Remote ignition control system of release torch

Abstract

The invention discloses a remote ignition control system for a release flare, and relates to the technical field of release flare control, the system comprises the following components: a fault detection module, a fault isolation module, a standby module, a redundant circuit switching module, a fault diagnosis module and a wireless communication module; by integrating multiple modules of fault detection, fault isolation, standby module and redundant circuit switching, fault diagnosis and wireless communication, comprehensive remote monitoring and management of the release flare ignition control system are achieved, when the system detects a fault, electrical connection of a fault component can be rapidly cut off, and the safety of the release flare ignition control system is improved. And the standby module or the redundant circuit is automatically switched, so that the continuous and stable operation of the system is ensured, the reliability and the safety of the system are remarkably improved, and the risk of production interruption caused by faults is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of flare control, and particularly to a remote ignition control system for a flare. Background Art

[0002] As a key device for safely discharging excess or harmful gases in industrial production, the stability and reliability of the ignition control system of a flare are crucial for ensuring production safety. With the development of industrial automation and intelligent technologies, higher requirements are put forward for the remote ignition control system of a flare. In modern industrial sites, it is often necessary to accurately ignite and monitor the flare through a remote control system at an actual operation position far from the flare, so as to ensure that gases can be discharged quickly and safely in case of an emergency and avoid accidents.

[0003] In traditional flare ignition control systems, manual on-site operation or simple automation control is usually relied on. These systems often lack real-time fault detection and isolation mechanisms. When a component in the system fails, manual intervention is often required for troubleshooting and repair, which is not only inefficient but also may lead to production interruption or safety accidents due to untimely handling. In addition, traditional systems usually do not have the design of standby modules and redundant circuits. Once the main circuit or key components fail, the entire system will not be able to work properly, seriously affecting the reliability and safety of the system. At the same time, traditional systems also have deficiencies in fault diagnosis, often relying on the experience and intuition of maintenance personnel, lacking scientific diagnosis methods and means, resulting in low accuracy and efficiency of fault diagnosis.

[0004] In summary, traditional flare ignition control systems have many deficiencies in aspects such as fault detection, isolation, standby module switching, fault diagnosis, and remote control, and cannot meet the requirements of modern industries for safe production and efficient management. Therefore, it is particularly important to develop a remote ignition control system for a flare. Summary of the Invention

[0005] The purpose of the present invention is to make up for the deficiencies of the existing technology and provide a remote ignition control system for a flare, which can improve the reliability and safety of the system through comprehensive remote monitoring and management of the flare ignition control system, reduce the risk of production interruption caused by faults, and provide strong guarantee for the safety and efficiency of industrial production.

[0006] To solve the above technical problems, the present invention provides the following technical solution: A remote ignition control system for a flare, the system includes the following components: a fault detection module, a fault isolation module, a standby module and a redundant circuit switching module, a fault diagnosis module, and a wireless communication module; The fault detection module: is used to monitor the operating status of each component in the system in real time; The fault isolation module: is used to quickly cut off the electrical connection between the faulty component and other parts of the system when the fault detection module detects a component fault; The spare module and redundant line switching module: is used to automatically connect the spare module or redundant line to the system according to the fault type and system configuration after the faulty component is isolated; The fault diagnosis module: is used to conduct a detailed diagnosis on the faulty component after it is isolated; The wireless communication module: is used to send the fault report generated by the fault diagnosis module to the maintenance personnel.

[0007] Furthermore, the intelligent algorithm adopted by the fault detection module is the adaptive dynamic threshold monitoring algorithm, and the algorithm formula is: , where is the predicted value at the current moment, is the actual value collected by the sensor at the current moment, is the moving average value at the previous moment, is the adaptive weight factor, The value range of is [0.2, 0.8], and its value is determined according to the historical data of the component operation stability. For components with relatively stable operation, takes a value close to 0.2 to refer more to the historical average value. For components with large fluctuations, takes a value close to 0.8 and pays more attention to the current real-time value. By continuously calculating and comparing it with the preset dynamic threshold range , when or , it is determined that the component may have a fault, where , and are coefficients determined according to the safe operating range of the component, Generally, the value is in the range of 0.8 - 0.9, takes a value in the range of 1.1 - 1.2. This algorithm can adapt to the changes in the component operation status and improve the accuracy of fault detection.

[0008] Furthermore, the intelligent switch in the fault isolation module adopts a multi-redundancy control logic. Inside the intelligent switch, there is a main control circuit and at least two groups of standby control circuits. When receiving a fault isolation instruction, the main control circuit acts first, cutting off the circuit connection of the faulty component by controlling the relay. If the main control circuit fails, the standby control circuit 1 automatically detects the abnormality of the main control circuit within 50 milliseconds and immediately takes over the control, starting the standby relay for circuit cutting operation. If the standby control circuit 1 also fails, the standby control circuit 2 responds within the same 50 milliseconds and attempts to cut off the circuit of the faulty component again. Each control circuit has an independent power supply, and the power supplies use different voltage levels. The main control circuit uses a 24V DC power supply, the standby control circuit 1 uses a 12V DC power supply, and the standby control circuit 2 uses a 36V DC power supply to prevent the intelligent switch from malfunctioning due to power supply faults and ensure the reliability of the fault isolation operation.

[0009] Furthermore, when the standby module and the redundant line switching module access the standby module, they adopt a parameter adaptive adjustment algorithm based on fuzzy logic. First, a fuzzy rule base is established. The input parameters are the fault type and the current torch working condition parameters. For the fault type, different fuzzy subsets are set. Let the ignition electrode fault be , and the ignition controller fault be . For the exhaust gas flow rate, low flow rate , medium flow rate , and high flow rate fuzzy subsets are set. The same applies to pressure. The output parameter is the adjustment parameter of the standby module, the ignition energy of the standby ignition electrode, and the electrode position . The fuzzy rule for ignition energy adjustment: If the fault type is and the flow rate is and the pressure is , then the ignition energy is adjusted to . The output parameter value is calculated through fuzzy inference. The formula is , where is the weight of each fuzzy rule, determined based on a large amount of historical experimental data and expert experience, and is the ignition energy value output by each rule. This algorithm enables the standby module to quickly adapt to different fault scenarios and torch working conditions.

[0010] Furthermore, when analyzing the fault data, the fault diagnosis module uses an improved Bayesian network causal inference algorithm. First, a Bayesian network structure is constructed. The nodes include various fault causes (electrode wear , carbon deposition , loose circuit connection ) and fault phenomena (abnormal current 、 Abnormal temperature ), the connection probability between each node is obtained by statistically analyzing historical fault data. It indicates that the probability of current abnormality in case of electrode wear is 0.7. When the fault phenomenon (current abnormality ) is detected, the posterior probability of each fault cause is calculated through Bayes' formula . Among them, is the prior probability of the fault cause , which is obtained by statistically analyzing the historical fault frequency of this component during the operation of the system. By comparing the magnitudes of each posterior probability, the most likely fault cause is determined, improving the accuracy and efficiency of fault diagnosis.

[0011] Further, the wireless communication module adopts adaptive modulation and coding technology. During data transmission, according to the real-time monitored channel quality indicators (signal-to-noise ratio , bit error rate ), the modulation and coding method is dynamically adjusted. When and , a high-order modulation and coding method is adopted. When and , it is switched to 16-QAM modulation, and the coding rate is adjusted to . When and , a more robust QPSK modulation is adopted, and the coding rate is . , , , and other thresholds are determined according to actual communication environment tests. Due to different interference situations in different industrial scenarios, these thresholds will vary. Through this adaptive modulation and coding technology, it is ensured that the fault report can be stably and timely transmitted to maintenance personnel in a complex communication environment.

[0012] Further, the redundant sensors in the fault detection module adopt a cross-check fusion algorithm. For two redundant sensors of the same component, the collected data are respectively and . First, calculate the difference between the data of the two sensors. At the same time, calculate the change rates and of the data of the two sensors. The reliability of the sensor data is judged according to the difference and the change rate. and , it is considered that the data of the two sensors are reliable, and the weighted average method is used to fuse the data. The fused data , where and is the weight, determined according to the accuracy of the sensor and the stability of historical data , the weight of a sensor with high accuracy and good stability is higher. If the above conditions are not met, it is determined that one of the sensors may malfunction, and a sensor fault warning is issued in a timely manner to improve the accuracy of fault detection data.

[0013] Furthermore, when the standby module and the redundant line switching module perform redundant line switching, a line selection algorithm based on priority is adopted. The priority of each redundant line is set in advance according to the bandwidth, stability, and delay performance indicators of the line. The line with a larger bandwidth, higher stability, and lower delay has a higher priority. When the main communication line fails, the switching module first detects the status of each redundant line, and only the redundant lines in the normal working state participate in the selection. Then, it tries to switch to each redundant line in the order of priority. At the same time, the system monitors the communication quality of the switched line in real time. If the communication quality of the switched line does not meet the requirements, it searches for other available lines for switching again in the order of priority to ensure the stable transmission of remote control instructions.

[0014] Furthermore, the fault knowledge base in the fault diagnosis module adopts a dynamic update mechanism. When the system detects a new fault and completes the diagnosis, the new fault case is stored in the fault knowledge base. At the same time, the historical fault cases in the fault knowledge base are statistically analyzed regularly. For the fault cases that have not occurred again within a certain period of time, they are evaluated according to their occurrence frequency and the degree of impact on the system. If the occurrence frequency is extremely low and the impact on the system is small, they can be deleted from the fault knowledge base to reduce the scale of the knowledge base and improve the retrieval efficiency of fault diagnosis. For the frequently occurring fault cases, their fault causes and solutions are optimized and updated according to the new fault data. If it is found that a certain type of ignition electrode fault has a new fault cause under the new working conditions, the fault cause description and corresponding solution of this case in the fault knowledge base are updated in a timely manner, so that the fault knowledge base always maintains the effectiveness of the current system fault diagnosis.

[0015] Compared with the prior art, the remote ignition control system of the blowdown torch has the following beneficial effects: First, by integrating multiple modules such as fault detection, fault isolation, standby module and redundant line switching, fault diagnosis, and wireless communication, the invention realizes the comprehensive remote monitoring and management of the blowdown torch ignition control system. When the system detects a fault, it can quickly cut off the electrical connection of the faulty component, automatically switch the standby module or redundant line, and ensure the continuous and stable operation of the system. This feature significantly improves the reliability and safety of the system and reduces the risk of production interruption caused by faults.

[0016] II. By adopting advanced intelligent algorithms and technologies, such as the adaptive dynamic threshold monitoring algorithm and the improved Bayesian network causal inference algorithm, the invention can accurately determine the type and location of faults based on the operating status and historical data of components, providing strong support for the rapid repair of faults. In addition, the wireless communication module adopts adaptive modulation and coding technology to ensure that fault reports can be stably and timely transmitted to maintenance personnel in complex communication environments, further improving the efficiency and accuracy of fault handling. The application of these technologies makes the system have a high level of intelligence and practicality in the remote ignition control of the flare.

[0017] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be learned from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0019] Figure 1 It is a flowchart of the function implementation of a remote ignition control system for a flare. Figure 2 It is a flowchart of the overall architecture of a remote ignition control system for a flare. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention objective, the following will, in conjunction with the accompanying drawings and preferred embodiments, describe in detail the specific embodiments, structures, features, and their effects of the present invention as follows.

[0021] Embodiment 1 In the flare system of a large chemical industrial park, the fault detection module monitors the operating status of the ignition electrode in real time through the adaptive dynamic threshold monitoring algorithm. Suppose at a certain moment , the sensor collects the actual value of the ignition electrode (unit: ampere, used to represent current, this is an assumed key operating parameter), the moving average value at the previous moment , the adaptive weight factor , according to the formula , it can be calculated that , it is known that the coefficient determined according to the safe operating range of the ignition electrode , the preset dynamic threshold range is , , since is within the threshold range, and the system is operating normally at this time.

[0022] After a period of time, , recalculate , at this time , the fault detection module determines that there may be a fault with the ignition electrode and transmits the fault information to the fault isolation module. After receiving the instruction, the main control circuit (using a 24V DC power supply) of the intelligent switch of the fault isolation module immediately operates, cuts off the circuit connection of the ignition electrode by controlling the relay. If the main control circuit fails, the standby control circuit 1 (using a 12V DC power supply) automatically detects the abnormality within 50 milliseconds and takes over the control, starting the standby relay to cut off the circuit.

[0023] After receiving the fault information, the standby module and the redundant line switching module determine that the fault type is an ignition electrode fault , the current torch emission gas flow rate is low , and the pressure is low , according to the parameter adaptive adjustment algorithm based on fuzzy logic, find the corresponding rule in the fuzzy rule base. If the fault type is and the flow rate is and the pressure is , then the ignition energy is adjusted to , assuming there are 3 relevant fuzzy rules, and the weights of each fuzzy rule are , , the ignition energy values output by each rule are joules, joules, joules. According to the formula , it can be calculated that joules, and then the standby ignition electrode with the adjusted ignition energy is automatically connected to the system.

[0024] Network structure. Assuming that the detected fault phenomenon is abnormal ignition electrode current, through the Bayesian formula The fault diagnosis module uses the improved Bayesian network causal reasoning algorithm to diagnose the faulty ignition electrode, determines the most likely fault cause according to the posterior probability of each fault cause calculated from the Bayesian network based on historical fault data, such as electrode aging. Finally, the fault diagnosis module sends the fault report to the maintenance personnel through the wireless communication module. The wireless communication module uses adaptive modulation and coding technology. If the current channel quality index meets and , then the high-order modulation and coding method is used to quickly transmit the fault report.

[0025] Embodiment 2 The fault detection module adopts an adaptive dynamic threshold monitoring algorithm to monitor each component in the system, such as the ignition electrode, ignition controller, gas flow sensor, pressure sensor, etc. in real time. This algorithm continuously calculates the predicted value at the current moment and compares it with the preset dynamic threshold range to determine whether there may be a fault in the component. For example, for the gas flow sensor, if the predicted value of the collected data exceeds the threshold range, the system will initially determine that the sensor may be abnormal.

[0026] Once the fault detection module detects a fault in the ignition controller, the fault isolation module responds quickly. The intelligent switch in the fault isolation module adopts a multiple redundant control logic and has a main control circuit and two sets of standby control circuits. After receiving the fault isolation instruction, the main control circuit acts immediately and cuts off the circuit connection of the faulty ignition controller by controlling the relay to prevent the fault from expanding further. If the main control circuit itself fails, standby control circuit 1 will automatically detect the abnormality of the main control circuit within 50 milliseconds by virtue of its internal fault detection mechanism and immediately take over the control work to start the standby relay for circuit cutting operation. If standby control circuit 1 also fails, standby control circuit 2 will respond quickly within the same 50 milliseconds and try to cut off the circuit of the faulty ignition controller again to ensure that the electrical connection between the faulty component and other parts of the system is reliably cut off.

[0027] After the ignition controller fault is isolated, the standby module and the redundant line switching module start to work. On the one hand, an adaptive parameter adjustment algorithm based on fuzzy logic is adopted to adjust the parameters of the standby ignition controller according to the fault type (i.e., ignition controller fault) and the current torch operating conditions parameters (such as emission gas flow rate, pressure, etc.). For example, if the emission gas flow rate is in a low flow state and the pressure is also low, according to the fuzzy rule base, the system will automatically adjust the ignition energy and ignition time parameters of the standby ignition controller to ensure successful ignition under the current operating conditions. On the other hand, when performing redundant line switching, the system adopts a line selection algorithm based on priority. The priority of each redundant line is set in advance according to the bandwidth, stability, and delay performance indicators of the line. The line with a larger bandwidth, higher stability, and lower delay has a higher priority. When the main communication line fails, the switching module first comprehensively detects the status of each redundant line. Only the redundant lines in the normal working state will participate in the subsequent selection process. Then, the switching module tries to switch to each redundant line in the order of priority. During the switching process, the system will real-time monitor the communication quality of the switched line. If the communication quality of the switched line does not meet the requirements, such as signal loss, high bit error rate, etc., the system will search for other available lines for switching again in the order of priority to ensure that the remote control instructions can be transmitted stably and reliably without being interfered by the complex electromagnetic environment of the refinery.

[0028] After the faulty component of the ignition controller is isolated, the fault diagnosis module uses an improved Bayesian network causal reasoning algorithm to conduct a detailed diagnosis. By constructing a Bayesian network structure, various possible fault causes and fault phenomena are used as nodes, and the connection probabilities between the nodes are obtained according to the statistical analysis of historical fault data. When a fault phenomenon is detected, the posterior probabilities of each fault cause are calculated using Bayes' formula and by comparing the magnitudes of these posterior probabilities, the most likely fault cause is determined. The wireless communication module adopts adaptive modulation and coding technology. During data transmission, according to the real-time monitored channel quality indicators, such as signal-to-noise ratio (SNR) and bit error rate (BER), the modulation and coding method is dynamically adjusted. When and is satisfied, a high-order modulation and coding method is adopted to improve the data transmission rate; when and is satisfied, it switches to 16-QAM modulation and the coding rate is adjusted to . When and is satisfied, a more robust QPSK modulation is adopted and the coding rate is , ensuring that the fault report generated by the fault diagnosis module is accurately and timely sent to the maintenance personnel so that they can quickly take measures for repair and restore the normal operation of the system.

[0029] As described above, these are only the preferred embodiments of the present invention and do not impose any formal restrictions on the present invention. Although the present invention has been disclosed above in its preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can, without departing from the scope of the technical solution of the present invention, make some changes or modifications using the above-disclosed technical content to obtain equivalent embodiments of equivalent changes. However, as long as it does not depart from the content of the technical solution of the present invention, any brief modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A remote ignition control system for a venting flare, characterized in that, The system includes the following components: a fault detection module, a fault isolation module, a spare module and a redundant line switching module, a fault diagnosis module, and a wireless communication module; The fault detection module: is used to monitor the operating status of each component in the system in real time; The fault isolation module: is used to quickly cut off the electrical connection between the faulty component and other parts of the system when the fault detection module detects a component fault; The spare module and the redundant line switching module: are used to automatically connect the spare module or the redundant line to the system according to the fault type and system configuration after the faulty component is isolated; The fault diagnosis module: is used to perform a detailed diagnosis on the faulty component after it is isolated; The wireless communication module: is used to send the fault report generated by the fault diagnosis module to the maintenance personnel.

2. The remote ignition control system of a venting flare according to claim 1, wherein The intelligent algorithm adopted by the fault detection module is the adaptive dynamic threshold monitoring algorithm, and the algorithm formula is as follows: where F(t) is the predicted value at the current moment, and X(t) is the actual value collected by the sensor at the current moment. is the moving average value at the previous moment, α is the adaptive weight factor. By continuously calculating the comparison between F(t) and the preset dynamic threshold range [L(t), U(t)], when F(t) < L(t) or F(t) > U(t), it is determined that the component may have a fault, where β1 and β2 are coefficients determined according to the safe operating range of the component.

3. The remote ignition control system of a venting flare according to claim 1, characterized in that, The intelligent switch in the fault isolation module adopts a multi-redundant control logic. There is a main control circuit and at least two groups of spare control circuits inside the intelligent switch. When receiving a fault isolation instruction, the main control circuit acts first, and cuts off the circuit connection of the faulty component by controlling the relay. If the main control circuit fails, the spare control circuit 1 automatically detects the abnormality of the main control circuit within 50 milliseconds and immediately takes over the control, starting the spare relay to perform the circuit cutting operation. If the spare control circuit 1 also fails, the spare control circuit 2 responds within the same 50 milliseconds and tries to cut off the circuit of the faulty component again. Each control circuit has an independent power supply, and the power supplies adopt different voltage levels. The main control circuit uses a 24V DC power supply, the spare control circuit 1 uses a 12V DC power supply, and the spare control circuit 2 uses a 36V DC power supply to prevent the intelligent switch from malfunctioning due to power supply failure.

4. The remote ignition control system for a blowdown torch according to claim 1, characterized in that, When the standby module and the redundant line switching module access the standby module, a parameter adaptive adjustment algorithm based on fuzzy logic is adopted. First, a fuzzy rule base is established. The input parameters are the fault type and the current torch operating conditions parameters. For the fault type, different fuzzy subsets are set. Let the ignition electrode fault be F1 and the ignition controller fault be F2. For the exhaust gas flow rate, low flow rate Q L , medium flow rate Q M , and high flow rate Q H fuzzy subsets are set. The same applies to the pressure. The output parameter is the adjustment parameter of the standby module, the ignition energy E of the standby ignition electrode, and the electrode position S. The ignition energy adjustment fuzzy rule: If the fault type is F1 and the flow rate is Q L and the pressure is P L , then the ignition energy E is adjusted to E1. The output parameter value is calculated through fuzzy inference. The formula is where w i is the weight of each fuzzy rule, and E i is the ignition energy value output by each rule.

5. The remote ignition control system of a venting flare according to claim 1, characterized in that, When analyzing the fault data, the fault diagnosis module uses an improved Bayesian network causal reasoning algorithm. First, it constructs a Bayesian network structure, where the nodes include various fault causes and fault phenomena. The connection probabilities between the nodes are obtained based on the statistics of historical fault data. When a fault phenomenon is detected, the posterior probabilities of each fault cause are calculated through Bayes' formula where P(C j ) is the prior probability of the fault cause C j . By comparing the magnitudes of the posterior probabilities, the most likely fault cause is determined.

6. The remote ignition control system of a venting flare according to claim 1, wherein, The wireless communication module adopts an adaptive modulation and coding technology. During the data transmission process, it dynamically adjusts the modulation and coding method according to the real-time monitored channel quality indicators. When SNR>SNR1 and BER<BER1, a high-order modulation and coding method is adopted. When SNR2<SNR≤SNR1 and BER2<BER≤BER1, it switches to 16-QAM modulation and the coding rate is adjusted to R2. When SNR≤SNR2 and BER≤BER2, a more robust QPSK modulation is adopted and the coding rate is R3. These thresholds will vary in different industrial scenarios due to different interference situations.

7. The remote ignition control system for a venting flare according to claim 1, characterized in that, The redundant sensors in the fault detection module adopt the cross-check fusion algorithm. For two redundant sensors of the same component, the collected data are D1(t) and D2(t) respectively. First, calculate the difference between the two sensor data ΔD(t) = |D1(t) - D2(t)|, and at the same time calculate the change rates of the two sensor data and Judge the reliability of the sensor data according to the difference and the change rate. If ΔD(t) < δ1 and |R1(t) - R2(t)| < δ2, it is considered that the two sensor data are reliable, and the weighted average method is used to fuse the data. The fused data D(t) = ω1×D1(t) + ω2×D2(t), where ω1 and ω2 are weights. If the above conditions are not met, it is determined that one of the sensors may have a fault, and a sensor fault warning is issued in time.

8. The remote ignition control system of a venting flare according to claim 1, wherein When the spare module and the redundant line switching module perform redundant line switching, it adopts a line selection algorithm based on priority. The priority of each redundant line is set in advance according to the bandwidth, stability, and delay performance indicators of the line. The line with a larger bandwidth, higher stability, and lower delay has a higher priority. When the main communication line fails, the switching module first detects the status of each redundant line. Only the redundant lines in the normal working state participate in the selection. Then it tries to switch to each redundant line in order of priority. At the same time, the system monitors the communication quality of the switched line in real time. If the communication quality of the switched line does not meet the requirements, it will search for other available lines to switch again in order of priority to ensure the stable transmission of remote control instructions.

9. The remote ignition control system of a venting flare according to claim 1, wherein The fault knowledge base in the fault diagnosis module adopts a dynamic update mechanism. When the system detects a new fault and completes the diagnosis, the new fault case is stored in the fault knowledge base. At the same time, the historical fault cases in the fault knowledge base are statistically analyzed regularly. For the fault cases that have not occurred again within a certain period of time, they are evaluated according to their occurrence frequency and the degree of impact on the system. If the occurrence frequency is extremely low and the impact on the system is small, they can be deleted from the fault knowledge base. For the frequently occurring fault cases, their fault causes and solutions are optimized and updated according to the new fault data.

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