault information exists, input, convolution, pooling and full-connection processing are carried out on each group of data through the detection neural network, an operation curve graph of the heating and ventilation electromechanical equipment is output, the trend difference value of the current time period and the previous time period of the operation curve graph is detected, if the difference value is gradually increased, the heating and ventilation electromechanical equipment is judged to have a fault, and the fault is sent to the maintenance feedback module.
As a further scheme of the invention, the step (1) of detecting the neural network specifically trains as follows:
the first step is as follows: collecting each group of detection data produced by detecting a neural network, selecting one group from N groups of collected detection data as verification data, fitting the rest detection models into a group of test models, verifying the precision of the test models by using the verification data, and calculating the detection capability of the test models by root mean square error to obtain precision parameters;
the second step: initializing a precision range, then listing all data results, selecting any one group of subsets as a test set for each group of data, using the rest subsets as a training set, training a test model through the training set, predicting the test set through the test model, simultaneously counting the root mean square error of the test result, then replacing another subset as the test set, repeating the operation until each group of data is predicted, and selecting a corresponding combination parameter when the root mean square error is minimum as an optimal parameter in a data interval;
the third step: and then, transmitting the selected optimal parameters to a detection neural network to replace the original parameters, simultaneously producing sample data to test the detection neural network, stopping training if the test accuracy meets the expected value, otherwise, continuing the steps, and finally, performing performance evaluation on the network intrusion detection model meeting the expected value, namely, performing accuracy rate, detectable rate and false alarm rate evaluation.
As a further scheme of the present invention, the specific steps of the maintenance feedback module selecting feedback are as follows:
s1: the maintenance feedback module receives fault information sent by the fault analysis module, then collects position information of the fault analysis module, generates a corresponding coordinate point, and receives regional information transmitted by a GPS satellite to generate a map model;
s2: the method comprises the steps that the fault heating and ventilation electromechanical equipment is marked on a map model according to coordinate information, then a maintenance feedback module collects information of related maintenance personnel in the area, the distance between each maintenance personnel and the fault heating and ventilation electromechanical equipment is calculated, a group of maintenance personnel in an idle state and closest to the heating and ventilation electromechanical equipment are selected, the maintenance personnel are prompted to maintain, and meanwhile the position of the heating and ventilation electromechanical equipment is sent to a mobile phone of the maintenance personnel.
As a further scheme of the present invention, the update and adjustment steps of the compression recovery module are as follows:
p1: the compression recovery module periodically performs statistical updating on each storage data according to a system default or manually set cycle time value, and then performs recovery rate calculation according to the number of the updated storage data;
p2: after the recovery rate is calculated, the compression recovery module extracts cache data uploaded by each heating and ventilating electrical device from old to new from the data server according to the proportion of the recovery rate, after the collection is completed, the collected data of each group are combined into a block, the block is analyzed to obtain a physical page of the block, then the physical page is copied into a buffer area, a compression algorithm is called to compress the physical page in the buffer area into a compression block, and when the number of the compression blocks reaches a certain threshold value, each group of the cached compression blocks is released.
Compared with the prior art, the invention has the beneficial effects that:
1. the method comprises the steps of constructing and training a group of detection neural networks through a fault analysis model, constructing a basic fault diagnosis tree, introducing operation information collected by an information collector into the detection neural networks, extracting characteristic data in each group of operation information, removing unimportant operation information through characteristic dimension reduction, carrying out normalization processing on each group of characteristic data, converting the processed group of data into a specified detection interval, inputting each group of data into the detection neural networks, comparing each group of data with the basic fault diagnosis tree through the detection neural networks, carrying out calculation processing on each group of data through the detection neural networks if no consistent fault information exists, outputting an operation curve graph of the heating and ventilation motor device, detecting the trend difference value of the operation curve graph between the current time period and the previous time period, judging that the heating and ventilation motor device has a fault if the difference value is gradually increased, sending the fault to a maintenance feedback module, constructing the detection neural networks and the fault diagnosis tree, improving the fault detection efficiency, analyzing data aiming at unknown operation information, and being small in use and convenient for users;
2. according to the system, each storage data is periodically counted and updated through a compression and recovery module according to a cycle time value which is set by default or manually, then the recovery rate is calculated according to the number of the updated storage data, after the recovery rate is calculated, the compression and recovery module extracts cache data uploaded by each heating and ventilating motor device from old to new from a data server according to the proportion of the recovery rate, after the collection is completed, each set of collected data is combined into a block, the block is analyzed to obtain a physical page of the block, then the physical page is copied into a buffer area, then a compression algorithm is called to compress the physical page in the buffer area into a compression block, when the number of the compression blocks reaches a certain threshold value, each set of cached compression blocks is released, the stored old data in the data server can be automatically recovered, abnormal data transmission caused by excessive stored data is avoided, and the data transmission stability is guaranteed.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a system block diagram of an intelligent online fault diagnosis system for heating and ventilating electrical equipment according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Example 1
Referring to fig. 1, an intelligent online fault diagnosis system for heating and ventilation electromechanical equipment comprises a client, an information collector, a simulation module, a fault analysis module, a maintenance feedback module, a compression recovery module and a data server.
The client is used for receiving the analysis results of the sub-modules for the user to check; the information collector is used for collecting the operation information of each group of heating and ventilating electrical equipment.
It should be further explained that the client specifically includes a smart phone, a tablet computer, a notebook computer, and a desktop computer.
The simulation module is used for acquiring parameter information of the related heating and ventilating electrical equipment from the data server to construct an equipment simulation model.
Specifically, the simulation module collects the model of the heating and ventilation electric device, then searches from the data server according to the model, extracts the parameter information of the device if the devices with the same model exist, then constructs a device simulation model according to the extracted parameter information of the device, if the devices with the same model do not exist in the data server, the simulation model receives the device information uploaded by the client and the device picture acquired by the external camera, then performs mutual transformation of an image space and a frequency space on the acquired device picture through Fourier forward-inverse transformation, then performs filtering processing on high-frequency components in the device picture in the frequency space to filter noise in the device picture, then constructs a device simulation model according to the device information, and then performs optimization processing on the generated simulation device through the processed device picture.
It should be further explained that the specific calculation formula of the fourier forward-inverse transform is as follows:
u and v are frequency variables, x and y are coordinates of each pixel point of the picture of the equipment, a formula (1) is Fourier forward transformation, and a formula (2) is Fourier inverse transformation.
And the fault analysis module is used for receiving the collected operation information of each group and carrying out matching analysis on the operation information and the simulation model.
The method comprises the steps that a fault analysis model builds and trains a group of detection neural networks, then the detection neural networks extract relevant expert knowledge, engineer experience and knowledge from a data server to build a basic fault diagnosis tree, then operation information collected by an information collector is led into the detection neural networks, feature data in each group of operation information is extracted, unimportant operation information is removed through feature dimension reduction, normalization processing is carried out on each group of feature data and is converted into a specified detection interval, each processed group of data is input into the detection neural networks, the detection neural networks compare each group of data with the basic fault diagnosis tree, if consistent fault information exists, the consistent fault information is fed back to a maintenance feedback module and a client, if the consistent fault information does not exist, input, convolution, pooling and full connection processing are carried out on each group of data through the detection neural networks, an operation curve graph of the heating and ventilation electric equipment is output, meanwhile, the trend difference value of the current operation time period and the previous operation time period of the heating and ventilation electric equipment is detected, if the difference value gradually increases, the heating and ventilation equipment is judged to have faults, and is sent to the maintenance feedback module.
It is further noted that the computer collects each set of detection data produced by the neural network, and selects one set from the collected N sets of detection data as verification data, then fits the remaining detection models into a set of test models, verifies the precision of the test models with the verification data, calculates the detection capability of the test models through root mean square error to obtain precision parameters, initializes the precision range, then lists all data results, selects any subset of each set as a test set for each set of data, uses the rest subsets as training sets, trains the test models through the training sets, predicts the test sets through the test models, meanwhile counts the root mean square error of the test results, then replaces another subset as a test set, repeats the above operations until each set of data is predicted, selects the corresponding combination parameter when the root mean square error is the smallest, then transmits the selected optimum parameter to the neural network to replace the original parameter, produces sample data to test the neural network, and if the test accuracy meets the expected value, stops training, otherwise continues the above steps, finally evaluates the expected value, namely, evaluates the intrusion rate and the detection model, namely, and evaluates the accurate detection rate.
Example 2
Referring to fig. 1, an intelligent online fault diagnosis system for heating and ventilation electromechanical equipment comprises a client, an information collector, a simulation module, a fault analysis module, a maintenance feedback module, a compression recovery module and a data server.
And the maintenance feedback module is used for receiving the analysis result and carrying out selection feedback on related maintenance personnel.
Specifically, the maintenance feedback module receives fault information sent by the fault analysis module, then collects position information of the fault analysis module, generates corresponding coordinate points, receives regional information transmitted by a GPS satellite to generate a map model, marks the fault heating and ventilation electromechanical equipment on the map model according to the coordinate information, collects information of related maintenance personnel in the region, calculates the distance between each maintenance personnel and the fault heating and ventilation electromechanical equipment, selects a group of maintenance personnel in an idle state and closest to the heating and ventilation electromechanical equipment, prompts the maintenance personnel to maintain, and sends the position of the heating and ventilation electromechanical equipment to a mobile phone of the maintenance personnel.
The compression and recovery module is used for compressing and recovering the internal space of the data server.
Specifically, the compression and recovery module periodically performs statistical updating on each storage data according to a cycle time value set by default or manually, then performs recovery rate calculation according to the number of the updated storage data, after the recovery rate calculation is completed, the compression and recovery module extracts cache data uploaded by each heating and ventilation electric device from old to new from a data server according to the proportion of the recovery rate, after the collection is completed, combines each collected group of data into one block, analyzes the block to obtain a physical page of the block, then copies the physical page into a buffer area, calls a compression algorithm to compress the physical page in the buffer area into a compression block, and releases each cached group of compression blocks when the number of the compression blocks reaches a certain threshold value.
The data server is used for receiving and storing data files related to all groups of heating and ventilating electric equipment.
Claims (8)
1. An intelligent online fault diagnosis system of heating and ventilation electromechanical equipment is characterized by comprising a client, an information collector, a simulation module, a fault analysis module, a maintenance feedback module, a compression recovery module and a data server;
the client is used for receiving the analysis results of the sub-modules for the user to view;
the information collector is used for collecting the operation information of each group of heating and ventilating electrical equipment;
the simulation module is used for acquiring parameter information of related heating and ventilating electrical equipment from the data server to construct an equipment simulation model;
the fault analysis module is used for receiving the collected operation information of each group and carrying out matching analysis on the operation information and the simulation model;
the maintenance feedback module is used for receiving the analysis result and selectively feeding back related maintenance personnel;
the compression and recovery module is used for compressing and recovering the internal space of the data server;
the data server is used for receiving and storing data files related to all groups of heating and ventilating electrical equipment.
2. The intelligent online fault diagnosis system for the heating and ventilation electromechanical device according to claim 1, wherein the client specifically comprises a smart phone, a tablet computer, a notebook computer and a desktop computer.
3. The intelligent online fault diagnosis system for the heating and ventilating electromechanical device according to claim 1, wherein the specific steps of the simulation module for constructing the device simulation model are as follows:
the method comprises the following steps: the simulation module collects the models of the heating and ventilating electrical equipment, then searches from the data server according to the models, extracts the parameter information of the equipment if the equipment with the same model exists, and then constructs an equipment simulation model according to the extracted parameter information of the equipment;
step two: if the data server does not have the equipment with the model, the simulation model receives equipment information uploaded by the client and equipment pictures acquired by an external camera, and then performs mutual transformation of an image space and a frequency space on the acquired equipment pictures through Fourier forward-inverse transformation;
step three: filtering high-frequency components in the equipment picture in the frequency space to filter noise in the equipment picture, then constructing an equipment simulation model according to the equipment information, and optimizing the generated simulation equipment through the processed equipment picture.
4. The intelligent online fault diagnosis system for the heating and ventilation electromechanical device according to claim 3, wherein the specific calculation formula of the Fourier forward and backward transformation in the second step is as follows:
u and v are frequency variables, x and y are coordinates of each pixel point of the picture of the equipment, a formula (1) is Fourier forward transformation, and a formula (2) is Fourier inverse transformation.
5. The intelligent online fault diagnosis system for the heating and ventilation electromechanical device according to claim 1, wherein the fault analysis model matching analysis comprises the following specific steps:
step (1): constructing and training a group of detection neural networks by using a fault analysis model, and extracting relevant expert knowledge, engineer experience and knowledge from a data server by using the detection neural networks to construct a basic fault diagnosis tree;
step (2): then, importing the operation information collected by the information collector into a detection neural network, extracting characteristic data in each group of operation information, then removing unimportant operation information through characteristic dimension reduction, and then carrying out normalization processing on each group of characteristic data to convert the characteristic data into a specified detection interval;
and (3): inputting the processed data into a detection neural network, comparing the data with a basic fault diagnosis tree by the detection neural network, and if consistent fault information exists, feeding the consistent fault information back to a maintenance feedback module and a client;
and (4): if no consistent fault information exists, input, convolution, pooling and full-connection processing are carried out on each group of data through the detection neural network, an operation curve graph of the heating and ventilation electromechanical equipment is output, the trend difference value of the current time period and the previous time period of the operation curve graph is detected, if the difference value is gradually increased, the heating and ventilation electromechanical equipment is judged to have a fault, and the fault is sent to the maintenance feedback module.
6. The intelligent online fault diagnosis system for the heating and ventilation electromechanical device according to claim 5, wherein the detecting neural network in step (1) is specifically trained as follows:
the first step is as follows: collecting each group of detection data produced by detecting a neural network, selecting one group from N groups of collected detection data as verification data, fitting the rest detection models into a group of test models, verifying the precision of the test models by using the verification data, and calculating the detection capability of the test models by root mean square error to obtain precision parameters;
the second step is that: initializing a precision range, then listing all data results, selecting any one group of subsets as a test set for each group of data, using the rest subsets as a training set, training a test model through the training set, predicting the test set through the test model, simultaneously counting the root mean square error of the test result, then replacing another subset as the test set, repeating the operation until each group of data is predicted, and selecting a corresponding combination parameter when the root mean square error is minimum as an optimal parameter in a data interval;
the third step: and then transmitting the selected optimal parameters to a detection neural network to replace the original parameters, simultaneously producing sample data to test the detection neural network, stopping training if the test accuracy meets an expected value, otherwise, continuing the steps, and finally, performing performance evaluation on the network intrusion detection model meeting the expected value, namely, performing accuracy rate, detection rate and false alarm rate evaluation.
7. The intelligent online fault diagnosis system for the heating and ventilation electromechanical device according to claim 1, wherein the maintenance feedback module selects the feedback specifically as follows:
s1: the maintenance feedback module receives fault information sent by the fault analysis module, then collects position information of the fault analysis module, generates a corresponding coordinate point, and receives regional information transmitted by a GPS satellite to generate a map model;
s2: the method comprises the steps that the fault heating and ventilation electromechanical equipment is marked on a map model according to coordinate information, then a maintenance feedback module collects information of related maintenance personnel in the area, the distance between each maintenance personnel and the fault heating and ventilation electromechanical equipment is calculated, a group of maintenance personnel in an idle state and closest to the heating and ventilation electromechanical equipment are selected, the maintenance personnel are prompted to maintain, and meanwhile the position of the heating and ventilation electromechanical equipment is sent to a mobile phone of the maintenance personnel.
8. The intelligent online fault diagnosis system for the heating and ventilation electromechanical device as claimed in claim 1, wherein the compression recovery module updating and adjusting specifically comprises the following steps:
p1: the compression recovery module periodically performs statistical updating on each storage data according to a system default or manually set cycle time value, and then performs recovery rate calculation according to the number of the updated storage data;
p2: after the recovery rate is calculated, the compression recovery module extracts cache data uploaded by each heating and ventilating electrical device from old to new from the data server according to the proportion of the recovery rate, after the collection is completed, the collected data of each group are combined into a block, the block is analyzed to obtain a physical page of the block, then the physical page is copied into a buffer area, a compression algorithm is called to compress the physical page in the buffer area into a compression block, and when the number of the compression blocks reaches a certain threshold value, each group of the cached compression blocks is released.
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| CN116934180B (en) * | 2023-09-15 | 2023-12-08 | 恒实建设管理股份有限公司 | Whole process consultation information management method, system, device and storage medium |
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Application publication date: 20221216 |