A compressor fault detection algorithm based on state parameter analysis

Abstract

The present invention relates to the technical field of compressors, and in particular to a compressor fault detection algorithm based on state parameter analysis, comprising: real-time collection of compressor operating state parameters, building a cloud database, and using the cloud database to store the compressor operating state parameters collected in real time; setting a compressor fault judgment threshold, traversing the latest compressor operating state parameters stored in the cloud database in real time, and comparing the traversed compressor operating state parameters with the set compressor fault judgment threshold; the present invention brings continuous fault judgment and hidden fault detection to the compressor through the collection and analysis of various operating parameters under the operating state of the compressor, so that the operating faults and hidden fault hazards of the compressor can be discovered in a timely and predictable manner, and then the compressor is started and stopped and maintained, ensuring that the compressor operating function is complete and the operation is stable, thereby ensuring that the compressor connection mechanical device can operate more stably and continuously.

Description

Technical Field

[0001] The present invention relates to the technical field of compressors, and in particular to a compressor fault detection algorithm based on state parameter analysis. Background Art

[0002] A compressor is a fluid machine that converts the mechanical energy of a prime mover (motor or engine) into the energy of a compressed medium and delivers gas. It is the heart of a gas booster system. It inhales low-temperature, low-pressure gas from the intake pipe, compresses it through the piston driven by the motor or engine, and then discharges high-temperature, high-pressure gas to the exhaust pipe.

[0003] As for the compressor itself, its main function is to convert low-pressure gas into high-pressure gas. However, during the operation of the compressor, various faults may cause the loss of gas compression function or deterioration of gas compression effect. If these problems are not discovered in time and the compressor is not maintained, it will cause failures in the mechanical devices that further serve the compressor. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, the present invention provides a compressor fault detection algorithm based on state parameter analysis, which solves the technical problems raised in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0006] A compressor fault detection algorithm based on state parameter analysis, comprising:

[0007] Collect the compressor operating status parameters in real time, build a cloud database, and use the cloud database to store the compressor operating status parameters collected in real time; set the compressor fault judgment threshold, traverse the latest compressor operating status parameters stored in the cloud database in real time, and compare the traversed compressor operating status parameters with the set compressor fault judgment threshold, and make a real-time judgment on the compressor operating fault based on the comparison result; retrieve the compressor operating status parameters from the cloud database, and perform hidden fault detection on the compressor based on the compressor operating status parameters; obtain the compressor hidden fault detection result, set the compressor hidden fault judgment threshold, and compare the hidden fault judgment threshold with the compressor hidden fault detection result to determine whether the compressor has a hidden fault; obtain the judgment result of whether the compressor has a hidden fault, and control the compressor operation based on the judgment result; generate a compressor operation message when the compressor is controlled to be shut down based on the judgment result.

[0008] Furthermore, the compressor operation state parameters include: compressor operation voltage, current, resistance, compressor operation inlet gas volume, compressor operation output gas volume, compressor operation output gas pressure, compressor operation vibration spectrum. When the cloud database stores the compressor operation state parameters, the compressor operation state parameters are differentiated and stored based on the types of the compressor operation state parameters.

[0009] When the compressor operating status parameters are stored in the cloud database, each set of compressor operating status parameters is marked with a timestamp of parameter generation.

[0010] Furthermore, the compressor fault judgment threshold is provided with several groups, and the several groups of compressor fault judgment threshold are respectively applied to the compressor operating voltage, current, resistance, compressor operating incoming gas volume, compressor operating output gas volume, and compressor operating output gas pressure. When it is determined based on the comparison result that there is a fault in the current operating state of the compressor, the compressor power supply is synchronously cut off, the compressor output and input ends are controlled to be closed, and the compressor is controlled to stop running.

[0011] Furthermore, when retrieving compressor operating status parameters from a cloud database, the retrieved parameter type target is the compressor operating vibration spectrum, and when performing hidden fault detection on the compressor, the marking timestamp of the retrieved compressor operating status parameters is synchronously obtained, and the compressor operating status parameters are filtered based on the marking timestamp of the compressor operating status parameters;

[0012] Among them, the compressor operation status parameter screening logic is expressed as:

[0013] Logic 1: Determine the time span covered by the compressor operating status parameter based on the marked timestamp corresponding to the compressor operating status parameter;

[0014] Logic2: Set the compressor operation status parameter screening ratio, and determine the time span of the compressor operation status parameter covering the corresponding ratio in the time span based on the set screening ratio;

[0015] Logic3: Based on the time span determined in Logic2, the compressor operation status parameters corresponding to the corresponding time span are intercepted at the center of the compressor operation status parameter coverage time span.

[0016] Furthermore, when performing hidden fault detection on the compressor, the intercepted compressor operation status parameters are synchronously divided into equal parts, so that the compressor operation status parameters are divided into several groups of sub-compressor operation vibration spectra. The hidden fault detection logic of the compressor is expressed as follows:

[0017]

[0018] Where: f1 is the first fault judgment value of the compressor; m is the set of sub-compressor operation vibration spectra; (n max ) i 、(n min ) i is the maximum and minimum frequency values ​​in the vibration spectrum of the i-th group of sub-compressors; (n max ) i+1 、(n min ) i+1 is the maximum and minimum frequency values ​​in the vibration spectrum of the i+1th group of sub-compressors; f2 is the second fault judgment value of the compressor; q is the set of frequency points in the vibration spectrum a and b of the sub-compressors; ax, bx are the corresponding frequency values ​​of the spectrum a and b of the i-th group of frequency points; (a, b) max The maximum frequency value in the vibration spectrum a and b of the sub-compressor operation; (a, b) min It is the minimum frequency value of the vibration spectrum a and b of the sub-compressor operation;

[0019] in, Express The closer f1 and f2 are to 1, the more stable the compressor operation state is. Conversely, the higher the risk of compressor failure is.

[0020] Furthermore, based on formula (2), the vibration spectrum of all sub-compressors is used to calculate the compressor second fault judgment value f2. After the judgment values ​​of each group are obtained, the judgment values ​​of each group are further summed and averaged, which is recorded as the compressor second fault comprehensive judgment value f2′.

[0021] Furthermore, after the first fault judgment value f1 of the compressor and the second comprehensive fault judgment value f2′ of the compressor are obtained, the two sets of judgment values ​​are combined to obtain the final fault judgment value of the compressor, and the formula for obtaining the final fault judgment value is expressed as:

[0022] F=f1×f2′×(λ+|f1-f2′);

[0023] Where: F is the final fault judgment value of the compressor; λ is the normalization factor.

[0024] Furthermore, the value of the normalization factor λ obeys the following rules: the more the sub-compressor operating vibration spectra are, the closer the value of (λ+f1-f2′) is to 1, and (λ+f1-f2′)>1; conversely, the closer the value of (λ+f1-f2′) is to 1, and (λ+f1-f2′)≤1.

[0025] Furthermore, based on the comparison between the hidden fault determination threshold and the compressor hidden fault detection result, when determining whether the compressor has a hidden fault, the compressor final fault determination value F is compared with the hidden fault determination threshold.

[0026] Furthermore, when it is determined that the compressor has a hidden fault, the power supply of the compressor is cut off, the output and input ends of the compressor are controlled to be closed, and the compressor is controlled to stop running. Conversely, the refresh step is executed, and the content of the compressor operation message is all the compressor operation status parameters within the corresponding time threshold when the compressor is determined to have a hidden fault.

[0027] Compared with the known public technology, the technical solution provided by the present invention has the following advantages:

[0028] Beneficial effects:

[0029] The present invention provides a compressor fault detection algorithm based on state parameter analysis. During the execution of the algorithm, the algorithm collects and analyzes various operating parameters under the operating state of the compressor, thereby providing continuous fault judgment and hidden fault detection for the compressor, so that the operating faults and potential fault hazards of the compressor can be discovered in a timely and predictable manner, and then the start and stop control and maintenance of the compressor can be carried out in time to ensure the integrity of the operating functions of the compressor and the stability of its operation, thereby ensuring that the compressor connection mechanical device can operate more stably and continuously. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0031] Figure 1 It is a flow chart of a compressor fault detection algorithm based on state parameter analysis. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] The present invention will be further described below in conjunction with the embodiments.

[0034] Embodiment 1:

[0035] A compressor fault detection algorithm based on state parameter analysis in this embodiment is as follows: Figure 1 As shown, the following steps are included:

[0036] Step 1: Collect compressor operating status parameters in real time, build a cloud database, and use the cloud database to store the compressor operating status parameters collected in real time;

[0037] Step 2: Set a compressor fault judgment threshold, traverse the latest compressor operating status parameters stored in the cloud database in real time, compare the traversed compressor operating status parameters with the set compressor fault judgment threshold, and make a real-time judgment on the compressor operating fault based on the comparison result;

[0038] Step 3: Retrieve compressor operating status parameters from the cloud database, and perform hidden fault detection on the compressor based on the compressor operating status parameters;

[0039] When retrieving compressor operating status parameters from a cloud database, the retrieved parameter type target is the compressor operating vibration spectrum. When performing hidden fault detection on the compressor, the marking timestamp of the retrieved compressor operating status parameters is synchronously obtained, and the compressor operating status parameters are filtered based on the marking timestamp of the compressor operating status parameters.

[0040] Among them, the compressor operation status parameter screening logic is expressed as:

[0041] Logic 1: Determine the time span covered by the compressor operating status parameter based on the marked timestamp corresponding to the compressor operating status parameter;

[0042] Logic2: Set the compressor operation status parameter screening ratio, and determine the time span of the compressor operation status parameter covering the corresponding ratio in the time span based on the set screening ratio;

[0043] Logic3: Based on the time span determined in Logic2, the compressor operation status parameter corresponding to the time span is intercepted at the center of the compressor operation status parameter coverage time span;

[0044] When performing hidden fault detection on the compressor, the intercepted compressor operation status parameters are synchronously divided into equal parts, so that the compressor operation status parameters are divided into several groups of sub-compressor operation vibration spectra. The hidden fault detection logic of the compressor is expressed as follows:

[0045]

[0046] Where: f1 is the first fault judgment value of the compressor; m is the set of sub-compressor operation vibration spectra; (n max ) i 、(n min ) i is the maximum and minimum frequency values ​​in the vibration spectrum of the i-th group of sub-compressors; (n max )i+1 、(n min ) i+1 is the maximum and minimum frequency values ​​in the vibration spectrum of the i+1th group of sub-compressors; f2 is the second fault judgment value of the compressor; q is the set of frequency points in the vibration spectrum a and b of the sub-compressors; ax, bx are the corresponding frequency values ​​of the spectrum a and b of the i-th group of frequency points; (a, b) max The maximum frequency value in the vibration spectrum a and b of the sub-compressor operation; (a, b) min It is the minimum frequency value of the vibration spectrum a and b of the sub-compressor operation;

[0047] in, Express The closer f1 and f2 are to 1, the more stable the compressor operation state is. Conversely, the higher the risk of compressor failure is.

[0048] Based on formula (2), the vibration spectrum of all sub-compressors is used to calculate the second fault judgment value f2 of the compressor. After the judgment values ​​of each group are obtained, the sum of the judgment values ​​of each group is further calculated and then averaged, which is recorded as the second fault comprehensive judgment value f2′ of the compressor;

[0049] After the compressor first fault judgment value f1 and the compressor second fault comprehensive judgment value f2′ are obtained, the two sets of judgment values ​​are combined to obtain the compressor final fault judgment value, and the formula for obtaining the value is expressed as:

[0050] F=f1×f2′×(λ+|f1-f2′);

[0051] Where: F is the final fault judgment value of the compressor; λ is the normalization factor;

[0052] The value of the normalization factor λ follows the following rules: the more the number of vibration spectra of the sub-compressors is, the closer the value of (λ+f1-f2′) is to 1, and (λ+f1-f2′)>1; conversely, the closer the value of (λ+f1-f2′) is to 1, and (λ+f1-f2′)≤1;

[0053] Based on the comparison between the hidden fault judgment threshold and the hidden fault detection result of the compressor, when determining whether the compressor has a hidden fault, the final fault judgment value F of the compressor is compared with the hidden fault judgment threshold;

[0054] Step 4: Obtain the hidden fault detection result of the compressor, set the hidden fault judgment threshold of the compressor, and compare the hidden fault judgment threshold with the hidden fault detection result of the compressor to determine whether the compressor has a hidden fault;

[0055] Step 5: Obtain a determination result of whether the compressor has a hidden fault, and control the operation of the compressor based on the determination result;

[0056] Step 6: When the compressor is controlled to be turned off based on the determination result, a compressor operation message is generated.

[0057] In this embodiment, through the multi-party collection of compressor operating status parameters, a variety of operating fault judgment conditions are provided for the compressor, so that the operating fault of the compressor can be comprehensively and timely monitored, so that the operating fault of the compressor can be detected by the compressor management user in time, and then the maintenance work of the compressor can be responded to more quickly, ensuring the safe and stable operation of the compressor;

[0058] Referring to the setting of the compressor operation state parameter screening logic in the above embodiment, when the algorithm performs fault detection on the compressor, it can detect the operation fault of the compressor and possible hidden faults with more refined compressor operation state parameters, thereby ensuring that the compressor can operate more safely and stably;

[0059] Furthermore, by setting the compressor hidden fault detection logic and the compressor final fault judgment value calculation formula, the compressor fault judgment process can be output in a digital form, thereby making the compressor fault judgment process more effective and accurate.

[0060] Embodiment 2:

[0061] In terms of specific implementation, based on Example 1, this example refers to Figure 1 A compressor fault detection algorithm based on state parameter analysis in Example 1 is further described in detail:

[0062] The compressor operation status parameters include: compressor operation voltage, current, resistance, compressor operation inlet gas volume, compressor operation output gas volume, compressor operation output gas pressure, compressor operation vibration spectrum. When the cloud database stores the compressor operation status parameters, the compressor operation status parameters are differentiated and stored based on the type of the compressor operation status parameters.

[0063] When the compressor operating status parameters are stored in the cloud database, each set of compressor operating status parameters is marked with a timestamp of parameter generation.

[0064] Through the above settings, the contents included in the compressor operating status parameters are further limited, providing further data support for the execution of the steps of the algorithm in Example 1.

[0065] like Figure 1As shown, several groups of compressor fault judgment thresholds are set, and the several groups of compressor fault judgment thresholds are respectively applied to the compressor operating voltage, current, resistance, compressor operating inlet gas volume, compressor operating output gas volume, and compressor operating output gas pressure. When it is determined based on the comparison result that there is a fault in the current operating state of the compressor, the compressor power supply is synchronously cut off, the compressor output and input ends are controlled to be closed, and the compressor is controlled to stop running.

[0066] Through the above settings, the algorithm's further control logic for the compressor is further limited when a compressor fault is detected, thereby preventing the compressor fault from further deteriorating, ensuring the safe operation of the mechanical system where the compressor is located, and bringing a better user experience to compressor users.

[0067] like Figure 1 As shown, when it is determined that the compressor has a hidden fault, the power supply of the compressor is cut off, the output and input ends of the compressor are controlled to be closed, and the compressor is controlled to stop running. On the contrary, the refresh step is executed, and the content of the compressor operation message is all the compressor operation status parameters within the corresponding time threshold when the compressor is determined to have a hidden fault.

[0068] Through the above settings, when the method in the above embodiment detects that the compressor has a fault, it further generates a compressor operation message, so that the compressor management user can perform faster inspection and maintenance on the current operation fault of the compressor based on the compressor operation message.

[0069] In summary, during the execution of the algorithm in the above embodiment, the collection and analysis of various operating parameters under the operating state of the compressor bring continuous fault judgment and hidden fault detection to the compressor, so that the operating faults and potential faults of the compressor can be discovered in a timely and predictable manner, and then the start and stop control and maintenance of the compressor can be carried out in time to ensure the integrity of the operating functions of the compressor and the stability of its operation, thereby ensuring that the compressor connection mechanical device can operate more stably and continuously.

[0070] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A compressor fault detection algorithm based on state parameter analysis, characterized in that: The following steps are involved: Step 1: Collect compressor operating status parameters in real time, build a cloud database, and use the cloud database to store the compressor operating status parameters collected in real time; Step 2: Set a compressor fault judgment threshold, traverse the latest compressor operating status parameters stored in the cloud database in real time, compare the traversed compressor operating status parameters with the set compressor fault judgment threshold, and make a real-time judgment on the compressor operating fault based on the comparison result; Step 3: Retrieve compressor operating status parameters from the cloud database, and perform hidden fault detection on the compressor based on the compressor operating status parameters; Step 4: Obtain the hidden fault detection result of the compressor, set the hidden fault judgment threshold of the compressor, and compare the hidden fault judgment threshold with the hidden fault detection result of the compressor to determine whether the compressor has a hidden fault; Step 5: Obtain a determination result of whether the compressor has a hidden fault, and control the operation of the compressor based on the determination result; Step 6: When the compressor is controlled to be turned off based on the determination result, a compressor operation message is generated; The compressor operation state parameters include: compressor operation voltage, current, resistance, compressor operation inlet gas volume, compressor operation output gas volume, compressor operation output gas pressure, compressor operation vibration spectrum. When the cloud database stores the compressor operation state parameters, the compressor operation state parameters are differentiated and stored based on the type of the compressor operation state parameters. When the compressor operating status parameters are stored in the cloud database, each set of compressor operating status parameters is marked with a timestamp of parameter generation; The compressor fault determination threshold is set to several groups, and the several groups of compressor fault determination thresholds are respectively applied to the compressor operation voltage, current, resistance, compressor operation inlet gas volume, compressor operation output gas volume, and compressor operation output gas pressure. When it is determined based on the comparison result that the compressor current operation state has a fault, the compressor power supply is synchronously cut off, the compressor output end and the input end are controlled to be closed, and the compressor is controlled to stop running; When retrieving compressor operating status parameters from a cloud database, the retrieved parameter type target is the compressor operating vibration spectrum. When performing hidden fault detection on the compressor, the marking timestamp of the retrieved compressor operating status parameters is synchronously obtained, and the compressor operating status parameters are filtered based on the marking timestamp of the compressor operating status parameters. Among them, the compressor operation status parameter screening logic is expressed as: Logic 1: Determine the time span covered by the compressor operating status parameter based on the marked timestamp corresponding to the compressor operating status parameter; Logic2: Set the compressor operation status parameter screening ratio, and determine the time span of the compressor operation status parameter covering the corresponding ratio in the time span based on the set screening ratio; Logic3: Based on the time span determined in Logic2, the compressor operation status parameter corresponding to the time span is intercepted at the center of the compressor operation status parameter coverage time span; When performing hidden fault detection on the compressor, the intercepted compressor operation status parameters are synchronously divided into equal parts, so that the compressor operation status parameters are divided into several groups of sub-compressor operation vibration spectra. The hidden fault detection logic of the compressor is expressed as follows: Where: f1 is the first fault judgment value of the compressor; m is the set of sub-compressor operation vibration spectra; (n max ) i 、(n min ) i is the maximum and minimum frequency values ​​in the vibration spectrum of the i-th group of sub-compressors; (n max ) i+1 、(n min ) i+1 is the maximum and minimum frequency values ​​in the vibration spectrum of the i+1th group of sub-compressors; f2 is the second fault judgment value of the compressor; q is the set of frequency points in the vibration spectrum a and b of the sub-compressors; a x 、b x is the corresponding frequency value of the spectrum a and b of the i-th group of frequency points; (a, b) max The maximum frequency value in the vibration spectrum a and b of the sub-compressor operation; (a, b) min It is the minimum frequency value of the vibration spectra a and b of the sub-compressor operation; in, Express The closer the values ​​of f1 and f2 are to 1, the more stable the compressor operation state is. Conversely, the higher the risk of compressor failure is. Based on formula (2), the vibration spectrum of all sub-compressors is used to calculate the second fault judgment value f2 of the compressor. After the judgment values ​​of each group are obtained, the sum of the judgment values ​​of each group is further calculated and then averaged, which is recorded as the second fault comprehensive judgment value f2′ of the compressor; After the compressor first fault judgment value f1 and the compressor second fault comprehensive judgment value f2′ are obtained, the two sets of judgment values ​​are combined to obtain the compressor final fault judgment value, and the formula for obtaining the value is expressed as: F=f1×f2′×(λ+|f1-f2′|); Where: F is the final fault judgment value of the compressor; λ is the normalization factor; The value of the normalization factor λ is subject to the following conditions: the more the number of sub-compressor operation vibration spectra is, the closer the value of (λ+|f1-f2′|) is to 1, and (λ+|f1-f2′|)>1; conversely, the closer the value of (λ+|f1-f2′|) is to 1, and (λ+|f1-f2′|)≤1; Based on the comparison between the hidden fault judgment threshold and the hidden fault detection result of the compressor, when determining whether the compressor has a hidden fault, the final fault judgment value F of the compressor is compared with the hidden fault judgment threshold; When it is determined that the compressor has a hidden fault, the power supply of the compressor is cut off, the output and input ends of the compressor are controlled to be closed, and the compressor is controlled to stop running. Conversely, the refresh step is executed, and the content of the compressor operation message is all the compressor operation status parameters within the corresponding time threshold when the compressor is determined to have a hidden fault.

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