The boundary between the good state and the fault state is ambiguous.
In addition, people's description of the symptoms of the failure is also vague.
For example, the noise of the hydraulic system is high, the oil temperature is high, the hydraulic pump is strong, and so on.
It can be seen that ambiguity exists in the fault diagnosis of hydraulic system. Therefore, fuzzy mathematics must be used to make an objective diagnosis of the fault.
Fuzzy Logic Diagnostic Method Mathematical Description of Fault Diagnosis In the fault diagnosis expert knowledge, a set is used to represent all possible fault causes, which are recorded as the total number of fault cause types.
Similarly, a set is used to indicate the various symptoms caused by the causes of these failures, which are recorded as the total number of types of failure signs.
Wang Si, the fuzzy logic diagnosis method for hydraulic system faults is called the fuzzy relation equation between the cause of the fault and the symptom.
The symbol is a fuzzy logic operator, which is a fuzzy relation matrix. It is also called fuzzy diagnosis matrix in fault diagnosis. It can be expressed as a dimension matrix. The fuzzy matrix is ​​the dimension matrix. The matrix element indicates the membership of the first symptom to the first cause. That is, coincidence, in this way, according to the fuzzy diagnosis matrix and the symptom fuzzy vector, the fault diagnosis can be performed by using the 7) formula.
The determination of the symptom fuzzy vector and the fuzzy diagnosis matrix is ​​due to the membership of the symptom fuzzy vector. Therefore, as long as the membership degree of each failure symptom can be determined, the symptom fuzzy vector is also determined.
General membership can be chosen by experience or by domain experts.
There are many ways to determine the fuzzy diagnostic matrix, but in hydraulic system fault diagnosis, the most common and effective method is determined empirically or given by domain experts.
The fuzzy diagnosis principle has the diagnostic matrix and the symptom fuzzy vector. After selecting the appropriate fuzzy logic operator, the fuzzy relation equation can be obtained through the fuzzy relational equation 7). Finally, the actual diagnosis can be diagnosed according to the appropriate diagnostic principle such as the principle of maximum membership. The cause of the failure.
Discussion on Fuzzy Logical Operators As mentioned above, the fault cause fuzzy vector is expressed as this equation is actually a comprehensive evaluation equation in fuzzy mathematics.
Let the definition of the fuzzy logic operator be one, which means that the maximum operation is taken, and eight represents the minimum operation.
After the above operation, the elements in the operation are actually only related to several elements in or, and have nothing to do with other elements, that is, such operations lose a lot of valuable information, and the diagnosis result is often unsatisfactory.
Aiming at the shortcomings of this kind of operation, many improved fuzzy logic operators have been proposed, but the diagnosis results are still not ideal.
In this paper, a fuzzy diagnosis example is given below to further illustrate the shortcomings of the above fuzzy logic operator, and then a new fuzzy logic operator is given.
Fuzzy Logic Diagnostic Example of Hydraulic System Faults We have diagnosed the fuzzy logic diagnostic method applied to the hydraulic system faults shown.
The diagnosis steps are as follows: The fault symptom set The fault symptom set of the hydraulic system is the motor speed is low, especially the motor housing temperature rise motor is leaking, the special moon motor use period, the hydraulic pump housing temperature rise, the one-to-one hydraulic pump leakage amount, one hydraulic pump When the system is in use, the system will change the oil pressure from the unloading state to the load state. The system pressure is not high when the various operations have not started. The system pressure adjustment range is small to the highest pressure, and the hydraulic pump vibration and noise are not changed.
List the causes of failures Here we only list three causes of faults that are difficult to diagnose. The causes of failure of other components can be diagnosed by simple methods. Hydraulic motor wear is caused by hydraulic pump wear.
The relationship between the establishment of the fault symptom fuzzy vector hydraulic motor speed and the failure symptom membership is shown in the table.
Since the normal rated speed of the hydraulic motor of the hydraulic system is such that the degree of membership in the table is the degree of membership of the hydraulic motor housing, especially the temperature of the hydraulic pump housing, as shown in the table.
Lilongjiang Maitonglong and other special sciences talk about the relationship between the motor speed and the membership degree of the Li report. The relationship between the temperature rise of the motor and the pump's casing and the degree of membership. The highest speed is not hot to the degree of membership. The hot hand is very hot hydraulic motor and hydraulic pump. The degree of membership of the excreted intestinal pill is determined.
The service life of the hydraulic motor and hydraulic pump is determined from the degree of membership of the intestine.
Table motor and pump leakage relationship and membership degree Table of the relationship between the motor and the pump's service life and the degree of membership. The leakage situation is obviously small. The rapid release of the subordinate degree of use. The annual subordinate degree hydraulic system changes from the unloading state to the load state. The degree of membership of the suction pressure rise amount is determined as the pressure at the load state as shown in the table is the suction pressure rise amount.
The degree of membership of the system is not high when the actions have not started. The degree of membership of the system with a small pressure adjustment range is determined as shown in the table. The highest pressure that the system can achieve is mP ax).
The relationship between the increase of the suction pressure and the degree of membership is not high. The pressure adjustment range is small and the relationship between the subordinates is less than the degree of membership. The degree of membership of the instrument is due to the rated working pressure of the system. Therefore, when the mP is normal, the system is normal. When the maximum pressure is adjusted, the vibration and noise of the hydraulic pump will become unsatisfactory. The membership degree of 1 is determined as shown in the table.
According to the relationship between the above failure symptoms and the membership degree, the symptom fuzzy vector can be obtained immediately from the actual symptom of the failure.
Establishing Fuzzy Diagnostic Matrix According to experience, the fuzzy diagnostic matrix is ​​the change of the relationship between the vibration and noise of the watch pump and the degree of membership. There is no change in the relationship between the vibration and the damage of the subordinate. The symptoms of the hydraulic system are slow. The maximum speed is 20 and the maximum speed is 20. The adjustable pressure is when the system is changed from the unloading state to the load state, and the suction pressure rises to the pump casing. The use period of the hot pump is the annual pump leakage.
Other system performance is normal.
According to the corresponding relationship between fault symptom and membership degree, the fuzzy vector is the second. If we take the operation as the aforementioned fuzzy logic operator, the fuzzy logic diagnosis method of the hydraulic system fault can be seen. The fault cause has the highest degree of membership, so according to the maximum membership degree. The principle concludes that the cause of the failure is the wear of the hydraulic pump. This diagnosis is consistent with the facts.
However, from the results, the first value and the third value in the fuzzy vector of the fault cause are also large. According to the above fuzzy diagnosis method, it can be considered that the corresponding hydraulic motor and the voltage regulator circuit also have different degrees. Failure, but this is not the case.
In addition, we also see that in the resulting fuzzy vector of fault causes, each element is ultimately only related to the three elements in the fuzzy diagnostic matrix, while other elements do not play their due role.
If we change a single element in the fuzzy diagnostic matrix corresponding to the three elements in the result, such as changing the element lr0 in the first row, then the diagnosis result is based on the principle of maximum membership. There are two faulty hydraulic pumps in the system. The fault and the voltage regulation loop are faulty, and in fact the latter fault does not exist.
This shows that the fuzzy logic operator defined above is too close to the elements in the fuzzy diagnostic matrix, which requires the fuzzy diagnostic matrix and the fault symptom fuzzy vector knife to be very accurate, which is difficult to achieve in practice.
According to this fuzzy, the size of each element indicates the credibility of each of the three fault causes, not the degree of membership of each fault cause.
It can be seen from the results that the corresponding fault cause has the highest degree of credibility and is therefore determined to be a hydraulic pump wear fault.
Because the first step of this operator is the ordinary matrix multiplication operation, which is equivalent to weighting the summation of the components of the fault symptom vector, the weight is the element in the fuzzy diagnosis matrix, and the second step is actually possible The cause of the failure is comprehensively compared, and the possibility of occurrence of each failure cause is determined to be reliable.
Therefore, we refer to this operator as a fuzzy weighted synthesis operator.
After using the fuzzy weighted synthesis operator, the sensitivity of the diagnosis results to the size of individual elements in the fuzzy diagnosis matrix is ​​greatly reduced, that is, the robustness of the diagnosis results to the element perturbation in the fuzzy diagnosis matrix is ​​increased.
In this paper, the fuzzy logic diagnosis method is used to diagnose the fault of the hydraulic system. At the same time, a new fuzzy logic operator fuzzy weighted comprehensive operator is proposed to overcome the defects of the traditional fuzzy logic operator, which can effectively improve the accuracy of the diagnosis result. .
We can produce the carbon steel pipes according to the EN10210,EN10216 and EN10297 standards, Such as the S235JRH steel pipe,S275J0H steel pipe,S275J2H steel pipe,S355J0H steel pipe,S355J2H steel pipe,E355 steel pipe,P235GH steel pipe and P265GH steel pipe.
Yangzhou Chengde Steel Pipe (YC) has been growing to a leading large diameter seamless pipe manufacturer with production range Ø219 ~ Ø1750 mm in OD and wall thickness from 8 mm up to 150mm. The product covers coal power generation, Nuclear power, Oil and gas. Chengde group acquired investment cooperation from American Carlyle Group and PCC (Precision Castparts Corp., Fortune 500 enterprise of the world, majorly in Aerospace and Power industries) successively as her special marketing strategy and the excellence of the product cost performance.
YC has been granted a number of approval certificate by international institutions, such as ASME Nuclear pipe certificate, China Nuclear HAF certificate, 914mm Cylinder certificate, API monogram and certificate, ISO9001 by TUV NORD, ISO14001/OHSAS18001 by CQC, approvals from Shipping Classification Bureaus like DNV, RINA, ABS, Lloyd, GL, Bureau Veritas, and permit of exporting to Europe as PED 2014/68/EU and AD 2000 certificates for pressure pipes, CPD (Construction Products Directive), etc. and these certificates support the development of international sales market.
Carbon Steel Seamless Pipes,En10210 Pipe,S355J2H Pipe,S235Jrh Pipe
YANGZHOU CHENGDE STEEL PIPE CO.,LTD , https://www.chengdepipe.com