Electrical equipment fault diagnosis early warning system

Abstract

The invention provides an electrical equipment fault diagnosis early warning system, and relates to the technical field of equipment fault diagnosis, and the system comprises a multi-sensor module which is used for obtaining electrical, thermal, mechanical and electromagnetic parameters of electrical equipment; the acquisition processing module is used for summarizing the acquired various parameters and carrying out preprocessing and feature extraction; the fault diagnosis module is used for analyzing indexes of electrical characteristics, thermal characteristics, mechanical characteristics and electromagnetic characteristics of the electrical equipment, fusing the indexes based on the type of the equipment and diagnosing the fault condition of the equipment; the early warning module is used for generating a fault trend image of the electrical equipment based on the fusion result; the man-machine interaction module is used for generating a corresponding signal based on the fault condition and the trend image; through the reasonable design and the processing mode based on the equipment working type, the method has good adaptability and universality to different electrical equipment and complex working conditions, overcomes the limitation of the prior art, improves the diagnosis accuracy, and provides time for maintenance personnel to avoid fault deterioration.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment fault diagnosis, and in particular to an electrical equipment fault diagnosis and early warning system. Background Art

[0002] With the rapid development of modern industry, electrical equipment has been widely used in various fields; the reliability and safety of electrical equipment are crucial to the continuity and stability of industrial production; early fault diagnosis mainly relied on manual regular inspections and empirical judgments, which were inefficient and difficult to guarantee accuracy; with the development of sensor technology, sensor-based fault diagnosis methods have gradually emerged; these methods can obtain some operating parameters of electrical equipment in real time, such as temperature, vibration, etc.; gradually, multi-sensor diagnostic technology has emerged; it uses information obtained by multiple sensors to perform more comprehensive diagnosis of electrical equipment;

[0003] However, most existing technologies still analyze each type of data separately, lack a fusion mechanism, and are unable to mine parameter associations, which limits the accuracy and comprehensiveness of diagnosis. They are also poorly adaptable to dynamic changes and complex working conditions, and are prone to misjudgment and missed judgments. They also have poor versatility. Models built for specific equipment or working conditions have reduced accuracy when applied to different situations, and need to be retrained.

[0004] Therefore, how to provide an electrical equipment fault diagnosis and early warning system that can comprehensively and accurately diagnose faults and effectively warn, and that has versatility and adaptability to complex working conditions has become a problem that technical personnel in this field are eager to solve. Summary of the invention

[0005] The present invention provides an electrical equipment fault diagnosis and early warning system to solve the above problems.

[0006] The present invention provides an electrical equipment fault diagnosis and early warning system, comprising:

[0007] A multi-sensor module is installed on the electrical equipment to obtain the electrical, thermal, mechanical and electromagnetic parameters of the electrical equipment;

[0008] An acquisition and processing module, which is connected to the multi-sensor module and is used to summarize various acquired parameters and perform preprocessing and feature extraction;

[0009] A fault diagnosis module, which is connected to the acquisition and processing module, analyzes the indexes of the electrical equipment in terms of electrical characteristics, thermal characteristics, mechanical characteristics and electromagnetic characteristics based on the processed parameters, fuses the indexes based on the working type of the electrical equipment, and diagnoses the equipment fault based on the fusion result;

[0010] An early warning module, which is connected to the fault diagnosis module and generates a fault trend image of the electrical equipment based on the fusion results at each moment;

[0011] The human-computer interaction module is connected to the fault diagnosis module and the early warning module, and generates corresponding alarm signals and early warning signals based on the equipment fault conditions and fault trend images obtained by diagnosis, so as to remind the user to check and handle them.

[0012] According to an electrical equipment fault diagnosis and early warning system provided by the present invention, the multi-sensor module comprises:

[0013] Electrical sensing units, which are used to measure the current, voltage and power of electrical equipment in real time;

[0014] A temperature sensing unit, which is used to monitor the surface temperature of the electrical equipment and the ambient temperature in real time;

[0015] A vibration sensing unit, which is used to measure the vibration acceleration, vibration velocity and resonance frequency of the electrical equipment in real time;

[0016] An electromagnetic sensing unit, which is used to monitor the magnetic induction intensity and magnetic field intensity around the electrical equipment in real time;

[0017] A timing unit is connected to the electrical sensing unit, the temperature sensing unit, the vibration sensing unit and the electromagnetic sensing unit, and is used to record the time when each sensing unit detects the parameter.

[0018] According to an electrical equipment fault diagnosis and early warning system provided by the present invention, the acquisition and processing module includes:

[0019] A data aggregation unit, which is connected to the electrical sensing unit, the temperature sensing unit, the vibration sensing unit, the electromagnetic sensing unit and the timing unit, and is used to aggregate various parameters detected, sort them according to the detection time recorded by the timing unit, and insert a corresponding timestamp;

[0020] A filtering unit connected to the data summarizing unit and configured to filter the summarized parameters to remove noise interference;

[0021] A feature extraction unit is connected to the filtering unit and is used to classify the filtered summary parameters according to electrical, thermal, mechanical and electromagnetic categories, and extract effective feature information at the same time; the effective feature information is data that can directly participate in index analysis.

[0022] According to an electrical equipment fault diagnosis and early warning system provided by the present invention, the effective characteristic information includes:

[0023] Current current value, current voltage value, current power value, rated current value, rated voltage value, rated power value;

[0024] Current device surface temperature, current ambient temperature, and temperature change value within a preset period;

[0025] The root mean square value of the current vibration acceleration, the root mean square value of the current vibration velocity, and the resonant frequency offset obtained based on spectrum analysis;

[0026] Current magnetic induction intensity deviation, current magnetic field intensity deviation, and magnetic induction intensity change value within a preset period.

[0027] According to an electrical equipment fault diagnosis and early warning system provided by the present invention, the fault diagnosis module comprises:

[0028] An index calculation unit, connected to the feature extraction unit, calculates the electrical characteristic index, thermal characteristic index, mechanical characteristic index and electromagnetic characteristic index of the current electrical equipment based on the effective feature information;

[0029] The calculation formula of the electrical characteristic index is:

[0030] ECI=(I a -I 0 ) 2 +(V a -V 0 ) 2 +(P a -P 0 ) 2

[0031] Among them, ECI is the electrical characteristics index, I a is the current value, V a is the current voltage value, P a is the current power value, I 0 is the rated current value, V 0 is the rated voltage value, P 0 is the rated power value; the electrical characteristic index ECI is used to reflect the degree of deviation of current, voltage and power, and to highlight the influence of the deviation of any parameter on the overall index;

[0032] The calculation formula of the thermal characteristic index is:

[0033]

[0034] Where TCI is the thermal characteristic index, T cur is the current device surface temperature, T amb is the current ambient temperature; is the temperature change rate, where dT is the temperature change value within the preset period, and dt is the time corresponding to the preset period; k 1 is the thermal conductivity coefficient, which is set based on the actual material conditions and environmental conditions of the electrical equipment; the thermal characteristic index TCI is used to reflect the degree of heat generation of the electrical equipment relative to the environment;

[0035] The calculation formula of the mechanical property index is:

[0036]

[0037] Among them, MCI is the mechanical property index, A rms is the root mean square value of the current vibration acceleration, V rms is the root mean square value of the current vibration velocity, f reson is the resonance frequency offset; the mechanical characteristic index MCI is used to reflect the potential fault conditions caused by the mechanical operation state of the electrical equipment;

[0038] The calculation formula of the electromagnetic characteristic index is:

[0039]

[0040] Among them, EMCI is the electromagnetic characteristic index, B dev is the current magnetic induction intensity deviation, H dev is the current magnetic field strength deviation; is the rate of change of magnetic induction intensity, where dB is the change value of magnetic induction intensity within a preset period, and dt is the time corresponding to the preset period; k 2 The electromagnetic induction coefficient is set based on the electromagnetic characteristics of the electrical equipment and environmental factors. The electromagnetic characteristic index EMCI is used to reflect the status of the electromagnetic field around the electrical equipment and potential electromagnetic faults.

[0041] a fusion unit connected to the index calculation unit and used to fuse the characteristic indexes according to the working type of the electrical equipment to obtain a comprehensive fault index;

[0042] The diagnosis unit is connected to the fusion unit and is used to pre-set a fault threshold, compare the comprehensive fault index with the fault threshold, and judge that the current electrical equipment is in a fault state if the comprehensive fault index is greater than the fault threshold.

[0043] According to an electrical equipment fault diagnosis and early warning system provided by the present invention, in the fusion unit, each characteristic index is fused according to the working type of the electrical equipment to obtain a fusion model of the comprehensive fault index:

[0044] CFI=ω 1 *ECI+ω 2 *TCI+ω 3*MCI+ω 4 *EMCI

[0045] Wherein, CFI is a comprehensive fault index, ECI, TCI, MCI, and EMCI are respectively an electrical characteristic index, a thermal characteristic index, a mechanical characteristic index, and an electromagnetic characteristic index obtained by the index calculation unit; ω 1 ,ω 2 ,ω 3 ,ω 4 are the weights corresponding to each index, which are pre-set based on the working type of the electrical equipment, and the weight ω 1 +ω 2 +ω 3 +ω 4 =1.

[0046] According to an electrical equipment fault diagnosis and early warning system provided by the present invention, the early warning module comprises:

[0047] The image generation module is connected to the diagnosis unit and is used to summarize the historical comprehensive fault index of the electrical equipment and draw a line chart of time-comprehensive fault index as a fault trend image.

[0048] According to an electrical equipment fault diagnosis and early warning system provided by the present invention, the human-computer interaction module comprises:

[0049] an alarm triggering unit connected to the diagnosis unit and configured to trigger an alarm signal when the comprehensive fault index CFI is greater than a fault threshold;

[0050] An early warning trigger unit, connected to the image generation module, is used to analyze the slope of the broken line from the current moment to the previous preset period in the fault trend image, and trigger an early warning signal when the slope exceeds a preset broken line threshold;

[0051] A display unit is connected to the alarm trigger unit and the early warning trigger unit, and displays corresponding warning content based on the alarm signal or the early warning signal to remind personnel to handle it.

[0052] Compared with the prior art, the present invention has the following beneficial effects:

[0053] It can obtain the electrical, thermal, mechanical and electromagnetic parameters of electrical equipment, realize all-round monitoring of the equipment operation status, and overcome the limitations of existing technologies;

[0054] It can summarize and filter various parameters acquired to remove noise interference and extract effective feature information, providing a good data basis for accurate diagnosis;

[0055] It can generate fault trend images of electrical equipment based on the fusion results at each moment, analyze the slope of the broken line according to the fault trend image, and trigger an early warning signal when the slope exceeds the preset threshold, thereby achieving effective prediction and early warning of fault trends, providing maintenance personnel with enough time to take measures to avoid further deterioration of equipment failures;

[0056] Through reasonable module design and processing methods based on the working types of electrical equipment, it has good adaptability and versatility to different types of electrical equipment and complex working conditions.

[0057] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0058] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0060] Figure 1 It is a structural diagram of an electrical equipment fault diagnosis and early warning system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0062] Embodiment 1:

[0063] This embodiment provides an electrical equipment fault diagnosis and early warning system. Figure 1 ,include:

[0064] A multi-sensor module is installed on the electrical equipment to obtain the electrical, thermal, mechanical and electromagnetic parameters of the electrical equipment;

[0065] The acquisition and processing module is connected to the multi-sensor module and is used to summarize the various parameters obtained and perform preprocessing and feature extraction;

[0066] A fault diagnosis module is connected to the acquisition and processing module, and analyzes the indexes of the electrical equipment in terms of electrical characteristics, thermal characteristics, mechanical characteristics, and electromagnetic characteristics based on the processed parameters, and fuses the indexes based on the working type of the electrical equipment, and diagnoses the equipment fault based on the fusion results;

[0067] An early warning module is connected to the fault diagnosis module and generates a fault trend image of the electrical equipment based on the fusion results at each moment;

[0068] The human-computer interaction module is connected to the fault diagnosis module and the early warning module, and generates corresponding alarm signals and early warning signals based on the equipment fault conditions and fault trend images obtained by diagnosis, reminding users to check and handle them.

[0069] The principle of the above embodiment is: the multi-sensor module obtains the multi-dimensional parameters of the electrical equipment in electrical, thermal, mechanical and electromagnetic aspects in real time through various types of sensors; the acquisition and processing module summarizes these parameters, and after filtering to remove noise interference, classifies them according to categories and extracts effective feature information; the fault diagnosis module calculates the electrical, thermal, mechanical and electromagnetic characteristic indexes based on these effective feature information, and then fuses each characteristic index into a comprehensive fault index according to the set weight according to the working type of the electrical equipment, and diagnoses the equipment fault by comparing it with the pre-set fault threshold; the early warning module draws a fault trend image based on the fusion result to intuitively present the development trend of the equipment fault; the human-computer interaction module triggers the corresponding alarm signal and early warning signal based on the diagnosis result and trend image, prompting the user to pay attention to and deal with the equipment problem;

[0070] The beneficial effects of the above embodiments are as follows: the multi-sensor module can obtain various parameters of the electrical equipment, overcome the problem of limited information of a single sensor, and realize comprehensive monitoring of the operating status of the electrical equipment; the acquisition and processing module's fine processing and feature extraction of data, and the fault diagnosis module's exponential fusion based on the working type of the electrical equipment, improve the accuracy of fault diagnosis and reduce the possibility of misjudgment and missed judgment; the fault trend image generated by the early warning module can enable maintenance personnel to understand the development trend of equipment failure in advance, and the early warning signal issued in time by the human-computer interaction module provides maintenance personnel with sufficient time to take measures to avoid further deterioration of equipment failure, thereby improving the safety and reliability of equipment operation; the fault diagnosis module of the system has good adaptability and versatility to different types of electrical equipment and complex working conditions through exponential fusion based on the working type of electrical equipment and effective processing of data by the acquisition and processing module.

[0071] In order to further optimize the above embodiment, the multi-sensor module includes:

[0072] Electrical sensing units, which are used to measure the current, voltage and power of electrical equipment in real time;

[0073] A temperature sensing unit, which is used to monitor the surface temperature of the electrical equipment and the ambient temperature in real time;

[0074] A vibration sensing unit, which is used to measure the vibration acceleration, vibration velocity and resonance frequency of the electrical equipment in real time;

[0075] An electromagnetic sensing unit, which is used to monitor the magnetic induction intensity and magnetic field intensity around the electrical equipment in real time;

[0076] The timing unit is connected with the electrical sensing unit, the temperature sensing unit, the vibration sensing unit and the electromagnetic sensing unit, and is used to record the time when each sensing unit detects the parameter.

[0077] It should be noted that current, voltage and power are key electrical parameters for the operation of electrical equipment. By measuring these parameters in real time, the electrical working status of electrical equipment can be directly understood. For example, abnormal changes in current may mean that there is a short circuit or overload in the circuit, voltage fluctuations may affect the normal operation of the equipment, and power changes reflect the energy consumption of the equipment and whether it is within the normal working power range.

[0078] The surface temperature of the equipment and the ambient temperature are crucial to determine the thermal performance of electrical equipment. Excessive surface temperature of the equipment may be caused by poor internal heat dissipation, circuit overload, etc., which may damage the components of the equipment. At the same time, the ambient temperature will also affect the heat dissipation effect of the equipment. If the ambient temperature is too high, it will increase the difficulty of heat dissipation of the equipment, further affecting the normal operation of the equipment.

[0079] Vibration acceleration, vibration velocity and resonant frequency are important indicators of the mechanical performance of electrical equipment. Excessive vibration acceleration may indicate that the mechanical structure of the equipment is loose or worn. Abnormal vibration velocity may be related to unbalanced operation of the equipment. Changes in resonant frequency may be due to changes in the structure of the equipment, such as damage or deformation of components. These may lead to equipment failure.

[0080] The monitoring of magnetic induction intensity and magnetic field intensity can reflect the status of the electromagnetic field around electrical equipment; the abnormality of the electromagnetic field may be due to the failure of electromagnetic components inside the equipment, such as damage to components such as transformers and inductors, or due to interference from external electromagnetic fields; such electromagnetic abnormality may affect the electromagnetic compatibility of the equipment and cause equipment failure;

[0081] The timing unit records the time when each sensor unit detects the parameters, which is very important for subsequent data processing and analysis; it can ensure the accuracy of the time sequence of the data, making it easier for the acquisition and processing module to aggregate, filter and extract features from the data in time sequence; at the same time, time information is also indispensable in analyzing the development of equipment failures. For example, the changing trend of equipment parameters can be observed by comparing data at different time points.

[0082] In order to further optimize the above embodiment, the acquisition processing module includes:

[0083] A data aggregation unit, which is connected to the electrical sensing unit, the temperature sensing unit, the vibration sensing unit, the electromagnetic sensing unit and the timing unit, is used to aggregate various parameters detected, sort them according to the detection time recorded by the timing unit, and insert a corresponding timestamp;

[0084] A filtering unit connected to the data summarizing unit is used to filter the summarized parameters to remove noise interference;

[0085] The feature extraction unit is connected to the filtering unit and is used to classify the filtered summary parameters according to electrical, thermal, mechanical and electromagnetic categories, and extract effective feature information; the effective feature information is data that can directly participate in index analysis; the effective feature information includes:

[0086] Current current value, current voltage value, current power value, rated current value, rated voltage value, rated power value;

[0087] Current device surface temperature, current ambient temperature, and temperature change value within a preset period;

[0088] The root mean square value of the current vibration acceleration, the root mean square value of the current vibration velocity, and the resonant frequency offset obtained based on spectrum analysis;

[0089] Current magnetic induction intensity deviation, current magnetic field intensity deviation, and magnetic induction intensity change value within a preset period.

[0090] It should be noted that the various sensing units of the multi-sensor module work simultaneously, which will generate a large amount of different types of parameter data; the data aggregation unit collects the current, voltage, and power data of the electrical sensing unit, the equipment surface temperature and ambient temperature data of the temperature sensing unit, the vibration acceleration, vibration velocity, and resonance frequency data of the vibration sensing unit, and the magnetic induction intensity and magnetic field intensity data of the electromagnetic sensing unit; sorts the data according to the time recorded by the timing unit and inserts a timestamp, so that the data are arranged in order in the time dimension;

[0091] The data obtained by the sensor will inevitably be interfered by noise; the filtering unit filters the aggregated parameters to remove noise and improve the accuracy and reliability of the data; for example, the voltage measured by the electrical sensor unit may be interfered by the surrounding electromagnetic environment and have slight fluctuations. The filtering unit can remove these interference fluctuations to make the obtained voltage value closer to the true value, thereby providing a more accurate data basis for subsequent analysis and diagnosis;

[0092] The filtered summary parameters are classified into electrical, thermal, mechanical and electromagnetic categories to better organize the data for analysis of different characteristics, specifically:

[0093] Electrical category: Current current value, current voltage value, current power value, and rated current value, rated voltage value, and rated power value are important characteristic information. By comparing the current value with the rated value, the degree of deviation of the electrical characteristics of the electrical equipment can be calculated. For example, the deviation of the current may indicate a problem in the circuit, the voltage deviation may affect the normal operation of the equipment, and the power deviation reflects whether the working status of the equipment is normal.

[0094] Thermal category: The current device surface temperature, the current ambient temperature, and the temperature change value within the preset period are key feature information; the difference between the device surface temperature and the ambient temperature and the temperature change rate can reflect the heat dissipation of the device and whether there is a risk of overheating. For example, if the device surface temperature is too high, it may be caused by poor internal heat dissipation or circuit overload.

[0095] Mechanical category: The root mean square value of the current vibration acceleration, the root mean square value of the current vibration velocity, and the resonant frequency offset obtained based on spectrum analysis are important characteristic information; the root mean square value of the vibration acceleration and vibration velocity can reflect the vibration intensity of the equipment, and the resonant frequency offset can indicate whether the mechanical structure of the equipment has changed, such as loose parts and wear, which may lead to equipment failure;

[0096] Electromagnetic category: The current magnetic induction intensity deviation, the current magnetic field intensity deviation, and the magnetic induction intensity change value within the preset period are key feature information; the deviation and change rate of the magnetic induction intensity and magnetic field intensity can reflect the condition of the electromagnetic field around the device. The abnormality of the electromagnetic field may be due to the failure of the internal electromagnetic components of the device or external electromagnetic field interference, thus affecting the normal operation of the device.

[0097] In order to further optimize the above embodiment, the fault diagnosis module includes:

[0098] An index calculation unit, which is connected to the feature extraction unit and calculates the electrical characteristic index, thermal characteristic index, mechanical characteristic index and electromagnetic characteristic index of the current electrical equipment based on the effective feature information;

[0099] The calculation formula of the electrical characteristic index is:

[0100] ECI=(I a -I 0 ) 2 +(V a -V 0 ) 2 +(P a -P 0 ) 2

[0101] Among them, ECI is the electrical characteristics index, I a is the current value, V a is the current voltage value, P a is the current power value, I 0 is the rated current value, V 0 is the rated voltage value, P 0 is the rated power value; the electrical characteristic index ECI is used to reflect the degree of deviation of current, voltage and power, and to highlight the influence of the deviation of any parameter on the overall index;

[0102] The calculation formula of thermal characteristic index is:

[0103]

[0104] Where TCI is the thermal characteristic index, T cur is the current device surface temperature, T amb is the current ambient temperature; is the temperature change rate, where dT is the temperature change value within the preset period, and dt is the time corresponding to the preset period; k 1 It is the thermal conductivity coefficient, which is set based on the actual material conditions and environmental conditions of the electrical equipment. The thermal characteristic index TCI is used to reflect the degree of heat generation of the electrical equipment relative to the environment.

[0105] The calculation formula of mechanical property index is:

[0106]

[0107] Among them, MCI is the mechanical property index, A rms is the root mean square value of the current vibration acceleration, V rms is the root mean square value of the current vibration velocity, f reson is the resonant frequency offset; the mechanical characteristic index MCI is used to reflect the potential fault conditions caused by the mechanical operation state of the electrical equipment;

[0108] The calculation formula of electromagnetic characteristic index is:

[0109]

[0110] Among them, EMCI is the electromagnetic characteristic index, B dev is the current magnetic induction intensity deviation, H dev is the current magnetic field strength deviation; is the rate of change of magnetic induction intensity, where dB is the change value of magnetic induction intensity within a preset period, and dt is the time corresponding to the preset period; k 2 It is the electromagnetic induction coefficient, which is set based on the electromagnetic characteristics of electrical equipment and environmental factors. The electromagnetic characteristic index EMCI is used to reflect the status of the electromagnetic field around electrical equipment and potential electromagnetic faults.

[0111] A fusion unit connected to the index calculation unit is used to fuse the characteristic indexes according to the working type of the electrical equipment to obtain a comprehensive fault index;

[0112] The diagnosis unit is connected to the fusion unit and is used to pre-set a fault threshold, compare the comprehensive fault index with the fault threshold, and if the comprehensive fault index is greater than the fault threshold, it is determined that the current electrical equipment is in a fault state.

[0113] It should be noted that the calculation formula of the electrical characteristic index comprehensively considers the deviations of the current current value, voltage value and power value from the rated value; by squaring and summing these deviations, the influence of the deviation of any parameter on the overall index can be highlighted; for example, if the current deviation is large, then when calculating the electrical characteristic index, the square term of the current deviation will significantly increase the index value, thus reflecting that there may be electrical problems with the electrical equipment;

[0114] The calculation formula of the thermal characteristic index takes into account the difference between the current device surface temperature and the ambient temperature, as well as the temperature change rate. The thermal conductivity coefficient is set according to the actual material conditions and environmental conditions of the electrical equipment. The thermal characteristic index calculated by this formula can accurately reflect the degree of heat generation of the electrical equipment relative to the environment. If the device surface temperature is too high and the temperature change rate is large, the thermal characteristic index will increase accordingly, indicating that the device may have heat dissipation problems or circuit overloads.

[0115] The calculation formula of the mechanical characteristic index includes the current root mean square value of vibration acceleration, the root mean square value of vibration velocity and the resonant frequency offset; these parameters can reflect the potential fault conditions caused by the mechanical operation status of the electrical equipment; for example, if the root mean square value of vibration acceleration is large, it means that the equipment may have problems such as loose mechanical structure or imbalance, and the mechanical characteristic index will increase accordingly, indicating that the mechanical part of the equipment needs to be checked;

[0116] The calculation formula of the electromagnetic characteristic index takes into account the current magnetic induction intensity deviation, magnetic field intensity deviation and magnetic induction intensity change rate; the electromagnetic induction coefficient is set according to the electromagnetic characteristics of the electrical equipment and environmental factors; the electromagnetic characteristic index calculated by this formula can reflect the status of the electromagnetic field around the electrical equipment and potential electromagnetic faults; if the magnetic induction intensity deviation is large or the change rate is abnormal, the electromagnetic characteristic index will increase, indicating that there may be problems such as electromagnetic component failure or external electromagnetic field interference;

[0117] It is a critical step to integrate the characteristic indexes according to the working type of the electrical equipment. Electrical equipment of different working types may have different sensitivities to various characteristics. For example, for some temperature-sensitive equipment, the weight of the thermal characteristic index in the comprehensive fault index may be larger; and for some equipment sensitive to electromagnetic interference, the weight of the electromagnetic characteristic index may be larger. By setting different weights (and the sum of the weights is 1), a comprehensive fault index that is more in line with the actual situation of the equipment can be obtained, thereby improving the accuracy of fault diagnosis.

[0118] Presetting the fault threshold is an important basis for fault diagnosis. When the comprehensive fault index is greater than the fault threshold, the current electrical equipment is judged to be in a fault state. This fault threshold is set based on a large amount of experimental data and practical experience, and it can distinguish between the normal operation state and the fault state of the equipment. In this way, it is possible to timely and accurately diagnose whether the equipment has a fault, providing a basis for subsequent maintenance and processing.

[0119] In order to further optimize the above embodiment, in the fusion unit, each characteristic index is fused according to the working type of the electrical equipment, and the fusion model of the comprehensive fault index is obtained as follows:

[0120] CFI=ω 1 *ECI+ω 2 *TCI+ω 3 *MCI+ω 4 *EMCI

[0121] Among them, CFI is the comprehensive fault index, ECI, TCI, MCI, and EMCI are the electrical characteristic index, thermal characteristic index, mechanical characteristic index, and electromagnetic characteristic index obtained by the index calculation unit respectively; ω 1 ,ω 2 ,ω 3 ,ω 4 are the weights corresponding to each index, which are pre-set based on the working type of the electrical equipment, and the weight ω 1 +ω 2 +ω 3 +ω 4 =1.

[0122] It should be noted that for motor equipment, mechanical characteristics may be more critical; assuming that the electrical characteristics index weight ω is set 1 =0.2, thermal characteristic index weight ω 2 =0.3, mechanical property index weight ω 3 =0.4, electromagnetic characteristic index weight ω 4 =0.1; This is because the normal operation of the motor depends on a good mechanical structure, and mechanical faults such as bearing wear and rotor imbalance will have a significant impact on the motor performance; at the same time, the motor will also generate heat during operation, and thermal characteristics also need to be considered, but the weight is slightly lower than that of mechanical characteristics; although electrical characteristics and electromagnetic characteristics are also important, they are usually not as critical as mechanical characteristics and thermal characteristics in motor fault diagnosis;

[0123] For transformer equipment, electromagnetic characteristics and thermal characteristics may be more important; setting the electrical characteristics index weight ω 1 =0.1, thermal characteristic index weight ω 2 =0.4, mechanical property index weight ω 3 =0.2, electromagnetic characteristic index weight ω 4 =0.3; Transformers mainly work on the principle of electromagnetic induction. Changes in electromagnetic characteristics will directly affect their performance. For example, winding short circuits and core failures will cause abnormal electromagnetic characteristics. At the same time, transformers will generate heat during operation. Poor heat dissipation will cause excessive temperatures, affecting the service life and performance of the transformer. Therefore, the weight of thermal characteristics is also high. Mechanical characteristics are relatively not the most critical factor, unless there are serious mechanical structural problems such as shell deformation.

[0124] For electronic circuit board equipment, electrical characteristics and electromagnetic characteristics may be more critical; setting the electrical characteristics index weight ω 1 =0.3, thermal characteristic index weight ω 2 =0.2, mechanical property index weight ω 3 =0.1, electromagnetic characteristic index weight ω 4 =0.4; The components on the electronic circuit board mainly realize their functions through electrical connections and electromagnetic signals. Deviations in electrical characteristics such as voltage instability and current overload can cause component damage; at the same time, electromagnetic interference can also affect the normal operation of the circuit board, so the electromagnetic characteristics have a higher weight; thermal characteristics mainly consider the heat dissipation of components, and have a relatively lower relative weight. Mechanical characteristics are generally not the main consideration unless there are serious problems such as circuit board deformation;

[0125] The same goes for other electrical equipment, which will not be elaborated one by one.

[0126] In order to further optimize the above embodiment, the early warning module includes:

[0127] The image generation module is connected to the diagnosis unit and is used to summarize the historical comprehensive fault index of the electrical equipment and draw a line chart of time-comprehensive fault index as a fault trend image.

[0128] It should be noted that the fault trend image can intuitively present the changing trend of the electrical equipment fault index over time; maintenance personnel can understand the development of equipment failure by observing the trend of the broken line; for example, if the broken line shows an upward trend, it means that the failure risk of the equipment is gradually increasing; if the broken line is relatively stable, it means that the equipment is in a relatively stable operating state; this provides an important basis for maintenance personnel to take measures in advance, which helps to avoid the occurrence of equipment failures or reduce the losses caused by failures.

[0129] In order to further optimize the above embodiment, the human-computer interaction module includes:

[0130] An alarm triggering unit, connected to the diagnosis unit, for triggering an alarm signal when the comprehensive fault index CFI is greater than a fault threshold;

[0131] An early warning trigger unit, which is connected to the image generation module and is used to analyze the slope of the broken line from the current moment to the previous preset period in the fault trend image, and trigger an early warning signal when the slope exceeds a preset broken line threshold;

[0132] The display unit is connected to the alarm trigger unit and the early warning trigger unit, and displays corresponding warning content based on the alarm signal or the early warning signal to remind personnel to handle it.

[0133] It should be noted that the core function of the alarm trigger unit is to determine whether to trigger an alarm signal based on the comprehensive fault index and the pre-set fault threshold. When the comprehensive fault index is greater than the fault threshold, it means that the equipment is likely to be in a fault state. At this time, the alarm signal is triggered to promptly notify relevant personnel that there is a problem with the equipment and that inspection and maintenance are required. This mechanism of triggering the alarm based on accurate fault diagnosis results ensures the timeliness and accuracy of the alarm, and avoids equipment failures being ignored and leading to more serious consequences.

[0134] The early warning trigger unit is connected to the image generation module. It focuses on the slope of the broken line in the fault trend image. It determines the development trend of the equipment fault by analyzing the slope of the broken line from the current moment to the previous preset period. When the slope exceeds the preset broken line threshold, it means that the equipment fault is developing rapidly and a serious fault may occur in a short period of time. At this time, the early warning signal is triggered to remind relevant personnel to pay attention to the equipment in advance and take corresponding preventive measures, such as increasing the number of inspections, preparing maintenance tools and spare parts, etc. This early warning mechanism for fault trends provides maintenance personnel with more time to deal with potential equipment failures and improves the safety and reliability of equipment operation.

[0135] The display unit is connected to the alarm trigger unit and the early warning trigger unit. Its function is to display the corresponding warning content according to the received alarm signal or early warning signal. These warning contents can be text messages, such as "Equipment failure, please repair in time" (corresponding to the alarm signal) or "Equipment failure trend is accelerating, please pay attention" (corresponding to the early warning signal), or some graphical symbols, such as a flashing red light (corresponding to the alarm signal) or a yellow exclamation mark (corresponding to the early warning signal). By displaying these warning contents, personnel can be intuitively reminded to deal with equipment-related issues, ensuring that relevant personnel can understand the status of the equipment in a timely manner and take corresponding actions.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An electrical equipment fault diagnosis and early warning system, characterized in that: include: A multi-sensor module is installed on the electrical equipment to obtain the electrical, thermal, mechanical and electromagnetic parameters of the electrical equipment; An acquisition and processing module, which is connected to the multi-sensor module and is used to summarize various acquired parameters and perform preprocessing and feature extraction; A fault diagnosis module, which is connected to the acquisition and processing module, analyzes the indexes of the electrical equipment in terms of electrical characteristics, thermal characteristics, mechanical characteristics and electromagnetic characteristics based on the processed parameters, fuses the indexes based on the working type of the electrical equipment, and diagnoses the equipment fault based on the fusion result; An early warning module, which is connected to the fault diagnosis module and generates a fault trend image of the electrical equipment based on the fusion results at each moment; The human-computer interaction module is connected to the fault diagnosis module and the early warning module, and generates corresponding alarm signals and early warning signals based on the equipment fault conditions and fault trend images obtained by diagnosis, so as to remind the user to check and handle them.

2. An electrical equipment fault diagnosis and early warning system according to claim 1, characterized in that: The multi-sensor module comprises: Electrical sensing units, which are used to measure the current, voltage and power of electrical equipment in real time; A temperature sensing unit, which is used to monitor the surface temperature of the electrical equipment and the ambient temperature in real time; A vibration sensing unit, which is used to measure the vibration acceleration, vibration velocity and resonance frequency of the electrical equipment in real time; An electromagnetic sensing unit, which is used to monitor the magnetic induction intensity and magnetic field intensity around the electrical equipment in real time; A timing unit is connected to the electrical sensing unit, the temperature sensing unit, the vibration sensing unit and the electromagnetic sensing unit, and is used to record the time when each sensing unit detects the parameter.

3. An electrical equipment fault diagnosis and early warning system according to claim 2, characterized in that: The acquisition processing module includes: A data aggregation unit, which is connected to the electrical sensing unit, the temperature sensing unit, the vibration sensing unit, the electromagnetic sensing unit and the timing unit, and is used to aggregate various parameters detected, sort them according to the detection time recorded by the timing unit, and insert a corresponding timestamp; A filtering unit connected to the data summarizing unit and configured to filter the summarized parameters to remove noise interference; A feature extraction unit is connected to the filtering unit and is used to classify the filtered summary parameters according to electrical, thermal, mechanical and electromagnetic categories, and extract effective feature information at the same time; the effective feature information is data that can directly participate in index analysis.

4. An electrical equipment fault diagnosis and early warning system according to claim 3, characterized in that: The effective feature information includes: Current current value, current voltage value, current power value, rated current value, rated voltage value, rated power value; Current device surface temperature, current ambient temperature, and temperature change value within a preset period; The root mean square value of the current vibration acceleration, the root mean square value of the current vibration velocity, and the resonant frequency offset obtained based on spectrum analysis; Current magnetic induction intensity deviation, current magnetic field intensity deviation, and magnetic induction intensity change value within a preset period.

5. The electrical equipment fault diagnosis and early warning system according to claim 4, characterized in that: The fault diagnosis module comprises: An index calculation unit, connected to the feature extraction unit, calculates the electrical characteristic index, thermal characteristic index, mechanical characteristic index and electromagnetic characteristic index of the current electrical equipment based on the effective feature information; The calculation formula of the electrical characteristic index is: ECI=(I a -I0)+ 2 (V a -V0) 2 +(P a -P0) 2 Among them, ECI is the electrical characteristics index, I a is the current value, V a is the current voltage value, P a is the current power value, I0 is the rated current value, V0 is the rated voltage value, and P0 is the rated power value; the electrical characteristic index ECI is used to reflect the degree of deviation of current, voltage and power, and highlights the influence of the deviation of any parameter on the overall index; The calculation formula of the thermal characteristic index is: Where TCI is the thermal characteristic index, T cur is the current device surface temperature, T amb is the current ambient temperature; is the temperature change rate, where dT is the temperature change value within a preset period, and dt is the time corresponding to the preset period; k1 is the thermal conductivity, which is set based on the actual material conditions and environmental conditions of the electrical equipment; the thermal characteristic index TCI is used to reflect the degree of heat generation of the electrical equipment relative to the environment; The calculation formula of the mechanical property index is: Among them, MCI is the mechanical property index, A rms is the root mean square value of the current vibration acceleration, V rms is the root mean square value of the current vibration velocity, f reson is the resonance frequency offset; the mechanical characteristic index MCI is used to reflect the potential fault conditions caused by the mechanical operation state of the electrical equipment; The calculation formula of the electromagnetic characteristic index is: Among them, EMCI is the electromagnetic characteristic index, B dev is the current magnetic induction intensity deviation, H dev is the current magnetic field strength deviation; is the rate of change of magnetic induction intensity, where dB is the change value of magnetic induction intensity within a preset period, and dt is the time corresponding to the preset period; k2 is the electromagnetic induction coefficient, which is set based on the electromagnetic characteristics of the electrical equipment and environmental factors; the electromagnetic characteristic index EMCI is used to reflect the status of the electromagnetic field around the electrical equipment and potential electromagnetic faults; A fusion unit connected to the index calculation unit and used to fuse the characteristic indexes according to the working type of the electrical equipment to obtain a comprehensive fault index; The diagnosis unit is connected to the fusion unit and is used to pre-set a fault threshold, compare the comprehensive fault index with the fault threshold, and judge that the current electrical equipment is in a fault state if the comprehensive fault index is greater than the fault threshold.

6. An electrical equipment fault diagnosis and early warning system according to claim 5, characterized in that: In the fusion unit, each characteristic index is fused according to the working type of the electrical equipment, and the fusion model of the comprehensive fault index is obtained as follows: CFI=ω1*ECI+ω2*TCI+ω3*MCI+ω4*EMCI Among them, CFI is a comprehensive fault index, ECI, TCI, MCI, and EMCI are respectively the electrical characteristic index, thermal characteristic index, mechanical characteristic index, and electromagnetic characteristic index obtained by the index calculation unit; ω1, ω2, ω3, and ω4 are respectively the weights corresponding to each index, which are pre-set based on the working type of the electrical equipment, and the weight ω1+ω2+ω3+ω4=1.

7. An electrical equipment fault diagnosis and early warning system according to claim 6, characterized in that: The early warning module comprises: The image generation module is connected to the diagnosis unit and is used to summarize the historical comprehensive fault index of the electrical equipment and draw a line chart of time-comprehensive fault index as a fault trend image.

8. The electrical equipment fault diagnosis and early warning system according to claim 7, characterized in that: The human-computer interaction module comprises: an alarm triggering unit, connected to the diagnosis unit, for triggering an alarm signal when the comprehensive fault index CFI is greater than a fault threshold; An early warning trigger unit, connected to the image generation module, is used to analyze the slope of the broken line from the current moment to the previous preset period in the fault trend image, and trigger an early warning signal when the slope exceeds a preset broken line threshold; A display unit is connected to the alarm trigger unit and the early warning trigger unit, and displays corresponding warning content based on the alarm signal or the early warning signal to remind personnel to handle it.

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