CN110879971B - Industrial production equipment operation abnormal condition prediction method and system - Google Patents
Industrial production equipment operation abnormal condition prediction method and system Download PDFInfo
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Abstract
The invention provides a method for predicting abnormal operation conditions of industrial production equipment, which comprises the following steps: step 1: collecting device-related data; step 2: preprocessing the collected related data; step 3: sample labeling is carried out on the preprocessed data; step 4: extracting features of the data marked by the sample to form feature set data; step 5: inputting the feature set data into a Gaussian mixture model algorithm for prediction model training to obtain a prediction result; step 6: expert analysis is carried out on the obtained prediction result; the analysis result which is consistent with the actual situation is normally output, and meanwhile, enterprises are reminded of producing users; the analysis result which does not accord with the actual situation is suggested by an expert, and the model algorithm is optimized by feedback. Through the steps, the user can know the abnormal condition of the operation equipment in advance, the unknown risk can be effectively reduced, and the production efficiency is improved.
Description
Technical Field
The invention relates to the field of data processing, in particular to a method and a system for predicting abnormal operation conditions of industrial production equipment.
Background
The normal operation of production equipment is very important for enterprises in the industrial field, the problems of the equipment are found early and reasonably solved, and the economic loss of the enterprises can be reduced. At present, most enterprises do not predict abnormal conditions of equipment in advance, but solve the problems after the equipment stops. A conventional plant diagnosis apparatus and a plant diagnosis method as disclosed in patent document CN107710089a include: and a comprehensive diagnosis unit that obtains accuracy of detection of the state abnormality by each of the plurality of diagnosis units based on measurement signal data related to the state of the plant equipment and equipment management information data related to past state abnormality, and evaluates a loss prediction amount based on the accuracy and a loss amount associated with the state abnormality.
However, the conventional method for predicting abnormal operation of industrial production equipment has the following problems:
1. the prediction accuracy is low.
2. Cannot be predicted in advance, and can not be detected until equipment fails to stop production.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method and a system for predicting the abnormal operation condition of industrial production equipment.
The invention provides a method for predicting abnormal operation conditions of industrial production equipment, which comprises the following steps:
step 1: collecting device-related data;
step 2: preprocessing the collected related data;
step 3: sample labeling is carried out on the preprocessed data;
step 4: extracting features of the data marked by the sample to form feature set data;
step 5: inputting the feature set data into a Gaussian mixture model algorithm for prediction model training to obtain a prediction result;
step 6: expert analysis is carried out on the obtained prediction result; the analysis result which is consistent with the actual situation is normally output, and meanwhile, enterprises are reminded of producing users; the analysis result which does not accord with the actual situation is suggested by an expert, and the model algorithm is optimized by feedback.
Preferably, the device-related data in step 1 includes: device parameter information, historical operating data.
Preferably, the preprocessing in step 2 includes: and cleaning the data and correcting the defect data.
Preferably, the historical operating data includes: temperature, pressure, vibration, audio signal and abnormal handling conclusions under normal and abnormal conditions.
Preferably, the gaussian mixture model algorithm comprises: taking the Gaussian mixture model as a model of various abnormal conditions, wherein each abnormal condition corresponds to one Gaussian mixture model:
m represents: number of gaussian mixtures;
p i the representation is: is the mixing weight of Gaussian components, satisfies
∑ i the variance matrix is the ith Gaussian distribution;
the complete gaussian model is denoted by λ, consisting of the expectation, variance matrix and weights of all gaussian components, denoted as
λ＝{p i ，μ i ，∑ i },i＝1,2,…,M (3)
Assuming that the training vector sequence of a certain anomaly model is
The likelihood function of the gaussian mixture model is +.>
The final result is calculated by the expectation maximization algorithm.
The invention provides an industrial production equipment operation abnormal condition prediction system, which comprises the following modules:
module M1: collecting device-related data;
module M2: preprocessing the collected related data;
module M3: sample labeling is carried out on the preprocessed data;
module M4: extracting features of the data marked by the sample to form feature set data;
module M5: inputting the feature set data into a Gaussian mixture model algorithm for prediction model training to obtain a prediction result;
module M6: expert analysis is carried out on the obtained prediction result; the analysis result which is consistent with the actual situation is normally output, and meanwhile, enterprises are reminded of producing users; the analysis result which does not accord with the actual situation is suggested by an expert, and the model algorithm is optimized by feedback.
Preferably, the device-related data in the module M1 includes: device parameter information, historical operating data.
Preferably, the preprocessing in the module M2 includes: and cleaning the data and correcting the defect data.
Preferably, the historical operating data includes: temperature, pressure, vibration, audio signal and abnormal handling conclusions under normal and abnormal conditions.
Preferably, the gaussian mixture model algorithm comprises: taking the Gaussian mixture model as a model of various abnormal conditions, wherein each abnormal condition corresponds to one Gaussian mixture model:
m represents: number of gaussian mixtures;
p i the representation is: is the mixing weight of Gaussian components, satisfies
∑ i the variance matrix is the ith Gaussian distribution;
the complete gaussian model is denoted by λ, consisting of the expectation, variance matrix and weights of all gaussian components, denoted as
λ＝{p i ，μ i ，∑ i },i＝1,2,…,M (3)
Assuming that the training vector sequence of a certain anomaly model is
The likelihood function of the gaussian mixture model is +.>
The final result is calculated by the expectation maximization algorithm.
Compared with the prior art, the invention has the following beneficial effects:
1. the method has the advantages that the steps of collecting data related to equipment, preprocessing the data, labeling samples, extracting data features, training a prediction model, collecting on-line data of the equipment, predicting the model, feeding back results or reminding the results and the like are realized, the user can know abnormal conditions of the running equipment in advance, the unknown risk can be effectively reduced, and the production efficiency is improved.
2. The prediction accuracy is high, and the prediction accuracy can be improved through sustainable iteration.
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Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a flow chart of a method for predicting abnormal operation conditions of industrial production equipment.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present invention.
As shown in fig. 1, the method for predicting the abnormal operation condition of the industrial production equipment provided by the invention is characterized by comprising the following steps:
step 1: collecting device-related data;
step 2: preprocessing the collected related data;
step 3: sample labeling is carried out on the preprocessed data;
step 4: extracting features of the data marked by the sample to form feature set data;
step 5: inputting the feature set data into a Gaussian mixture model algorithm for prediction model training to obtain a prediction result;
step 6: expert analysis is carried out on the obtained prediction result; the analysis result which is consistent with the actual situation is normally output, and meanwhile, enterprises are reminded of producing users; the analysis result which does not accord with the actual situation is suggested by an expert, and the model algorithm is optimized by feedback.
Further, the device-related data in step 1 includes: device parameter information, historical operating data. The preprocessing in the step 2 comprises the following steps: data cleaning and defect data correction facilitate better use of the data in later processes. The historical operating data includes: temperature, pressure, vibration, audio signal and abnormal handling conclusions under normal and abnormal conditions. In a preferred embodiment, the data of the production industrial equipment is collected for a specific period of time and the collected data is preprocessed.
Still further, the gaussian mixture model algorithm comprises: taking the Gaussian mixture model as a model of various abnormal conditions, wherein each abnormal condition corresponds to one Gaussian mixture model:
m represents: number of gaussian mixtures;
p i The representation is: is the mixing weight of Gaussian components, satisfies
∑ i the variance matrix is the ith Gaussian distribution;
the complete gaussian model is denoted by λ, consisting of the expectation, variance matrix and weights of all gaussian components, denoted as
λ＝{p i ，μ i ，∑ i },i＝1,2,…,M (3)
Assuming that the training vector sequence of a certain anomaly model is
The likelihood function of the gaussian mixture model is +.>
The final result is calculated by the expectation maximization algorithm.
The invention provides an industrial production equipment operation abnormal condition prediction system, which comprises the following modules:
module M1: collecting device-related data;
module M2: preprocessing the collected related data;
module M3: sample labeling is carried out on the preprocessed data;
module M4: extracting features of the data marked by the sample to form feature set data;
module M5: inputting the feature set data into a Gaussian mixture model algorithm for prediction model training to obtain a prediction result;
module M6: expert analysis is carried out on the obtained prediction result; the analysis result which is consistent with the actual situation is normally output, and meanwhile, enterprises are reminded of producing users; the analysis result which does not accord with the actual situation is suggested by an expert, and the model algorithm is optimized by feedback.
Further, the device-related data in the module M1 includes: device parameter information, historical operating data. The preprocessing in the module M2 includes: and cleaning the data and correcting the defect data. The historical operating data includes: temperature, pressure, vibration, audio signal and abnormal handling conclusions under normal and abnormal conditions.
Still further, the gaussian mixture model algorithm comprises: taking the Gaussian mixture model as a model of various abnormal conditions, wherein each abnormal condition corresponds to one Gaussian mixture model:
m represents: number of gaussian mixtures;
p i the representation is: is the mixing weight of Gaussian components, satisfies
∑ i the variance matrix is the ith Gaussian distribution;
the complete gaussian model is denoted by λ, consisting of the expectation, variance matrix and weights of all gaussian components, denoted as
λ＝{p i ，μ i ，∑ i },i＝1,2,…,M (3)
Assuming that the training vector sequence of a certain anomaly model is
Gaussian mixtureLikelihood function of the composite model is +.>
The final result is calculated by the expectation maximization algorithm.
In the preferred embodiment: for certain industrial equipment, collecting related equipment operation and state parameters and data information including temperature, pressure difference, vibration signals, audio signals and the like when the equipment is in abnormal conditions in the current use process;
preprocessing the acquired information, and repairing error data and defect data which possibly exist so as to meet the processing requirements of the following signals;
manually labeling the processed data for later model training and result verification;
the processed data are imported into an algorithm for feature extraction, including short-time energy, frequency spectrum, amplitude and other features of the audio signal are extracted, and corresponding normalization processing is carried out;
inputting the characteristics into a Gaussian mixture model for model training;
acquiring state data of equipment operation in a specific time period online, preprocessing and extracting features, and inputting the state data into a trained model for predictive analysis;
in order to make the model prediction more robust, a prediction result obtained by the model is submitted to relevant experts to compare and analyze according to the actual condition of the equipment, the final judgment is carried out, if the actual condition is not met, the result is fed back to a training model, and the model parameters are adjusted; and if the actual conditions are met, reminding the user of making relevant arrangement.
Those skilled in the art will appreciate that the invention provides a system and its individual devices, modules, units, etc. that can be implemented entirely by logic programming of method steps, in addition to being implemented as pure computer readable program code, in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units for realizing various functions included in the system can also be regarded as structures in the hardware component; means, modules, and units for implementing the various functions may also be considered as either software modules for implementing the methods or structures within hardware components.
In the description of the present application, it should be understood that the terms "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientations or positional relationships illustrated in the drawings, merely to facilitate description of the present application and simplify the description, and do not indicate or imply that the devices or elements being referred to must have a specific orientation, be configured and operated in a specific orientation, and are not to be construed as limiting the present application.
The foregoing describes specific embodiments of the present invention. It is to be understood that the invention is not limited to the particular embodiments described above, and that various changes or modifications may be made by those skilled in the art within the scope of the appended claims without affecting the spirit of the invention. The embodiments of the present application and features in the embodiments may be combined with each other arbitrarily without conflict.
Claims (8)
1. The industrial production equipment operation abnormal condition prediction method is characterized by comprising the following steps of:
step 1: collecting device-related data;
step 2: preprocessing the collected related data;
step 3: sample labeling is carried out on the preprocessed data;
step 4: extracting features of the data marked by the sample to form feature set data;
step 5: inputting the feature set data into a Gaussian mixture model algorithm for prediction model training to obtain a prediction result;
step 6: expert analysis is carried out on the obtained prediction result; the analysis result which is consistent with the actual situation is normally output, and meanwhile, enterprises are reminded of producing users; the analysis result which does not accord with the actual situation is proposed by an expert, and the model algorithm is optimized by feedback;
the Gaussian mixture model algorithm comprises the following steps: taking the Gaussian mixture model as a model of various abnormal conditions, wherein each abnormal condition corresponds to one Gaussian mixture model:
m represents: number of gaussian mixtures;
p i the representation is: is the mixing weight of Gaussian components, satisfies
∑ i the variance matrix is the ith Gaussian distribution;
the complete gaussian model is denoted by λ and is represented by all gaussian componentsThe expectation, variance matrix and weight composition, expressed as λ= { p i, μ i ,∑ i }, i＝1,2,…,M (3)
Assuming that the training vector sequence of a certain anomaly model is
The likelihood function of the gaussian mixture model is +.>
The final result is calculated by the expectation maximization algorithm.
2. The method for predicting abnormal operation of industrial production equipment according to claim 1, wherein the equipment-related data in step 1 comprises: device parameter information, historical operating data.
3. The method for predicting abnormal operation of industrial production facility according to claim 1, wherein the preprocessing in step 2 comprises: and cleaning the data and correcting the defect data.
4. The industrial production facility operation anomaly prediction method of claim 2 wherein the historical operating data comprises: temperature, pressure, vibration, audio signal and abnormal handling conclusions under normal and abnormal conditions.
5. An industrial production equipment operation abnormal condition prediction system is characterized by comprising the following modules:
module M1: collecting device-related data;
module M2: preprocessing the collected related data;
module M3: sample labeling is carried out on the preprocessed data;
module M4: extracting features of the data marked by the sample to form feature set data;
module M5: inputting the feature set data into a Gaussian mixture model algorithm for prediction model training to obtain a prediction result;
module M6: expert analysis is carried out on the obtained prediction result; the analysis result which is consistent with the actual situation is normally output, and meanwhile, enterprises are reminded of producing users; the analysis result which does not accord with the actual situation is proposed by an expert, and the model algorithm is optimized by feedback;
the Gaussian mixture model algorithm comprises the following steps: taking the Gaussian mixture model as a model of various abnormal conditions, wherein each abnormal condition corresponds to one Gaussian mixture model:
m represents: number of gaussian mixtures;
p i the representation is: is the mixing weight of Gaussian components, satisfies
∑ i the variance matrix is the ith Gaussian distribution;
the complete gaussian model is denoted by λ, consisting of the expectation, variance matrix and weights of all gaussian components, denoted λ= { p i, μ i ,∑ i }, i＝1,2,…,M (3)
Assuming that the training vector sequence of a certain anomaly model is
The likelihood function of the gaussian mixture model is +.>
The final result is calculated by the expectation maximization algorithm.
6. The industrial production facility operation anomaly prediction system of claim 5 wherein the facility related data in module M1 comprises: device parameter information, historical operating data.
7. The system for predicting abnormal operation of an industrial production facility as claimed in claim 6, wherein the preprocessing in the module M2 comprises: and cleaning the data and correcting the defect data.
8. The industrial production facility abnormal operation prediction system of claim 7 wherein the historical operating data comprises: temperature, pressure, vibration, audio signal and abnormal handling conclusions under normal and abnormal conditions.
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