CN115268418A - Electrical control equipment fault alarm system and method - Google Patents
Electrical control equipment fault alarm system and method Download PDFInfo
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Abstract
The invention relates to a fault alarm system and a method for electric control equipment, which relate to the technical field of alarm, and adopt the scheme that the fault alarm system and the method for the electric control equipment detect the abnormal operation state of the electric control equipment through an abnormal information detection module; performing alarm control through abnormal data information detected by an alarm controller; various abnormal data information in the running process of the electric appliance is controlled by the alarm judging module to alarm and output; various data information output alarms in the running process of the electric appliance are controlled in an audible and visual alarm mode through the audible and visual alarm; the alarm judging module comprises an improved EMD mixed distribution algorithm model which comprises a data preprocessing module, a data comparison module, a data classification module, an abnormal information calculating module and an information updating module. The invention can greatly improve the fault alarm capability of the electric appliance control equipment.
Description
Technical Field
The invention relates to the technical field of alarm communication, in particular to a fault alarm system and method for electric appliance control equipment.
Background
The electrical control system is generally called an electrical equipment secondary control circuit, different equipment has different control circuits, and the control modes of high-voltage electrical equipment and low-voltage electrical equipment are different. Specifically, the electrical control system is a combination of a plurality of electrical elements, and is used for controlling a certain object or certain objects, so as to ensure that a controlled device safely and reliably operates, and the electrical control system mainly has the following functions: automatic control, protection, monitoring and measurement. Different forms of faults can easily occur to the electric control equipment in the working process, for example, how to realize fault alarm of the electric control equipment directly determines the key of whether the electric control equipment can normally operate. In the conventional technology, data information alarm is usually implemented by using a detection device, and when data information is detected, data abnormal data information output is implemented by using an alarm device. The method only stops at macroscopic detection, and needs to apply detection equipment to realize fault detection of data information, and when the detection equipment has faults, the fault data information analysis and alarm of the electric control equipment are difficult to realize.
Disclosure of Invention
Aiming at the defects of the technology, the invention discloses a fault alarm system and a fault alarm method for electric control equipment, which greatly improve the fault detection and analysis capability.
The invention adopts the following technical scheme:
an appliance control device fault alarm system comprising:
the electric appliance control equipment is used for controlling various data information output in the operation process of the electric appliance;
the abnormal information detection module is used for detecting the abnormal operation state of the electrical control equipment;
an alarm controller for performing alarm control for the detected abnormal data information;
the alarm judgment module is used for carrying out alarm output on various abnormal data information in the process of controlling the operation of the electric appliance;
the audible and visual alarm realizes the output alarm of various data information in the process of controlling the operation of the electric appliance in an audible and visual alarm mode;
the alarm controller is respectively connected with the abnormal information detection module, the alarm judgment module and the audible and visual alarm;
wherein the alarm judging module comprises an improved EMD mixed distribution algorithm model which comprises a data preprocessing module, a data comparison module, a data classification module, an abnormal information calculating module and an information updating module,
the output end of the data preprocessing module is connected with the input end of the data comparison module, the output end of the data comparison module is connected with the input end of the data classification module, the output end of the data classification module is connected with the input end of the abnormal information calculation module, the output end of the abnormal information calculation module is connected with the input end of the information updating module, and the data preprocessing module is used for preprocessing the information output of the electric appliance control equipment; the data comparison module is used for respectively comparing and analyzing the detected normal data information or abnormal data information with the set normal data information or abnormal data information data threshold value; the data classification module is used for classifying the compared normal data information or abnormal data information data, the normal data information is classified into one type, and the abnormal data information is classified into one type; the abnormal information calculation module is used for calculating the abnormal information of the electric appliance control equipment; and the information updating module is used for updating the output data information of the electric appliance control equipment in real time so as to diagnose the fault of the electric appliance control equipment in real time.
As a further technical scheme of the invention, the alarm controller is an STM8L151K4T6 singlechip control unit.
As a further technical scheme of the invention, the abnormal information detection module is composed of a sensor group and a detection circuit.
As a further technical scheme of the invention, the audible and visual alarm consists of a + l2V voltage stabilizing circuit, a switch control circuit, an oscillator, a bistable trigger and an audible and visual alarm circuit.
The invention also adopts the following technical scheme:
an electric appliance control equipment fault alarm method comprises the following steps:
various data information output in the running process of the electric appliance is controlled through the electric appliance control equipment; detecting the abnormal operation state of the electrical control equipment through an abnormal information detection module; performing alarm control through abnormal data information detected by the alarm controller; various abnormal data information in the operation process of the control electric appliance is output in an alarm mode through an alarm judging module; various data information output alarms in the running process of the electric appliance are controlled in an audible and visual alarm mode through the audible and visual alarm; alarm judgment of data information is realized through an improved EMD mixed distribution algorithm model; the working method of the improved EMD mixed distribution algorithm model comprises the following steps:
an EMD mixed distribution algorithm model is built, and preprocessing of data information of the electrical equipment, data comparison and analysis, data classification and abnormal information calculation are achieved;
and constructing a GAPSO algorithm model, and realizing information updating and processing of the data information of the electrical equipment.
As a further technical scheme of the invention, the working method of the EMD mixed distribution algorithm model comprises the following steps:
constructing a signal processing function to realize the classification of data information of the electric appliance control equipment, wherein the output function is as follows:
in the formula (1), the first and second groups,
an output signal function representing the operating state of the appliance control device,
the operation state of the fault electric appliance control equipment after classification is represented, data are summarized,
indicating the running state of the electric appliance control equipment and outputting normal data;
the number of the normal data output by the electric appliance control equipment in the running state is shown,
a sequence for outputting normal data representing the running state of the electric appliance control equipment;
reflecting the operation state of the electric control equipment according to the average value of the theoretical value and the actual value to output a maximum allowable fault information function:
in the formula (2), the first and second groups,
represents the operation state of the electrical control equipment and outputs the maximum allowable fault output quantity,
the data which represents the running state of the electrical control equipment and outputs rated bearing fault data,
the minimum bearing load is output according to the running state of the electric control equipment;
then combining the formula (1) and the formula (2), and converting the maximum fault information output quantity output by the operation state of the electric control equipment into a signal function, namely:
in the formula (3), the first and second groups,
the function represents the operation state of the electric control equipment and outputs the maximum fault signal; converting into recognizable first-order input signals in an algorithm programming mode:
in the formula (4), the first and second groups,
a first order signal that represents an algorithm's programming recognition,
representing analog input data that satisfies the EMD algorithm conditions,
the abnormal information simulation data component represents the running state of the electrical control equipment which is successfully programmed;
then will be
And comparing with the set fault alarm threshold information.
As a further technical scheme of the invention, the construction method of the GAPSO algorithm model comprises the following steps:
set one
Target search space of dimension, including in the population
Particles, in the multidimensional vector, the electric appliance control equipment outputs data information to the first
The position of the generation represents
The speed of the electric appliance control equipment output data information individual is expressed as
The position of each particle is a potential solution, before the iteration number does not reach a preset maximum value, the coordinates of each particle are continuously updated to seek an optimal solution, and the speed and the position of each particle are updated according to a rule, which is expressed as:
in the formula (5), the first and second groups,
representing updated particlesThe speed of the motor is controlled by the speed of the motor,
indicating the updated position of the particle or particles,
the coefficient of inertia is expressed as a function of,
which is indicative of the velocity of the initial particles,
the cognitive learning coefficient is represented by a coefficient of cognitive learning,
the social learning coefficient is represented by a social learning coefficient,
、
representing the transfer coefficient, then determining the network topology, and initially setting and using the parameters
Describing individual position vectors of data information output by the electrical control equipment, then defining the fitness in the particle swarm algorithm, calculating the fitness and obtaining the minimum value through sequencing
；
Showing the update state of the particles under the cognitive learning coefficient,
the update state of the entire particle under the social learning coefficient is shown.
As a further technical solution of the present inventionThe alarm output process also comprises the membership degree, and the membership degree is solved, so that the membership degree is obtained
The formula is as follows:
in the formula (6), the first and second groups,
is shown as
The actual value of the individual indicator is,
is shown as
Preset alarm values for individual indicators.
The invention has the following positive beneficial effects:
the abnormal operation state of the electrical control equipment is detected through the abnormal information detection module; performing alarm control through abnormal data information detected by an alarm controller; various abnormal data information in the running process of the electric appliance is controlled by the alarm judging module to carry out alarm output; various data information output alarms in the running process of the electric appliance are controlled in an audible and visual alarm mode through the audible and visual alarm; the alarm judging module comprises an improved EMD mixed distribution algorithm model which comprises a data preprocessing module, a data comparison module, a data classification module, an abnormal information calculating module and an information updating module. The invention can greatly improve the fault alarm capability of the electrical control equipment.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without inventive exercise, wherein:
FIG. 1 is a schematic diagram of a system architecture according to the present invention;
FIG. 2 is a schematic diagram of a schematic structure of an improved EMD hybrid distribution algorithm model in the system of the present invention;
FIG. 3 is a schematic diagram of a gas concentration detection circuit according to the present invention;
FIG. 4 is a schematic circuit diagram of a flame sensor monitoring module according to the present invention;
FIG. 5 is a schematic circuit diagram of a smoke sensor monitoring module according to the present invention;
FIG. 6 is a schematic diagram of an acousto-optic alarm circuit of the present invention;
FIG. 7 is a GAPSO particle swarm optimization algorithm flow of the genetic thought optimization of the invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, and it should be understood that the embodiments described herein are merely for the purpose of illustrating and explaining the present invention and are not intended to limit the present invention.
Example (1)
An appliance control device fault alarm system comprising:
the electric appliance control equipment is used for controlling various data information output in the operation process of the electric appliance;
the abnormal information detection module is used for detecting the abnormal operation state of the electrical control equipment;
an alarm controller for performing alarm control for the detected abnormal data information;
the alarm judgment module is used for carrying out alarm output on various abnormal data information in the operation process of the control electric appliance;
the audible and visual alarm realizes the output alarm of various data information in the process of controlling the operation of the electric appliance in an audible and visual alarm mode;
the alarm controller is respectively connected with the abnormal information detection module, the alarm judgment module and the audible and visual alarm;
wherein the alarm judging module comprises an improved EMD mixed distribution algorithm model which comprises a data preprocessing module, a data comparison module, a data classification module, an abnormal information calculating module and an information updating module,
the output end of the data preprocessing module is connected with the input end of the data comparison module, the output end of the data comparison module is connected with the input end of the data classification module, the output end of the data classification module is connected with the input end of the abnormal information calculation module, the output end of the abnormal information calculation module is connected with the input end of the information updating module, and the data preprocessing module is used for preprocessing the information output of the electric control equipment; the data comparison module is used for respectively comparing and analyzing the detected normal data information or abnormal data information with the set normal data information or abnormal data information data threshold value; the data classification module is used for classifying the compared normal data information or abnormal data information data, the normal data information is classified into one type, and the abnormal data information is classified into one type; the abnormal information calculation module is used for calculating the abnormal information of the electric appliance control equipment; and the information updating module is used for updating the output data information of the electric appliance control equipment in real time so as to diagnose the fault of the electric appliance control equipment in real time.
In the above embodiment, the alarm controller is an STM8L151K4T6 single chip microcomputer control unit.
In a specific embodiment, the alarm controller uses an STM32F103RCT6 singlechip and is added with a RISC kernel to strengthen input and output capabilities.
In the above embodiment, the abnormal information detecting module is constituted by a sensor group and a detecting circuit.
As shown in fig. 3, the sensor group may be, for example, MQ-2 gas sensor in a specific embodiment, and a TTL level is added to implement a dual output. The alarm threshold value of the flame sensor monitoring module is set through the single chip microcomputer, and the LM393 wide voltage comparator is added to output high and low levels to realize alarm. When MQ-2 surveys to obtain airWhen the concentration of the diffused harmful gas is higher than the standard value, the related resistance value in fig. 2 is decreased, and the Ground (GND) voltage outputted from the circuit is increased. Resistance in gas sensor
The resistance value of (d) is expressed as:
wherein
Which is representative of the loop voltage and,
which is indicative of the output voltage of the voltage converter,
representing the load resistance. The flame sensor can catch infrared signals in the monitored environment, and the built-in module is used for signal analysis to identify different infrared intensities in the monitored environment to make different judgments.
As shown in fig. 4, in a specific embodiment, the sensor group may be, for example, a flame sensor, an infrared alarm threshold is set in the flame sensor by programming an STM32F103RCT6 single chip microcomputer, the module outputs a high level when a measurement value does not reach the alarm threshold, the module outputs a low level when the measurement value exceeds a preset alarm threshold, and the single chip microcomputer drives a corresponding alarm module after receiving a low level signal. LM393 in the flame sensor monitoring module is a wide voltage comparator, Q7 uses an NPN photodiode for receiving a flame signal, and a resistor R2 is used for adjusting the sensitivity of the monitoring module. When the flame sensor works normally, the port 3 of the LM393 is at a high level, and after the NPN light-sensitive diode monitors that an infrared signal appears in the environment, the resistance is reduced, and the current is increased. The smoke sensor monitoring module is used for detecting the smoke concentration emitted by combustible substances during fire burning in the monitoring environment, and sends out an alarm signal when the smoke in the environment reaches a certain concentration. The smoke sensor is provided with a light-emitting device and a light-receiving device, when particulate matters emitted by combustible burning enter the smoke sensor, the light-emitting device transmits digital signals to the light-receiving device, so that the light-emitting device reacts to generate current.
As shown in fig. 5, the sensor group may be, for example, a smoke sensor in a specific embodiment, and the smoke sensor monitoring module is configured to detect a smoke concentration emitted from combustible substances burned by a fire in a monitored environment, and to send an alarm signal when the smoke in the monitored environment reaches a certain concentration. The smoke sensor is provided with a light-emitting device and a light-receiving device, when particulate matters emitted by combustible burning enter the smoke sensor, the light-emitting device transmits digital signals to the light-receiving device, so that the light-emitting device reacts to generate current. Wherein the LED1 is a light emitting device, the LED2 is a light receiving device, and TLV6001 is used as a low power consumption operational amplifier, and the LED light source is provided with
Low input bias current, high bandwidth of 1MHz and
low quiescent current. And integrating RF/EMI suppression filters in the monitoring module up to
The unit gain is stable under the capacitive load condition, the phase inversion can not occur under the overdrive condition, and the high electrostatic discharge protection is realized. The independent wireless alarm is provided with an LED drive circuit which is used for displaying the working state of the independent wireless alarm, and the LED lamp is continuously lightened in the alarm state.
In a specific embodiment, the detection circuit is controlled by an STM32F103RCT6 singlechip, and different sensors are connected around the STM32F103RCT6 singlechip.
In the above embodiment, the audible and visual alarm consists of a + l2V voltage stabilizing circuit, a switch control circuit, an oscillator, a bistable trigger and an audible and visual alarm circuit.
As shown in fig. 6, the + l2V regulator circuit is composed of resistors R2, R3, filter capacitors Cl, C5, C6, and a zener diode VS. The switch control circuit consists of resistors Rl, rl2, an optical coupler VLC and a control contact (controlled electric contact) Kx of the industrial instrument. The oscillator is composed of a diode VD2, a resistor Rll and capacitors C2 and C3, and Dl and D2 inside an NOT gate integrated circuit IC (Dl-D5). The flip-flop consists of D3-D5 and resistor R7 internal to the IC. The sound-light alarm circuit consists of transistors Vl-V3, resistors R4-R6, R9, rlO, a light-emitting diode VL, diodes VD3, VD4 and VD6, a field effect transistor VF, a relay K and an alarm HA. At ordinary times, kx is in a disconnected state, a light emitting diode and a photosensitive transistor in VLC are in a cut-off state, IC and Vl have no + l2V working voltage, and the audible and visual alarm does not work. When Kx is switched on due to the fact that control signals or monitoring parameters of the instrument are out of limit, VLC is switched on to provide + l2V working voltage for IC, the oscillator is switched on to work, and oscillation signals output by the oscillator are amplified by Vl and V2 to drive VL to flicker. Meanwhile, the + l2V voltage output by the VLC is also output to the large output end of the D4 through the C4, so that the bistable trigger is turned to another state, the D3 and the D5 both output high levels, the V3 and the VF are conducted to work, the K is electrified and attracted, the normally open contact of the K is connected, and the HA gives an alarm sound. When the audible alarm release button Sl is pressed, VD5 is conducted, D4 outputs high level, D5 and D3 output low level, V3 and VF are cut off, K is released, and HA stops sounding. And S2 is a test button used for checking whether the working state of the audible and visual alarm is normal or not, and the Kx is switched on after the S2 is pressed.
Example (2)
An electrical appliance control equipment fault alarm method comprises the following steps:
various data information output in the running process of the electric appliance is controlled through the electric appliance control equipment; detecting the abnormal operation state of the electrical control equipment through an abnormal information detection module; performing alarm control through abnormal data information detected by the alarm controller; various abnormal data information in the operation process of the control electric appliance is output in an alarm mode through an alarm judging module; the audible and visual alarm controls various data information output alarms in the running process of the electric appliance in an audible and visual alarm mode; alarm judgment of data information is realized through an improved EMD mixed distribution algorithm model; the working method of the improved EMD mixed distribution algorithm model comprises the following steps:
an EMD mixed distribution algorithm model is built, and preprocessing of data information of the electrical equipment, data comparison and analysis, data classification and abnormal information calculation are achieved;
and constructing a GAPSO algorithm model, and realizing information updating and processing of the data information of the electrical equipment.
In the above embodiment, the operation method of the EMD mixed distribution algorithm model is as follows:
constructing a signal processing function to realize the classification of data information of the electric appliance control equipment, wherein the output function is as follows:
in the formula (1), the first and second groups,
an output signal function representing the running state of the electric appliance control equipment,
the operation state of the fault electric appliance control equipment after classification is represented, data are summarized,
indicating the running state of the electric appliance control equipment and outputting normal data;
the number of normal data output by the electric appliance control equipment in the running state is shown,
a sequence for outputting normal data representing the running state of the electric appliance control equipment;
reflecting the operation state of the electric control equipment according to the average value of the theoretical value and the actual value to output a maximum allowable fault information function:
in the formula (2), the first and second groups,
represents the operation state of the electrical control equipment and outputs the maximum allowable fault output quantity,
the data which represents the running state of the electrical control equipment and outputs rated bearing fault data,
the minimum bearing load is output according to the running state of the electric control equipment;
then combining the formula (1) and the formula (2), and converting the maximum fault information output quantity output by the operation state of the electric control equipment into a signal function, namely:
in the formula (3), the first and second groups of the compound,
the function represents the operation state of the electric control equipment and outputs the maximum fault signal; converting into recognizable first-order input signals in an algorithm programming mode:
in the formula (4), the first and second groups,
a first order signal that represents an algorithm's programming recognition,
representing analog input data that satisfies the EMD algorithm conditions,
indicating success of programmingSimulating data components by the abnormal information of the running state of the electrical control equipment;
then will be
And comparing with the set fault alarm threshold information.
In order to better understand the above embodiments, the following further explanation of the above embodiments is made.
When complex data information of electrical appliance control faults is encountered, any complex signal can be regarded as the sum of superposition of a plurality of different inherent mode functions, any mode function can be linear or nonlinear, and any two modes are independent of each other. On the basis of this assumption, the EMD decomposition steps for complex signals are as follows:
step 1: and searching all extreme points of the signal, connecting the local extreme points into an upper envelope curve through a cubic spline curve, and connecting the local minimum points into a lower envelope curve. The upper and lower envelopes contain all data points.
Step 2: from the average of the upper and lower envelopes, the first IMF component is derived, which if the IMF condition is met, can be considered.
And step 3: if the IMF condition is not met, the mean value of the upper envelope and the lower envelope is obtained by repeating the step 1 and the step 2 as the original data, and the required condition whether the IMF component is met or not is calculated, if the IMF component is not met, the steps are repeated until the IMF component is met.
And 4, step 4: separating the signals, repeating the three steps as original signals for a cycle time to obtain a second IMF component and a third IMF component, and obtaining data information in another form;
and 5: when becoming a monotonic function, the remainder becomes a residual component. The sum of all IMF and residual components is the original signal:
the basic idea of filtering with EMD is to select only the part related to the characteristic signal to reconstruct the signal after EMD decomposition of the original signal. In the following figure, a part a is an original signal, b part b is a signal obtained by performing EMD decomposition on the original signal to obtain 6 IMF components and 1 residual component, and c part c is a signal obtained by reconstructing the 6 IMF components and 1 residual component obtained by performing the decomposition.
The fault alarm threshold comprises a current abnormity threshold, a voltage abnormity threshold, a ripple abnormity threshold and a load abnormity threshold, wherein the EMD mixed distribution algorithm model interface realizes EMD mixed distribution algorithm model data information output through PLC control programming.
As shown in fig. 7, the method for constructing the GAPSO algorithm model includes:
set one
A target search space of dimensions, including in the population
Particles, in the multidimensional vector, the electric appliance control equipment outputs data information to the first
The position of the generation represents
The speed of the electric appliance control equipment output data information individual is expressed as
The position of each particle is a potential solution, and before the iteration number does not reach a preset maximum value, the coordinates of each particle are continuously updated to seek an optimal solution, and the speed and the position of each particle are updated according to a rule, which is expressed as follows:
wherein
Indicating the velocity of the particles after the update,
indicating the updated position of the particle or particles,
the coefficient of inertia is expressed as a function of,
which is indicative of the velocity of the initial particles,
the cognitive learning coefficient is represented by a coefficient of cognitive learning,
the social learning coefficient is represented by a social learning coefficient,
、
representing the transfer coefficient. Then, the network topology is determined, and the parameters are initially set and used
Describing individual position vectors of data information output by the electric control equipment, defining the fitness in the particle swarm algorithm, calculating the fitness and obtaining the minimum value through sequencing
；
Showing the update state of the particles under the cognitive learning coefficient,
the update state of the entire particle under the social learning coefficient is shown.
In the above embodiment, the alarm output process further includes a membership degree, and the membership degree is solved if the membership degree is solved
The formula is as follows:
in the case of the formula (6),
is shown as
The actual value of the individual indicator is,
is shown as
Preset alarm values for individual indicators. .
According to the monitoring alarm system structure, three fault types of input end faults, electric appliance control equipment operation state faults and load end faults are selected as output variables. The neural network after the learning and training stage is applied to an application layer of the multifunctional intelligent monitoring and alarming system, so that alarming of various fault types is realized.
The GAPSO is an efficient genetic particle hybrid algorithm and application search thereof, data information is rich in the running process of the electric control equipment, and how to realize calculation and application of a large amount of data information in the electric control equipment, the method can effectively carry out intelligent alarm.
The core meaning of the group algorithm is that the invention simulates an optimization process of fault data information of control equipment, the group optimization algorithm simulates individuals in various electrical control equipment data groups by designing a particle without quality, and the individuals only have two attributes: speed, which represents how fast the movement is, and position, which represents the direction of the movement. Each control equipment fault data information monomer independently searches an optimal solution in a search space, the optimal solution is recorded as a current control equipment fault data information individual extreme value, the control equipment fault data information individual extreme value is shared with other control equipment fault data information individuals in the whole control equipment fault data information group, the optimal control equipment fault data information individual extreme value is found to be used as the current global optimal solution of the whole group, all control equipment fault data information monomers in the group adjust the speed and the position of the control equipment fault data information monomer according to the current control equipment fault data information individual extreme value found by the control equipment fault data information monomer and the current global optimal solution shared by the whole group, and the method is not found in the control equipment fault data information optimization problem.
Although specific embodiments of the present invention have been described above, it will be understood by those skilled in the art that these specific embodiments are merely illustrative and that various omissions, substitutions and changes in the form and details of the methods and inventions described may be made by those skilled in the art without departing from the spirit and scope of the invention. For example, it is within the scope of the present invention to combine the steps of the methods described above to perform substantially the same function in substantially the same way to achieve substantially the same result. Accordingly, the scope of the invention is to be limited only by the following claims.
Claims (8)
1. The utility model provides an electrical control equipment fault alarm system which characterized in that: the method comprises the following steps:
the electric appliance control equipment is used for controlling various data information output in the operation process of the electric appliance;
the abnormal information detection module is used for detecting the abnormal operation state of the electrical control equipment;
an alarm controller for performing alarm control for the detected abnormal data information;
the alarm judgment module is used for carrying out alarm output on various abnormal data information in the process of controlling the operation of the electric appliance;
the audible and visual alarm realizes the output alarm of various data information in the process of controlling the operation of the electric appliance in an audible and visual alarm mode;
the alarm controller is respectively connected with the abnormal information detection module, the alarm judgment module and the audible and visual alarm;
wherein the alarm judging module comprises an improved EMD mixed distribution algorithm model which comprises a data preprocessing module, a data comparison module, a data classification module, an abnormal information calculating module and an information updating module,
the output end of the data preprocessing module is connected with the input end of the data comparison module, the output end of the data comparison module is connected with the input end of the data classification module, the output end of the data classification module is connected with the input end of the abnormal information calculation module, the output end of the abnormal information calculation module is connected with the input end of the information updating module, and the data preprocessing module is used for preprocessing the information output of the electric appliance control equipment; the data comparison module is used for respectively comparing and analyzing the detected normal data information or abnormal data information with the set normal data information or abnormal data information data threshold value; the data classification module is used for classifying the compared normal data information or abnormal data information data, the normal data information is classified into one type, and the abnormal data information is classified into one type; the abnormal information calculation module is used for calculating the abnormal information of the electric appliance control equipment; and the information updating module is used for updating the output data information of the electric appliance control equipment in real time so as to diagnose the fault of the electric appliance control equipment in real time.
2. The electrical control equipment fault alarm system of claim 1, wherein: the alarm controller is an STM8L151K4T6 single-chip microcomputer control unit.
3. The electrical control equipment fault alarm system of claim 1, wherein: the abnormal information detection module is composed of a sensor group and a detection circuit.
4. The electrical control equipment fault alarm system of claim 1, wherein: the audible and visual alarm consists of a + l2V voltage stabilizing circuit, a switch control circuit, an oscillator, a bistable trigger and an audible and visual alarm circuit.
5. A fault alarm method for electric control equipment is characterized in that: the method comprises the following steps:
various data information output in the running process of the electric appliance is controlled through the electric appliance control equipment; detecting the abnormal operation state of the electrical control equipment through an abnormal information detection module; performing alarm control through abnormal data information detected by an alarm controller; various abnormal data information in the operation process of the control electric appliance is output in an alarm way through an alarm judging module; the audible and visual alarm controls various data information output alarms in the running process of the electric appliance in an audible and visual alarm mode; alarm judgment of data information is realized through an improved EMD mixed distribution algorithm model; the working method of the improved EMD mixed distribution algorithm model comprises the following steps:
an EMD mixed distribution algorithm model is built, and preprocessing of data information of the electrical equipment, data comparison and analysis, data classification and abnormal information calculation are achieved;
and constructing a GAPSO algorithm model to realize information updating and processing of the data information of the electrical equipment.
6. The electric appliance control device fault alarm method according to claim 5, characterized in that: the working method of the EMD mixed distribution algorithm model comprises the following steps:
constructing a signal processing function to realize the classification of data information of the electric appliance control equipment, wherein the output function is as follows:
in the formula (1), the first and second groups of the compound,
indicating the running state of the electric control equipmentThe function of the signal is output, and the signal is output,
the operation state of the fault electrical appliance control equipment is output to summarize data after the classification,
indicating the running state of the electric appliance control equipment and outputting normal data;
the number of normal data output by the electric appliance control equipment in the running state is shown,
a sequence for outputting normal data representing the running state of the electric appliance control equipment;
reflecting the operation state of the electric control equipment according to the average value of the theoretical value and the actual value to output a maximum allowable fault information function:
in the formula (2), the first and second groups,
represents the operation state of the electrical control equipment and outputs the maximum allowable fault output quantity,
output rated fault data representing the operation state of the electrical control equipment,
the minimum bearing load is output according to the running state of the electric control equipment;
then combining the formula (1) and the formula (2), and converting the maximum fault information output quantity output by the operation state of the electric control equipment into a signal function, namely:
in the formula (3), the first and second groups,
the maximum fault signal function is output to represent the running state of the electrical control equipment; converting into recognizable first-order input signals in an algorithm programming mode:
in the formula (4), the first and second groups,
a first order signal recognizable to the programming of the algorithm,
representing analog input data that satisfies the EMD algorithm conditions,
the abnormal information simulation data component represents the running state of the electrical control equipment which is successfully programmed;
then will be
And comparing with the set fault alarm threshold information.
7. The electric appliance control equipment fault alarm method according to claim 5, characterized in that: the construction method of the GAPSO algorithm model comprises the following steps:
set one
Target search space of dimension, including in the population
Particles, in the multidimensional vector, the output data information of the electric appliance control equipment is individually related to
The position of the generation represents
The speed of the electric appliance control equipment output data information individual is expressed as
The position of each particle is a potential solution, before the iteration number does not reach a preset maximum value, the coordinates of each particle are continuously updated to seek an optimal solution, and the speed and the position of each particle are updated according to a rule, which is expressed as:
in the formula (5), the first and second groups,
indicating the velocity of the particles after the update,
indicating the updated position of the particle or particles,
the coefficient of inertia is expressed as a function of,
which is indicative of the velocity of the initial particles,
the cognitive learning coefficient is represented by a coefficient of cognitive learning,
a social learning coefficient is expressed by the expression,
、
representing the transfer coefficient, then determining the network topology, and initially setting and using the parameters
Describing individual position vectors of data information output by the electrical control equipment, then defining the fitness in the particle swarm algorithm, calculating the fitness and obtaining the minimum value through sequencing
；
Showing the update state of the particles under the cognitive learning coefficient,
the update state of the entire particles under the social learning coefficient is represented.
8. The electrical control equipment fault alarm system of claim 1, wherein: the alarm output process also comprises the membership degree, and the membership degree is solved, so that the membership degree
The formula is as follows:
in the formula (6), the first and second groups,
denotes the first
The actual value of the individual index is,
denotes the first
Preset alarm values for individual indicators.
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