CN115600695A - Fault diagnosis method of metering equipment - Google Patents
Fault diagnosis method of metering equipment Download PDFInfo
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- CN115600695A CN115600695A CN202211085965.6A CN202211085965A CN115600695A CN 115600695 A CN115600695 A CN 115600695A CN 202211085965 A CN202211085965 A CN 202211085965A CN 115600695 A CN115600695 A CN 115600695A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/20—Administration of product repair or maintenance
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/50—Testing of electric apparatus, lines, cables or components for short-circuits, continuity, leakage current or incorrect line connections
- G01R31/56—Testing of electric apparatus
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
Abstract
The invention discloses a fault diagnosis method of metering equipment, and relates to the technical field of fault diagnosis of intelligent metering. The specific implementation mode of the method comprises the following steps: receiving a diagnosis request of a metering device to be diagnosed; preprocessing the historical calibration certificate, and determining the structured environmental characteristics and project characteristics; inputting the environmental characteristics and the project characteristics into a pre-trained fault diagnosis model; determining a target diagnosis result of the metering equipment to be diagnosed according to the output of the fault diagnosis model; the target diagnosis result comprises a predicted fault position and a predicted fault degree of the metering equipment to be diagnosed in the target prediction time. The embodiment can analyze the existing metering big data, so that the health condition of each metering device is monitored, the fault condition of the metering device is diagnosed and pre-warned in advance, an auxiliary decision is provided for workers, the fault of the metering device is timely processed, the safety production of the metering device is guaranteed, and the metering efficiency is improved.
Description
Technical Field
The invention belongs to the field of fault diagnosis of intelligent metering, and particularly relates to a fault diagnosis method of metering equipment.
Background
In the conventional fault diagnosis of the metering equipment, the fault equipment is reported by each metering site, and then equipment maintenance personnel rush to the site to diagnose and maintain (namely, repair after the fact), or the equipment maintenance personnel rush to the working site of each equipment periodically to perform on-site detection to find defects or faults (namely, plan maintenance), so that the normal operation of the metering equipment is guaranteed.
However, in the existing fault diagnosis mode, on one hand, with the rapid increase of the metering equipment, the workload of the manual diagnosis mode is very large, and equipment maintenance personnel cannot cope with the manual diagnosis mode, so that the labor cost consumed by the existing fault diagnosis is very high, and the failure of the metering equipment and the failure of the production site due to the failure of timely handling can not be solved; on the other hand, the efficiency of the manual diagnosis mode is too low, the metering equipment cannot be judged in real time, the early warning capability is poor, the possible equipment hidden danger cannot be predicted in advance and eliminated in time, and the equipment failure rate is increased and even worsened; on the other hand, the increase of the metering equipment is also accompanied by the exponential increase of the equipment use data, the storage space is occupied while the data is not fully utilized, the metering data use rate is low, and the potential value of the data which can provide a reference for fault diagnosis is not fully mined.
Disclosure of Invention
In view of the above, the present invention provides a method and an apparatus for diagnosing faults of metering devices, which can analyze existing big metering data, so as to monitor health conditions of each metering device, diagnose and early warn fault conditions of the metering device in advance, thereby providing an assistant decision for workers, timely processing device faults, ensuring safe production of the metering devices, and improving metering efficiency.
The technical scheme for realizing the invention is as follows:
a method of fault diagnosis of a metrology device, comprising:
receiving a diagnosis request of a metering device to be diagnosed; the diagnosis request comprises target prediction time and historical calibration certificate of the metering equipment to be diagnosed;
preprocessing the historical calibration certificate, and determining the structured environmental characteristics and project characteristics;
inputting the environmental characteristics and the project characteristics into a pre-trained fault diagnosis model;
determining a target diagnosis result of the metering equipment to be diagnosed according to the output of the fault diagnosis model; and the target diagnosis result comprises a predicted fault position and a predicted fault degree of the metering equipment to be diagnosed in the target prediction time.
Optionally, the method further comprises:
obtaining a sample calibration certificate and a sample verification certificate of each sample metering device;
preprocessing the sample calibration certificate and the sample verification certificate to obtain structured sample structural characteristics, sample environment characteristics, sample project characteristics and sample fault characteristics;
decomposing the sample environment characteristics, the sample project characteristics and the sample fault characteristics into a sample environment subsequence, a sample project subsequence and a sample fault subsequence respectively by empirical mode decomposition;
reconstructing the sample environment subsequence, the sample project subsequence and the sample fault subsequence, and determining a target environment variable subsequence, a target project variable subsequence and a target fault parameter subsequence;
taking the target environment variable sub-sequence and the target project variable sub-sequence as inputs, taking the target fault parameter sub-sequence as an output, and performing iterative training on a fault prediction model;
and generating a final fault prediction model according to the training result.
Optionally, the preprocessing the sample calibration certificate and the sample verification certificate to obtain a structured sample structure feature, a sample environment feature, a sample item feature, and a sample fault feature includes:
and extracting sample structure characteristics, sample environment characteristics, sample item characteristics and sample fault characteristics of the sample certificate in the sample calibration certificate by using a pre-constructed certificate entity dictionary.
Optionally, the extracting sample structure features, sample environment features, sample item features, and sample failure features of the sample certificate in the sample calibration certificate includes:
determining, from the sample calibration time of the sample calibration certificate, the sample environment characteristic comprising a corresponding sequence of respective sample environment variables and the sample calibration time, the sample item characteristic comprising a corresponding sequence of respective sample item variables and the sample calibration time, and the sample fault characteristic comprising a corresponding sequence of respective sample fault parameters and the sample calibration time.
Optionally, constructing the certificate entity dictionary comprises:
acquiring a plurality of historical calibration certificates and historical verification certificates of each historical metering device in the verification process of each historical metering device; and the historical calibration certificate and the historical verification certificate are in a Word document format.
Converting the Word document data of the plurality of historical calibration certificates and historical verification certificates into calibration certificate semi-structured data and verification certificate semi-structured data in an XML format;
extracting the mapping relation between XML labels and label values of the XML labels in the calibration certificate semi-structured data and the verification certificate semi-structured data;
and constructing a certificate entity dictionary according to the XML label and the mapping relation of the label values of the XML label.
Optionally, the sample environment variables include sample temperature, sample humidity, sample air pressure, etc., the sample item variables include sample current, sample voltage, sample resistance, sample noise, etc., and the sample fault parameters include sample fault location and sample fault extent.
Optionally, before the preprocessing the historical calibration certificate, the method further includes:
determining a structured structural feature of the historical calibration certificate;
and judging whether the structural characteristics conform to a specified data form, and if so, executing the determination of the structured environmental characteristics and the project characteristics.
Has the advantages that:
(1) The workload of fault diagnosis of the metering equipment is greatly reduced, the consumption of cost management is reduced, the fault of the equipment can be diagnosed in time, and the normal work and the normal production of the metering equipment are guaranteed;
(2) The efficiency of fault diagnosis of the metering equipment is improved, real-time judgment and early warning of the metering equipment can be realized, potential fault hazards possibly existing in the metering equipment are eliminated in advance, the safety of the metering equipment of a shift is eliminated in time, and the service life of the metering equipment is prolonged;
(3) Metering data of the metering equipment is fully utilized and mined, and reference is provided for fault diagnosis of the metering equipment while the utilization rate of the metering data is improved;
(4) Based on structural characteristics, environment variables, project variables and fault parameters of the metering equipment in the conventional verification process, the state of the metering equipment is analyzed and monitored by using a fault prediction model, the future health condition of the metering equipment is predicted, the problems of insufficient maintenance, excessive maintenance and the like existing in the planned maintenance of after-service maintenance and rigidification are prevented through targeted prevention and maintenance, and the service life of the metering equipment can be effectively prolonged.
Drawings
Fig. 1 is a schematic view of a main flow of a fault diagnosis method of a metering apparatus according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a main flow of a method for generating a fault prediction model according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a main flow of a method for constructing a certificate entity dictionary according to an embodiment of the present invention.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
EMD: the Empirical Mode Decomposition means that signal Decomposition is performed according to the time scale characteristics of data itself, without presetting any basis function, and can be theoretically applied to the Decomposition of any type of signals, including linear and stationary signal serial numbers and nonlinear and non-stationary signal sequences.
IMF: intrinsic Mode Functions, that is, the connate modal components, or the Intrinsic Mode Functions, are signal components or single-component signals of each layer obtained after the original signal is decomposed by the EMD.
The invention provides a fault diagnosis method of metering equipment, which comprises the following steps of:
In the embodiment of the present invention, the calibration certificate includes a unique certificate number, a calibration location, a calibration time, a technical specification file of a calibration basis, a name of a metering device, a serial number of the metering device, a number of certificate pages, a name of a consignor, an auditor, a calibration environment variable, a calibration item, a measurement result of the calibration item, and an uncertainty of the measurement result of the calibration item (or referred to as a measurement uncertainty).
And step 12, preprocessing the historical calibration certificate, and determining the structured environmental characteristics and project characteristics.
In this embodiment of the present invention, before the preprocessing the historical calibration certificate, the method further includes:
determining a structured structural feature of the historical calibration certificate;
judging whether the structural features conform to a specified data form, and if so, executing the determined structured environmental features and project features;
if not, rejecting the diagnosis request and sending out reminding information that the historical calibration certificate does not conform to the specified data form.
In the embodiment of the invention, the structural characteristics are extracted according to the structural characteristics in the calibration certificate, the structural characteristics refer to the data form of the calibration certificate, including the forms of lists, characters, signatures (pictures) and the like, and can be used for judging whether the calibration certificate conforms to the specified data form, and the calibration certificate which does not conform to the specified data form is directly excluded without judgment; and extracting the environmental characteristics and the project characteristics only for the calibration certificate which conforms to the specified data form.
Further, before preprocessing the calibration certificate, irrelevant diagnostic data in the calibration certificate can be removed, wherein the irrelevant diagnostic data comprises a unique serial number of the certificate, a calibration place, a technical specification file of a quasi-basis, the number of pages of the certificate, a name of a consignor, an auditor and the like.
And step 13, inputting the environmental characteristics and the project characteristics into a pre-trained fault diagnosis model.
In the embodiment of the invention, the fault diagnosis model can predict the future health condition of the metering equipment, including possible faults, the fault degree and position, the trend of fault development, the residual service life and the like according to the input environmental characteristics and project characteristics, so as to determine whether the influence on the task execution of the metering equipment is generated.
The invention provides a method for generating a fault diagnosis model, which comprises the following steps of:
In the embodiment of the present invention, the sample database stores the sample calibration certificate and the sample certification certificate of each sample metering device in the course of the verification process.
In the embodiment of the invention, the certification certificate is a conclusion whether the metering equipment is qualified or not according to the calibration certificate.
And step 22, preprocessing the sample calibration certificate and the sample verification certificate to obtain structured sample structure characteristics, sample environment characteristics, sample project characteristics and sample fault characteristics.
In the embodiment of the invention, a pre-constructed certificate entity dictionary is utilized to extract a sample structure characteristic value, a sample environment characteristic, a sample project characteristic and a sample fault characteristic of a sample verification certificate in the sample calibration certificate; wherein:
in the embodiment of the present invention, as shown in fig. 3, the method for constructing a certificate entity dictionary of the present invention includes the following steps:
And 32, converting the Word document data of the plurality of historical calibration certificates and historical verification certificates into calibration certificate semi-structured data and verification certificate semi-structured data in an XML format.
And step 34, constructing a certificate entity dictionary according to the mapping relation between the XML tags and the tag values of the XML tags.
In the embodiment of the invention, the certificate entity dictionary extracted by the plurality of historical calibration certificates and the historical verification certificates is utilized, so that the certificate data of each calibration certificate and verification certificate, including the label value of each entity of the calibration certificate and the verification certificate, can be extracted based on the certificate entity dictionary in the subsequent use process.
In an embodiment of the present invention, the sample environment characteristic comprising a corresponding sequence of respective sample environment variables and the sample calibration time, the sample item characteristic comprising a corresponding sequence of respective sample item variables and the sample calibration time, and the sample fault characteristic comprising a corresponding sequence of respective sample fault parameters and the sample calibration time are determined from the sample calibration time of the sample calibration certificate.
Further, the sample environment variables include sample temperature, sample humidity, sample air pressure, etc., the sample item variables include sample current, sample voltage, sample resistance, sample noise, etc., and the sample fault parameters include sample fault location and sample fault extent.
And step 23, decomposing the sample environment characteristics, the sample project characteristics and the sample fault characteristics into a sample environment subsequence, a sample project subsequence and a sample fault subsequence respectively by using empirical mode decomposition.
In an embodiment of the present invention, the EMD decomposes each corresponding sequence of the sample environment variable and the sample calibration time into a plurality of sample environment subsequences, for example, the sample environment variable is the sample temperature, and the EMD decomposes each corresponding sequence of the sample temperature and the sample calibration time into a plurality of sample temperature subsequences.
The EMD decomposes each corresponding sequence of sample item variables and sample calibration times into a plurality of sample item subsequences, e.g., the sample item variable is a sample current, and the EMD decomposes each corresponding sequence of sample currents and sample calibration times into a plurality of sample current subsequences.
The EMD decomposes each sample failure parameter and sample calibration time corresponding sequence into a plurality of sample failure location subsequences, for example, the sample failure parameter is a sample failure location, and the EMD decomposes each sample failure location and sample calibration time corresponding sequence into a plurality of sample failure location subsequences.
Further, after the EMD decomposes each sequence, trend terms (i.e., the variation trend of each sequence) of each subsequence can be obtained.
And 24, reconstructing the sample environment subsequence, the sample item subsequence and the sample fault subsequence, and determining a target environment variable subsequence, a target item variable subsequence and a target fault parameter subsequence.
In the embodiment of the invention, the EMD can decompose the data sequence into an intrinsic mode function subsequence IMF and a trend item, and when the IMF is obtained, the EMD is a sequence which automatically identifies and successively extracts high frequency to low frequency, so that the obtained IMF is a sequence with an average value tending to zero and no obvious rising or falling trend, and the requirement of the time sequence on the stability is met. And reconstructing the sample environment subsequence, the sample item subsequence and the sample fault subsequence of each sample metering device, and determining a diagnostic sequence (namely a target environment variable subsequence, a target item variable subsequence and a target fault parameter subsequence, or a high-frequency sequence) which is useful for the sample metering device, for example, the air pressure is not valuable for fault diagnosis of the engine, and the temperature is valuable for fault diagnosis of the engine, so that the target environment variable subsequence of the engine comprises the sample temperature sequence but does not comprise the sample air pressure sequence.
Further, the reconstruction means that each sample subsequence is converted into a linear graph by filtering to judge.
In the embodiment of the invention, taking an engine as an example, when the engine runs normally, the temperature of the environmental variable of the engine and the signal value corresponding to the noise of the project variable are both in a normal range, once the temperature signal value and the noise signal value collected in a certain period seriously exceed the range of the normal signal value, the engine is indicated to be possible to have a fault, and the EMD analyzes the sample temperature subsequence and the sample noise subsequence to find that the sequence of the temperature signal value and the noise signal value exceeds the normal signal value for multiple times, and the sequence corresponding to the temperature signal value and the noise signal value is a high-frequency sequence.
In the embodiment of the invention, the sample environment subsequence, the sample project subsequence and the sample failure subsequence are decomposed into a series of more stable components after being subjected to EMD processing, but each component cannot independently describe the characteristics of the original sequence, and the original sequence can be more accurately described only by integrating the characteristics of each component, so that each component is recombined by using the reconstruction idea, and the reconstructed sequence characteristic information can more comprehensively represent each characteristic of corresponding sample metering equipment, thereby providing reliable analysis for equipment performance prediction and reliable analysis and prediction for the health condition of the metering equipment.
And 25, taking the target environment variable sub-sequence and the target project variable sub-sequence as inputs, taking the target fault parameter sub-sequence as an output, and performing iterative training on a fault prediction model.
In the embodiment of the invention, a neural network algorithm is adopted as the fault prediction model, a gradient descent and back propagation algorithm is adopted for training the fault prediction model of the neural network algorithm, the error of the network output node is minimized through a back learning process, the weight and the threshold of each parameter of the initially assumed neural network are trained, and correction is carried out along the negative gradient rapid descent direction of an error function in the training process until the final weight of each parameter of the neural network is determined. In the neural network, the nonlinear learning process is completed by the joint action of the hidden layer and the output layer. And when the output of the fault prediction model is inconsistent with the expected value given by the target fault parameter subsequence, returning an error signal of the fault prediction model back from the output end, and continuously correcting the weight value in the returning process.
And step 26, generating a final fault prediction model according to the training result.
In the embodiment of the invention, in the training process of the fault prediction model, the training set and the test set can be divided, so that the accuracy of the fault prediction model is further improved.
In the embodiment of the invention, the neural network of the fault diagnosis model can carry out forward calculation on the input environmental characteristics and the project characteristics to obtain the target diagnosis result, and carry out fault prediction on the metering equipment to be diagnosed, so that predictive maintenance can be provided when the fault does not occur, measures can be taken in time to solve the fault, and the fault reason can be traced back to avoid the generation of unnecessary faults. By analyzing various data of the metering equipment in the verification process of the past times and applying EMD to carry out sequence decomposition according to the analysis of the time sequence, a reasonable, simple and practical fault prediction model is established, the accuracy of fault prediction is improved, and the safety production of the equipment is guaranteed.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A method of diagnosing a fault in a metering apparatus, comprising:
receiving a diagnosis request of a metering device to be diagnosed; the diagnosis request comprises target prediction time and historical calibration certificate of the metering equipment to be diagnosed;
preprocessing the historical calibration certificate, and determining the structured environmental characteristics and project characteristics;
inputting the environmental characteristics and the project characteristics into a pre-trained fault diagnosis model;
determining a target diagnosis result of the metering equipment to be diagnosed according to the output of the fault diagnosis model; and the target diagnosis result comprises a predicted fault position and a predicted fault degree of the metering equipment to be diagnosed in the target prediction time.
2. The method of claim 1, further comprising:
obtaining a sample calibration certificate and a sample verification certificate of each sample metering device;
preprocessing the sample calibration certificate and the sample verification certificate to obtain structured sample structural characteristics, sample environment characteristics, sample project characteristics and sample fault characteristics;
decomposing the sample environment characteristics, the sample project characteristics and the sample fault characteristics into a sample environment subsequence, a sample project subsequence and a sample fault subsequence respectively by using empirical mode decomposition;
reconstructing the sample environment subsequence, the sample project subsequence and the sample fault subsequence, and determining a target environment variable subsequence, a target project variable subsequence and a target fault parameter subsequence;
taking the target environment variable sub-sequence and the target project variable sub-sequence as input, taking the target fault parameter sub-sequence as output, and performing iterative training on a fault prediction model;
and generating a final fault prediction model according to the training result.
3. The method of claim 2, wherein said preprocessing said sample calibration certificate and said sample certification certificate to obtain structured sample structural features, sample environmental features, sample item features, and sample failure features comprises:
and extracting sample structure characteristics, sample environment characteristics, sample item characteristics and sample fault characteristics of the sample certificate in the sample calibration certificate by using a pre-constructed certificate entity dictionary.
4. The method of claim 3, wherein said extracting sample structural features, sample environmental features, sample item features, and sample failure features of a sample certificate of authenticity in said sample calibration certificate comprises:
determining, from sample calibration times of the sample calibration certificate, the sample environment characteristic comprising a corresponding sequence of respective sample environment variables and the sample calibration times, the sample item characteristic comprising a corresponding sequence of respective sample item variables and the sample calibration times, and the sample fault characteristic comprising a corresponding sequence of respective sample fault parameters and the sample calibration times.
5. The method of claim 3, wherein constructing the certificate entity dictionary comprises:
acquiring a plurality of historical calibration certificates and historical verification certificates of each historical metering device in the verification process of each historical metering device; and the historical calibration certificate and the historical verification certificate are in Word document format.
Converting the Word document data of the plurality of historical calibration certificates and historical verification certificates into calibration certificate semi-structured data and verification certificate semi-structured data in an XML format;
extracting the mapping relation between XML labels and label values of the XML labels in the calibration certificate semi-structured data and the verification certificate semi-structured data;
and constructing a certificate entity dictionary according to the XML label and the mapping relation of the label values of the XML label.
6. The method of claim 4, wherein the sample environmental variables include sample temperature, sample humidity, sample air pressure, etc., the sample item variables include sample current, sample voltage, sample resistance, sample noise, etc., and the sample fault parameters include sample fault location and sample fault level.
7. The method of claim 1, prior to the preprocessing the historical calibration certificate, further comprising:
determining a structured structural feature of the historical calibration certificate;
and judging whether the structural characteristics conform to a specified data form, and if so, executing the determined structured environmental characteristics and project characteristics.
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