Fault diagnosis method, device and system for guide bearing of hydraulic generator

Abstract

The invention relates to a fault diagnosis method, device and system for a guide bearing of a hydraulic generator. The method comprises the following steps: acquiring operation data of the guide bearing of the hydraulic generator in a preset time period; the operation data comprises throw data and bush temperature data of the guide bearing; determining throw change trend information of the guide bearing based on the throw data by using a least square method; determining tile temperature change trend information of the guide bearing based on the tile temperature data by using a least square method; and determining the fault type of the guide bearing based on the throw change trend information and the tile temperature change trend information. According to the scheme, the accuracy of fault identification of the guide bearing is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of hydro-generators, and particularly to a fault diagnosis method, device and system for a guide bearing of a hydro-generator. Background Art

[0002] In the related art, the guide bearing of a hydro-generator is an important component in a hydro-generator unit, and its operating state directly affects the stability and safety of the unit. If the faults of the guide bearing are not diagnosed and processed in time, it may lead to the shutdown of the unit or even serious damage. The existing fault diagnosis methods mainly rely on manual experience judgment and simple threshold alarms, and it is difficult to accurately and timely detect and process faults. Summary of the Invention

[0003] To overcome the problems existing in the related art, the present disclosure provides a fault diagnosis method, device and system for a guide bearing of a hydro-generator.

[0004] According to the first aspect of the embodiments of the present disclosure, a fault diagnosis method for a guide bearing of a hydro-generator is provided, including:

[0005] Obtaining the operation data of the guide bearing of the hydro-generator within a preset time period; the operation data includes the swing data and the tile temperature data of the guide bearing;

[0006] Using the least squares method, determining the swing change trend information of the guide bearing based on the swing data;

[0007] Using the least squares method, determining the tile temperature change trend information of the guide bearing based on the tile temperature data;

[0008] Determining the fault category of the guide bearing based on the swing change trend information and the tile temperature change trend information.

[0009] In some embodiments of the present disclosure, the swing data includes the first swing in the first direction, the acquisition time of the first swing, the second swing in the second direction and the acquisition time of the second swing, and the first direction is perpendicular to the second direction;

[0010] The step of using the least squares method to determine the swing change trend information of the guide bearing based on the swing data includes:

[0011] Determining the average swing of the guide bearing within the preset time period based on the first swing, and determining the average time of the guide bearing within the preset time period based on the acquisition time of the first swing;

[0012] Based on the mean value of the swing, the mean value of the time, the first swing, and the acquisition time of the first swing, the first slope of the first swing is determined by using the least squares method to obtain the change trend information of the first swing;

[0013] Based on the second swing, the mean value of the swing of the guide bearing within the preset time period is determined, and based on the acquisition time of the second swing, the mean value of the time of the guide bearing within the preset time period is determined;

[0014] Based on the mean value of the swing, the mean value of the time, the second swing, and the acquisition time of the second swing, the second slope of the second swing is determined by using the least squares method to obtain the change trend information of the second swing.

[0015] In some embodiments of the present disclosure, the using the least squares method to determine the change trend information of the bearing temperature of the guide bearing based on the bearing temperature data includes:

[0016] Based on the bearing temperature data, the mean value of the bearing temperature, the bearing temperature deviation value, and the deviation coefficient of the guide bearing within the preset time period are determined;

[0017] Using the least squares method to calculate the third slope of the mean value of the bearing temperature, the fourth slope of the bearing temperature deviation value, and the fifth slope of the deviation coefficient to obtain the change trend information of the bearing temperature of the guide bearing.

[0018] In some embodiments of the present disclosure, the determining the fault category of the guide bearing based on the change trend information of the swing and the change trend information of the bearing temperature includes:

[0019] In the case where the first slope of the first swing data is greater than the first threshold or the second slope of the second swing data is greater than the second threshold, and the fifth slope of the deviation coefficient is greater than the third preset threshold, it is determined that the guide bearing has a fault of loose bearing bush.

[0020] In some embodiments of the present disclosure, the determining the fault category of the guide bearing based on the change trend information of the swing and the change trend information of the bearing temperature further includes:

[0021] In the case where the first slope of the first swing data is greater than the fourth threshold and the absolute value of the third slope of the mean value of the bearing temperature is less than or equal to the fifth threshold, it is determined that the guide bearing has a fault of radial unbalanced friction.

[0022] In some embodiments of the present disclosure, the determining the fault category of the guide bearing based on the change trend information of the swing and the change trend information of the bearing temperature further includes:

[0023] When the first slope of the first swing data is greater than the sixth threshold, or the second slope of the second swing data is greater than the seventh threshold, and the eighth slope of the bearing temperature deviation value is greater than the eighth threshold, it is determined that there is a fault of foreign object jamming in the guide bearing.

[0024] In some embodiments of the present disclosure, the determining the fault category of the guide bearing based on the swing change trend information and the bearing temperature change trend information further includes:

[0025] When the first slope of the first swing data is less than the ninth threshold, or the second slope of the second swing data is greater than the tenth threshold, and the third slope of the average bearing temperature is greater than the eleventh threshold, it is determined that there is a fault of too small bearing clearance in the guide bearing.

[0026] In some embodiments of the present disclosure, the determining the fault category of the guide bearing based on the swing change trend information and the bearing temperature change trend information further includes:

[0027] When the first slope of the first swing data is greater than the twelfth threshold, or the second slope of the second swing data is greater than the thirteenth threshold, and the third slope of the average bearing temperature is less than the fourteenth threshold, it is determined that there is a fault of too large bearing clearance in the guide bearing.

[0028] According to a second aspect of the embodiments of the present disclosure, there is provided a fault diagnosis device for a guide bearing of a hydrogenerator, characterized by including:

[0029] An acquisition unit, configured to acquire the operation data of the guide bearing of the hydrogenerator within a preset time period; the operation data includes the swing data and the bearing temperature data of the guide bearing;

[0030] A first determination unit, configured to use the least squares method to determine the swing change trend information of the guide bearing based on the swing data;

[0031] A second determination unit, configured to use the least squares method to determine the bearing temperature change trend information of the guide bearing based on the bearing temperature data;

[0032] A third determination unit, configured to determine the fault category of the guide bearing based on the swing change trend information and the bearing temperature change trend information.

[0033] According to a third aspect of the embodiments of the present disclosure, an electronic device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the method described in any item of the first aspect is implemented.

[0034] According to a fourth aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method described in any one of the first aspects is implemented.

[0035] According to a fifth aspect of the embodiments of the present disclosure, there is provided a computer program product including a computer program, and when the computer program is executed by a processor, the method described in any one of the first aspects is implemented.

[0036] The technical solutions provided by the embodiments of the present disclosure may include the following beneficial effects: by obtaining the operation data of the guide bearing of the hydro-generator within a preset time period; the operation data includes the swing data and the pad temperature data of the guide bearing; using the least squares method, based on the swing data, to determine the swing change trend information of the guide bearing; using the least squares method, based on the pad temperature data, to determine the pad temperature change trend information of the guide bearing; based on the swing change trend information and the pad temperature change trend information, to determine the fault category of the guide bearing. Thus, it is possible to accurately judge the fault type of the guide bearing by using the change trend of the operation data of the guide bearing, and the accuracy of fault identification of the guide bearing is improved.

[0037] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings here are incorporated into the specification and form a part of this specification, showing embodiments consistent with the present invention, and are used together with the specification to explain the principles of the present invention.

[0039] Figure 1 is a flowchart of a fault diagnosis method for a guide bearing of a hydro-generator shown according to an exemplary embodiment.

[0040] Figure 2 is a block diagram of a fault diagnosis device for a guide bearing of a hydro-generator shown according to an exemplary embodiment.

[0041] Figure 3 is a block diagram of a device for a fault diagnosis method for a guide bearing of a hydro-generator shown according to an exemplary embodiment. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. On the contrary, they are only examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0043] The terms used in the embodiments of the present disclosure are for the purpose of describing specific embodiments only and are not intended to limit the embodiments of the present disclosure. The singular forms "a" and "the" used in the embodiments of the present disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0044] It should be understood that although the terms first, second, third, etc. may be used in the embodiments of the present disclosure to describe various information, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the embodiments of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "when" as used herein may be interpreted as "when...", "while...", or "in response to determining".

[0045] In addition, various forms of processes shown in the embodiments of the present disclosure can be used, with steps reordered, added, or deleted. For example, the steps described in this application can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and no limitations are imposed herein.

[0046] In the related art, the guide bearing of a hydrogenerator is an important component in a hydrogenerator unit, and its operating state directly affects the stability and safety of the unit. If the faults of the guide bearing are not diagnosed and processed in a timely manner, it may lead to the shutdown of the unit or even serious damage. The existing fault diagnosis methods mainly rely on manual experience judgment and simple threshold alarms, and it is difficult to accurately and timely detect and handle faults.

[0047] To solve the above problems, the present disclosure provides a fault diagnosis method, device, and system for the guide bearing of a hydrogenerator. By obtaining the operating data of the guide bearing of the hydrogenerator within a preset time period; the operating data includes the swing data and the tile temperature data of the guide bearing; using the least squares method, based on the swing data, to determine the swing change trend information of the guide bearing; using the least squares method, based on the tile temperature data, to determine the tile temperature change trend information of the guide bearing; based on the swing change trend information and the tile temperature change trend information, to determine the fault category of the guide bearing. Thus, it is possible to accurately judge the fault type of the guide bearing by using the change trend of the operating data of the guide bearing, and the accuracy of fault identification for the guide bearing is improved.

[0048] Figure 1 is a flowchart of a fault diagnosis method for the guide bearing of a hydrogenerator shown according to an exemplary embodiment. As Figure 1 shown, it should be noted that the fault diagnosis method for the guide bearing of the hydrogenerator in the embodiments of the present disclosure is applied to a fault diagnosis device for the guide bearing of the hydrogenerator. AsFigure 1 As shown, the method may include the following steps:

[0049] Step 101, obtaining the operation data of the guide bearing of the hydrogenerator within a preset time period.

[0050] Wherein, the operation data includes the swing data and the tile temperature data of the guide bearing.

[0051] In some embodiments of the present application, the operation data may further include the collection time of the swing data and the tile temperature data respectively.

[0052] In some embodiments of the present application, the operation data may further include the active power of the generator and the collection time of the active power.

[0053] In one embodiment, the above preset time period may be a time period preset according to actual needs, and the operation data may be collected at a preset frequency within the above preset time period.

[0054] Step 102, using the least squares method to determine the swing change trend information of the guide bearing based on the swing data.

[0055] In one embodiment, the slope of the swing data may be calculated by using the least squares method, and the change trend of the swing data may be determined according to the magnitude of the slope value. Among them, the above change trend may include an upward trend, a downward trend and a stable state.

[0056] In some embodiments of the present application, the swing data includes the first swing in the first direction, the collection time of the first swing, the second swing in the second direction and the collection time of the second swing. The first direction is perpendicular to the second direction. Step 102 may specifically include:

[0057] Step a1, determining the average swing of the guide bearing within the preset time period based on the first swing, and determining the average time of the guide bearing within the preset time period based on the collection time of the first swing.

[0058] Step a2, using the least squares method to determine the first slope of the first swing based on the average swing, the average time, the first swing and the collection time of the first swing, and obtaining the change trend information of the first swing.

[0059] In the embodiments of the present application, the following formula may be used to calculate the average swing of the guide bearing within the preset time period and determining the average time of the guide bearing within the preset time period based on the collection time of the first swing

[0060]

[0061] Wherein, x iis the i-th first deflection data among n first deflection data, t i is the acquisition time node of the i-th first deflection data.

[0062] In the embodiments of the present application, based on the mean deflection mean time first deflection x i and the acquisition time t of the first deflection i , the first slope mV of the first deflection is calculated using the following formula X :

[0063]

[0064] As an example, a smaller threshold ∈ can be set to determine whether the trend is significant: if mV X > ∈, the trend is rising; if mV X < - ∈, the trend is falling; if ∣mV X ∣≤ ∈, it is stable.

[0065] Step a3, determining the mean deflection of the guide bearing within a preset time period based on the second deflection, and determining the mean time of the guide bearing within the preset time period based on the acquisition time of the second deflection.

[0066] Step a4, determining the second slope of the second deflection using the least squares method based on the mean deflection, mean time, second deflection, and the acquisition time of the second deflection, to obtain the change trend information of the second deflection.

[0067] In the embodiments of the present application, the mean deflection of the guide bearing within a preset time period can be calculated using the following formula and determining the mean time of the guide bearing within the preset time period based on the acquisition time of the second deflection

[0068]

[0069] where y i is the i-th second deflection data among n second deflection data, t i is the acquisition time node of the i-th second deflection data.

[0070] In the embodiments of the present application, based on the mean deflection mean time second deflection y i and the acquisition time t of the second deflection i , the second slope mV of the second deflection is calculated using the following formula Y :

[0071]

[0072] As an example, a relatively small threshold ∈ can be set to determine whether the trend is significant: if mV Y > ∈, the trend is upward; if mV Y < - ∈, the trend is downward; if ∣mV Y ∣ ≤ ∈, it is stable.

[0073] In some embodiments of the present application, the swing degrees V X 、V Y of the guide bearings in the X direction (i.e., the first direction) and the Y direction (i.e., the second direction) under the condition that the active power of the unit is ≧ 70% of the rated active power can be selected as the swing characteristic values (i.e., the first swing data and the second swing data).

[0074] Step 103: Use the least squares method to determine the temperature change trend information of the guide bearing based on the pad temperature data.

[0075] In one embodiment, the slope of the pad temperature data can be calculated by the least squares method, and the change trend of the pad temperature data can be determined according to the magnitude of the pad temperature data values. Among them, the above change trend can include an upward trend, a downward trend, and a stable state.

[0076] In some embodiments of the present application, step 103 may specifically include:

[0077] Determine the average pad temperature, the pad temperature deviation value, and the deviation coefficient of the guide bearing within a preset time period based on the pad temperature data;

[0078] Use the least squares method to calculate the third slope of the average pad temperature, the fourth slope of the pad temperature deviation value, and the fifth slope of the deviation coefficient to obtain the temperature change trend information of the guide bearing.

[0079] In the embodiments of the present application, the following formulas can be used to determine the average pad temperature W, the pad temperature deviation value R, and the deviation coefficient CV of the guide bearing within a preset time period:

[0080]

[0081] R = W max - W min ;

[0082]

[0083] Among them, W i is the temperature of the i-th pad, k is the number of bearing pads, W max is the maximum pad temperature value among the k pads, W min is the minimum pad temperature value among the k pads, σ is the standard deviation of the k pad temperatures, and μ is the average value of the k pad temperatures.

[0084] As an example, the least squares method proposed in this application can be adopted to calculate the third slope of the bearing temperature average value by using the bearing temperature average value, the bearing temperature deviation value R and the deviation coefficient CV respectively. The fourth slope mR of the bearing temperature deviation value and the fifth slope mCV of the deviation coefficient. The fourth slope mR of the bearing temperature deviation value and the fifth slope mCV of the deviation coefficient.

[0085] In some embodiments of this application, data with a generator speed ≥ 5% of the rated speed and continuous operation for more than 2 hours can be used as valid data. Based on the valid data, all bearing temperature average values, the maximum value W, the maximum value W, the bearing temperature deviation R, and the deviation coefficient CV are calculated as characteristic values. The maximum value W max The maximum value W min The bearing temperature deviation R and the deviation coefficient CV are used as characteristic values.

[0086] Step 104: Determine the fault type of the guide bearing based on the change trend information of the swing and the change trend information of the bearing temperature.

[0087] It should be noted that, in order to more accurately identify whether there is a fault in the guide bearing, this application comprehensively discriminates the fault type of the guide bearing based on the change trend of the swing of the guide bearing and the change trend of the bearing temperature over a period of time, so as to effectively avoid the problem of one-sidedness in the diagnostic results caused by diagnosing the fault of the guide bearing based only on the operation data at a single time node. In addition, since the data collected at a certain time node may be inaccurate due to some sudden reasons during the process of collecting operation data, the above fault diagnosis method adopted in this application can avoid the situation of false fault alarms caused by inaccurate individual operation data.

[0088] In some embodiments of this application, step 104 may specifically include:

[0089] When the first slope of the first swing data is greater than the first threshold or the second slope of the second swing data is greater than the second threshold, and the fifth slope of the deviation coefficient is greater than the third preset threshold, it is determined that the guide bearing has a fault of loose bearing bush.

[0090] It should be noted that after the bearing bush is loose, the vibration amplitude (especially the radial vibration) will increase significantly. Therefore, it is possible to judge whether the guide bearing has a fault of loose bearing bush based on the first slope of the first swing data, the second slope of the second swing data, and the fifth slope of the deviation coefficient.

[0091] In one embodiment, when the guide bearing meets the following determination conditions, it is determined that the guide bearing has a fault of loose bearing bush:

[0092] (mV X > a1 or mV Y > b1) and mCV > c1

[0093] Wherein, a1 is the first threshold, b1 is the second threshold, and c1 is the third threshold.

[0094] In some embodiments of the present application, step 104 may specifically further include:

[0095] When the first slope of the first swing data is greater than the fourth threshold and the absolute value of the third slope of the average bearing temperature is less than or equal to the fifth threshold, it is determined that there is a fault of radial unbalanced friction in the guide bearing.

[0096] It should be noted that when there is a seal friction in the guide bearing, the first swing of the guide bearing will continuously increase with the increase of the operation duration. Therefore, it is possible to judge whether there is a fault of radial unbalanced friction in the bearing based on the first slope of the first swing data and the fifth slope mCV of the deviation coefficient.

[0097] In one embodiment, when the guide bearing meets the following determination conditions, it is determined that there is a fault of radial unbalanced friction in the bearing:

[0098]

[0099] Wherein, a2 is the fourth threshold and d1 is the fifth threshold.

[0100] In some embodiments of the present application, when the guide bearing meets the above determination conditions within 1 day, it is determined that there is a fault of radial unbalanced friction in the bearing.

[0101] In some embodiments of the present application, step 104 may specifically further include:

[0102] When the first slope of the first swing data is greater than the sixth threshold or the second slope of the second swing data is greater than the seventh threshold, and the eighth slope of the bearing temperature deviation value is greater than the eighth threshold, it is determined that there is a fault of foreign object jamming in the guide bearing.

[0103] It should be noted that after a foreign object enters the guide bearing, it may cause an increase in local friction, thereby causing a temperature rise and a sudden change in the swing. Therefore, it is possible to judge whether there is a fault of loose bearing bush in the guide bearing based on the first slope of the first swing data, the second slope of the second swing data, and the fourth slope of the bearing temperature deviation value.

[0104] In one embodiment, when the guide bearing meets the following determination conditions, it is determined that there is a fault of loose bearing bush in the guide bearing:

[0105] mR>e1 and(mV X >a3 or mV Y >b2)

[0106] Wherein, e1 is the sixth threshold, a3 is the seventh threshold, and b2 is the eighth threshold.

[0107] In some embodiments of the present application, step 104 may specifically further include:

[0108] When the first slope of the first swing data is less than the ninth threshold or the second slope of the second swing data is greater than the tenth threshold, and the third slope of the average bearing temperature is greater than the eleventh threshold, it is determined that there is a fault of too small bearing clearance in the guide bearing.

[0109] It should be noted that too small bearing clearance is one of the common problems in the operation of hydro-generator units, which may lead to serious consequences such as increased bearing temperature, increased vibration, and even bearing burning. It is possible to judge whether there is a fault of too small bearing clearance in the guide bearing based on the first slope of the first swing data, the second slope of the second swing data, and the third slope of the average bearing temperature.

[0110] In one embodiment, when the guide bearing meets the following determination conditions, it is determined that there is a fault of too small bearing clearance in the guide bearing:

[0111]

[0112] Wherein, d2 is the ninth threshold, a4 is the tenth threshold, and b3 is the eleventh threshold.

[0113] In some embodiments of the present application, step 104 may specifically further include:

[0114] When the first slope of the first swing data is greater than the twelfth threshold or the second slope of the second swing data is greater than the thirteenth threshold, and the third slope of the average bearing temperature is less than the fourteenth threshold, it is determined that there is a fault of too large bearing clearance in the guide bearing.

[0115] It should be noted that too large bearing clearance will cause an increase in the movement range of the journal in the bearing, which will in turn cause abnormal vibration, a decrease in the overall bearing temperature, and possible fluctuations in the local bearing temperature. It is possible to judge whether there is a fault of too large bearing clearance in the guide bearing based on the first slope of the first swing data, the second slope of the second swing data, and the third slope of the average bearing temperature.

[0116] In one embodiment, when the guide bearing meets the following determination conditions, it is determined that there is a fault of too large bearing clearance in the guide bearing:

[0117]

[0118] Wherein, d3 is the twelfth threshold, a5 is the thirteenth threshold, and b4 is the fourteenth threshold.

[0119] It should be noted that in the embodiments of the present application, based on the possible changes in the operation data during different types of failures of the guide bearing, the value range of the operation data during the failure process is determined, and corresponding determination conditions are set accordingly, so that the failure types of the guide bearing can be comprehensively identified based on the change trend of the operation data, effectively improving the accuracy and rationality of the failure discrimination of the guide bearing.

[0120] According to the fault diagnosis method of the guide bearing of a hydrogenerator proposed by an embodiment of the present disclosure, the operation data of the guide bearing of the hydrogenerator within a preset time period is obtained; the operation data includes the swing data and the tile temperature data of the guide bearing; the least squares method is used to determine the swing change trend information of the guide bearing based on the swing data; the least squares method is used to determine the tile temperature change trend information of the guide bearing based on the tile temperature data; and the fault category of the guide bearing is determined based on the swing change trend information and the tile temperature change trend information. Therefore, the fault type of the guide bearing can be accurately judged by using the change trend of the operation data of the guide bearing, and the accuracy of fault identification of the guide bearing is improved.

[0121] Figure 2 It is a block diagram of a fault diagnosis device for a guide bearing of a hydrogenerator shown according to an exemplary embodiment. Refer to Figure 2 and the device includes an acquisition unit 201, a first determination unit 202, a second determination unit 203, and a third determination unit 204.

[0122] Among them, the acquisition unit 201 is configured to acquire the operation data of the guide bearing of the hydrogenerator within a preset time period; the operation data includes the swing data and the tile temperature data of the guide bearing;

[0123] The first determination unit 202 is configured to use the least squares method to determine the swing change trend information of the guide bearing based on the swing data;

[0124] The second determination unit 203 is configured to use the least squares method to determine the tile temperature change trend information of the guide bearing based on the tile temperature data;

[0125] The third determination unit 204 is configured to determine the fault category of the guide bearing based on the swing change trend information and the tile temperature change trend information.

[0126] In some embodiments of the present application, the swing data includes the first swing in the first direction, the acquisition time of the first swing, the second swing in the second direction, and the acquisition time of the second swing, the first direction is perpendicular to the second direction, and the first determination unit 202 may specifically be configured to:

[0127] Determine the average swing of the guide bearing within a preset time period based on the first swing, and determine the average time of the guide bearing within a preset time period based on the acquisition time of the first swing;

[0128] Based on the mean value of the swing, the mean value of time, the first swing, and the acquisition time of the first swing, the first slope of the first swing is determined by using the least squares method, and the change trend information of the first swing is obtained;

[0129] Based on the second swing, the mean value of the swing of the guide bearing within a preset time period is determined, and based on the acquisition time of the second swing, the mean value of time of the guide bearing within a preset time period is determined;

[0130] Based on the mean value of the swing, the mean value of time, the second swing, and the acquisition time of the second swing, the second slope of the second swing is determined by using the least squares method, and the change trend information of the second swing is obtained.

[0131] In some embodiments of the present application, the second determination unit 203 may specifically be used for:

[0132] Based on the tile temperature data, the mean value of the tile temperature, the tile temperature deviation value, and the deviation coefficient of the guide bearing within a preset time period are determined;

[0133] Using the least squares method to calculate the third slope of the mean value of the tile temperature, the fourth slope of the tile temperature deviation value, and the fifth slope of the deviation coefficient, and obtaining the change trend information of the tile temperature of the guide bearing.

[0134] In some embodiments of the present application, the third determination unit 204 may specifically further be used for:

[0135] When the first slope of the first swing data is greater than the first threshold or the second slope of the second swing data is greater than the second threshold, and the fifth slope of the deviation coefficient is greater than the third preset threshold, it is determined that the guide bearing has a fault of loose bearing bush.

[0136] In some embodiments of the present application, the third determination unit 204 may specifically further be used for:

[0137] When the first slope of the first swing data is greater than the fourth threshold and the absolute value of the third slope of the mean value of the tile temperature is less than or equal to the fifth threshold, it is determined that the guide bearing has a fault of radial unbalanced friction.

[0138] In some embodiments of the present application, the third determination unit 204 may specifically further be used for:

[0139] When the first slope of the first swing data is greater than the sixth threshold or the second slope of the second swing data is greater than the seventh threshold, and the eighth slope of the tile temperature deviation value is greater than the eighth threshold, it is determined that the guide bearing has a fault of foreign object jamming.

[0140] In some embodiments of the present application, the third determination unit 204 may specifically further be used for:

[0141] When the first slope of the first swing data is less than the ninth threshold value, or the second slope of the second swing data is greater than the tenth threshold value, and the third slope of the average bearing temperature is greater than the eleventh threshold value, it is determined that there is a fault of too small bearing clearance in the guide bearing.

[0142] In some embodiments of the present application, the third determination unit 204 may specifically be further configured to:

[0143] When the first slope of the first swing data is greater than the twelfth threshold value, or the second slope of the second swing data is greater than the thirteenth threshold value, and the third slope of the average bearing temperature is less than the fourteenth threshold value, it is determined that there is a fault of too large bearing clearance in the guide bearing.

[0144] Regarding the device in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated herein.

[0145] A fault diagnosis device for a guide bearing of a hydrogenerator according to an embodiment of the present disclosure obtains operation data of the guide bearing of the hydrogenerator within a preset time period; the operation data includes swing data and bearing temperature data of the guide bearing; uses the least squares method to determine the swing change trend information of the guide bearing based on the swing data; uses the least squares method to determine the bearing temperature change trend information of the guide bearing based on the bearing temperature data; and determines the fault category of the guide bearing based on the swing change trend information and the bearing temperature change trend information. Thus, it is possible to accurately judge the fault type of the guide bearing by using the change trend of the operation data of the guide bearing, and improve the accuracy of fault identification of the guide bearing.

[0146] Figure 3 It is a block diagram of a device for a fault diagnosis method for a guide bearing of a hydrogenerator shown according to an exemplary embodiment. For example, the device 300 may be an electronic device, such as a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0147] Referring to Figure 3 , the device 300 may include one or more of the following components: a processing component 302, a memory 304, a power component 306, a multimedia component 308, an audio component 310, an input / output (I / O) interface 312, a sensor component 314, and a communication component 316.

[0148] The processing component 302 generally controls the overall operation of the device 300, such as operations associated with display, telephone calls, data communication, camera operations, and recording operations. The processing component 302 may include one or more processors 320 to execute instructions to complete all or part of the steps of the above-described methods. In addition, the processing component 302 may include one or more modules to facilitate the interaction between the processing component 302 and other components. For example, the processing component 302 may include a multimedia module to facilitate the interaction between the multimedia component 308 and the processing component 302.

[0149] The memory 304 is configured to store various types of data to support the operation of the device 300. Examples of such data include instructions for any application or method operating on the device 300, contact data, phone book data, messages, pictures, videos, etc. The memory 304 may be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0150] The power component 306 provides power to the various components of the device 300. The power component 306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device 300.

[0151] The multimedia component 308 includes a screen that provides an output interface between the device 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe actions, but also detect the duration and pressure associated with the touch or swipe operations. In some embodiments, the multimedia component 308 includes a front camera and / or a rear camera. When the device 300 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each of the front camera and the rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0152] The audio component 310 is configured to output and / or input audio signals. For example, the audio component 310 includes a microphone (MIC) that is configured to receive external audio signals when the device 300 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 304 or transmitted via the communication component 316. In some embodiments, the audio component 310 further includes a speaker for outputting audio signals.

[0153] The I / O interface 312 provides an interface between the processing component 302 and peripheral interface modules, and the peripheral interface modules may be a keyboard, a click wheel, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a power button, and a lock button.

[0154] The sensor component 314 includes one or more sensors for providing an assessment of various aspects of the state of the device 300. For example, the sensor component 314 can detect the on / off state of the device 300, the relative positioning of components, such as the display and keypad of the device 300. The sensor component 314 can also detect a change in the position of the device 300 or a component of the device 300, the presence or absence of user contact with the device 300, the orientation or acceleration / deceleration of the device 300, and the temperature change of the device 300. The sensor component 314 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor component 314 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor component 314 may further include an acceleration sensor, a gyro sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0155] The communication component 316 is configured to facilitate communication between the device 300 and other devices in a wired or wireless manner. The device 300 can access a wireless network based on communication standards, such as WiFi, 2G, or 3G, or a combination thereof. In an exemplary embodiment, the communication component 316 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 316 further includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0156] In an exemplary embodiment, the apparatus 300 may be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components for performing the above method.

[0157] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 304 including instructions, and the above instructions can be executed by a processor 320 of the apparatus 300 to complete the above method. For example, the non-transitory computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0158] In an exemplary embodiment, a computer program product including a computer program is also provided, and the computer program implements the above method when executed by a processor 320 of the apparatus 300.

[0159] Those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common general knowledge or conventional technical means in the technical field not disclosed in this disclosure. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of the present invention are pointed out by the following claims.

[0160] It should be understood that the present invention is not limited to the exact structures described above and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A fault diagnosis method for a guide bearing of a hydro-turbine generator, characterized in that: include: Acquiring the operating data of the guide bearing of the hydro-generator within a preset time period; the operating data includes the swing data and bearing temperature data of the guide bearing; Determining the swing variation trend information of the guide bearing based on the swing data by using the least square method; Determine the bearing temperature variation trend information of the guide bearing based on the bearing temperature data by using the least square method; The fault category of the guide bearing is determined based on the swing variation trend information and the bearing temperature variation trend information.

2. The fault diagnosis method for a guide bearing of a hydro-turbine generator according to claim 1, characterized in that: The swing data includes a first swing in a first direction, a collection time of the first swing, a second swing in a second direction, and a collection time of the second swing, the first direction being perpendicular to the second direction; The method of using the least square method to determine the swing change trend information of the guide bearing based on the swing data includes: Determine a mean value of the swing of the guide bearing within the preset time period based on the first swing, and determine a time mean value of the guide bearing within the preset time period based on the acquisition time of the first swing; Based on the swing mean, the time mean, the first swing and the acquisition time of the first swing, a first slope of the first swing is determined by using a least square method to obtain change trend information of the first swing; Determining a mean value of the swing of the guide bearing within the preset time period based on the second swing, and determining a time mean value of the guide bearing within the preset time period based on a collection time of the second swing; Based on the swing mean, the time mean, the second swing and the acquisition time of the second swing, a second slope of the second swing is determined by using a least square method to obtain change trend information of the second swing.

3. The fault diagnosis method for a guide bearing of a hydro-turbine generator according to claim 2, characterized in that: The method of using the least square method to determine the bearing temperature variation trend information of the guide bearing based on the bearing temperature data includes: Determine the bearing temperature mean value, bearing temperature deviation value and deviation coefficient of the guide bearing within the preset time period based on the bearing temperature data; The third slope of the bearing temperature mean, the fourth slope of the bearing temperature deviation, and the fifth slope of the deviation coefficient are calculated using the least squares method to obtain the bearing temperature change trend information of the guide bearing.

4. The fault diagnosis method for a guide bearing of a hydro-turbine generator according to claim 3, characterized in that: The determining the fault type of the guide bearing based on the swing variation trend information and the bearing temperature variation trend information comprises: When the first slope of the first swing data is greater than the first threshold or the second slope of the second swing data is greater than the second threshold, and the fifth slope of the deviation coefficient is greater than the third preset threshold, it is determined that the guide bearing has a loose bearing failure.

5. The fault diagnosis method for a guide bearing of a hydro-turbine generator according to claim 3, characterized in that: The method of determining the fault type of the guide bearing based on the swing variation trend information and the bearing temperature variation trend information further includes: When the first slope of the first swing data is greater than a fourth threshold and the absolute value of the third slope of the bearing temperature average is less than or equal to a fifth threshold, it is determined that the guide bearing has a radial unbalanced friction fault.

6. The fault diagnosis method for a guide bearing of a hydro-turbine generator according to claim 3, characterized in that: The method of determining the fault type of the guide bearing based on the swing variation trend information and the bearing temperature variation trend information further includes: When the first slope of the first swing data is greater than the sixth threshold or the second slope of the second swing data is greater than the seventh threshold, and the eighth slope of the bearing temperature deviation value is greater than the eighth threshold, it is determined that the guide bearing has a foreign object stuck fault.

7. The fault diagnosis method for a guide bearing of a hydro-turbine generator according to claim 3, characterized in that: The method of determining the fault type of the guide bearing based on the swing variation trend information and the bearing temperature variation trend information further includes: When the first slope of the first swing data is less than the ninth threshold or the second slope of the second swing data is greater than the tenth threshold, and the third slope of the bearing temperature average is greater than the eleventh threshold, it is determined that the guide bearing has a bearing clearance that is too small.

8. The fault diagnosis method for a guide bearing of a hydro-turbine generator according to claim 3, characterized in that: The method of determining the fault type of the guide bearing based on the swing variation trend information and the bearing temperature variation trend information further includes: When the first slope of the first swing data is greater than the twelfth threshold or the second slope of the second swing data is greater than the thirteenth threshold, and the third slope of the bearing temperature average is less than the fourteenth threshold, it is determined that the guide bearing has a fault of excessive bearing clearance.

9. A fault diagnosis device for a guide bearing of a hydro-turbine generator, characterized in that: include: An acquisition unit, used for acquiring the operation data of the guide bearing of the hydro-generator within a preset time period; the operation data includes the swing data and bearing temperature data of the guide bearing; A first determining unit, configured to determine the swing variation trend information of the guide bearing based on the swing data by using a least square method; A second determining unit is used to determine the bearing temperature variation trend information of the guide bearing based on the bearing temperature data by using a least square method; The third determination unit is used to determine the fault category of the guide bearing based on the swing change trend information and the bearing temperature change trend information.

10. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

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