CN117391443A - Dust removal equipment state monitoring and early warning method and system - Google Patents
Dust removal equipment state monitoring and early warning method and system Download PDFInfo
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Abstract
The invention provides a dust removing equipment state monitoring and early warning method and a system, which relate to the technical field of equipment state monitoring and early warning and comprise the following steps: acquiring basic parameter information of the dust removing equipment to perform risk assessment, acquiring equipment risk level, acquiring working condition data to construct a dynamic working condition data set, acquiring environment data to construct a dynamic environment data set, and constructing a dynamic risk model by association between the dynamic environment data set and the dynamic working condition data set, wherein the dynamic risk model comprises an environment anomaly identification module and a working condition anomaly identification module, acquiring real-time working condition data and real-time environment data, inputting the dynamic risk model, acquiring operation parameter risk level, performing fusion assessment, acquiring a state risk assessment result, and performing state early warning of the dust removing equipment. The invention solves the technical problems that the traditional method can not accurately capture the influence of dynamic working condition change and environmental influence on the performance of equipment, and the parameters can not be combined, so that the state monitoring is not accurate and comprehensive.
Description
Technical Field
The invention relates to the technical field of equipment state monitoring and early warning, in particular to a dust removing equipment state monitoring and early warning method and system.
Background
Currently, state monitoring and early warning of dust removing equipment mainly depend on a traditional basic parameter monitoring method, for example, basic parameters such as temperature, pressure and flow of the equipment are detected regularly. On one hand, the traditional method mainly focuses on static basic parameter information of equipment, can not comprehensively evaluate the risk condition of the equipment, and can not capture the influence of dynamic working condition change and environmental influence on the performance and health state of the equipment; on the other hand, the monitoring data are generally recorded and stored in a scattered way, and lack of correlation analysis between different data sources, so that the working condition data and the environmental parameters cannot be combined, and the health state and the risk level of the equipment can be accurately and comprehensively judged.
Therefore, a new dust removing equipment state monitoring and early warning method is needed to realize more accurate and comprehensive equipment state monitoring and early warning.
Disclosure of Invention
The utility model provides a dust collecting equipment state monitoring early warning method and system, aims at solving the traditional method and can't accurately catch dynamic operating condition change and environmental impact to the influence of equipment performance to can't combine operating condition data with environmental parameter, only provide the early warning signal of single index, lead to the state monitoring not accurate, comprehensive technical problem.
In view of the above problems, the present application provides a dust removing device status monitoring and early warning method and system.
In a first aspect of the disclosure, a method for monitoring and early warning a dust removing device state is provided, the method comprising: basic parameter information of dust removing equipment to be monitored is obtained, risk assessment is carried out on the basic parameter information, and equipment risk level is obtained; collecting working condition data of the dust removing equipment, and constructing a dynamic working condition data set, wherein the dynamic working condition data set is collected through a voltage sensor, an equipment temperature sensor and a vibration sensor; collecting environment data of the dust removing equipment, and constructing a dynamic environment data set, wherein the dynamic environment data set is collected through an environment temperature sensor, a humidity sensor and a particulate matter sensor, and has an association relation with the dynamic working condition data set; constructing a dynamic risk model according to the dynamic environment data set and the dynamic working condition data set, wherein the dynamic risk model comprises an environment abnormality recognition module and a working condition abnormality recognition module; acquiring real-time working condition data and real-time environment data of the dust removing equipment, inputting the real-time working condition data and the real-time environment data into the dynamic risk model, and acquiring the risk level of the operation parameters; performing fusion evaluation on the operation parameter risk level and the equipment risk level to obtain a state risk evaluation result of the dust removing equipment; and carrying out state early warning on the dust removing equipment according to the state risk assessment result.
In another aspect of the disclosure, a dust removing device status monitoring and early warning system is provided, the system is used in the method, and the system includes: the system comprises a basic parameter acquisition unit, a risk assessment unit and a power supply unit, wherein the basic parameter acquisition unit is used for acquiring basic parameter information of dust removal equipment to be monitored, performing risk assessment on the basic parameter information and acquiring equipment risk level; the working condition data acquisition unit is used for acquiring working condition data of the dust removal equipment and constructing a dynamic working condition data set, and the dynamic working condition data set is acquired through a voltage sensor, an equipment temperature sensor and a vibration sensor; the environment data acquisition unit is used for acquiring environment data of the dust removal equipment and constructing a dynamic environment data set, the dynamic environment data set is acquired through an environment temperature sensor, a humidity sensor and a particulate matter sensor, and the dynamic environment data set and the dynamic working condition data set have an association relation; the model construction unit is used for constructing a dynamic risk model according to the dynamic environment data set and the dynamic working condition data set, and the dynamic risk model comprises an environment abnormality recognition module and a working condition abnormality recognition module; the real-time data acquisition unit is used for acquiring real-time working condition data and real-time environment data of the dust removal equipment, inputting the real-time working condition data and the real-time environment data into the dynamic risk model and acquiring the risk level of the operation parameters; the fusion evaluation unit is used for carrying out fusion evaluation on the running parameter risk level and the equipment risk level to obtain a state risk evaluation result of the dust removal equipment; and the state early warning unit is used for carrying out state early warning on the dust removing equipment according to the state risk assessment result.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
by comprehensively considering basic parameters, dynamic working conditions and environmental data, the risk level of the equipment can be estimated more accurately, and abnormal conditions can be monitored in time; the real-time working condition and environment data are collected and analyzed, and the abnormal condition of the equipment in operation can be rapidly identified by combining with the dynamic risk model, and corresponding early warning and response are carried out, so that the shutdown time and loss can be reduced; by carrying out fusion evaluation on the equipment risk level based on risk evaluation and the operation parameter risk level based on dynamic data, a more comprehensive equipment state risk evaluation result is provided, and scientific basis is provided for decision and resource allocation. In general, the dust removing equipment state monitoring and early warning method realizes the improvement of monitoring accuracy, the realization of real-time early warning and response and the provision of comprehensive evaluation results, and is beneficial to the improvement of the operation efficiency and the safety of the dust removing equipment.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
Fig. 1 is a schematic flow chart of a dust removing device state monitoring and early warning method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a dust removing device status monitoring and early warning system according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a basic parameter acquisition unit 10, a working condition data acquisition unit 20, an environment data acquisition unit 30, a model construction unit 40, a real-time data acquisition unit 50, a fusion evaluation unit 60 and a state early warning unit 70.
Detailed Description
According to the method for monitoring and early warning the state of the dust removing equipment, the technical problems that the traditional method cannot accurately capture the influence of dynamic working condition change and environmental influence on equipment performance, and cannot combine working condition data with environmental parameters, only single-index early warning signals are provided, so that state monitoring is not accurate and comprehensive are solved.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Example 1
As shown in fig. 1, an embodiment of the present application provides a dust removing device status monitoring and early warning method, where the method includes:
basic parameter information of dust removing equipment to be monitored is obtained, risk assessment is carried out on the basic parameter information, and equipment risk level is obtained;
further, performing risk assessment on the basic parameter information further includes:
carrying out risk factor identification on the basic parameter information, and defining a risk assessment index according to an identification result;
collecting actual operation data corresponding to the basic parameter information;
and carrying out risk assessment on the actual operation data based on the risk assessment index, and converting a risk assessment result into a device risk grade.
Basic parameter information of the dust removing equipment to be monitored comprises the model number, the year, the manufacturer, the installation position and the like of the equipment, and the information can be collected through an automatic system. Identifying factors related to equipment risk, such as longer equipment age, possibly with more potential fault risk, based on the collected basic parameter information; the manufacturer of the device is reputable and may have a low risk level. Based on the identified risk factors, corresponding risk assessment indicators are defined, each of which may be used to measure the degree of risk of the device in this regard, e.g., using the device age as a risk assessment indicator, classifying the device age into different levels, such as new device, middle-aged device, old device, etc.
Actual operation data corresponding to the basic parameter information is collected, and the data comprises running time, maintenance record, failure times and the like of equipment. By comparing the actual operation data with the predefined risk assessment indicators, a risk level corresponding to each indicator is determined, e.g. if the equipment age is greater than 10 years, it is classified as old equipment, corresponding to a higher risk level. And comprehensively considering the risk levels of all the risk assessment indexes, and calculating the overall risk level of the equipment through weighted average so as to more accurately reflect the overall risk condition of the equipment.
And basic data are provided for subsequent state monitoring and early warning by acquiring the risk level of the dust removing equipment to be monitored.
Collecting working condition data of the dust removing equipment, and constructing a dynamic working condition data set, wherein the dynamic working condition data set is collected through a voltage sensor, an equipment temperature sensor and a vibration sensor;
in order to collect the working condition data of the dust removing equipment, three sensors are selected, including a voltage sensor, an equipment temperature sensor and a vibration sensor, and the sensors are used for collecting relevant information about the running state of the equipment. The selected sensor is arranged on the dust removing equipment, the voltage sensor is used for measuring the voltage change of the equipment, the equipment temperature sensor is used for measuring the temperature of the equipment, and the vibration sensor is used for detecting the vibration degree of the equipment.
By continuously collecting working condition data and recording the working condition data according to time sequence, a dynamic working condition data set is constructed, wherein the dynamic working condition data set contains data of the change of parameters such as voltage, temperature and vibration of equipment along with time, so that the change condition of key parameters of the equipment in the operation process can be obtained, and basic data is provided for subsequent state monitoring, anomaly detection and risk assessment.
Collecting environment data of the dust removing equipment, and constructing a dynamic environment data set, wherein the dynamic environment data set is collected through an environment temperature sensor, a humidity sensor and a particulate matter sensor, and has an association relation with the dynamic working condition data set;
for the acquisition of environmental data of the dust-removing device, three sensors are selected, including an ambient temperature sensor, a humidity sensor and a particulate matter sensor, which are used for collecting parameter information related to the surrounding environment of the device. The selected sensor is installed at a proper position near the dust removing device, the ambient temperature sensor is used for measuring the temperature change around the device, the humidity sensor is used for measuring the humidity level in the air, and the particulate matter sensor is used for detecting the concentration of particulate matters in the air.
By continuously collecting environmental data and correlating the environmental data with a dynamic working condition data set, a dynamic environmental data set is constructed, wherein the dynamic environmental data set contains data of the temperature, humidity, particulate matter concentration and other parameters around the equipment, which change along with time. In this way, the change of the environmental conditions around the equipment can be obtained, and meanwhile, the correlation with the dynamic working condition data set is used for analyzing the interaction between the operation of the equipment and the environment.
Constructing a dynamic risk model according to the dynamic environment data set and the dynamic working condition data set, wherein the dynamic risk model comprises an environment abnormality recognition module and a working condition abnormality recognition module;
using the dynamic environmental data set, constructing an environmental anomaly identification module aimed at detecting anomalies in the environmental data, such as temperature anomalies, humidity anomalies, or particulate matter concentration anomalies; and constructing a working condition abnormality identification module by utilizing the dynamic working condition data set, wherein the module aims to detect abnormal conditions in the working condition data, such as voltage abnormality, equipment temperature abnormality or vibration abnormality.
The constructed environment abnormality recognition module and the working condition abnormality recognition module are integrated to construct a complete dynamic risk model, and the model can receive real-time dynamic environment data and working condition data as input and output corresponding risk levels, so that abnormal conditions of the running state of the equipment can be found in time, and data support is provided for subsequent early warning and risk assessment.
Acquiring real-time working condition data and real-time environment data of the dust removing equipment, inputting the real-time working condition data and the real-time environment data into the dynamic risk model, and acquiring the risk level of the operation parameters;
the installed sensors are used for collecting working condition data of the dust removing equipment in real time, wherein the data comprise parameters such as voltage, equipment temperature, vibration and the like, so that the frequency of data collection is high enough to capture real-time change of the working condition of the equipment; similarly, data of the surrounding environment of the dust removing device is collected in real time, and the real-time environmental data such as temperature, humidity, concentration of particles and the like are collected by using devices such as an environmental temperature sensor, a humidity sensor, a particle sensor and the like.
The real-time working condition data and the real-time environment data are input into a pre-constructed dynamic risk model, and the dynamic risk model comprises an environment abnormality recognition module and a working condition abnormality recognition module. The model evaluates the running state of the equipment according to the real-time working condition data and the real-time environment data through the working condition abnormality recognition module and the environment abnormality recognition module respectively, and gives corresponding working condition risk levels and environment risk levels, for example, the working condition data are evaluated to be low risk, medium risk and high risk levels, the working condition risk levels and the environment risk levels are subjected to weighted average calculation, the calculation result is used as the output of the dynamic risk model, and the risk levels of the running parameters are obtained.
Performing fusion evaluation on the operation parameter risk level and the equipment risk level to obtain a state risk evaluation result of the dust removing equipment;
further, performing fusion assessment on the operation parameter risk level and the equipment risk level, including:
according to the equipment safety and the environmental influence, assigning an associated weight to the operation parameter risk level and the equipment risk level;
and carrying out weighted fusion on the running parameter risk level and the equipment risk level according to the association weight, and calculating to obtain a state risk assessment result.
Firstly, considering the safety factors of equipment, including the structure, design, reliability and the like of the equipment, wherein higher safety means lower risk potential and more reliable operation, and the weight related to the safety is distributed according to the characteristics and service requirements of the equipment; secondly, considering the influence of environmental factors on risk assessment, including the conditions of the environment in which the equipment is located, surrounding facilities, possible risk sources and the like, if the environment has an important influence on the running stability and safety of the equipment, corresponding weights are given. And associating the weight values of the equipment safety and the environmental influence with the risk level of the operation parameters and the risk level of the equipment, ensuring reasonable weight distribution, and meeting the actual conditions and requirements, wherein the weight values reflect the importance degree of the equipment safety and the environmental influence on the risk assessment.
Multiplying the running parameter risk level by the corresponding association weight, multiplying the equipment risk level by the association weight, adding the two results, namely performing weighted fusion calculation, acquiring and obtaining a state risk assessment result according to the result of the weighted fusion calculation, wherein the result reflects comprehensive state risk assessment comprehensively considering the running parameter and the equipment risk level, and obtaining a more accurate state risk assessment result, which is helpful for providing more reliable information support so as to take necessary measures in time to manage and alleviate potential risks.
And carrying out state early warning on the dust removing equipment according to the state risk assessment result.
And formulating preset warning thresholds according to specific conditions and service requirements of the equipment, wherein the thresholds are used for judging whether the state of the equipment reaches the early warning or dangerous degree or not. Based on the state risk assessment result and a preset warning threshold value, a corresponding state early warning strategy is formulated, including triggering an alarm, sending a notification or taking other early warning measures, for example, triggering the alarm to notify related personnel when the risk level exceeds a certain threshold value. And according to the evaluation result and the state of the early warning strategy monitoring equipment, once the state early warning notification is received, corresponding personnel take appropriate response measures, including checking the equipment, maintaining and the like, so as to remove the faults.
Further, the method further comprises the following steps:
extracting characteristics of the dynamic working condition data set to obtain working condition characteristic data;
performing abnormal marking on the working condition characteristic data, and dividing the working condition characteristic data into a training set and a testing set according to marking results, wherein the marking results comprise abnormal grades;
and constructing a network structure of the working condition abnormal recognition module based on the neural network, training the working condition abnormal recognition module by adopting the training set, performing training evaluation on the trained working condition abnormal recognition module by using the testing set, and optimizing the module according to the evaluation result until the working condition abnormal recognition module meeting the preset requirement is obtained.
The feature extraction method is selected to extract useful information from dynamic working condition data, and common feature extraction methods comprise statistical features, frequency domain features, time domain features and the like, and the feature extraction method is applied to a dynamic working condition data set to obtain corresponding working condition feature data, wherein the feature data reflects key information and variation trend of the dynamic working condition data, for example, the features such as average value, standard deviation, energy spectrum density and the like are extracted. The extracted working condition characteristic data are processed and arranged, and the steps of data cleaning, normalization, dimension reduction and the like are included, so that the quality and the applicability of the characteristic data are ensured.
Based on methods such as statistical analysis and expert knowledge, the working condition characteristic data are marked abnormally, whether abnormality exists is determined by comparing the difference between the working condition characteristic data and the normal running state, and corresponding abnormality grades are given. Each sample of operating characteristic data is marked as normal or abnormal, with an abnormality rating, which may be a discrete classification label, such as low, medium, high, or a continuous number, such as a score between 0 and 1, indicating the severity of the abnormality.
According to the marking result, the working condition characteristic data is divided into a training set and a testing set, the consistency of the sample distribution and the abnormal category distribution of the training set and the testing set is maintained, for example, the data set is divided according to a certain proportion, such as a 70% training set and a 30% testing set, and meanwhile, the samples of each abnormal level are ensured to have proper distribution in the training set and the testing set.
Training the working condition abnormality recognition module by using a training set, taking the working condition characteristic data as input, taking the corresponding abnormality label as target output, and adjusting the weight and the parameters of the neural network through a back propagation algorithm and an optimization method such as gradient descent, so that the abnormal state of the working condition can be accurately recognized. And evaluating the trained abnormal working condition recognition module by using the test set, inputting working condition characteristic data of the test set into the module, comparing the working condition characteristic data with a real label according to an output result, and calculating evaluation indexes such as accuracy, precision, recall rate or F1 score and the like, wherein the indexes reflect the performance and recognition capability of the module.
And optimizing the abnormal working condition identification module according to the evaluation result, wherein the abnormal working condition identification module comprises the steps of adjusting a network structure, adjusting super parameters, increasing the number of training samples, introducing regularization technology and the like, and the performance and the generalization capability of the module are improved through continuous iteration and optimization, so that the module meets the preset requirement. The above steps are repeated until the condition abnormality recognition module reaches a preset requirement, which requires multiple adjustments and optimizations to obtain optimal model performance and reliability.
Further, the method further comprises the following steps:
acquiring initial environmental data before dust removal and purified environmental data after dust removal;
acquiring the environmental cleanliness according to the initial environmental data and the purified environmental data;
and compensating the state risk assessment result according to the environmental cleanliness.
Before the dust removing equipment is operated, initial environmental data before dust removal is collected and recorded by using installed sensors, wherein the data comprise information such as particulate matter concentration, harmful gas concentration, temperature, humidity and the like in the air. After the dust removing equipment operates, the sensor is also used for collecting the data of the purified environment after dust removal, the data reflect the removal effect of the dust removing equipment on the environmental pollutants, and the information of the concentration of the particulate matters, the concentration of the harmful gases, the temperature, the humidity and the like in the air is also collected.
The initial environmental data is compared with the clean environmental data, and parameters in the two sets of data, such as particulate matter concentration, harmful gas concentration, etc., are compared. The method comprises the steps of evaluating the cleanliness of the environment by calculating the removal efficiency of the dust removal equipment, wherein the removal efficiency is calculated by dividing the difference between the initial concentration and the purification concentration by the initial concentration, the initial concentration is a concentration value in the initial environment, and the purification concentration is a concentration value in the purified environment after dust removal.
The environmental pollution level and improvement thereof are judged by evaluating the environmental cleanliness based on a preset standard according to the calculated removal efficiency, for example, the environmental is classified into different grades from high pollution to cleaning according to the percentage of the removal efficiency.
According to the environmental cleanliness, a corresponding compensation method is formulated, for example, if the environment is cleaner, the weight of the risk assessment result can be reduced or the safety coefficient can be increased, according to the formulated compensation method, the state risk assessment result is adjusted to obtain a final state risk assessment result, the result comprehensively considers the environmental cleanliness factor, more accurately reflects the actual risk level, and according to the result, corresponding measures can be taken to manage and reduce the risk. And further, the accuracy and pertinence of the evaluation are improved, so that the safety of personnel and the environmental health are better protected.
Further, the method further comprises the following steps:
synchronously acquiring and obtaining electric power data corresponding to the real-time working condition data of the dust removing equipment;
performing power influence evaluation on the power data to generate a power influence factor;
and compensating the state risk assessment result according to the electric power influence factor.
And collecting and recording the electric power data of the dust removing equipment through the electric power monitoring device while collecting the real-time working condition data, wherein the data relate to the aspects of current, voltage, power consumption and the like of the equipment. And synchronizing the real-time working condition data and the electric power data, namely matching the two groups of data in the corresponding time period, and ensuring that the working condition data and the electric power data have the same time stamp so as to facilitate subsequent analysis.
The power influence factor, i.e. an index for measuring the influence degree of the power data on the performance of the equipment, can be defined according to specific situations and requirements, such as the operation efficiency of the equipment, the energy utilization rate and the like.
The collected power data is analyzed, including data preprocessing, feature extraction, statistical analysis, and the like, with the objective of obtaining information related to the defined power impact factors from the power data.
Based on the analyzed power data information, calculating the value of a power influence factor, and carrying out influence evaluation and generating a corresponding influence factor result according to the calculated power influence factor. This reflects the extent to which power consumption affects the performance of the device, and the results can be visually presented to more intuitively understand the impact of power. By performing power impact evaluation on the power data and generating a power impact factor, the impact of power consumption on the device can be quantified.
Based on the magnitude of the power impact factor, a corresponding compensation method is formulated, for example, if the power impact factor is higher, the weight of the risk assessment result can be increased or a corresponding correction coefficient can be introduced. According to the formulated compensation method and strategy, the state risk assessment result is adjusted, and the final state risk assessment result is obtained through the compensation of the electric power influence factors, the influence of the electric power consumption on the risk is comprehensively considered by the result, the actual risk level is reflected more accurately, and corresponding measures can be taken to reduce the risk according to the result. By compensating the power influence factors on the state risk assessment result, the risk can be assessed and managed more comprehensively, and the influence of power consumption is considered, so that the assessment accuracy and pertinence are improved, and the personnel safety and the equipment operation are better protected.
In summary, the dust removing equipment state monitoring and early warning method and system provided by the embodiment of the application have the following technical effects:
1. by comprehensively considering basic parameters, dynamic working conditions and environmental data, the risk level of the equipment can be estimated more accurately, and abnormal conditions can be monitored in time;
2. the real-time working condition and environment data are collected and analyzed, and the abnormal condition of the equipment in operation can be rapidly identified by combining with the dynamic risk model, and corresponding early warning and response are carried out, so that the shutdown time and loss can be reduced;
3. by carrying out fusion evaluation on the equipment risk level based on risk evaluation and the operation parameter risk level based on dynamic data, a more comprehensive equipment state risk evaluation result is provided, and scientific basis is provided for decision and resource allocation.
In general, the dust removing equipment state monitoring and early warning method realizes the improvement of monitoring accuracy, the realization of real-time early warning and response and the provision of comprehensive evaluation results, and is beneficial to the improvement of the operation efficiency and the safety of the dust removing equipment.
Example two
Based on the same inventive concept as the dust removing device state monitoring and early warning method in the foregoing embodiment, as shown in fig. 2, the present application provides a dust removing device state monitoring and early warning system, where the system includes:
a basic parameter acquiring unit 10, where the basic parameter acquiring unit 10 is configured to acquire basic parameter information of a dust removing device to be monitored, perform risk assessment on the basic parameter information, and acquire a device risk level;
the working condition data acquisition unit 20 is used for acquiring working condition data of the dust removal equipment, and constructing a dynamic working condition data set, wherein the dynamic working condition data set is acquired through a voltage sensor, an equipment temperature sensor and a vibration sensor;
the environment data acquisition unit 30 is used for acquiring environment data of the dust removing equipment, constructing a dynamic environment data set, wherein the dynamic environment data set is acquired through an environment temperature sensor, a humidity sensor and a particulate matter sensor, and has an association relation with the dynamic working condition data set;
the model construction unit 40 is configured to construct a dynamic risk model according to the dynamic environment data set and the dynamic working condition data set, where the dynamic risk model includes an environment anomaly identification module and a working condition anomaly identification module;
the real-time data acquisition unit 50 is used for acquiring real-time working condition data and real-time environment data of the dust removal equipment, inputting the real-time working condition data and the real-time environment data into the dynamic risk model, and acquiring the risk level of the operation parameters;
the fusion evaluation unit 60 is configured to perform fusion evaluation on the operation parameter risk level and the equipment risk level, and obtain a state risk evaluation result of the dust removing equipment;
and the state early-warning unit 70 is used for carrying out state early-warning on the dust removing equipment according to the state risk assessment result.
Further, the system also includes a risk assessment module to perform the following operational steps:
carrying out risk factor identification on the basic parameter information, and defining a risk assessment index according to an identification result;
collecting actual operation data corresponding to the basic parameter information;
and carrying out risk assessment on the actual operation data based on the risk assessment index, and converting a risk assessment result into a device risk grade.
Further, the system also comprises a risk model construction module for executing the following operation steps:
extracting characteristics of the dynamic working condition data set to obtain working condition characteristic data;
performing abnormal marking on the working condition characteristic data, and dividing the working condition characteristic data into a training set and a testing set according to marking results, wherein the marking results comprise abnormal grades;
and constructing a network structure of the working condition abnormal recognition module based on the neural network, training the working condition abnormal recognition module by adopting the training set, performing training evaluation on the trained working condition abnormal recognition module by using the testing set, and optimizing the module according to the evaluation result until the working condition abnormal recognition module meeting the preset requirement is obtained.
Further, the system also comprises a level fusion evaluation module for executing the following operation steps:
according to the equipment safety and the environmental influence, assigning an associated weight to the operation parameter risk level and the equipment risk level;
and carrying out weighted fusion on the running parameter risk level and the equipment risk level according to the association weight, and calculating to obtain a state risk assessment result.
Further, the system also includes a first compensation module to perform the following operation steps:
acquiring initial environmental data before dust removal and purified environmental data after dust removal;
acquiring the environmental cleanliness according to the initial environmental data and the purified environmental data;
and compensating the state risk assessment result according to the environmental cleanliness.
Further, the system also includes a second compensation module to perform the following operation steps:
synchronously acquiring and obtaining electric power data corresponding to the real-time working condition data of the dust removing equipment;
performing power influence evaluation on the power data to generate a power influence factor;
and compensating the state risk assessment result according to the electric power influence factor.
Through the foregoing detailed description of the dust-collecting equipment state monitoring and early warning method, those skilled in the art can clearly know the dust-collecting equipment state monitoring and early warning method and system in this embodiment, and for the apparatus disclosed in the embodiment, the description is relatively simple because it corresponds to the method disclosed in the embodiment, and relevant places refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. The method for monitoring and early warning the state of the dust removing equipment is characterized by comprising the following steps of:
basic parameter information of dust removing equipment to be monitored is obtained, risk assessment is carried out on the basic parameter information, and equipment risk level is obtained;
collecting working condition data of the dust removing equipment, and constructing a dynamic working condition data set, wherein the dynamic working condition data set is collected through a voltage sensor, an equipment temperature sensor and a vibration sensor;
collecting environment data of the dust removing equipment, and constructing a dynamic environment data set, wherein the dynamic environment data set is collected through an environment temperature sensor, a humidity sensor and a particulate matter sensor, and has an association relation with the dynamic working condition data set;
constructing a dynamic risk model according to the dynamic environment data set and the dynamic working condition data set, wherein the dynamic risk model comprises an environment abnormality recognition module and a working condition abnormality recognition module;
acquiring real-time working condition data and real-time environment data of the dust removing equipment, inputting the real-time working condition data and the real-time environment data into the dynamic risk model, and acquiring the risk level of the operation parameters;
performing fusion evaluation on the operation parameter risk level and the equipment risk level to obtain a state risk evaluation result of the dust removing equipment;
and carrying out state early warning on the dust removing equipment according to the state risk assessment result.
2. The method of claim 1, wherein risk assessment is performed on the base parameter information, further comprising:
carrying out risk factor identification on the basic parameter information, and defining a risk assessment index according to an identification result;
collecting actual operation data corresponding to the basic parameter information;
and carrying out risk assessment on the actual operation data based on the risk assessment index, and converting a risk assessment result into a device risk grade.
3. The method of claim 1, wherein constructing a dynamic risk model from the dynamic environment dataset and the dynamic operating condition dataset comprises:
extracting characteristics of the dynamic working condition data set to obtain working condition characteristic data;
performing abnormal marking on the working condition characteristic data, and dividing the working condition characteristic data into a training set and a testing set according to marking results, wherein the marking results comprise abnormal grades;
and constructing a network structure of the working condition abnormal recognition module based on the neural network, training the working condition abnormal recognition module by adopting the training set, performing training evaluation on the trained working condition abnormal recognition module by using the testing set, and optimizing the module according to the evaluation result until the working condition abnormal recognition module meeting the preset requirement is obtained.
4. The method of claim 1, wherein performing a fusion assessment of the operating parameter risk level and the device risk level comprises:
according to the equipment safety and the environmental influence, assigning an associated weight to the operation parameter risk level and the equipment risk level;
and carrying out weighted fusion on the running parameter risk level and the equipment risk level according to the association weight, and calculating to obtain a state risk assessment result.
5. The method as recited in claim 1, further comprising:
acquiring initial environmental data before dust removal and purified environmental data after dust removal;
acquiring the environmental cleanliness according to the initial environmental data and the purified environmental data;
and compensating the state risk assessment result according to the environmental cleanliness.
6. The method as recited in claim 1, further comprising:
synchronously acquiring and obtaining electric power data corresponding to the real-time working condition data of the dust removing equipment;
performing power influence evaluation on the power data to generate a power influence factor;
and compensating the state risk assessment result according to the electric power influence factor.
7. The dust collecting equipment state monitoring and early warning system is characterized by being used for implementing the dust collecting equipment state monitoring and early warning method according to any one of claims 1-6, and comprises the following steps:
the system comprises a basic parameter acquisition unit, a risk assessment unit and a power supply unit, wherein the basic parameter acquisition unit is used for acquiring basic parameter information of dust removal equipment to be monitored, performing risk assessment on the basic parameter information and acquiring equipment risk level;
the working condition data acquisition unit is used for acquiring working condition data of the dust removal equipment and constructing a dynamic working condition data set, and the dynamic working condition data set is acquired through a voltage sensor, an equipment temperature sensor and a vibration sensor;
the environment data acquisition unit is used for acquiring environment data of the dust removal equipment and constructing a dynamic environment data set, the dynamic environment data set is acquired through an environment temperature sensor, a humidity sensor and a particulate matter sensor, and the dynamic environment data set and the dynamic working condition data set have an association relation;
the model construction unit is used for constructing a dynamic risk model according to the dynamic environment data set and the dynamic working condition data set, and the dynamic risk model comprises an environment abnormality recognition module and a working condition abnormality recognition module;
the real-time data acquisition unit is used for acquiring real-time working condition data and real-time environment data of the dust removal equipment, inputting the real-time working condition data and the real-time environment data into the dynamic risk model and acquiring the risk level of the operation parameters;
the fusion evaluation unit is used for carrying out fusion evaluation on the running parameter risk level and the equipment risk level to obtain a state risk evaluation result of the dust removal equipment;
and the state early warning unit is used for carrying out state early warning on the dust removing equipment according to the state risk assessment result.
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