Radar servo control system fault prediction and diagnosis method

Abstract

The invention belongs to the technical field of radars, and particularly relates to a radar servo control system fault prediction and diagnosis method. By analyzing composition characteristics, working principles and working states of an electromechanical hybrid device or a complex electronic system in a servo control system, considering factors such as maintenance strategies, use modes, maintenance levels and replacement methods of the products, and combining historical operation conditions, fault data statistical analysis and the like, the method and the system can be used for solving the problems in the prior art. And reasonable use and maintenance suggestions are given for a product guarantee decision.

Description

Technical Field

[0001] The invention belongs to the field of radar technology, and particularly relates to a method for fault prediction and diagnosis of a radar servo control system. Background Art

[0002] The radar servo control system is an important part of the radar, mainly completing functions such as automatic leveling of the antenna vehicle, automatic installation and removal of the antenna array, and driving the antenna azimuth rotation. Compared with other subsystems of the radar, the failure of the servo control system will have a direct and harmful impact on the safety and usability of the radar, and may even cause the loss of radar detection performance or maneuverability.

[0003] The servo control system integrates mechanical, electrical and hydraulic technologies. Once a failure occurs, due to its complex structure and difficult disassembly and assembly, it is easy to have problems such as long maintenance time and difficulty. In response to the above problems:

[0004] (1) Compared with electronic components, the failure modes of electromechanical equipment, such as wear, fatigue, and aging, all have a process of developing from potential failure to functional failure. When potential failures are discovered, preventive maintenance can be performed to reduce the impact on radar safety and usability.

[0005] (2) For failures caused by failure modes other than wear, fatigue, and aging, corrective repairs can be performed after the functional failure is discovered and the fault point is accurately located, thus reducing troubleshooting time and improving system maintainability.

[0006] Therefore, the servo control system can monitor system failures by combining the detection of potential failures and functional failures, complete the prediction of failures or accurately locate the fault points, realize system preventive maintenance or corrective maintenance, reduce the impact of servo control system failures on the radar, and meet the radar safety and usability requirements. Summary of the invention

[0007] In view of the shortcomings of the prior art, the present invention proposes a method for predicting and diagnosing faults of a radar servo control system. The radar fault prediction of the present invention analyzes the composition characteristics, working principle and working status of the electromechanical hybrid device or complex electronic system in the servo control system, and takes into account the maintenance strategy, usage, maintenance level and replacement method of such products, combined with historical operation conditions and statistical analysis of fault data, to provide reasonable use and maintenance suggestions for product assurance decisions.

[0008] The technical solution of the present invention is: a method for predicting and diagnosing faults of a radar servo control system, characterized in that it comprises the following steps:

[0009] Step 1: Fault prediction through measurement data;

[0010] Step 1.1 Motor fault prediction method:

[0011] The aging of insulation structure will lead to the decline of motor performance, and the aging of insulation structure can be effectively predicted;

[0012] Step 1.2 Implementation method of fault prediction

[0013] Step 2: Troubleshooting

[0014] Step 2.1: Establish the fault tree model of radar servo control system

[0015] The fault tree model of the radar servo control system is divided into three functional faults: "the antenna cannot rotate normally", "the antenna cannot be erected and withdrawn normally" and "the leveling system does not work properly". With "the servo control system does not work properly" as the top event, the fault tree is established by deductive method, and the fault tree cause event analysis is hierarchical;

[0016] Step 2.2: After a fault occurs, isolate the faults according to "the antenna cannot rotate normally", "the antenna cannot be erected or withdrawn normally" and "the leveling system does not work properly", and handle the three faults separately;

[0017] 2.2.1 "Antenna cannot rotate normally" fault location

[0018] 2.2.2 "Antenna cannot be erected or removed normally" fault location

[0019] 2.3.3 "Leveling system does not work properly" fault location

[0020] In steps 2.2.1, 2.2.2 and 2.2.3, the fault point is determined and located through the correlation model between the functional failure mode of each fault event component unit and the test point;

[0021] Step 2.3, determine the fault level according to the fault point;

[0022] Step 2.4, fault compensation measures: According to the degree of fault hazard and the mutual influence on system functions, compensation measures are taken for serious faults with high probability of occurrence and high fault level.

[0023] According to the method for fault prediction and diagnosis of a radar servo control system as described above, it is characterized in that: in step 1.1, the following methods are used to predict: 1) the minimum allowable value of the hot insulation resistance of the winding

[0024]

[0025] Among them: U N ━Rated voltage of motor winding; P N ━ Rated power of motor;

[0026] 2) Winding insulation resistance steady-state coefficient

[0027]

[0028] Where: R tn ━After the motor has been running for a long time, the cold insulation resistance of the winding at a temperature of t℃;

[0029] R to ━The cold insulation resistance of the winding at a temperature of t℃ when the motor is newly purchased;

[0030] R co ━The minimum allowable value of cold insulation resistance of qualified motor windings;

[0031]

[0032] Where: Tr━winding temperature;

[0033] 3) Winding hot insulation resistance: When the winding temperature is not lower than 75°C, measure the hot insulation resistance at this time.

[0034] According to the method for fault prediction and diagnosis of a radar servo control system as described above, it is characterized in that: the implementation method of step 1.2 fault prediction includes: data acquisition, collecting motor winding temperature, cold insulation resistance, and hot insulation resistance data; motor insulation performance evaluation; health assessment.

[0035] According to the method for fault prediction and diagnosis of a radar servo control system as described above, it is characterized in that: step 2.3, the fault level is divided into four levels: alarm fault, minor fault, critical fault and fatal fault.

[0036] According to the method for fault prediction and diagnosis of a radar servo control system as described above, it is characterized in that: in step 2.4, a backup switching and automatic / manual mode switching compensation measure are set for the PCC fault; backup switching compensation measures are set for the azimuth servo driver, azimuth motor, electromagnetic clutch, and azimuth reading mechanism; and component replacement compensation measures are set for the leveling servo driver.

[0037] The beneficial effects of the present invention are: first, it realizes the fault prediction of the radar servo system, warns of potential faults in advance, and reduces the impact of faults on radar performance; second, it improves the fault diagnosis efficiency and accuracy of the radar servo system, and timely discovers and locates faults through real-time detection and data analysis; third, it reduces the maintenance cost of the radar servo system, and reduces the number of manual inspections and repairs through intelligent diagnosis and prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 : Fault tree model of radar servo control system.

[0039] Figure 2: “Antenna cannot rotate normally” fault correlation model.

[0040] Figure 3 : “Antenna cannot be installed or removed normally” fault correlation model.

[0041] Figure 4 : “Leveling system not working properly” fault correlation model. DETAILED DESCRIPTION

[0042] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0043] like Figures 1 to 4 As shown, a method for predicting and diagnosing faults of a radar servo control system of the present invention comprises the following steps:

[0044] Step 1: Predict faults by measuring data.

[0045] The radar servo control system is an important part of the radar. Its failure will cause the radar to not work properly, which will affect the radar's detection performance and accuracy. Through fault prediction, appropriate preventive measures can be taken before the fault occurs to avoid or reduce the impact of the fault on the radar system and improve the reliability and stability of the overall radar system. The servo control system fault prediction objects are mainly important components or units that affect the completion of subsystem tasks. The specific situation is shown in Table 1.

[0046] Table 1 List of servo control system fault prediction conditions

[0047]

[0048] Step 1.1 Motor fault prediction method

[0049] The insulation structure is an important component of all kinds of electrical equipment, and its performance directly affects the safety, reliability and service life of electrical equipment. The insulation performance of the motor will gradually deteriorate after long-term operation. The aging of the insulation structure is a comprehensive representation of various degradations. Therefore, by monitoring the insulation performance of the motor, a qualitative analysis of the motor's service life can be performed to estimate the remaining life of the motor.

[0050] The insulation performance estimation method mainly estimates the motor life by analyzing the data of the winding insulation resistance steady-state coefficient and the winding thermal insulation resistance. The specific judgment basis is shown in Table 2.

[0051] Table 2 Motor remaining life estimation criteria

[0052]

[0053] Note: Rn in the table is the minimum allowable value of hot insulation resistance of qualified motor windings specified in national standards.

[0054] The relevant parameters involved in the above method are introduced one by one as follows:

[0055] 1) Minimum allowable value of winding hot insulation resistance

[0056]

[0057] According to the national standard GB755, the minimum allowable value of the hot insulation resistance of qualified motor windings (when the winding temperature is 75°C) is:

[0058] Among them: U N ━Rated voltage of motor winding, V;

[0059] P N ━Motor rated power, Kw.

[0060] 2) Winding insulation resistance steady-state coefficient

[0061]

[0062] The steady-state coefficient of motor winding insulation resistance is:

[0063] Where: R tn ━After the motor has been running for a long time, the cold insulation resistance of the winding at a temperature of t℃, MΩ;

[0064] R to ━The cold insulation resistance of the winding at t℃ when the motor is newly purchased, MΩ;

[0065] R co ━Minimum allowable value of cold insulation resistance of qualified motor windings, MΩ;

[0066]

[0067] Where: Tr━winding temperature, °C.

[0068] 3) Winding hot insulation resistance

[0069] For motors that have been running for a long time, when the winding temperature is not lower than 75°C, measure the hot insulation resistance at this time, which is Rt.

[0070] Step 1.2 Implementation method of fault prediction

[0071] The servo control system needs to implement functions such as data collection, motor insulation performance evaluation, and health assessment to estimate the life of the motor used.

[0072] 1) Data collection

[0073] The data to be collected and the collection methods in the above method are shown in Table 3.

[0074] Table 3 Data collection status

[0075] Serial number data Collection method 1 Motor winding temperature Motor drivers provide 2 Cold insulation resistance Manual measurement 3 Hot insulation resistance Manual measurement

[0076] The remaining data are calculated or measured during system design.

[0077] 2) Motor insulation performance evaluation

[0078] After the data is manually measured and injected into the health management system, the winding insulation performance level (five levels including "good", "qualified", "slightly deteriorated", "severely deteriorated", and "insulation damaged") is determined by analyzing the relevant data as shown in Table 2.

[0079] 3) Health Assessment

[0080] The motor winding insulation performance level corresponding to the radar health management system health assessment level is shown in Table 4.

[0081] Table 4 Evaluation level comparison table

[0082]

[0083] Step 2: Troubleshooting

[0084] Step 2.1: Establish the fault tree model of radar servo control system

[0085] Fault diagnosis of radar servo control system is a key link to ensure the normal operation of radar system. When a fault occurs, the fault diagnosis and isolation should be carried out first, and then the fault level should be determined, and corresponding compensation measures should be taken according to different fault types and levels. Figure 1 As shown in the figure, there are three functional faults: "the antenna cannot rotate normally", "the antenna cannot be erected and withdrawn normally" and "the leveling system does not work properly". "The servo control system does not work properly" is determined as the top event, and the fault tree is established by deduction. According to the design principles of the radar health management system, the fault tree cause event analysis level is up to LRU and LRC.

[0086] Step 2.2: After a fault occurs, isolate the faults according to "the antenna cannot rotate normally", "the antenna cannot be erected or withdrawn normally" and "the leveling system does not work properly", and handle the three faults separately;

[0087] Fault diagnosis and isolation for servo control systems can quickly and accurately identify the source of faults and reduce the impact of faults on the overall operation of the system. This system uses the correlation matrix reasoning method to isolate faults, and the depth of fault isolation covers LRU and safety-critical components.

[0088] According to the above Figure 1The fault event situation in the system is analyzed, and the correlation model and correlation matrix of the functional failure mode and test point of each fault event component unit are established. The fault correlation model is an important tool for analyzing the mutual influence relationship between components in the system, and can realize the quantitative analysis and prediction of the fault state of the servo control system. The use of correlation matrix reasoning diagnosis method for fault isolation can improve the fault isolation rate, reduce the false alarm rate, and improve the diagnostic accuracy.

[0089] 2.2.1 "Antenna cannot rotate normally" fault location

[0090] The fault correlation model of "antenna cannot rotate normally" can be found in Figure 2 . Among them, "limit sensor failure" or "azimuth reading mechanism failure" will lead to "PCC failure"; "PCC failure" will lead to "azimuth servo driver failure"; "azimuth servo driver failure" will lead to "azimuth servo motor failure"; "azimuth servo motor failure" will lead to "electromagnetic clutch failure"; "electromagnetic clutch failure" will lead to "reducer failure"; "reducer failure" will lead to "slewing support failure".

[0091] In steps 2.2.1, 2.2.2, and 2.2.3, the correlation model between the functional failure mode of each fault event component unit and the test point, combined with the fault prediction information, can effectively and quickly locate the fault point, providing reasonable background requirements for subsequent optimization of system performance and implementation of compensation measures.

[0092] The fault correlation matrix of "the antenna cannot rotate normally" is shown in Table 5. The fault correlation matrix can more clearly and accurately show the correlation between the functional failure mode of each fault event component unit and the monitoring point, and investigate the causes of the fault phenomenon in the complex servo control system. Among them, Ti in the correlation matrix is ​​the performance of each monitoring point, and the correlation 0 / 1 indicates the change of the monitoring point from normal to fault, 0 means no effect, and 1 means effect. According to the Ti value, the specific fault information can be inferred to determine which LRU has a fault.

[0093] Table 5 “Antenna cannot rotate normally” fault correlation matrix

[0094]

[0095] 2.2.2 "Antenna cannot be erected or removed normally" fault location

[0096] The fault correlation model and matrix of "the antenna cannot be normally installed and removed" can be found in Figure 3and Table 6. Among them, "limit sensor failure" or "inclinometer failure" will lead to "PCC failure"; "PCC failure" will lead to "low-frequency merge ring failure"; "low-frequency merge ring failure" will lead to "hydraulic station failure"; "hydraulic station failure" will lead to "electromagnetic reversing valve failure"; "electromagnetic reversing valve failure" will lead to "hydraulic cylinder failure" and "hydraulic motor failure".

[0097] Table 6 "Antenna cannot be erected or removed normally" fault correlation matrix

[0098]

[0099]

[0100] 2.3.3 "Leveling system does not work properly" fault location

[0101] The fault correlation model and matrix of "leveling system does not work properly" can be found in Figure 4 And Table 7. Among them, "limit sensor failure" or "limit sensor failure" will cause "PCC failure"; "PCC failure" will cause "leveling servo drive failure"; "leveling servo drive failure" will cause "leveling servo motor failure"; "leveling servo motor failure" will cause "reducer failure"; "reducer failure" will cause "leveling leg structure failure".

[0102] Table 7 "Leveling system does not work properly" fault correlation matrix

[0103]

[0104] Step 2.3: Determine the fault level

[0105] For the servo control system, the specific fault point is isolated through fault diagnosis, and then the fault is divided into different levels according to the impact of the fault on the overall radar system, providing priority guidance for fault handling, ensuring that key faults are responded to and handled in a timely manner, and resources are allocated reasonably.

[0106] The faults of this radar servo control system are divided into four levels: alarm fault, minor fault, critical fault and fatal fault.

[0107] 1) Alarm fault (Level IV): Fault causes alarm (such as temperature alarm, protection lock);

[0108] 2) Minor fault (Level III): Fault that does not affect radar performance;

[0109] 3) Critical failure (Level II): Failure that affects radar performance and will spread;

[0110] 4) Fatal failure (Level I): A failure that causes the radar to not work properly or personal safety.

[0111] According to the servo control system status monitoring and fault diagnosis isolation, a judgment matrix is ​​formed according to the system function classification. The judgment matrix is ​​listed in Table 8.

[0112] Table 8 Fault level determination matrix

[0113]

[0114] 1) PCC

[0115] The servo control adopts dual-master PCC, which has only two states: normal and fault. There is a manual working bypass in case of full fault. One fault is judged as an alarm and two faults are judged as critical.

[0116] 2) Servo drive

[0117] The servo drive uses dual drivers and has only two states: normal and fault. In case of fault, there is bypass backup switching. One fault is judged as an alarm and two faults are judged as fatal.

[0118] 3) Azimuth motor

[0119] The azimuth drive uses dual motors and has only two states: normal and fault. In case of fault, there is bypass backup switching. One fault is judged as an alarm and two faults are judged as fatal.

[0120] 4) Electromagnetic clutch

[0121] The azimuth dual drive is controlled by electromagnetic clutch, and has only two states: normal and fault. In case of fault, there is bypass backup switching. One fault is judged as an alarm, and two faults are judged as fatal.

[0122] 5) Azimuth reading mechanism

[0123] The azimuth signal adopts a dual reading mechanism, with only two states: normal and fault. In case of fault, there is bypass backup switching. One fault is judged as an alarm, and two faults are judged as fatal.

[0124] 6) Limit sensor

[0125] The motion position signal adopts dual detection of software and hardware, and has dual insurance of software and hardware in case of failure, and the failure is judged as secondary.

[0126] 7) Hydraulic station

[0127] The hydraulic station has only two states: normal and faulty. In the event of a fault, the array cannot be unfolded or folded, and the fault is considered fatal.

[0128] 8) Hydraulic cylinder

[0129] The antenna lifting adopts dual hydraulic cylinder follow-up, and there are only two states: normal and faulty. One fault is judged as critical, and two faults are judged as fatal.

[0130] 9) Hydraulic motor

[0131] The hydraulic motor has only two states: normal and faulty. When faulty, the array rotation function cannot be completed and the fault is judged as fatal.

[0132] 10) Solenoid reversing valve

[0133] The solenoid reversing valve has only two states: normal and faulty. In case of fault, it can be manually bypassed and is judged as critical.

[0134] 11) Inclinometer

[0135] The inclinometer has only two states: normal and faulty. It can be manually bypassed when faulty and is judged as critical when faulty.

[0136] 12) Leveling the servo drive

[0137] The leveling servo drive has only two states: normal and faulty. It can be manually bypassed in case of fault and is judged as critical in case of fault.

[0138] 13) Leveling servo motor

[0139] Step 2.4: Fault compensation measures

[0140] According to the degree of fault hazard and the mutual impact on system functions, the radar has designed corresponding compensation measures for serious faults with high probability of occurrence and high fault level to improve mission reliability and availability.

[0141] After the servo control system diagnoses and isolates the fault and determines the level, the compensation measures are prompted to the user through the human-machine interface. The prompt content is generally a functional state change or operation steps. This article mainly introduces the compensation measures for LRU and safety-critical components, as shown in Table 9.

[0142] Table 9 Main compensation measures for faults

[0143]

[0144] The radar fault diagnosis of the present invention has a clear information transmission path and causal relationship, and reflects the main working characteristics of the radar by improving the status monitoring information. At the same time, the fault diagnosis model and the fault expert knowledge base are used to detect and isolate the fault, classify different faults, and give corresponding diagnosis results.

Claims

1. A method for fault prediction and diagnosis of a radar servo control system, characterized in that: The following steps are involved: Step 1: Fault prediction through measurement data; Step 1.1 Motor fault prediction method: The aging of insulation structure will lead to the decline of motor performance, and the aging of insulation structure can be effectively predicted; Step 1.2 Implementation method of fault prediction Step 2: Troubleshooting Step 2.1: Establish the fault tree model of radar servo control system The fault tree model of the radar servo control system is divided into three functional faults: "the antenna cannot rotate normally", "the antenna cannot be erected and withdrawn normally" and "the leveling system does not work properly". With "the servo control system does not work properly" as the top event, the fault tree is established by deductive method, and the fault tree cause event analysis is hierarchical; Step 2.2: After a fault occurs, isolate the faults according to "the antenna cannot rotate normally", "the antenna cannot be erected or withdrawn normally" and "the leveling system does not work properly", and handle the three faults separately; 2.2.1 "Antenna cannot rotate normally" fault location The fault correlation matrix of "antenna cannot rotate normally" is established as follows: 2.2.2 "Antenna cannot be erected or removed normally" fault location The fault correlation matrix of "the antenna cannot be erected or removed normally" is established as follows: 2.3.3 "Leveling system does not work properly" fault location The fault correlation matrix of "leveling system does not work properly" is established as follows: In steps 2.2.1, 2.2.2 and 2.2.3, the fault point is determined and located through the correlation model between the functional failure mode of each fault event component unit and the test point; Step 2.3, determine the fault level according to the fault point; Step 2.4, fault compensation measures: According to the degree of fault hazard and the mutual influence on system functions, compensation measures are taken for serious faults with high probability of occurrence and high fault level.

2. A method for fault prediction and diagnosis of a radar servo control system according to claim 1, characterized in that: Step 1.1 uses the following methods to predict: 1) the minimum allowable value of the hot insulation resistance of the winding Among them: U N ━Rated voltage of motor winding; P N ━ Rated power of motor; 2) Winding insulation resistance steady-state coefficient Where: R tn ━After the motor has been running for a long time, the cold insulation resistance of the winding at a temperature of t℃; R to ━The cold insulation resistance of the winding at a temperature of t℃ when the motor is newly purchased; R co ━The minimum allowable value of cold insulation resistance of qualified motor windings; Where: Tr━winding temperature; 3) Winding hot insulation resistance: When the winding temperature is not lower than 75°C, measure the hot insulation resistance at this time.

3. The method for fault prediction and diagnosis of a radar servo control system according to claim 1, characterized in that: Step 1.2: The implementation method of fault prediction includes: data collection, collecting motor winding temperature, cold insulation resistance, and hot insulation resistance data; motor insulation performance evaluation; and health evaluation.

4. The method for fault prediction and diagnosis of a radar servo control system according to claim 1, characterized in that: Step 2.3: The fault levels are divided into four levels: alarm fault, minor fault, critical fault and fatal fault.

5. The method for fault prediction and diagnosis of a radar servo control system according to claim 1, characterized in that: In step 2.4, PCC fault settings include backup switching and automatic / manual mode switching compensation measures; azimuth servo drive, azimuth motor, electromagnetic clutch, and azimuth reading mechanism settings include backup switching compensation measures; and leveling servo drive settings include component replacement compensation measures.

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