Circuit breaker state monitoring and self-diagnosis system

Abstract

The invention provides a circuit breaker state monitoring and self-diagnosis system, which relates to the technical field of power monitoring, and comprises a plurality of heterogeneous sensor modules including a temperature sensor, a current sensor, a pressure sensor, a vibration sensor and a voltage sensor, the sensor modules are installed at a plurality of key positions of the circuit breaker and used for collecting multi-dimensional operation data of equipment, and the heterogeneous sensor collaborative sensing unit is used for receiving and processing real-time data from the sensor modules and sending the real-time data to the circuit breaker. And the heterogeneous sensor collaborative sensing unit performs association analysis on data of different types of sensors through data fusion. According to the circuit breaker state monitoring and self-diagnosis system, fault prediction and trend analysis can be efficiently carried out by combining the grey prediction model GM (1, 1) and real-time sensor data. By performing accumulation generation and grey differential equation modeling on the key indexes, the scheme can predict the potential time of the equipment fault in advance and generate a trend analysis result.

Description

Technical Field

[0001] The present invention relates to the technical field of power monitoring, and in particular to a circuit breaker state monitoring and self-diagnosis system. Background Art

[0002] Currently, circuit breaker status monitoring in power systems primarily relies on traditional monitoring methods, including sensor monitoring, manual inspections, and regular maintenance. While these methods have ensured the normal operation of power equipment to a certain extent, they also have significant shortcomings and are unable to meet the demands of increasingly complex power systems and high-efficiency management. Sensor monitoring technology installs various sensors on circuit breakers, such as temperature, pressure, and current sensors, to collect real-time operational data from the equipment. These sensors detect basic operating parameters and monitor the equipment's status in real time through a data acquisition system. However, this approach typically only provides basic operational data and, due to limitations in the number and location of sensors, cannot fully detect all potential equipment faults. Furthermore, sensors may malfunction or fail, resulting in loss or inaccuracy of monitoring data, seriously impacting the accuracy of fault detection. Manual inspections rely on personnel to regularly check the appearance and operating status of circuit breaker equipment to ensure its proper operation. While manual inspections can detect external anomalies, such as aging, damage, or other obvious signs of faults, to a certain extent, they also have significant limitations. Manual inspections typically require regular testing, but the operating environment of power equipment can be complex and unpredictable. Manual inspections are limited in frequency and cannot provide 24 / 7 monitoring. Due to negligence or operational errors, potential faults may be missed during inspections, resulting in equipment problems not being discovered in time, which in turn affects the stability and safety of the power system.

[0003] Traditional monitoring technologies rely primarily on manual inspections or scheduled maintenance, failing to provide 24 / 7 real-time monitoring. Potential equipment failures cannot be detected promptly, and fault diagnosis methods rely on manual experience and regular testing, lacking intelligent support. This results in low diagnostic accuracy. Faults are difficult to detect using traditional methods, making it easy to miss the optimal repair opportunity. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the present invention provides a circuit breaker status monitoring and self-diagnosis system. The technical problem to be solved by the invention is: how to improve the safety and reliability of the power system through fault warning and emergency response mechanisms.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A circuit breaker status monitoring and self-diagnosis system, comprising:

[0006] Multiple heterogeneous sensor modules, including temperature sensors, current sensors, pressure sensors, vibration sensors, and voltage sensors, are installed at multiple key locations on the circuit breaker to collect multi-dimensional operating data of the device;

[0007] A heterogeneous sensor collaborative sensing unit is used to receive and process real-time data from various sensor modules. The heterogeneous sensor collaborative sensing unit performs correlation analysis on data from different types of sensors through data fusion to obtain standardized data to capture characteristic information of various potential faults.

[0008] A fault diagnosis and analysis unit, which performs fault pattern recognition, fault prediction, and trend analysis based on the fused data from the heterogeneous sensor collaborative sensing unit using a grey prediction model, automatically diagnoses the equipment status, and predicts potential faults;

[0009] A fault warning and response unit automatically issues a fault warning and executes corresponding emergency response measures based on the diagnosis results of the fault diagnosis and analysis unit;

[0010] The data transmission and storage unit is responsible for transmitting the data of the sensor module, fault diagnosis and analysis results, and early warning information to the remote monitoring platform, and storing and managing the relevant data.

[0011] Preferably, the output data of the multiple heterogeneous sensor modules are digitized signals, and are transmitted wirelessly to the heterogeneous sensor collaborative sensing unit for processing.

[0012] Preferably, the data fusion comprises the following steps:

[0013] S3.1. Preprocessing the real-time data, including denoising, standardization, and normalization, to eliminate interference factors in the sensor data and unify the data scale;

[0014] S3.2. Perform weighted averaging on the preprocessed data to fuse different types of data and generate a comprehensive device health index.

[0015] S3.3. Based on the equipment health index, a fault judgment is made through a set threshold. If the equipment health index exceeds the predetermined threshold, a fault alarm signal is triggered to indicate equipment failure.

[0016] Preferably, the grey prediction model is performed by the following steps:

[0017] S4.1. Filter the standardized data to identify key indicators related to the fault. These key indicators are used to reflect the current health status of the equipment and serve as input data for subsequent predictive analysis.

[0018] S4.2. Use the key indicators as input to a grey prediction model GM(1,1) for modeling. The grey prediction model uses a cumulative generation method to construct a cumulative data sequence. The accumulated data sequence is used to establish a grey differential equation for trend analysis, and a trend analysis result is generated.

[0019] S4.3. Based on the trend analysis results, generate an equipment health prediction report and perform fault diagnosis. By comparing the prediction results with the set fault threshold, if the equipment health index is lower than the set threshold or the predicted fault time is approaching, a fault alarm signal is triggered, indicating that the equipment may be in a fault state or close to a fault state, and guiding maintenance personnel to intervene in a timely manner.

[0020] Preferably, the specific steps of the trend analysis are as follows:

[0021] S4.2.1 Accumulate the normalized data x0(k) to obtain an accumulated sequence x1(k);

[0022] S4.2.2 Use the least squares method to fit the grey differential equation and estimate the development coefficient a and background value b;

[0023] S4.2.3 Based on the estimated parameters a and b, perform prediction calculations at future time points to obtain the predicted value of the equipment's operating status. This step can analyze future state changes of the equipment and calculate the potential time and evolution path of the failure.

[0024] Preferably, the accumulation sequence is specifically:

[0025]

[0026] Among them, x1(k) is the accumulated data, x0(k) is the original data, and n is the length of the data sequence;

[0027] The grey differential equation is in the form of:

[0028]

[0029] Among them, a is the development coefficient, which represents the rate of change of the data, and b is the background value, which represents the normal fluctuation of the system. The equation is solved by the least squares method to obtain the appropriate values ​​of a and b.

[0030] Preferably, the data transmission and storage unit includes: a real-time data transmission module, which uses wireless communication technology to transmit data to the remote monitoring platform in real time, ensuring that the remote platform can obtain the operating status and fault information of the circuit breaker in real time.

[0031] The data encryption module is used to encrypt the data from the sensor module, fault diagnosis and analysis results, and early warning information to ensure the security of the data during transmission.

[0032] The data storage module includes local storage and cloud storage. Local storage is used to temporarily store data and diagnostic results from various sensors, and cloud storage is used for long-term storage and backup of all device data, fault analysis reports, and early warning records.

[0033] Preferably, the fault warning and response unit uses an adaptive threshold adjustment algorithm to dynamically adjust the fault warning threshold of the device health index, and the adaptive threshold adjustment algorithm is calculated based on the following parameters:

[0034] T a =T b +k1·Δ(avg)+k2·std(x)

[0035] Among them, T a is the warning threshold after dynamic adjustment, T b is the basic health index threshold of the device, Δ(avg) is the average change in the health index of the device in the most recent time window, std(x) is the standard deviation of the device's historical operating data, and k1 and k2 are constants adjusted according to the device's operating characteristics.

[0036] The present invention provides a circuit breaker status monitoring and self-diagnosis system. It has the following beneficial effects:

[0037] This circuit breaker condition monitoring and self-diagnosis system combines the gray prediction model GM(1,1) with real-time sensor data to efficiently perform fault prediction and trend analysis. By accumulating key indicators and modeling them using gray differential equations, the solution can predict the potential time of equipment failure and generate trend analysis results. This helps maintenance personnel understand the health status of equipment in real time and take timely maintenance measures, effectively reducing downtime and repair costs caused by sudden failures.

[0038] This technical solution introduces dynamic adjustment and intelligent response mechanisms, enabling the system to automatically adjust warning thresholds based on real-time changes in the device's operating status and trigger appropriate fault warnings and emergency response measures based on the predicted results. The adaptive threshold adjustment algorithm dynamically optimizes fault diagnosis thresholds based on the device's historical operating data and current status, ensuring more accurate warnings. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic diagram of a structure for realizing the invention;

[0040] Figure 2 The present invention is a flowchart for implementing the present invention. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] Example 1

[0043] like Figure 1-2 As shown, an embodiment of the present invention provides a circuit breaker state monitoring and self-diagnosis system, including multiple heterogeneous sensor modules, which include temperature sensors, current sensors, pressure sensors, vibration sensors and voltage sensors. The sensor modules are installed at multiple key positions of the circuit breaker to collect multi-dimensional operating data of the equipment. The output data of the multiple heterogeneous sensor modules are digitized signals and are transmitted wirelessly to the heterogeneous sensor collaborative perception unit for processing.

[0044] The heterogeneous sensor collaborative perception unit is used to receive and process real-time data from various sensor modules. It uses data fusion to correlate and analyze data from different types of sensors to obtain standardized data to capture characteristic information of various potential faults. Data fusion includes the following steps:

[0045] S3.1. Preprocess the real-time data, including denoising, standardization, and normalization, to eliminate interference factors in the sensor data and unify the data scale.

[0046] S3.2. Perform weighted averaging on the preprocessed data to fuse different types of data and generate a comprehensive equipment health index.

[0047] S3.3. Fault judgment is performed based on the device health index using the set threshold. If the device health index exceeds the predetermined threshold, a fault alarm signal is triggered, indicating a device fault.

[0048] Assume that the standardized data collected in real time is:

[0049] Temperature sensor data: [50,52,55,58,60]

[0050] Current sensor data: [10, 15, 20, 25, 30]

[0051] Vibration sensor data: [0.02, 0.03, 0.04, 0.05, 0.06]

[0052] After denoising, standardization, and normalization, the following standardized data is obtained:

[0053] Temperature sensor: [0.0, 0.1, 0.3, 0.4, 0.5]

[0054] Current sensor: [-1.41, -0.71, 0.0, 0.71, 1.41]

[0055] Vibration sensor: [0.0, 0.2, 0.4, 0.6, 0.8]

[0056] Assume the weights are set as:

[0057] Temperature sensor: weight 0.4

[0058] Current sensor: weight 0.3

[0059] Vibration sensor: weight 0.3

[0060] Calculate the device health index:

[0061] DHI=0.4·0.3+0.3·0.0+0.3·0.4=0.12+0.12=0.24.

[0062] The fault judgment threshold is set to T t =0.5. Since the device health index DHI = 0.24 is lower than the threshold, the system triggers a fault alarm, indicating that the device needs maintenance.

[0063] The fault diagnosis and analysis unit, based on the fused data from the heterogeneous sensor collaborative perception unit, uses the grey prediction model to perform fault pattern recognition, fault prediction, and trend analysis, automatically diagnosing the equipment status and predicting potential faults. The grey prediction model is implemented through the following steps:

[0064] S4.1. Filter out key indicators related to faults from the standardized data. These key indicators are used to reflect the current health status of the equipment and serve as input data for subsequent predictive analysis.

[0065] S4.2. Use the key indicators as input to the grey prediction model GM(1,1) for modeling. The grey prediction model uses the cumulative generation method to construct the cumulative data sequence. The accumulated data sequence establishes the grey differential equation for trend analysis and generates the trend analysis results. The specific steps of trend analysis are as follows:

[0066] S4.2.1 performs accumulation processing on the standardized data x0(k) to obtain an accumulated sequence x1(k).

[0067] S4.2.2 Use the least squares method to fit the grey differential equation and estimate the development coefficient a and background value b. The cumulative sequence is as follows:

[0068]

[0069] Among them, x1(k) is the accumulated data, x0(k) is the original data, and n is the length of the data sequence.

[0070] S4.2.3 Based on the estimated parameters a and b, perform prediction calculations at future time points to obtain the predicted value of the equipment's operating status. This step can analyze the future state changes of the equipment and calculate the potential time and evolution path of the fault. The grey differential equation form is:

[0071]

[0072] Among them, a is the development coefficient, which represents the rate of change of the data, and b is the background value, which represents the normal fluctuation of the system. The equation is solved by the least squares method to obtain the appropriate values ​​of a and b.

[0073] S4.3. Based on the trend analysis results, generate an equipment health prediction report and perform fault diagnosis. By comparing the prediction results with the set fault threshold, if the equipment health index is lower than the set threshold or the predicted fault time is approaching, a fault alarm signal is triggered, indicating that the equipment may be in a fault state or close to a fault state, and guiding maintenance personnel to intervene in a timely manner.

[0074] The fault warning and response unit automatically issues fault warnings and executes corresponding emergency response measures based on the diagnosis results of the fault diagnosis and analysis unit. The fault warning and response unit uses an adaptive threshold adjustment algorithm to dynamically adjust the fault warning threshold of the equipment health index. The adaptive threshold adjustment algorithm is calculated based on the following parameters:

[0075] T a =T b +k1·Δ(avg)+k2·std(x)

[0076] Among them, T a is the warning threshold after dynamic adjustment, T b is the basic health index threshold of the device, Δ(avg) is the average change in the health index of the device in the most recent time window, std(x) is the standard deviation of the device's historical operating data, and k1 and k2 are constants adjusted according to the device's operating characteristics.

[0077] The following are the detailed implementation steps of the algorithm, including how to calculate the dynamically adjusted warning threshold, and are illustrated with specific data.

[0078] Step 1: Input data preparation

[0079] Table 1: Standardized data collected by sensors

[0080] Time point Temperature (℃) Current (A) Voltage (V) Health Index t1 50 15 380 75 t2 52 16 381 76 t3 55 16.5 380.5 77 t4 58 17 380.8 78 t5 60 18 381 80

[0081] The health index is an indicator of the device's health. The warning threshold is adjusted based on the health index.

[0082] Step 2: Calculate the average change in health index

[0083] First, you need to calculate the average change in the device's health index over the most recent period (Δavg). To calculate the health index, select the data from the last four time points:

[0084] Calculate the change in health index:

[0085] ΔDHI1=76-75=1

[0086] ΔDHI2=77-76=1

[0087] ΔDHI3=78-77=1

[0088] ΔDHI4=80-78=2

[0089] Calculate the average change (Δavg):

[0090]

[0091] Step 3: Calculate the standard deviation of the equipment's historical operating data

[0092] Calculate the standard deviation of the device health index. Calculate the average value of the health index (μ D ):

[0093]

[0094] Then, calculate the square of the difference of each data point from the mean:

[0095] (75-77.2) 2 =(-2.2) 2 =4.84

[0096] (76-77.2) 2 =(-1.2) 2 =1.44

[0097] (77-77.2) 2 =(-0.2) 2 =0.04

[0098] (78-77.2) 2 =(0.8) 2 =0.64

[0099] (80-77.2)2 =(2.8) 2 =7.84

[0100] Find the average of the squared differences:

[0101]

[0102] Finally, calculate the standard deviation:

[0103]

[0104] Step 4: Calculate the dynamically adjusted warning threshold

[0105] The average change value (Δavg=1.25) and standard deviation (s(D)=1.6) of the health index calculated according to the above steps, as well as the basic health index threshold (T b =80), we can calculate the dynamically adjusted warning threshold (T a ).

[0106] The formula is:

[0107] T a =T b +k1·Δavg+k2·s(D)

[0108] Assuming constants k1 = 0.5 and k2 = 0.3, then:

[0109] T a =80+0.5·1.25+0.3·1.6

[0110] T a =80+0.625+0.48=81.105

[0111] Therefore, the dynamically adjusted warning threshold is 81.105.

[0112] Step 5: Fault warning and alarm triggering

[0113] If the device health index is lower than the dynamically adjusted threshold (T a =81.105), the system will trigger a fault warning. For example, if the health index forecast at the next time point is 79 (below the threshold of 81.105), the system will automatically issue a fault alarm signal and prompt maintenance personnel to take necessary intervention. The steps of the adaptive threshold adjustment algorithm are demonstrated, including how to calculate the average change value and standard deviation based on the changes in the device health index and how to dynamically adjust the warning threshold. This method can make intelligent adjustments in real time based on the operating status of the device, thereby improving the accuracy of fault warnings and reducing false positives or negatives caused by improper threshold settings.

[0114] The data transmission and storage unit is responsible for transmitting the sensor module data, fault diagnosis and analysis results, and early warning information to the remote monitoring platform, and storing and managing the relevant data. The data transmission and storage unit includes: a real-time data transmission module that uses wireless communication technology to transmit data to the remote monitoring platform in real time, ensuring that the remote platform can obtain the operating status and fault information of the circuit breaker in real time.

[0115] The data encryption module is used to encrypt the data from the sensor module, fault diagnosis and analysis results, and early warning information to ensure the security of the data during transmission.

[0116] The data storage module includes local storage and cloud storage. Local storage is used to temporarily store data and diagnostic results from various sensors, and cloud storage is used for long-term storage and backup of all device data, fault analysis reports, and early warning records.

[0117] Example 2

[0118] Unlike Example 1, this example describes in detail how to perform fault pattern identification, fault prediction, and trend analysis based on the gray prediction model GM(1,1). The following is a detailed description of this step, including the specific process of screening key indicators, establishing a gray prediction model, performing trend analysis, generating a health report, and triggering a fault alarm.

[0119] S4.1 Screening of key indicators related to failures

[0120] Before performing fault pattern identification and trend analysis, it's first necessary to filter out key indicators related to failures from the standardized data. These key indicators are typically high-impact factors in sensor data and can reflect the operational health of the equipment. Common key indicators include temperature, current, voltage, vibration, and pressure, which are closely related to equipment failure risk and operational efficiency.

[0121] Table 2: Data collected by the sensor

[0122] Time point Temperature (℃) Current (A) Voltage (V) Vibration (g) t1 50 15 380 0.02 t2 52 16 381 0.03 t3 55 16.5 380.5 0.04 t4 58 17 380.8 0.05 t5 60 18 381 0.06

[0123] S4.2 Input key indicators into the grey prediction model

[0124] S4.2.1 Accumulative generation method:

[0125] In this step, we first use the cumulative generation method to accumulate the filtered key indicators. For each key indicator, the cumulative sequence is constructed as follows:

[0126] Accumulate the temperature data to generate:

[0127] x1(1)=x0(1)=50

[0128] x1(2)=x0(1)+x0(2)=50+52=102

[0129] x1(3)=x0(1)+x0(2)+x0(3)=50+52+55=157

[0130] x1(4)=x0(1)+x0(2)+x0(3)+x0(4)=50+52+55+58=215

[0131] x1(5)=x0(1)+x0(2)+x0(3)+x0(4)+x0(5)=50+52+55+58+60=275

[0132] The key indicators of current, voltage and vibration are accumulated in the same way to obtain the corresponding cumulative data series.

[0133] S4.2.2 Least squares method to fit grey differential equation:

[0134] The grey differential equation is fitted using the least squares method to estimate the development coefficient a and background value b.

[0135] Assume that the cumulative series of device temperature has been obtained:

[0136] x1=[50,102,157,215,275]

[0137] Use Gray's differential equations:

[0138]

[0139] Calculate a and b using the least squares method. The least squares formula can be obtained by solving the following linear equations:

[0140] A·B=Y

[0141] Matrix A and vector Y are constructed by accumulating data. By solving for a and b, we can determine the failure trend of the equipment.

[0142] Least squares parameter calculation based on cumulative sequence:

[0143] 1. Definition of cumulative amount

[0144] For N groups of samples {(x k ,y k )}, and add them up in sequence to get:

[0145]

[0146] 2. Constructing normal equations

[0147] Fill in the above cumulative amount directly into:

[0148]

[0149] We get the linear equations:

[0150]

[0151] 3. Solution

[0152] Use any standard algorithm such as Gaussian elimination, matrix inversion, or Cramer's rule to find the coefficients a and b:

[0153]

[0154] S4.2.3 Make future predictions

[0155] Using the parameters a and b of the grey differential equation, we can perform predictions at future time points. In practice, the development coefficient a = 0.3 and the background value b = 4.0 to predict the device temperature at the next time point. The prediction formula is:

[0156]

[0157] The above calculations generate prediction results, which can then be used to deduce the time and trend of equipment failure. For example, if the equipment temperature exceeds a set threshold (such as 75°C) at a certain time in the future, the system will trigger a fault warning, alerting maintenance personnel that the equipment may fail.

[0158] S4.3 Generate equipment health prediction report and trigger fault alarm

[0159] Based on the forecast results and trend analysis, the system generates a device health prediction report and compares it with the set fault threshold. For example, if the set device temperature fault threshold is 70°C, if the forecast results indicate that the temperature will reach or exceed this threshold at some point in the future, the system will automatically trigger a fault alarm.

[0160] For example, if the device temperature is predicted to reach 75°C at the next time point, exceeding the safety threshold, the system will perform the following operations:

[0161] Generate a health prediction report that includes the current status of the equipment, predicted failure value, warning time, and recommended maintenance or shutdown operations.

[0162] A fault alarm signal is triggered, which is sent to maintenance personnel through the communication module and may be notified by SMS, email, or mobile phone APP notification.

[0163] Timely intervention provides maintenance personnel with detailed fault diagnosis reports and recommended actions, such as adjusting equipment parameters or arranging repairs.

[0164] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A circuit breaker status monitoring and self-diagnosis system, characterized in that: include: Multiple heterogeneous sensor modules, including temperature sensors, current sensors, pressure sensors, vibration sensors, and voltage sensors; A heterogeneous sensor collaborative sensing unit is used to receive and process real-time data from various sensor modules. The heterogeneous sensor collaborative sensing unit performs correlation analysis on data from different types of sensors through data fusion to obtain standardized data. A fault diagnosis and analysis unit, which performs fault pattern recognition, fault prediction, and trend analysis based on the fused data from the heterogeneous sensor collaborative sensing unit using a grey prediction model, automatically diagnoses the equipment status, and predicts potential faults; A fault warning and response unit automatically issues a fault warning and executes corresponding emergency response measures based on the diagnosis results of the fault diagnosis and analysis unit; The data transmission and storage unit is responsible for transmitting the data of the sensor module, fault diagnosis and analysis results, and early warning information to the remote monitoring platform, and storing and managing the relevant data.

2. A circuit breaker status monitoring and self-diagnosis system according to claim 1, characterized in that: The output data of the multiple heterogeneous sensor modules are digitized signals and are transmitted wirelessly to the heterogeneous sensor collaborative sensing unit for processing.

3. The circuit breaker status monitoring and self-diagnosis system according to claim 1, characterized in that: The data fusion comprises the following steps: S3.

1. Preprocessing the real-time data, including denoising, standardization, and normalization; S3.

2. Perform a weighted average on the preprocessed data to generate a comprehensive equipment health index; S3.

3. Based on the equipment health index, a fault judgment is made through a set threshold. If the equipment health index exceeds the predetermined threshold, a fault alarm signal is triggered to indicate equipment failure.

4. A circuit breaker status monitoring and self-diagnosis system according to claim 1, characterized in that: The grey prediction model is carried out by the following steps: S4.

1. Filter key indicators related to the fault from the standardized data; S4.

2. Use the key indicators as input to a grey prediction model GM(1,1) for modeling. The grey prediction model uses a cumulative generation method to construct a cumulative data sequence. The accumulated data sequence is used to establish a grey differential equation for trend analysis, and a trend analysis result is generated. S4.

3. Generate equipment health prediction reports and perform fault diagnosis based on trend analysis results.

5. A circuit breaker status monitoring and self-diagnosis system according to claim 4, characterized in that: The specific steps of the trend analysis are as follows: S4.2.1 Accumulate the normalized data x0(k) to obtain an accumulated sequence x1(k); S4.2.2 Use the least squares method to fit the grey differential equation and estimate the development coefficient a and background value b; S4.2.3 Based on the estimated parameters a and b, perform prediction calculations at future time points to obtain the predicted value of the equipment operating status.

6. A circuit breaker status monitoring and self-diagnosis system according to claim 5, characterized in that: The accumulation sequence is specifically: Among them, x1(k) is the accumulated data, x0(k) is the original data, and n is the length of the data sequence; The grey differential equation is in the form of: Among them, a is the development coefficient, which represents the rate of change of the data, and b is the background value, which represents the normal fluctuation of the system.

7. The circuit breaker status monitoring and self-diagnosis system according to claim 1, characterized in that: The data transmission and storage unit includes: a real-time data transmission module, a data encryption module and a data storage module.

8. The circuit breaker status monitoring and self-diagnosis system according to claim 1, characterized in that: The fault warning and response unit adopts an adaptive threshold adjustment algorithm to dynamically adjust the fault warning threshold of the equipment health index.

Images