阅读说明：本技术 用于断路器的故障诊断装置和方法 (Fault diagnosis apparatus and method for circuit breaker ) 是由 张欣 庄志坚 于 2018-03-28 设计创作，主要内容包括：一种用于断路器(200)的故障诊断装置(100)和方法(1200),包括至少一个传感器(101),被耦合至布置在断路器(200)中的至少一个机构(201),并且被配置为获得参数随时间的波形数据,波形数据与至少一个机构(201)的操作状态相关；以及处理单元(102),被耦合至至少一个传感器(101),并且被配置为分析波形数据以获得至少一个特征值(1220)；确定至少一个特征值与阈值矩阵(1230)之间的相异度；并且响应于该相异度大于阈值相异度,确定至少一个机构(201)存在故障(1240)。利用该故障诊断装置(100),可以提前确定断路器(200)的至少一个机构(201)中的故障。(A fault diagnosis apparatus (100) and method (1200) for a circuit breaker (200), comprising at least one sensor (101) coupled to at least one mechanism (201) arranged in the circuit breaker (200) and configured to obtain waveform data of a parameter over time, the waveform data relating to an operating state of the at least one mechanism (201); and a processing unit (102) coupled to the at least one sensor (101) and configured to analyze the waveform data to obtain at least one characteristic value (1220); determining a degree of dissimilarity between the at least one eigenvalue and a threshold matrix (1230); and determining (1240) that the at least one mechanism (201) is malfunctioning in response to the dissimilarity being greater than a threshold dissimilarity. With the fault diagnosis device (100), a fault in at least one mechanism (201) of a circuit breaker (200) can be determined in advance.)

1. A fault diagnosis device (100) for a circuit breaker (200), comprising:

at least one sensor (101) coupled to at least one mechanism (201) arranged in the circuit breaker (200) and configured to obtain waveform data of a parameter over time, the waveform data relating to an operating state of the at least one mechanism (201); and

a processing unit (102) coupled to the at least one sensor (101) and configured to:

analyzing the waveform data to obtain at least one characteristic value;

determining a degree of dissimilarity between the at least one eigenvalue and a threshold matrix; and is

Determining that the at least one mechanism (201) is malfunctioning in response to the dissimilarity being greater than the threshold dissimilarity.

2. The fault diagnosing apparatus (100) according to claim 1, wherein the processing unit (102) determines the dissimilarity based on a non-linear state estimation technique (NSET).

3. The fault diagnosing apparatus (100) according to claim 1, wherein the threshold matrix records characteristic values corresponding to a normal operation state of the at least one mechanism (201).

4. The fault diagnosis apparatus (100) according to claim 1, wherein the at least one mechanism (201) includes an operating mechanism (2011) of the circuit breaker (200), and the at least one sensor (101) includes a vibration sensor (1011) arranged on the operating mechanism (2011), the vibration sensor (1011) being configured to obtain vibration waveform data relating to an opening/closing operation of the operating mechanism (2011).

5. The fault diagnostic device (100) of claim 1, wherein the at least one mechanism (201) comprises a trip coil (2013) of the circuit breaker (200) and the at least one sensor (101) comprises a first hall sensor (1012) coupled to the trip coil (2013), the first hall sensor (1012) configured to obtain first current waveform data related to a trip operation of the trip coil (2013).

6. The fault diagnosis apparatus (100) of claim 1, wherein the at least one mechanism (201) comprises an energy storage motor (2015) of the circuit breaker (200), and the at least one sensor (101) comprises a second hall sensor (1013) coupled to the energy storage motor (2015), the second hall sensor (1013) configured to obtain second current waveform data related to energy storage operation of the energy storage motor (2014).

7. The fault diagnosis device (100) according to claim 4, wherein the processing unit (102) is further configured to filter the vibration waveform data based on a Wavelet Transform (WT).

8. The fault diagnosing apparatus (100) according to claim 7, wherein the processing unit (201) is configured to analyze the filtered vibration waveform data to obtain at least one vibration feature value including a peak value determined from the filtered vibration waveform data.

9. The fault diagnosing apparatus (100) according to claim 5, wherein the processing unit (201) is configured to analyze the first current waveform data to obtain at least one opening characteristic value, the at least one opening characteristic value comprising an operating peak and/or an operating time determined from the first current waveform data.

10. The fault diagnosis device (100) according to claim 6, wherein the processing unit (201) is configured to analyze the second current waveform data to obtain at least one energy storage characteristic value comprising a start-up current, a cut-off current, an average energy storage current and/or an energy storage time determined from the second current waveform data.

11. A circuit breaker (200) comprising the fault diagnosis device (100) of any one of claims 1-10.

12. A fault diagnosis method (1200) for a circuit breaker (200), comprising:

receiving (1210) waveform data of a parameter over time from at least one sensor (101) coupled to at least one mechanism (201) arranged in a circuit breaker (200), the waveform data relating to an operating state of the at least one mechanism (201);

analyzing (1220) the waveform data to obtain at least one characteristic value;

determining (1230) a degree of dissimilarity between the at least one characteristic value and a threshold matrix; and

determining (1240) that a fault exists with the at least one mechanism (201) in response to the degree of dissimilarity being greater than a threshold degree of dissimilarity.

13. The fault diagnostic method (1200) of claim 12, wherein the dissimilarity is determined based on a non-linear state estimation technique.

14. The fault diagnostic method (1200) of claim 12, further comprising:

-establishing said threshold matrix using eigenvalues corresponding to normal operation states of said at least one mechanism (201).

15. The fault diagnosis method (1200) according to claim 12, comprising:

vibration waveform data relating to an opening/closing operation of an operating mechanism (2011) of the circuit breaker (200) is received from a vibration sensor (1011) arranged on the operating mechanism (2011).

16. The fault diagnosis method (1200) according to claim 12, comprising:

receiving first current waveform data related to a switching-off operation of the switching-off coil (2013) from a first Hall sensor (1012) coupled to the switching-off coil (2013) of the circuit breaker (200).

17. The fault diagnosis method (1200) according to claim 12, comprising:

receiving second current waveform data related to a charging operation of the charging motor (2014) from a second Hall sensor (1013) coupled to a charging motor (2015) of the circuit breaker (200).

18. The fault diagnostic method (1200) of claim 12, further comprising:

filtering the vibration waveform data based on a Wavelet Transform (WT).

19. The fault diagnostic method (1200) of claim 18, comprising:

analyzing the filtered vibration waveform data to obtain at least one vibration characteristic value, the at least one vibration characteristic value comprising a peak value determined from the filtered vibration waveform data.

20. The fault diagnostic method (1200) of claim 16, comprising:

analyzing the first current waveform data to obtain at least one opening characteristic value, wherein the at least one opening characteristic value comprises an operation peak value and/or an operation time determined from the first current waveform data.

21. The fault diagnostic method (1200) of claim 18, further comprising:

analyzing the second current waveform data to obtain at least one energy storage characteristic value, wherein the at least one energy storage characteristic value comprises starting current, cut-off current, average energy storage current and/or energy storage time determined from the second current waveform data.

Technical Field

The disclosed embodiments relate generally to a circuit breaker, and more particularly, to a fault diagnosis apparatus and method for a circuit breaker.

Background

Circuit breakers, which are widely used in industrial and domestic applications, are well known. Circuit breakers are automatically operated electrical switches designed to protect an electrical circuit from damage due to overcurrent, typically caused by an overload or a short circuit. Once a circuit fault is detected, the circuit breaker contacts must be opened to interrupt the circuit, which is typically done using mechanically stored energy contained within the circuit breaker (such as springs or compressed air used to separate the contacts). Circuit breakers may also use high currents caused by faults (such as by thermal expansion or magnetic fields) to separate contacts. Circuit breakers typically use an opening coil to open the operating mechanism and an energy storage motor to recover the energy of the spring.

It can be seen that the stability of the circuit breaker is mainly determined by the health of the operating mechanism, the opening coil and the energy storage motor. With long-term use, the operating mechanism, the transmission mechanism between the operating mechanism and the opening coil and the transmission mechanism between the spring and the energy storage motor can be in failure. For example, components in the operating mechanism or the transmission mechanism may be worn, deformed, or broken, or joints between the components may be prevented from rotating due to deformation or increased spacing.

The above problems may cause the circuit breaker to operate poorly and eventually cause the circuit breaker to malfunction. In conventional solutions, these problems can only be detected or discovered after the problem causes a circuit breaker failure. This may result in damage to the electrical equipment in the circuit. Furthermore, in this case, the circuit breaker can only be passively operated (e.g., by replacement) to solve the above-described problems. Therefore, the replacement time of the circuit breaker is delayed compared to the case of actively or early replacing the circuit breaker.

Disclosure of Invention

The disclosed embodiments provide a solution for providing a circuit breaker fault diagnosis apparatus and method.

In a first aspect, a fault diagnosis apparatus for a circuit breaker is provided. The apparatus includes at least one sensor coupled to at least one mechanism disposed in the circuit breaker and configured to obtain waveform data of the parameter over time, the waveform data being related to an operating state of the at least one mechanism; and a processing unit coupled to the at least one sensor and configured to analyze the waveform data to obtain at least one characteristic value; determining a degree of dissimilarity between the at least one eigenvalue and the threshold matrix; and responsive to the dissimilarity being greater than a threshold dissimilarity, determining that a fault exists with at least one of the mechanisms.

In some embodiments, the processing unit determines the dissimilarity based on a non-linear state estimation technique.

In some embodiments, the threshold matrix records characteristic values corresponding to a normal operating state of the at least one mechanism.

In some embodiments, the at least one mechanism comprises an operating mechanism of the circuit breaker and the at least one sensor comprises a vibration sensor disposed on the operating mechanism, the vibration sensor configured to obtain vibration waveform data related to an opening/closing operation of the operating mechanism.

In some embodiments, the at least one mechanism includes a trip coil of the circuit breaker, and the at least one sensor includes a first hall sensor coupled to the trip coil, the first hall sensor configured to obtain first current waveform data related to a trip operation of the trip coil.

In some embodiments, the at least one mechanism includes a storage motor of the circuit breaker, and the at least one sensor includes a second hall sensor coupled to the storage motor, the second hall sensor configured to obtain second current waveform data related to the storage operation of the storage motor.

In some embodiments, the processing unit is further configured to filter the vibration waveform data based on a wavelet transform.

In some embodiments, the processing unit is configured to analyze the filtered vibration waveform data to obtain at least one vibration characteristic value, the at least one vibration characteristic value comprising a peak value determined from the filtered vibration waveform data.

In some embodiments, the processing unit is configured to analyze the first current waveform data to obtain at least one opening characteristic value, the at least one opening characteristic value comprising an operating peak and/or an operating time determined from the first current waveform data.

In some embodiments, the processing unit is configured to analyze the second current waveform data to obtain at least one energy storage characteristic value, the at least one energy storage characteristic value comprising an on-current, an off-current, an average energy storage current and/or an energy storage time determined from the second current waveform data.

In a second aspect, there is provided a circuit breaker including the above-described fault diagnosis apparatus.

In a third aspect, a fault diagnosis method for a circuit breaker is provided. The method includes receiving waveform data of a parameter over time from at least one sensor coupled to at least one mechanism disposed in the circuit breaker, the waveform data relating to an operating state of the at least one mechanism; analyzing the waveform data to obtain at least one characteristic value; determining a degree of dissimilarity between the at least one eigenvalue and the threshold matrix; and determining that the at least one mechanism is malfunctioning in response to the dissimilarity being greater than a threshold dissimilarity.

In some embodiments, the dissimilarity is determined based on a non-linear state estimation technique.

In some embodiments, the method further comprises establishing a threshold matrix using the eigenvalues corresponding to the normal operating state of the at least one mechanism.

In some embodiments, the method includes receiving vibration waveform data related to an opening/closing operation of an operating mechanism of the circuit breaker from a vibration sensor disposed on the operating mechanism.

In some embodiments, the method includes receiving first current waveform data related to a switching-off operation of a switching-off coil of a circuit breaker from a first hall sensor coupled to the switching-off coil.

In some embodiments, the method includes receiving second current waveform data related to the charging operation of the charging motor from a second hall sensor coupled to the charging motor of the circuit breaker.

In some embodiments, the method further comprises filtering the vibration waveform data based on a wavelet transform.

In some embodiments, the method includes analyzing the filtered vibration waveform data to obtain at least one vibration characteristic value, the at least one vibration characteristic value including a peak value determined from the vibration waveform data.

In some embodiments, the method includes analyzing the first current waveform data to obtain at least one opening characteristic, the at least one opening characteristic including an operating peak and/or an operating time determined from the first current waveform data.

In some embodiments, the method includes analyzing the second current waveform data to obtain at least one energy storage characteristic value, the at least one energy storage characteristic value including a start-up current, a cut-off current, an average energy storage current, and/or an energy storage time determined from the second current waveform data.

It should be understood that this summary is not intended to identify key or essential features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The foregoing and other objects, features and advantages of the disclosure will be apparent from the following more particular descriptions of exemplary embodiments of the disclosure as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the disclosure.

Fig. 1 shows a schematic diagram of a circuit breaker having a fault diagnostic device according to an embodiment of the present disclosure;

fig. 2 illustrates a perspective view of a circuit breaker having a fault diagnosis device according to an embodiment of the present disclosure;

FIG. 3 illustrates a perspective view of an operating mechanism having a vibration sensor disposed thereon according to an embodiment of the present disclosure;

fig. 4A and 4B show schematic diagrams of a trip coil and an energy storage motor coupled to a hall sensor according to an embodiment of the disclosure;

fig. 5A and 5B show schematic diagrams of vibration waveform data and filtered vibration waveform data, respectively, according to an embodiment of the present disclosure;

fig. 6 shows a diagram of the vibration characteristic value as a function of the number of closing operations of the operating mechanism;

fig. 7 shows a perspective view of a switching-off coil according to an embodiment of the present disclosure;

FIG. 8 illustrates a first current waveform data plot associated with a brake-off coil in accordance with an embodiment of the present disclosure;

fig. 9 shows a diagram of the switching-off characteristic value as a function of the number of switching-off operations of the switching-off coil;

fig. 10 illustrates a second current waveform data plot associated with an energy storage motor according to an embodiment of the present disclosure;

fig. 11 shows a graph of energy storage characteristic values as a function of the number of energy storage operations of the energy storage motor;

fig. 12 illustrates a flow diagram of a fault diagnosis method for a circuit breaker according to some other embodiments of the present disclosure.

Throughout the drawings, the same or similar reference numerals are used to designate the same or similar elements.

Detailed Description

The present disclosure will now be discussed in connection with several exemplary embodiments. It should be understood that these examples are discussed only for the purpose of enabling those skilled in the art to better understand and to further carry out the disclosure, and do not suggest any limitation as to the scope of the technical solution.

As used herein, the term "include" and its variants are to be understood as open-ended terms, meaning "including, but not limited to. The term "based on" is to be understood as "based at least in part on". The terms "one embodiment" and "an embodiment" should be understood as "at least one embodiment". The term "another embodiment" should be understood as "at least one other embodiment". The terms "first," "second," and the like may refer to different or the same object. Other explicit and implicit definitions may be included below. The definitions of the terms are consistent throughout the specification unless the context clearly dictates otherwise.

In circuit breakers, the operating and transmission mechanisms associated with the opening coil and the stored energy motor may fail over extended periods of use. Failure of the mechanism may result in a circuit breaker failure. In conventional solutions, these problems can only be detected or discovered after the problem causes the circuit breaker to fail. This may cause damage to electrical equipment in the circuit and delay the replacement time of the circuit breaker.

In order to detect or determine a fault in the above-described mechanism before the fault occurs, the disclosed embodiment provides a fault diagnosis device 100 for a circuit breaker 200. Some exemplary embodiments will now be described with reference to fig. 1 to 11.

Fig. 1 shows a schematic diagram of a circuit breaker 200 having a fault diagnostic device 100 according to an embodiment of the present disclosure; and fig. 2 illustrates a perspective view of a circuit breaker 200 having the fault diagnosis apparatus 100 according to an embodiment of the present disclosure.

As shown in fig. 1 and 2, the fault diagnosis device 100 generally includes at least one sensor 101 and a processing unit 102 coupled to the at least one sensor 101. The sensor 101 is coupled to at least one mechanism 201 disposed in the circuit breaker 200 to detect and obtain waveform data of the parameter over time. The waveform data is related to the operating state of at least one of the mechanisms 201.

The inventors have found that waveform data of parameters such as vibration amplitude, current, and the like may change with time before the above mechanism malfunctions. Although no failure occurs, the state of health of the mechanism deteriorates due to deformation or the like. Poor health may cause failure of the circuit breaker 200 at any time.

The inventors have also found that poor health can be detected by analyzing the waveform data described above. Thus, the processing unit 102 is configured to analyze the above-mentioned waveform data to obtain at least one characteristic value. The processing unit 102 then determines a degree of dissimilarity between the at least one eigenvalue and the threshold matrix. In response to the dissimilarity being greater than a threshold dissimilarity, the processing unit 102 determines that a fault exists with at least one of the mechanisms 201.

It should be appreciated that by analyzing waveform data related to the operating state of the mechanism 201 in the circuit breaker 200, a fault in the mechanism 201 can be determined in advance. Before the circuit breaker 200 suddenly fails, the user can take action(s) in advance to resolve the problem. For example, when the processing unit 102 determines that there is a fault in at least one mechanism 201, which means that the circuit breaker 200 needs to be replaced, the user can shut down the electrical equipment in the circuit in advance to prevent the electrical equipment from being damaged due to the sudden failure of the circuit breaker 200.

Further, the fault diagnosis apparatus 100 according to the embodiment of the present disclosure may be easily applied to a brand-new circuit breaker 200 or a modification of an existing circuit breaker 200. Fault diagnosis may be performed by coupling the sensor 101 to a mechanism 201 arranged in the circuit breaker 200 in a cost-effective manner.

In some embodiments, the processing unit 102 may take action when at least one mechanism 201 has a fault. For example, the processing unit 102 may send an alarm signal to an alarm device (not shown) to cause the alarm device to alert a user of the fault. In addition, the processing unit 102 can also actively cut off the current in the circuit to avoid unnecessary loss.

In some embodiments, the processing unit 102 may be a computer in communication with the at least one sensor 101, as shown in fig. 2. In this case, the processing unit 102 may alert the user by displaying an alert on the screen of the computer. Alternatively, in some embodiments, the processing unit 102 may be a control module disposed in the circuit breaker 200. The control module may be the control unit of the circuit breaker itself, or alternatively it may be another independent control unit 200.

It should be understood that the above-described embodiments of the processing unit 102 are illustrative only and do not imply any limitation on the scope of the disclosure. Any other suitable arrangement or components are also possible. For example, processing unit 102 may be a cellular telephone or a Personal Digital Assistant (PDA). Furthermore, the processing unit 102 may be coupled to the at least one sensor 101 in a wired manner or in a wireless manner.

In some embodiments, the threshold matrix may record characteristic values corresponding to normal operating conditions of at least one mechanism 201. For example, by analyzing waveform data, n eigenvalues (n is a natural number greater than 0) can be obtained in one operation, and these eigenvalues can be expressed as a matrix as follows:

X(i)＝[x₁x₂… x_n]^T(1)

in the above matrix, "T" represents a transpose of the matrix. The threshold matrix may record m eigenvalues (m is a natural number greater than 0) corresponding to m normal operations of the at least one mechanism 201, which may be represented as follows:

in the above equation, x_n(m) denotes an nth characteristic value among characteristic values obtained in the mth operation of the circuit breaker 200.

It will be appreciated that the larger the value of m, the more accurate the results of the dissimilarity. The value of m may be selected as desired. After the feature values have been obtained, in some embodiments, the dissimilarity may be determined based on a non-linear state estimation technique (NSET), which will be discussed further below. The NSET algorithm is a simple algorithm so that the processing unit 102 can more easily execute the algorithm, thereby improving the response speed of the fault diagnosis apparatus 100.

In particular, by way of example only, it is assumed that n characteristic values x relating to the operating state of at least one mechanism 201 have been obtained₁、x₂……x_nThese eigenvalues can then be recorded in the following matrix:

X_obs＝[x₁x₂… x_n]^T(3)

the dissimilarity may be determined using the following equation:

＝X_obs-X_est(4)

X_estmay be determined by multiplying the threshold matrix by the coefficient matrix W. The coefficient matrix W may be obtained by the following equation:

in the above-described equation, the equation,representing a non-linear operator. Non-linear operatorMay be implemented in various ways. For example,

can mean that:

after obtaining the coefficient matrix w through the above equation (5), X may be determined using the following equation_est：

Thus, the dissimilarity can be determined using equation (4) above. An exemplary determination process based on the dissimilarity of NSETs is described above. It should be understood that the above-described embodiments for determining the degree of dissimilarity are only illustrative and do not imply any limitation on the scope of the present disclosure. Any other suitable method and/or algorithm is also possible. For example, in some embodiments, regression analysis or the like may be used to determine the degree of dissimilarity.

After determining the dissimilarity, the processing unit 102 then compares the dissimilarity with a threshold matrix. On the one hand, if the dissimilarity is greater than the threshold matrix, it means that the state of health of the mechanism 201 is deteriorated, and the mechanism 201 or the circuit breaker 200 should be replaced to avoid the circuit breaker 200 from suddenly failing.

On the other hand, if the dissimilarity is smaller than the threshold matrix, it means that the mechanism 201 is in normal operation. In this case, the health state may be determined by calculating the similarity between the dissimilarity degree and the threshold matrix. For example, if the dissimilarity is very close but not greater than the threshold matrix, it means that the mechanism 201 is in normal operation but not flawless. The processing unit 102 may then shorten the detection and analysis interval to determine the dissimilarity more frequently. That is, the degree of dissimilarity may be obtained and analyzed periodically, and the detection interval may be adjusted. How the above process is performed is described below by several embodiments.

In some embodiments, at least one mechanism 201 may include an operating mechanism 2011. The at least one sensor 101 may be coupled to the operating mechanism 2011 in various ways. For example, the sensor 101 may include a vibration sensor 1011 disposed on the operating mechanism 2011. The vibration sensor 1011 may be disposed at any suitable location on the operating mechanism 2011, for example, the vibration sensor 1011 may be disposed on a mounting bracket of the operating mechanism 2011, as shown in fig. 3.

The vibration sensor 1011 can obtain vibration waveform data, such as vibration amplitude, relating to the opening/closing operation of the operating mechanism 2011. It should be appreciated that any suitable vibration sensor 1011 may be used to obtain vibration waveform data. For example, the vibration sensor 1011 may have a measurement range of greater than 300g ("g" stands for acceleration due to gravity) and a frequency range of greater than 5kHz, preferably 10-30 kHz.

Fig. 5A shows a diagram of vibration waveform data obtained by the vibration sensor 1011. As shown in fig. 5A, in the closing operation of the operating mechanism 2011, the amplitude of the vibration changes with time. To facilitate analysis of the vibration waveform data, in some embodiments, the vibration waveform data may be filtered. For example, in some embodiments, the vibration waveform data may be filtered based on a Wavelet Transform (WT) such as the Mallat algorithm. It should be understood that the above-described embodiments of filtering the vibration waveform data are illustrative only and do not imply any limitation on the scope of the present disclosure. Any other suitable method and/or algorithm is also possible. For example, the vibration waveform data may be filtered using low-pass filtering or the like.

Accordingly, the filtered vibration waveform data may be obtained by filtering to remove noise in the vibration waveform data. The inventors have found through experiments that when operating mechanism 2011 is in a poor health state, some values (such as peaks in vibration amplitude) may change compared to the normal operating state of operating mechanism 2011. In this case, as shown in fig. 5B, a peak value determined from the filtered vibration waveform data may be selected as the vibration characteristic value. Accordingly, the vibration threshold matrix may record a peak value obtained when the operating mechanism 2011 is in a normal operating state (such as when the circuit breaker 200 has just been placed into use).

The degree of dissimilarity of the vibration characteristic values can be determined by the above-described method. For example, the vibration characteristic value corresponding to one normal operation of the operating mechanism 2011 is 1225.4. Then, the vibration eigenvalues can be represented as the following matrix:

X(1)＝[1225.4]^T。

the threshold matrix may record 50 such vibration eigenvalues and may be represented as follows:

for the sake of discussion, it is assumed that one vibration characteristic value corresponding to one operation of the operating mechanism 2011 is 1005, which is expressed as follows:

X_obs＝[1005]^T。

then, X_estCan be determined by the above equation (7). Through the calculation, the method has the advantages that,

X_est＝[3994]^T。

thus, the dissimilarity can be determined using equation (4) above. The dissimilarity is then compared to a threshold dissimilarity. As described above, if the degree of dissimilarity is greater than the threshold degree of dissimilarity, this means that the operating mechanism 2011 may be malfunctioning or in a poor health state.

Fig. 6 shows a graph of the vibration characteristic value as a function of the number of operations of the operating mechanism, in which the threshold dissimilarity degree is designated as 0, as indicated by a broken line. As shown in fig. 6, as the number of closing operations increases, the degree of dissimilarity gradually approaches the threshold degree of dissimilarity, and eventually exceeds the threshold degree of dissimilarity after about 3900 operations. This means that the operating mechanism 2011 is in a poor health condition and needs to be replaced after 3900 operations.

It should be understood that the circuit breaker 200 may be operated to perform its function at this time. If the circuit breaker 200 continues to be used without replacement, the degree of dissimilarity exceeds the threshold degree of dissimilarity more and more until the operating mechanism 2011 is completely damaged after about 4200 operations, e.g., one of the components in the operating mechanism 2011 may break. It should be understood that the worse the state of health, the greater the difference in the degree of dissimilarity from the threshold.

Further, as can be seen from the above, the failure can be predicted approximately 300 times in advance before the operating mechanism 2011 fails. In this case, the user may replace the circuit breaker 200 or the operating mechanism 2011 more actively or in advance. This effectively prevents damage to the electrical equipment in the circuit due to a sudden failure of the operating mechanism 2011 or the circuit breaker 200. It should be understood that the above-described embodiment of selecting "0" as the threshold degree of dissimilarity is merely illustrative and does not imply any limitation on the scope of the present disclosure. Any other suitable value is also possible. For example, a greater threshold dissimilarity may be selected to save cost, or a lesser threshold dissimilarity may be selected to determine faults earlier.

It should also be understood that the above-described embodiments in which the threshold dissimilarity of the vibration characteristic values includes a peak value are merely illustrative and do not imply any limitation on the scope of the present disclosure. Any other suitable value as characteristic value is also possible. For example, in some embodiments, a valley or operating time determined from the filtered vibration waveform data may also be selected.

In some embodiments, at least one mechanism 201 may include a trip coil 2013. The at least one sensor 101 may be coupled to the opening coil 2013 in a variety of ways. For example, the sensor 101 may include a hall sensor (referred to as a first hall sensor 1012 for ease of discussion) coupled to the opening coil 2013, as shown in fig. 4A. The first hall sensor 1012 may be coupled to a wire 2012 for connecting the opening coil 2013 to a power supply 2016. It should be understood that the above-described embodiment in which the first hall sensor 1012 is coupled to the wire 2012 is only illustrative and does not imply any limitation on the scope of the present disclosure. Any other suitable arrangement is also possible. For example, in some embodiments, the first hall sensor 1012 may be coupled to the opening coil itself or any suitable location associated with the opening coil 2013.

The first hall sensor 1012 can obtain current waveform data (referred to as first current waveform data for ease of discussion) related to the opening operation of the opening coil 2013. It should be appreciated that any suitable sensor may be used to obtain waveform data related to the opening operation of the opening coil 2013. For example, the sensor 101 may include a load sensor (not shown) to obtain a load on the transmission connected to the opening coil 2013.

The inventors found that with long-term use, the load on the transmission mechanism connected to the opening coil 2013 increases due to deformation, increased clearance between components, component wear, and the like. Accordingly, the electric power required for the opening coil 2013 to perform the opening operation through the transmission mechanism is also gradually increased. To simulate this phenomenon, weights 300 having different weights (e.g., 100g, 200g, and 300g) are loaded on a driving mechanism connected to the opening coil 2013, as shown in fig. 7. These different weights correspond to loads on the transmission mechanism due to deformation, increased clearances between components, and the like.

Through experimentation, the inventors further discovered that as the load on the transmission mechanism connected to the opening coil 2013 increases, some values (such as the operating peak and/or operating time determined from the first current waveform data) may change as compared to the normal operating state, as shown in fig. 8. The opening peak corresponds to a peak of a current passing through the wire 2012 in the opening operation, and the opening time corresponds to a time to open the operating mechanism 2011.

Fig. 8 shows a first current waveform data plot corresponding to different loads applied to the actuator. As can be seen from fig. 8, as the load increases, the opening peak and/or the opening time increases. The load on the transmission may correspond to the health of the transmission. The greater the load on the transmission connected to the opening coil 2013, the worse the health of the transmission.

In this case, the opening peak value and/or the opening time determined from the first current waveform data as shown in fig. 8 may be selected as the opening characteristic value. Accordingly, the opening threshold matrix may record the opening peak and/or opening time obtained when the opening coil 2013 and its associated drive mechanism are in a normal operating state, such as when the circuit breaker 200 has just been placed in service.

The degree of dissimilarity of the opening characteristic values can be determined by the above-described method. The determined dissimilarity is then compared to a threshold dissimilarity. As described above, if the degree of difference is greater than the threshold degree of difference, it means that the transmission mechanism connected to the opening coil 2013 may be in a malfunction or in a poor health state.

Fig. 9 shows a graph of the opening characteristic value as a function of the number of opening operations of the opening coil 2013, with a threshold degree of dissimilarity designated as 3, as indicated by the dashed line. As shown in fig. 9, as the load on the transmission mechanism connected to the opening coil 2013 increases, the degree of dissimilarity exceeds the threshold degree of dissimilarity more and more. This means that the transmission mechanism connected to the opening coil 2013 is in a poor health state and needs to be replaced.

It should be noted that the circuit breaker 200 can be operated to achieve its function when a load occurs on the transmission mechanism connected to the opening coil 2013 due to deformation or the like. If the circuit breaker 200 is continued to be used without replacement, the degree of dissimilarity exceeds the threshold degree of dissimilarity more and more until the transmission mechanism is completely damaged. That is, an increase in load on the transmission mechanism connected to the opening coil 2013 may cause the transmission mechanism 2011 to malfunction.

When the load increases to a degree of dissimilarity in the opening characteristic value exceeding the threshold dissimilarity degree, the processing unit 102 determines that the transmission mechanism is in a poor health state and needs to be replaced. In this case, the user can more actively or prematurely replace the circuit breaker 200 or the transmission mechanism connected to the opening coil 2013. It should be understood that the above-described embodiment of selecting "3" as the threshold dissimilarity is merely illustrative and does not imply any limitation on the scope of the present disclosure. Any other suitable value is also possible. For example, a greater threshold dissimilarity may be selected to save cost, or a lesser threshold dissimilarity may be selected to determine faults earlier.

Furthermore, it should be understood that the above-described embodiments in which the threshold dissimilarity of the opening characteristic values includes an opening peak value and/or an opening time are merely illustrative and do not set any limit to the scope of the present disclosure. Any other suitable value is also possible. For example, in some embodiments, the total operating time of the opening coil determined from the first current waveform data may also be selected.

In some embodiments, at least one mechanism 201 may include an energy storage motor 2015. The at least one sensor 101 may be coupled to the energy storage motor 2015 in various ways. For example, the sensor 101 may include a hall sensor (referred to as a second hall sensor 1013 for ease of discussion) coupled to an energy storage motor 2015, as shown in fig. 4B. The second hall sensor 1013 can be coupled to a wire 2014 for connecting the storage motor 2015 to the power supply 2016. It should be understood that the above-described embodiment in which the second hall sensor 1013 is coupled to the wire 2014 is merely illustrative and does not imply any limitation on the scope of the disclosure. Any other suitable arrangement is possible. For example, in some embodiments, the second hall sensor 1013 can be coupled to the storage motor 2015 itself or any suitable location associated with the storage motor 2015.

The second hall sensor 1013 can obtain current waveform data (referred to as second current waveform data for ease of discussion) related to the charging operation of the charging motor 2015. It should be appreciated that any suitable sensor may be used to obtain waveform data related to the charging operation of charging motor 2015. For example, the sensor 101 may include a load sensor (not shown) to obtain a load on a transmission connected to the energy storage motor 2015.

Similar to the above-described process of determining the opening characteristic values of the opening coil 2013 (which is not repeated here), the inventors further found that when the load on the transmission mechanism connected to the energy storage motor 2015 increases, some values such as the starting current, the off-current, the average energy storage current and/or the energy storage time determined from the second current waveform data may change as compared to the normal operation state, as shown in fig. 10.

As shown, the startup current corresponds to the peak value of the current through the wire 2014 at the beginning of the energy storage; the cutoff current corresponds to the value of the current through the wire 2014 at the end of the stored energy; the average stored current corresponds to the average of the current through the wire 2014 during storage and the storage time corresponds to the time for storing energy to the spring.

As described above, when the load on the transmission mechanism connected to the energy storage motor 2015 increases, at least one of the above values may change as compared to the normal operation state, as shown in fig. 10. For example, in a state where the energy storage motor 2015 is normally operated, the current may be cut off at the end of the energy storage process, and thus, the cut-off current may be "0". However, if some mechanism, such as a transmission, connected to the energy storage motor 2015 is in a poor health state, the off current may not be "0" but other values.

In this case, as shown in fig. 10, the starting current, the off current, the average energy storage current, and/or the energy storage time determined from the second current waveform data may be selected as the energy storage characteristic value. Accordingly, the energy storage threshold matrix may record the on-current, off-current, average energy storage current, and/or energy storage time obtained when the energy storage motor 2015 and its associated drive mechanism are in a normal operating state, such as when the circuit breaker 200 has just been placed into service.

Fig. 11 shows a graph of the energy storage characteristic value as a function of the number of opening operations of the energy storage motor 2015, with the threshold dissimilarity degree designated as 2, as indicated by the dashed line. As shown in fig. 11, as the number of energy storing operations increases, the degree of dissimilarity gradually approaches the threshold degree of dissimilarity, and eventually exceeds the threshold degree of dissimilarity after about 780 operations. This means that the transmission connected to the energy storage motor 2015 is in a poor health state and needs to be replaced.

If an appropriate threshold degree of dissimilarity has been selected, a fault can be predicted before a transmission connected to the energy storage motor 2015 fails. In this case, the user can more actively or prematurely replace the circuit breaker 200 or the transmission connected to the energy storage motor 2015. This effectively prevents damage to the electrical equipment in the circuit due to sudden failure of the transmission or circuit breaker 200 connected to the energy storage motor 2015.

It should be understood that the above-described embodiment in which "2" is selected as the threshold degree of dissimilarity is merely illustrative and does not imply any limitation on the scope of the present disclosure. Any other suitable value is also possible. For example, a greater threshold dissimilarity may be selected to save cost, or a lesser threshold dissimilarity may be selected to determine faults earlier.

Moreover, it should be understood that the above-described embodiments in which the threshold degree of dissimilarity of the energy storage characteristic values includes the on-current, off-current, average energy storage current, and/or energy storage time are merely illustrative and do not imply any limitation on the scope of the present disclosure. Any other suitable value is also possible. For example, in some embodiments, a valley or operating time determined from the second current waveform data may also be selected.

The above describes embodiments of the fault diagnosis apparatus 100 applied to the operating mechanism 2011, the opening coil 2013 and/or the energy storage motor 2015 respectively according to the embodiments of the present disclosure. It should be understood that the above-described embodiments in which the fault diagnosis apparatus 100 is applied to the operating mechanism 2011, the opening coil 2013, or the energy storage motor 2015 are merely illustrative, and do not imply any limitation on the scope of the present disclosure. Any other suitable mechanism to be applied is also possible. For example, the failure diagnosis apparatus 100 may be applied to a drive mechanism (not shown).

Fig. 12 illustrates a flow diagram of a fault diagnosis method for a circuit breaker according to some other embodiments of the present disclosure. The method 1200 may be implemented by the processing unit 102 to perform fault diagnosis. As shown, at block 1210, waveform data of a parameter over time is received from at least one sensor 101 coupled to at least one mechanism 201 disposed in a circuit breaker. The waveform data is related to the operating state of at least one of the mechanisms 201.

At block 1220, the waveform data is analyzed to obtain at least one characteristic value. At block 1230, a degree of dissimilarity between the at least one eigenvalue and the threshold matrix is determined. At block 1240, a determination is made that at least one mechanism 201 has failed in response to the dissimilarity being greater than a threshold dissimilarity.

As can be seen from the above embodiments of the present disclosure, a failure of at least one mechanism 201 in the circuit breaker 200 can be determined in advance. In this case, the user can replace the circuit breaker 200 or the operating mechanism 2011 more actively. This effectively prevents damage to the electrical equipment in the circuit due to a sudden failure of the operating mechanism 2011 or the circuit breaker 200.

It is to be understood that the above detailed embodiments of the disclosure are merely illustrative of or explaining the principles of the disclosure and are not intended to limit the disclosure. Therefore, any modification, equivalent replacement, and improvement, etc. should be included within the protection scope of the present disclosure without departing from the spirit and scope of the present disclosure. Also, it is intended that the appended claims cover all such modifications and variations as fall within the scope and range of equivalents of the claims.