CN118224795B - Refrigerating system based on multisource data analysis control - Google Patents
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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- F25B—REFRIGERATION MACHINES, PLANTS OR SYSTEMS; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS
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- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25B—REFRIGERATION MACHINES, PLANTS OR SYSTEMS; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS
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Abstract
The invention relates to the field of refrigeration system safety, in particular to a refrigeration system based on multi-source data analysis control, which comprises a refrigeration group, a detection module, an upper control module and an early warning module, wherein the detection module is used for acquiring pressure values and acoustic emission signals at different positions of a circulating pipeline, the upper control module is used for determining steady state propagation characterization coefficients aiming at a circulating pipeline section, dividing steady state propagation categories of the circulating pipeline section, selecting analysis modes aiming at data acquired at each position in different circulating pipeline sections in a follow-up adaptive manner, in particular to a weak steady state circulating pipeline section, analyzing and judging whether the circulating pipeline section is abnormal or not according to frequency domain characteristics of the acoustic emission signals in a characteristic time domain section, extracting pressure values in a non-characteristic time domain section and judging whether the circulating pipeline section is abnormal or not, the processing amount of mass data brought when the leakage detection is carried out on the large-area refrigeration system is reduced through the processes, and the detection accuracy of tiny breakage and tiny leakage of the pipeline is improved.
Description
Technical Field
The invention relates to the field of refrigeration system safety, in particular to a refrigeration system based on multi-source data analysis control.
Background
A refrigeration system is a device for reducing the temperature of a space or an object, and is widely used in industry and daily life, and the possibility of leakage is increased for the refrigeration system in a large area due to the wide pipeline laying area, so that the leakage detection for the refrigeration system is more and more emphasized, and the related refrigeration system with a self-checking function is generated.
For example, chinese patent publication No.: CN114688774a discloses a method of monitoring the risk of refrigerant leakage. The vibration sensor is applied to a refrigerating system, the refrigerating system is provided with a plurality of vibration sensors, the vibration sensors are arranged on different pipelines of the refrigerating system, and pipeline amplitudes of the pipelines where the vibration sensors are collected are obtained; and determining that the refrigerant in the pipeline of the refrigeration system is at risk of leakage when the amplitude of the pipeline acquired by the one or more vibration sensors is not lower than a preset value. A plurality of vibration sensors are arranged in the refrigerating system, vibration parameters of all pipelines of the system are collected through the plurality of sensors, and the vibration parameters are compared with preset values.
There are problems in the prior art that,
In the prior art, detection when leakage occurs to a circulating pipeline of a large-area or large-area refrigerating system is not considered, the form of collecting a pipeline vibration value or a pressure value is usually adopted for leakage detection in the prior art, but the background noise is carried by the large-area refrigerating system due to pipeline bending and laying and the operation of related equipment, and the detection accuracy of fine damage and fine leakage of the circulating pipeline of the large-area refrigerating system is poor.
Disclosure of Invention
Therefore, the invention provides a refrigerating system based on multi-source data analysis control, which is used for solving the problems that in the prior art, the large-area refrigerating system carries background noise due to pipeline bending and laying and the operation of related equipment, and the detection accuracy of fine damage and fine leakage of a circulating pipeline of the large-area refrigerating system is poor.
To achieve the above object, the present invention provides a refrigeration system based on multi-source data analysis control, comprising:
the refrigerating unit comprises a plurality of refrigerating mechanisms, wherein each refrigerating mechanism comprises a compressor, a condenser, an expansion valve and an evaporator which are connected into a circulating pipeline;
the detection module comprises a plurality of acoustic emission detection units which are arranged on the outer wall of the circulating pipeline at preset intervals and used for acquiring acoustic emission signals, and a plurality of pressure detection units which are arranged on the inner wall of the circulating pipeline at preset intervals and used for acquiring pressure values;
the upper control module is connected with the detection module and comprises a hidden identification unit and an early warning analysis unit,
The implicit identification unit is used for determining steady-state propagation characterization coefficients for the circulating pipe section based on the pressure discrete values of a plurality of positions in different circulating pipe sections in a period and the difference of basic characteristics of acoustic emission signals of different positions so as to divide steady-state propagation categories of the circulating pipe section, wherein the basic characteristics comprise rising time and pulse width of the acoustic emission signals before pretreatment;
The early warning analysis unit is used for selecting an analysis mode of data collected for each position in the circulating pipe section based on the steady state propagation category of the circulating pipe section, the analysis mode comprises,
Determining a characteristic time domain segment in the period based on a discrete fluctuation value of basic characteristics of the acoustic emission signals, preprocessing the acoustic emission signals in the characteristic time domain segment, judging whether the circulating pipe segment is abnormal based on frequency domain characteristics, and extracting a pressure value in a non-characteristic time domain segment to judge whether the circulating pipe segment is abnormal;
or, acquiring acoustic emission signal fragments in a preset sampling period, preprocessing, and judging whether the circulation pipeline is abnormal or not based on frequency domain characteristics;
And the early warning module is connected with the upper control module and is used for sending an early warning signal according to the analysis result of the early warning analysis unit.
Further, the implicit identification unit calculates steady-state propagation characterization coefficients according to equation (1),
，
In the formula (1), G represents a steady state propagation characterization coefficient, fe represents a pressure discrete value, sti represents an average rising time of an acoustic emission signal corresponding to an ith position in a period, dti represents a pulse width of the acoustic emission signal corresponding to the ith position in the period, st0 represents a rising time average value of the acoustic emission signal at each position in the period, dt0 represents a pulse width average value of the acoustic emission signal at each position in the period, fi represents a pressure value of the ith position in the period, F0 represents a pressure average value at each position in the period, n1 represents the number of pressure detection units of a circulation pipe section, n2 represents the number of acoustic emission detection units of the circulation pipe section, alpha represents a pressure weight coefficient, and beta represents an acoustic emission weight coefficient.
Further, the implicit identification unit classifies steady state propagation categories of the circulation pipe section, including,
The implicit identification unit compares the steady-state propagation characterization coefficient with a preset steady-state propagation standard threshold,
If the steady-state propagation characterization coefficient is greater than or equal to a preset steady-state propagation standard threshold value, the implicit identification unit judges that the circulating pipe section is of a weak steady-state propagation type;
And if the steady-state propagation characterization coefficient is smaller than a preset steady-state propagation standard threshold value, the implicit identification unit judges that the circulating pipe section is of a strong steady-state propagation type.
Further, the early warning analysis unit selects analysis modes of the data collected for each position in the circulating pipe section to comprise,
If the circulating pipe section is in a weak steady state propagation type, the early warning analysis unit selects a characteristic time domain section in the period to be determined based on a discrete fluctuation value of basic characteristics of acoustic emission signals, judges whether the circulating pipe section is abnormal based on frequency domain characteristics after preprocessing the acoustic emission signals in the characteristic time domain section, and extracts a pressure value in a non-characteristic time domain section to judge whether the circulating pipe section is abnormal;
If the circulating pipeline section is of a strong steady state transmission type, the early warning analysis unit selects acoustic emission signal fragments to be collected in a preset sampling period for preprocessing and then judges whether the circulating pipeline is abnormal or not based on frequency domain characteristics.
Further, the early warning analysis unit constructs a time domain waveform image in the period based on the acoustic emission signal, divides the period into a plurality of time domain segments, calculates discrete fluctuation values of the time domain segments according to a formula (2),
，
In the formula (2), E represents a discrete fluctuation value, nt represents the number of peaks of the time domain waveform image in a time domain segment, se (i) represents the rising time corresponding to the ith peak, se (i-1) represents the rising time corresponding to the ith-1 peak, de (i) represents the pulse width corresponding to the ith peak, and De (i-1) represents the pulse width corresponding to the ith-1 peak.
Further, the pre-alarm analysis unit determining a characteristic time domain segment within the period based on discrete fluctuation values of a fundamental characteristic of the acoustic emission signal comprises,
The early warning analysis unit compares the discrete fluctuation value corresponding to the time domain segment with a preset discrete fluctuation threshold value,
And if the discrete fluctuation value corresponding to the time domain segment is smaller than the preset discrete fluctuation threshold value, the early warning analysis unit judges that the time domain segment is a characteristic time domain segment.
Further, the implicit identification unit determines whether there is an abnormality in the circulation pipe section based on the frequency domain characteristics,
The hidden identification unit is used for preprocessing the acoustic emission signal, wherein the preprocessing comprises the step of acquiring frequency domain characteristics of different frequency domains of the acoustic emission signal, and the frequency domain characteristics comprise frequency domain amplitude sub-characteristics and frequency domain frequency sub-characteristics;
The hidden identification unit compares the frequency domain sub-features of different frequency domains with the risk frequency domain sub-feature standard range in the corresponding frequency domain, and if any frequency domain sub-feature does not belong to the corresponding risk frequency domain sub-feature standard range, the hidden identification unit judges that the circulation pipeline section is abnormal.
Further, the hidden identification unit extracts the pressure value in the non-characteristic time domain segment to judge whether the circulation pipeline segment has abnormality or not,
And the hidden identification unit judges whether the pressure value is in a preset standard pressure interval, and if the pressure value is not in the standard pressure interval, the hidden identification unit judges that the circulation pipe section is abnormal.
Further, the early warning module sends out an early warning signal according to the analysis result of the early warning analysis unit,
And if the hidden identification unit judges that the circulating pipe section is abnormal, the early warning analysis unit sends out early warning prompt.
Further, the display module is connected with the detection module and used for displaying corresponding content based on the data acquired by the detection module.
Compared with the prior art, the invention comprises a refrigeration group, a detection module, an upper control module and an early warning module, wherein the detection module is used for acquiring pressure values and acoustic emission signals at different positions of a circulating pipeline, the upper control module is used for determining steady-state propagation characterization coefficients for the circulating pipeline section, the analysis mode of dividing steady-state propagation category follow-up adaptability of the circulating pipeline section and selecting data acquired at each position in different circulating pipeline sections is used for determining a characteristic time domain section especially for a weak steady-state circulating pipeline section by considering discrete fluctuation values based on basic characteristics of the acoustic emission signals, analyzing and judging whether the circulating pipeline section is abnormal or not according to frequency domain characteristics of the acoustic emission signals in the characteristic time domain section, extracting pressure values in a non-characteristic time domain section and judging whether the circulating pipeline section is abnormal or not, and the process quantity of massive data brought when leakage detection is carried out on a large-area refrigeration system is reduced through the process, so that the detection accuracy of fine breakage and tiny leakage of the pipeline is ensured.
In particular, the steady-state propagation characterization coefficients are calculated according to the invention, in the actual situation, the structural forms of different circulating pipe sections in a large-area refrigeration system may be different, the different structural forms can influence the propagation of a medium in a circulating pipe, for example, the pressure and speed distribution in the air dynamics can be uneven due to the change of the medium in the propagation direction in the circulating pipe with more circulating pipe sections, and the background noise of the circulating pipe section can be obvious due to the stress of the medium on the pipeline.
In particular, according to the invention, different analysis modes are adopted for data acquired by the circulating pipe sections of different steady-state propagation types, in the actual situation, the circulating pipe sections of the weak steady-state propagation type bring more background noise due to the stability of propagation of a medium in a pipeline, especially the influence on the acoustic emission signals, and influence on identifying leakage characteristics, so that in the situation, the characteristic time domain section with stronger data characterizability is selected by considering the discrete fluctuation value of the basic characteristics of the acoustic emission signals preferentially, the acoustic emission signals in the characteristic time domain section are relatively steady, and the basic characteristics of the extracted acoustic emission signals do not need to be subjected to frequency domain conversion or wavelet analysis, the characteristic time domain section is identified in a convenient way, the data characterizability of the acoustic emission signals in the characteristic time domain section is stronger, the characteristic comparison is convenient after the subsequent preprocessing, but the acoustic emission signals in the non-characteristic time domain section are poorer in steady state, the characteristic comparison is carried out after the subsequent preprocessing, and the condition that whether the circulating pipeline is abnormal or not is judged by taking into consideration the pressure dimension, therefore, the calculation quantity is reduced in a targeted analysis when the condition of mass detection data is faced, and the detection accuracy of the pipeline is ensured.
In particular, the invention considers extracting the acoustic emission signal segments collected by the circulation pipe section of the strong steady-state transmission type, the medium transmission of the circulation pipe section of the strong steady-state transmission type is relatively stable, the introduced background noise is less, the acoustic emission signal is relatively stable, and the overall data characterization is stronger, so the periodic analysis of the data collected by the circulation pipe section is considered, and the detection accuracy of the fine damage and the fine leakage of the pipeline is ensured.
Drawings
FIG. 1 is a schematic diagram of a refrigeration system based on multi-source data analysis control in accordance with an embodiment of the invention;
FIG. 2 is a logic block diagram of dividing steady state propagation categories for a circulation pipe segment according to an embodiment of the invention;
FIG. 3 is a logic block diagram of an embodiment of the invention for determining whether an anomaly exists in a circulation loop segment based on frequency domain characteristics;
fig. 4 is a logic block diagram of determining whether an abnormality exists in a circulation pipe section based on a pressure value according to an embodiment of the present invention.
Detailed Description
In order that the objects and advantages of the invention will become more apparent, the invention will be further described with reference to the following examples; it should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are merely for explaining the technical principles of the present invention, and are not intended to limit the scope of the present invention.
Furthermore, it should be noted that, in the description of the present invention, unless explicitly specified and limited otherwise, the term "connected" should be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those skilled in the art according to the specific circumstances.
Referring to fig. 1 to 4, fig. 1 is a schematic diagram of a refrigeration system based on multi-source data analysis control according to an embodiment of the present invention, fig. 2 is a logic block diagram for dividing steady-state propagation categories of circulation pipe sections according to an embodiment of the present invention, fig. 3 is a logic block diagram for determining whether abnormality exists in a circulation pipe section based on frequency domain characteristics according to an embodiment of the present invention, and fig. 4 is a logic block diagram for determining whether abnormality exists in a circulation pipe section based on pressure values according to an embodiment of the present invention, the refrigeration system based on multi-source data analysis control according to the present invention includes:
the refrigerating unit comprises a plurality of refrigerating mechanisms, wherein each refrigerating mechanism comprises a compressor, a condenser, an expansion valve and an evaporator which are connected into a circulating pipeline;
the detection module comprises a plurality of acoustic emission detection units which are arranged on the outer wall of the circulating pipeline at preset intervals and used for acquiring acoustic emission signals, and a plurality of pressure detection units which are arranged on the inner wall of the circulating pipeline at preset intervals and used for acquiring pressure values;
the upper control module is connected with the detection module and comprises a hidden identification unit and an early warning analysis unit,
The implicit identification unit is used for determining steady-state propagation characterization coefficients for the circulating pipe section based on the pressure discrete values of a plurality of positions in different circulating pipe sections in a period and the difference of basic characteristics of acoustic emission signals of different positions so as to divide steady-state propagation categories of the circulating pipe section, wherein the basic characteristics comprise rising time and pulse width of the acoustic emission signals before pretreatment;
The early warning analysis unit is used for selecting an analysis mode of data collected for each position in the circulating pipe section based on the steady state propagation category of the circulating pipe section, the analysis mode comprises,
Determining a characteristic time domain segment in the period based on a discrete fluctuation value of basic characteristics of the acoustic emission signals, preprocessing the acoustic emission signals in the characteristic time domain segment, judging whether the circulating pipe segment is abnormal based on frequency domain characteristics, and extracting a pressure value in a non-characteristic time domain segment to judge whether the circulating pipe segment is abnormal;
or, acquiring acoustic emission signal fragments in a preset sampling period, preprocessing, and judging whether the circulation pipeline is abnormal or not based on frequency domain characteristics;
And the early warning module is connected with the upper control module and is used for sending an early warning signal according to the analysis result of the early warning analysis unit.
Specifically, the specific structure of the refrigeration mechanism is not limited, and it can be understood that the basic structure of the refrigeration mechanism includes a compressor, a condenser, an expansion valve and an evaporator, the compressor is used for compressing low-pressure refrigerant vapor into high-pressure hot vapor, heat in the high-pressure hot vapor is released through the condenser, the refrigerant is converted from a gas state into a high-pressure liquid state, when the high-pressure liquid state refrigerant passes through the expansion valve, the pressure is suddenly reduced, a part of the refrigerant is evaporated to form a low-temperature low-pressure refrigerant mixture, in the evaporator, the refrigerant mixture absorbs heat of surrounding space, the space temperature is reduced, the refrigerant is completely evaporated into a low-pressure gas state, and then is sucked by the compressor again to complete circulation, and a person skilled in the art can adaptively select a connection mode of a circulation pipeline and the types of the compressor, the condenser, the expansion valve and the evaporator according to requirements, which are not described in detail in the prior art.
Specifically, the specific structures of the acoustic emission detection unit and the pressure detection unit are not limited, the acoustic emission detection unit can be an acoustic emission sensor and can only have the function of detecting acoustic emission signals, the pressure detection unit can be a pressure sensor and can only be used for measuring the pressure value born by the inside of the circulating pipeline, and the detailed description is omitted.
Specifically, the specific structure of the upper control module is not limited, and the upper control module itself or each unit thereof may be constituted by a logic component including a field programmable part, a computer, and a microprocessor in the computer.
Specifically, the specific structure of the early warning module is not limited, and the early warning module may be a sound horn, so as to send an early warning signal through the sound horn.
In particular, the implicit identification unit calculates steady-state propagation characterization coefficients according to equation (1),
，
In the formula (1), G represents a steady state propagation characterization coefficient, fe represents a pressure discrete value, sti represents an average rising time of an acoustic emission signal corresponding to an ith position in a period, dti represents a pulse width of the acoustic emission signal corresponding to the ith position in the period, st0 represents a rising time average value of the acoustic emission signal at each position in the period, dt0 represents a pulse width average value of the acoustic emission signal at each position in the period, fi represents a pressure value of the ith position in the period, F0 represents a pressure average value at each position in the period, n1 represents the number of pressure detection units of a circulation pipe section, n2 represents the number of acoustic emission detection units of the circulation pipe section, alpha represents a pressure weight coefficient, and beta represents an acoustic emission weight coefficient.
Specifically, the pressure weight coefficient α takes a value of 0.6, and the acoustic emission weight coefficient β takes a value of 0.4.
Specifically, the steady-state propagation characterization coefficients are calculated according to the invention, in the actual situation, the structural forms of different circulating pipe sections in a large-area refrigeration system may be different, the different structural forms can influence the propagation of a medium in a circulating pipe, for example, the pressure and speed distribution in the gas dynamics is uneven due to the change of the medium in the propagation direction in the circulating pipe with more circulating pipe sections, and the background noise of the circulating pipe section is obvious due to the stress of the medium on the pipeline.
In particular, the implicit identification unit classifies steady state propagation categories of the circulation pipe section, including,
The implicit identification unit compares the steady-state propagation characterization coefficient with a preset steady-state propagation standard threshold,
If the steady-state propagation characterization coefficient is greater than or equal to a preset steady-state propagation standard threshold value, the implicit identification unit judges that the circulating pipe section is of a weak steady-state propagation type;
And if the steady-state propagation characterization coefficient is smaller than a preset steady-state propagation standard threshold value, the implicit identification unit judges that the circulating pipe section is of a strong steady-state propagation type.
Specifically, the steady-state propagation standard threshold value G0 is obtained by pre-measurement, a plurality of circulating pipe sections comprising bending sections are detected in advance, and a steady-state propagation characterization coefficient is determined, wherein the single circulating pipe section is 5m;
The steady state propagation characterization coefficient mean Δg is solved, and g0=Δg×γ is set, γ represents the offset coefficient, 1.05 < γ < 1.1.
Specifically, the early warning analysis unit selects analysis modes of data collected for each position in the circulating pipe section to include,
If the circulating pipe section is in a weak steady state propagation type, the early warning analysis unit selects a characteristic time domain section in the period to be determined based on a discrete fluctuation value of basic characteristics of acoustic emission signals, judges whether the circulating pipe section is abnormal based on frequency domain characteristics after preprocessing the acoustic emission signals in the characteristic time domain section, and extracts a pressure value in a non-characteristic time domain section to judge whether the circulating pipe section is abnormal;
If the circulating pipeline section is of a strong steady state transmission type, the early warning analysis unit selects acoustic emission signal fragments to be collected in a preset sampling period for preprocessing and then judges whether the circulating pipeline is abnormal or not based on frequency domain characteristics.
Specifically, the invention considers that the data collected by the circulating pipe sections of different steady-state propagation types adopts different analysis modes, in the actual situation, the circulating pipe section of the weak steady-state propagation type brings more background noise due to the stability of propagation of a medium in a pipeline, especially the influence on the acoustic emission signal affects the identification of leakage characteristics, therefore, in the situation, the characteristic time domain section with stronger data characterizability is selected by considering the discrete fluctuation value of the basic characteristic preferentially based on the acoustic emission signal, the acoustic emission signal in the characteristic time domain section is relatively steady, the basic characteristic of the extracted acoustic emission signal does not need to be subjected to frequency domain conversion or wavelet analysis, the characteristic time domain section is identified in a convenient way, the data characterizability of the acoustic emission signal in the characteristic time domain section is stronger, the characteristic comparison is convenient after the subsequent preprocessing, but the acoustic emission signal in the non-characteristic time domain section is poorer, the characteristic comparison is carried out after the subsequent preprocessing, and the condition that the circulating pipeline is judged whether the pressure dimension exists abnormally is considered, therefore, the operation quantity is reduced in a targeted analysis when the condition of mass detection data is faced, and the detection accuracy of the pipeline is ensured.
Specifically, the invention considers extracting the acoustic emission signal segments collected by the circulation pipe section of the strong steady-state transmission type, the medium transmission of the circulation pipe section of the strong steady-state transmission type is relatively stable, the introduced background noise is less, the acoustic emission signal is relatively stable, and the overall data characterization is stronger, so the periodic analysis of the data collected by the circulation pipe section is considered, and the detection accuracy of the fine damage and the fine leakage of the pipeline is ensured.
Specifically, the early warning analysis unit constructs a time domain waveform image in the period based on the acoustic emission signal, divides the period into a plurality of time domain segments, calculates discrete fluctuation values of the time domain segments according to a formula (2),
，
In the formula (2), E represents a discrete fluctuation value, nt represents the number of peaks of the time domain waveform image in a time domain segment, se (i) represents the rising time corresponding to the ith peak, se (i-1) represents the rising time corresponding to the ith-1 peak, de (i) represents the pulse width corresponding to the ith peak, and De (i-1) represents the pulse width corresponding to the ith-1 peak.
In particular, the pre-alarm analysis unit determines a characteristic time domain segment within the period based on discrete fluctuation values of a fundamental characteristic of the acoustic emission signal comprises,
The early warning analysis unit compares the discrete fluctuation value corresponding to the time domain segment with a preset discrete fluctuation threshold value,
And if the discrete fluctuation value corresponding to the time domain segment is smaller than the preset discrete fluctuation threshold value, the early warning analysis unit judges that the time domain segment is a characteristic time domain segment.
Specifically, the discrete fluctuation threshold E0 is obtained by pre-measurement, wherein, a plurality of circulating pipe sections of weak steady-state propagation types are detected in advance, acoustic emission signals are obtained, a time domain waveform diagram is constructed, the average value delta E of the discrete fluctuation values of the circulating pipe sections in a single period is solved,
Let e0=Δe×c, c denote discrete precision coefficients, 0.5 < c < 0.8.
Specifically, the implicit identification unit determines whether or not there is an abnormality in the circulation pipe section based on the frequency domain characteristics,
The hidden identification unit is used for preprocessing the acoustic emission signal, wherein the preprocessing comprises the step of acquiring frequency domain characteristics of different frequency domains of the acoustic emission signal, and the frequency domain characteristics comprise frequency domain amplitude sub-characteristics and frequency domain frequency sub-characteristics;
The hidden identification unit compares the frequency domain sub-features of different frequency domains with the risk frequency domain sub-feature standard range in the corresponding frequency domain, and if any frequency domain sub-feature does not belong to the corresponding risk frequency domain sub-feature standard range, the hidden identification unit judges that the circulation pipeline section is abnormal.
Specifically, the mode of acquiring the frequency domain features is not limited, for example, fourier transform may be used to process the acoustic emission signal, so as to acquire the frequency domain features in different frequency domains, which is not described herein.
Specifically, the risk frequency domain sub-feature standard range is obtained by pre-measurement, and comprises a risk frequency domain amplitude sub-feature standard range and a risk frequency domain frequency sub-feature standard range, acoustic emission signals under stable operation of a circulation pipeline in a plurality of periods are obtained, after preprocessing, frequency domain amplitude upper limits and frequency domain amplitude lower limits under different frequency domains are counted to construct the risk frequency domain amplitude sub-feature standard range under different frequency domains, similarly, frequency domain frequency upper limits and frequency domain frequency lower limits under different frequency domains are counted to construct the risk frequency domain frequency sub-feature standard range under different frequency domains, and of course, a person skilled in the art can also adjust the risk frequency domain sub-feature standard range according to specific requirements and are not repeated herein.
Specifically, the implicit recognition unit extracts the pressure value in the non-characteristic time domain segment to judge whether the circulation pipeline segment has abnormality or not,
And the hidden identification unit judges whether the pressure value is in a preset standard pressure interval, and if the pressure value is not in the standard pressure interval, the hidden identification unit judges that the circulation pipe section is abnormal.
The standard pressure interval is obtained by pre-measuring the pressure value of the circulating pipeline under stable operation in a plurality of periods, and the upper limit and the lower limit of the pressure value are counted to obtain the standard pressure interval.
Specifically, the early warning module sends out an early warning signal according to the analysis result of the early warning analysis unit,
And if the hidden identification unit judges that the circulating pipe section is abnormal, the early warning analysis unit sends out early warning prompt.
Specifically, the display module is connected with the detection module and used for displaying corresponding content based on the data acquired by the detection module.
Specifically, the display module may be a display screen, and only needs to display the corresponding content according to the data acquired by the detection module, for example, the data detected by each pressure detection unit and the acoustic emission detection unit are displayed, which is not described herein.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will be within the scope of the present invention.
Claims (6)
1. A refrigeration system based on multi-source data analysis control, comprising:
the refrigerating unit comprises a plurality of refrigerating mechanisms, wherein each refrigerating mechanism comprises a compressor, a condenser, an expansion valve and an evaporator which are connected into a circulating pipeline;
the detection module comprises a plurality of acoustic emission detection units which are arranged on the outer wall of the circulating pipeline at preset intervals and used for acquiring acoustic emission signals, and a plurality of pressure detection units which are arranged on the inner wall of the circulating pipeline at preset intervals and used for acquiring pressure values;
the upper control module is connected with the detection module and comprises a hidden identification unit and an early warning analysis unit,
The implicit identification unit is used for determining steady-state propagation characterization coefficients for the circulating pipe section based on the pressure discrete values of a plurality of positions in different circulating pipe sections in a period and the difference of basic characteristics of acoustic emission signals of different positions so as to divide steady-state propagation categories of the circulating pipe section, wherein the basic characteristics comprise rising time and pulse width of the acoustic emission signals before pretreatment;
The early warning analysis unit is used for selecting an analysis mode of data collected for each position in the circulating pipe section based on the steady state propagation category of the circulating pipe section, the analysis mode comprises,
Determining a characteristic time domain segment in the period based on a discrete fluctuation value of basic characteristics of the acoustic emission signals, preprocessing the acoustic emission signals in the characteristic time domain segment, judging whether the circulating pipe segment is abnormal based on frequency domain characteristics, and extracting a pressure value in a non-characteristic time domain segment to judge whether the circulating pipe segment is abnormal;
or, acquiring acoustic emission signal fragments in a preset sampling period, preprocessing, and judging whether the circulation pipeline is abnormal or not based on frequency domain characteristics;
the early warning module is connected with the upper control module and used for sending out an early warning signal according to the analysis result of the early warning analysis unit;
The implicit identification unit calculates steady-state propagation characterization coefficients according to equation (1),
(1) In the formula (1), G represents a steady state propagation characterization coefficient, fe represents a pressure discrete value, sti represents the average rising time of an acoustic emission signal corresponding to the ith position in a period, dti represents the pulse width of the acoustic emission signal corresponding to the ith position in the period, st0 represents the rising time average value of the acoustic emission signal at each position in the period, dt0 represents the pulse width average value of the acoustic emission signal at each position in the period, fi represents the pressure value of the ith position in the period, F0 represents the pressure average value at each position in the period, n1 represents the number of pressure detection units of a circulating pipe section, n2 represents the number of acoustic emission detection units of the circulating pipe section, alpha represents a pressure weight coefficient, and beta represents an acoustic emission weight coefficient;
The implicit identification unit divides steady state propagation categories of the circulation pipe section, including,
The implicit identification unit compares the steady-state propagation characterization coefficient with a preset steady-state propagation standard threshold,
If the steady-state propagation characterization coefficient is greater than or equal to a preset steady-state propagation standard threshold value, the implicit identification unit judges that the circulating pipe section is of a weak steady-state propagation type;
If the steady-state propagation characterization coefficient is smaller than a preset steady-state propagation standard threshold value, the implicit identification unit judges that the circulating pipe section is of a strong steady-state propagation type;
The early warning analysis unit selects analysis modes of the data collected for each position in the circulating pipe section to comprise,
If the circulating pipe section is in a weak steady state propagation type, the early warning analysis unit selects a characteristic time domain section in the period to be determined based on a discrete fluctuation value of basic characteristics of acoustic emission signals, judges whether the circulating pipe section is abnormal based on frequency domain characteristics after preprocessing the acoustic emission signals in the characteristic time domain section, and extracts a pressure value in a non-characteristic time domain section to judge whether the circulating pipe section is abnormal;
If the circulating pipeline section is of a strong steady-state propagation type, the early warning analysis unit selects acoustic emission signal fragments to be collected in a preset sampling period for preprocessing and then judges whether the circulating pipeline is abnormal or not based on frequency domain characteristics;
the implicit identification unit determines whether there is an abnormal inclusion in the circulation pipe section based on the frequency domain characteristics,
The hidden identification unit is used for preprocessing the acoustic emission signal, wherein the preprocessing comprises the step of acquiring frequency domain characteristics of different frequency domains of the acoustic emission signal, and the frequency domain characteristics comprise frequency domain amplitude sub-characteristics and frequency domain frequency sub-characteristics;
The hidden identification unit compares the frequency domain sub-features of different frequency domains with the risk frequency domain sub-feature standard range in the corresponding frequency domain, and if any frequency domain sub-feature does not belong to the corresponding risk frequency domain sub-feature standard range, the hidden identification unit judges that the circulation pipeline section is abnormal.
2. The refrigeration system based on multi-source data analysis control according to claim 1, wherein the pre-warning analysis unit constructs a time domain waveform image within a period based on the acoustic emission signal, divides the period into a plurality of time domain segments, calculates a discrete fluctuation value of each time domain segment according to formula (2),
(2) In the formula (2), E represents a discrete fluctuation value, nt represents the number of peaks of the time domain waveform image in a time domain segment, se (i) represents the rising time corresponding to the ith peak, se (i-1) represents the rising time corresponding to the ith-1 peak, de (i) represents the pulse width corresponding to the ith peak, and De (i-1) represents the pulse width corresponding to the ith-1 peak.
3. The multi-source data analysis control based refrigeration system of claim 2 wherein the pre-alarm analysis unit determining a characteristic time domain segment within the cycle based on discrete fluctuation values of a fundamental characteristic of an acoustic emission signal comprises,
The early warning analysis unit compares the discrete fluctuation value corresponding to the time domain segment with a preset discrete fluctuation threshold value,
And if the discrete fluctuation value corresponding to the time domain segment is smaller than the preset discrete fluctuation threshold value, the early warning analysis unit judges that the time domain segment is a characteristic time domain segment.
4. The refrigeration system based on multi-source data analysis control according to claim 1, wherein the implicit identification unit extracting the pressure value in the non-characteristic time domain segment to determine whether there is an abnormality in the circulation pipe segment includes,
And the hidden identification unit judges whether the pressure value is in a preset standard pressure interval, and if the pressure value is not in the standard pressure interval, the hidden identification unit judges that the circulation pipe section is abnormal.
5. The refrigeration system based on multi-source data analysis control according to claim 1, wherein the pre-alarm module sends out a pre-alarm signal according to the analysis result of the pre-alarm analysis unit,
And if the hidden identification unit judges that the circulating pipe section is abnormal, the early warning analysis unit sends out early warning prompt.
6. The refrigeration system based on multi-source data analysis control of claim 1, further comprising a display module coupled to the detection module for displaying corresponding content based on the data acquired by the detection module.
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