CN110716528A - Large hydraulic machine remote fault diagnosis method and device based on expert system - Google Patents
Large hydraulic machine remote fault diagnosis method and device based on expert system Download PDFInfo
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- CN110716528A CN110716528A CN201910875456.5A CN201910875456A CN110716528A CN 110716528 A CN110716528 A CN 110716528A CN 201910875456 A CN201910875456 A CN 201910875456A CN 110716528 A CN110716528 A CN 110716528A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
- G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/24—Pc safety
- G05B2219/24065—Real time diagnostics
Abstract
The invention provides a large hydraulic machine remote fault diagnosis method and device based on an expert system, and belongs to the technical field of hydraulic machine fault diagnosis. The large hydraulic machine remote fault diagnosis method and device based on the expert system comprises the following steps: s1: collecting a diagnosis signal of the hydraulic machine; s2: the acquisition card transmits the diagnosis signal to the field workstation; s3: the field workstation performs data processing on the diagnosis signal and extracts a characteristic value, and the field workstation transmits the characteristic value to an engineer station; s4: the engineer station sends the characteristic value to an enterprise server; s5: the enterprise server processes the characteristic value data through the data processing model to obtain processed data, and the enterprise server sends the data to be analyzed to the expert system; s6: the expert system analyzes the processed data to obtain fault information. The invention adopts an expert system to automatically collect fault data, and compares and analyzes the fault data with knowledge in an expert knowledge base to determine a fault, and the fault is automatically analyzed in real time.
Description
Technical Field
The invention belongs to the technical field of hydraulic machine fault diagnosis, and relates to a large hydraulic machine remote fault diagnosis method and device based on an expert system.
Background
The hydraulic technology becomes one of key technologies in the industrial fields of all countries in the world, according to incomplete statistics, more than 95% of mechanical equipment adopts the hydraulic technology and devices, the hydraulic machine is a core device which must be adopted for processing and forging various high-strength steel, carbon steel and alloy steel, is widely used in equipment in the heavy industrial fields of aerospace, steel, large-scale bearing parts, nuclear industry, military, ships, cranes, artificial boards and the like, is key equipment in national economic strut industries of energy, petroleum, metallurgy and the like, and some hydraulic machines are strategic equipment required by industrial systems and national defense, are basic equipment for developing large military equipment and large industrial equipment in China, mark the national comprehensive production capacity and technical development level, and are of great importance in reliability and safe operation. The hydraulic machine is essentially a system integrating electro-hydraulic control, and has complex control and difficult fault diagnosis. The fault shutdown not only reduces the production efficiency of enterprises and causes huge economic loss, but also brings great difficulty to the production enterprises because the maintenance technology of the hydraulic equipment is locked abroad, thereby having great practical significance for the reliable operation, fault diagnosis and health prediction of the hydraulic equipment.
The large hydraulic machine has the characteristics of concealment, staggering, randomness, difference, uncertainty, complexity, timeliness dispersion and the like, and researchers never stop exploring the diagnosis method because of the fact that the large hydraulic machine has multiple working states, multiple elements, difficult fault diagnosis and long time consumption. However, even experienced technicians require a long time to find the fault. Therefore, mathematical-based, signal processing methods have been widely used in hydraulic fault diagnosis in the early days. With the development of network technology, remote monitoring and fault diagnosis technology based on web is widely researched, for example, a remote monitoring and diagnosis module is embedded in a product, error information and fault reasons are displayed and alarmed through a network control system, and fault early warning and diagnosis can be realized through a global system for mobile communications (GSM) global transaction information system and a VMS (virtual vehicle system) key information management system. The GPRS wireless communication technology and the GIS geographic information technology are developed in three-in-one industry in China to realize remote monitoring of intelligent hydraulic products. The hydraulic fault diagnosis technology of the remote intelligent fault diagnosis technology is developed vigorously, so that high-quality service for diagnosing complex problems is improved for products, various resources are effectively integrated, a large amount of resources are saved, and the fault diagnosis time is shortened.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a large hydraulic machine remote fault diagnosis method and device based on an expert system, and the technical problems to be solved by the invention are as follows: a method and a device for remotely diagnosing faults of a large hydraulic machine based on an expert system are provided.
The purpose of the invention can be realized by the following technical scheme:
the large hydraulic machine remote fault diagnosis method based on the expert system comprises the following steps:
s1: collecting a diagnosis signal of the hydraulic machine;
s2: the acquisition card transmits the diagnosis signal to the field workstation;
s3: the field workstation performs data processing on the diagnosis signal and extracts a characteristic value, and the field workstation transmits the characteristic value to an engineer station;
s4: the engineer station sends the characteristic value to an enterprise server;
s5: the enterprise server processes the characteristic value data through the data processing model to obtain processed data, and the enterprise server sends the data to be analyzed to the expert system;
s6: the expert system analyzes the processed data to obtain fault information.
Preferably, the system further comprises a first database for receiving the characteristic value data sent by the enterprise server.
Preferably, the classifier is trained by inputting the feature vectors into the classifier in step S3, and the classification precision of the classifier is verified so that the accuracy of the classifier reaches the preset precision.
Preferably, the expert system includes a knowledge base, an inference engine, and a second database.
Preferably, the inference engine is written by CLIPS artificial intelligence language.
Preferably, the engineer station sends the characteristic value data to the enterprise server through an enterprise service bus.
Preferably, the inference process of the inference engine comprises: acquiring a characteristic value of a target fault; starting an inference machine, opening and searching a knowledge base, and matching with all knowledge; and determining fault information which is successfully identified.
The large hydraulic machine remote fault diagnosis device based on the expert system comprises an acquisition card used for acquiring diagnosis signals of the hydraulic machine, a field workstation used for carrying out data processing on the diagnosis signals and extracting characteristic values, an engineer station used for receiving the characteristic values sent by the field workstation, an enterprise server used for receiving the characteristic values sent by the engineer station and processing characteristic value data through a data processing model to obtain processed data, a signal processing unit used for receiving the processed data sent by the enterprise server and carrying out signal processing on the processed data, and the expert system used for analyzing the characteristic vectors to obtain fault information.
Preferably, the system further comprises a first database for receiving the characteristic value data sent by the enterprise server.
Preferably, the expert system comprises a knowledge base, an inference engine and a second database, wherein the inference engine is written by adopting CLIPS artificial intelligence language.
The method comprises the steps of firstly collecting diagnosis signals of a hydraulic machine, then transmitting the diagnosis signals to a field workstation by a collection card, then carrying out data processing on the diagnosis signals by the field workstation and extracting characteristic values, sending the characteristic values to an engineer station by the field workstation, then sending the characteristic values to an enterprise server by the engineer station, then processing characteristic value data by the enterprise server through a data processing model to obtain processed data, sending the data to be analyzed to an expert system by the enterprise server, finally analyzing the processed data by the expert system to obtain fault information, automatically collecting fault data by adopting the expert system, comparing and analyzing the fault data with knowledge in an expert knowledge base, and further determining faults.
Drawings
FIG. 1 is a schematic flow diagram of the present invention.
Detailed Description
The following are specific embodiments of the present invention and are further described with reference to the drawings, but the present invention is not limited to these embodiments.
Referring to FIG. 1, in the present embodiment
1. The large hydraulic machine remote fault diagnosis method based on the expert system comprises the following steps:
s1: collecting a diagnosis signal of the hydraulic machine;
s2: the acquisition card transmits the diagnosis signal to the field workstation;
s3: the field workstation performs data processing on the diagnosis signal and extracts a characteristic value, and the field workstation transmits the characteristic value to an engineer station;
s4: the engineer station sends the characteristic value to an enterprise server;
s5: the enterprise server processes the characteristic value data through the data processing model to obtain processed data, and the enterprise server sends the data to be analyzed to the expert system;
s6: the expert system analyzes the processed data to obtain fault information.
The method comprises the steps of firstly collecting diagnostic signals of the hydraulic machine, then transmitting the diagnostic signals to a field workstation by a collecting card, then carrying out data processing on the diagnostic signals by the field workstation and extracting characteristic values, sending the characteristic values to an engineer station by the field workstation, then sending the characteristic values to an enterprise server by the engineer station, then processing characteristic value data by the enterprise server through a data processing model to obtain processed data, sending the data to be analyzed to an expert system by the enterprise server, finally analyzing the processed data by the expert system to obtain fault information, automatically collecting fault data by adopting the expert system, comparing and analyzing the fault data with knowledge in an expert knowledge base, and further determining a fault.
The remote fault diagnosis method for the large hydraulic machine in the embodiment further comprises a first database used for receiving the characteristic value data sent by the enterprise server.
In step S3, the classifier is trained by inputting the feature vectors into the classifier, and the classification precision of the classifier is verified so that the accuracy of the classifier reaches a preset precision.
The expert system includes a knowledge base, an inference engine, and a second database.
The inference engine adopts CLIPS artificial intelligence language to compile the inference engine.
The engineer station sends the characteristic value data to the enterprise server through the enterprise service bus.
The reasoning process of the reasoning machine comprises the following steps: acquiring a characteristic value of a target fault; starting an inference machine, opening and searching a knowledge base, and matching with all knowledge; and determining fault information which is successfully identified.
The large hydraulic machine remote fault diagnosis device based on the expert system comprises an acquisition card used for acquiring diagnosis signals of the hydraulic machine, a field workstation used for carrying out data processing on the diagnosis signals and extracting characteristic values, an engineer station used for receiving the characteristic values sent by the field workstation, an enterprise server used for receiving the characteristic values sent by the engineer station and processing characteristic value data through a data processing model to obtain processed data, a signal processing unit used for receiving the processed data sent by the enterprise server and carrying out signal processing on the processed data, and the expert system used for analyzing the characteristic vectors to obtain fault information.
The remote fault diagnosis device for the large hydraulic machine based on the expert system in the embodiment further comprises a first database used for receiving characteristic value data sent by the enterprise server.
Aiming at a hydraulic press system, firstly, a measuring point needs to be selected, because various fault possibilities need to be considered, the measuring point is designed on a main control loop and a main loop, the measured data are transmitted to a field workstation through a collecting card, the workstation processes the data, extracts a characteristic value and sends the characteristic value to an engineer station, a fault classification and performance evaluation algorithm is designed on the station, the data are received and stored in a database by an enterprise server, the enterprise server further processes the data through a related data processing model, and the processed data are transmitted to a remote server through a network.
The expert system comprises a knowledge base, an inference engine and a second database, wherein the inference engine adopts CLIPS artificial intelligence language to compile the inference engine.
The inference machine is written by CLIPS artificial intelligence language and human-computer interface is written by VC + +. The fault model library and the inference engine operate independently, and the inference engine does not need to be changed when the fault model library is changed. The HSMM-SVM analysis system may further include a semantic converter for converting the featuresKnowledge representation of feature transformation modules, e.g. of the typeWhere i =1, 2, 3 … N, N is the name of the feature, V is the value corresponding to the feature, and δ represents the allowable error, i.e., the difference between the current sample value and the feature value known in the fault model library. (+, /) indicates that features may be grouped into new features according to the operation. In the reasoning process, a fault knowledge model is required to be established for characterization and reasoning of fault characteristics. According to which the definition knowledge is expressed as
Where m is the number of faults, n is the number of signatures of a certain fault, w1+ w2+ … wn =1, λ is the threshold, and the range [0,1]. Where FNi denotes the name of the ith fault, Aij denotes the jth signature name of the ith fault, and Vij denotes the signature value of the jth signature of the ith fault. δ ij is the closeness, i.e. the difference between the sampled value of the ith feature and the value of the knowledge feature. (OR, AND) indicates that these features can be OR 'ed OR AND' ed. wi is objective confidence and is determined by domain experts. CFi is the learning reliability of the Aij feature, and the value of CFi can be changed during the learning process. The CF value is continuously strengthened along with learning, the CF value with small support degree is continuously reduced and weakened, and the CF value is more reasonable along with learning. λ i is a confidence threshold.
The reasoning cycle of CLIPS can be divided into four phases: pattern matching, conflict resolution, rule activation, and action. The reasoning process of the reasoning machine is as follows:
step 1: the eigenvalue V = { V1, V2, V3, … vn, n is the eigenvalue }, and the corresponding eigenvalue name is a = { a1, a2, A3, … An }, of a certain fault is obtained.
Step 2: the inference engine starts, opens and searches the fault model library to match with all knowledge. Assuming a matching failure F11, from the characteristic name a, a match can be found: the eigenvalues VB = { VB1, VB2, VB3, … VBn, n is the eigenvalue }, the allowable errors δ = { δ 1, δ 2, δ 3, … δ n, n is the eigenvalue }, the objective reliability w = { w1, w2,w3, … wn, n is a feature number, and the learning reliability CF = { CF1, CF2, CF3, … CFn, n is a feature number }, then the difference value of VB and V is calculated and compared with delta, features smaller than delta are reserved, and the reliability of each feature is calculated, so that the total reliability FCF1 of the fault F11 is a formula
Wherein
All knowledge in the fault model base is matched in this way, and the total credibility FCFj of each knowledge is obtained as the formula
Where FCFj is the total confidence of knowledge j and m is the number of "knowledge" in the fault model library.
Step 3: it is determined that the fault was identified successfully. Assuming that the overall confidence level of the kth fault that is expected to be identified is FCFk, then
。
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (10)
1. The large hydraulic machine remote fault diagnosis method based on the expert system is characterized by comprising the following steps:
s1: collecting a diagnosis signal of the hydraulic machine;
s2: the acquisition card transmits the diagnosis signal to the field workstation;
s3: the field workstation performs data processing on the diagnosis signal and extracts a characteristic value, and the field workstation transmits the characteristic value to an engineer station;
s4: the engineer station sends the characteristic value to an enterprise server;
s5: the enterprise server processes the characteristic value data through the data processing model to obtain processed data, and the enterprise server sends the data to be analyzed to the expert system;
s6: the expert system analyzes the processed data to obtain fault information.
2. The expert system based large hydraulic machine remote fault diagnosis method as claimed in claim 1, wherein: a first database is also included to receive the characteristic value data sent by the enterprise server.
3. The expert system based remote fault diagnosis method for large hydraulic machines according to claim 1 or 2, characterized in that: in step S3, the classifier is trained by inputting the feature vectors into the classifier, and the classification precision of the classifier is verified so that the accuracy of the classifier reaches a preset precision.
4. The expert system based large hydraulic machine remote fault diagnosis method as claimed in claim 3, wherein: the expert system includes a knowledge base, an inference engine, and a second database.
5. The expert system based large hydraulic machine remote fault diagnosis method as claimed in claim 4, wherein: the inference engine adopts CLIPS artificial intelligence language to compile the inference engine.
6. The expert system based remote fault diagnosis method for large hydraulic machines according to claim 1 or 2, characterized in that: and the engineer station sends the characteristic value data to an enterprise server through an enterprise service bus.
7. The expert system based large hydraulic machine remote fault diagnosis method as claimed in claim 5, wherein the inference process of the inference engine comprises: acquiring a characteristic value of a target fault; starting an inference machine, opening and searching a knowledge base, and matching with all knowledge; and determining fault information which is successfully identified.
8. The large hydraulic machine remote fault diagnosis device based on the expert system is characterized in that: the system comprises an acquisition card for acquiring a diagnosis signal of the hydraulic machine, a field workstation for carrying out data processing on the diagnosis signal and extracting a characteristic value, an engineer station for receiving the characteristic value sent by the field workstation, an enterprise server for receiving the characteristic value sent by the engineer station and processing characteristic value data through a data processing model to obtain processed data, a signal processing unit for receiving the processed data sent by the enterprise server and carrying out signal processing on the processed data, and an expert system for analyzing the characteristic vector to obtain fault information.
9. The remote fault diagnosis device for large hydraulic machines based on expert system as claimed in claim 8, characterized in that: a first database is also included to receive the characteristic value data sent by the enterprise server.
10. The remote fault diagnosis device for large hydraulic machines based on expert system as claimed in claim 8 or 9, characterized in that: the expert system comprises a knowledge base, an inference engine and a second database, wherein the inference engine adopts CLIPS artificial intelligence language to compile the inference engine.
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