Conical floating sleeve drill bearing analysis method based on cloud computing

Abstract

The invention belongs to the field of industrial control, particularly relates to a cone floating sleeve drill bit bearing analysis method based on cloud computing, and aims to solve the problem that systematic errors are generated in fault diagnosis due to the fact that the actual working condition influence is ignored in the related technology. The method comprises the following steps: acquiring a bearing vibration signal; working parameters are determined, signal decomposition operation is executed according to the working parameters, and bearing characteristic components are obtained; according to the bearing characteristic component and the environmental parameters, determining an equivalent stiffness tensor of the cone floating sleeve drill bit bearing working under a preset working condition, and according to the equivalent stiffness tensor, solving a bearing system dynamic model to obtain evolution field data; determining a target feature vector according to the bearing feature component and the evolution field data, and mapping the target feature vector to a preset fault feature space; and determining the distance between the target feature vector and the plurality of typical fault feature vectors, and determining a bearing control strategy of the cone floating sleeve drill bit according to the distance. When the method is applied to bearing analysis, the accuracy is higher.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of industrial control, and particularly relates to a bearing analysis method for a cone floating sleeve drill bit based on cloud computing. BACKGROUND

[0002] The cone floating sleeve drill bit is a key tool for deep well drilling, and its core structure is composed of a cone bit body, a floating sleeve assembly and a bearing system. The bearing system is a core transmission component of the drilling tool system, and its health status directly determines the safety and timeliness of the drilling operation. At present, the industry generally adopts a scheme of "on-site data acquisition + online analysis on the cloud platform" to monitor the bearing system, that is, the working data of the bearing are collected by multiple sensors arranged on site and analyzed to determine the bearing stiffness model, and the current working state of the bearing is determined and regulated accordingly.

[0003] However, this scheme has significant limitations, that is, it relies on a static bearing stiffness model for analysis, while ignoring the continuous and dynamic influence of complex working condition parameters in the drilling process on the bearing equivalent stiffness tensor. For example, the axial load in the drilling process is not constant, but presents significant fluctuation and impact characteristics with rock breaking, drilling pressure adjustment, equipment vibration, etc., continuously affecting the contact state of the bearing rollers and raceways, and thus causing dynamic changes in the contact stress distribution inside the bearing. The static bearing stiffness model is difficult to respond to such changes in a timely manner, resulting in deviations in the bearing internal clearance and actual working conditions calculated based on this, and the real stress state and early damage signals of the bearing cannot be accurately reflected. In addition, factors such as rock hardness and cutting fluid temperature will also simultaneously affect the bearing equivalent stiffness tensor, ultimately distorting the analysis results of the cloud platform and producing systematic errors. SUMMARY

[0004] In order to solve the above-mentioned problems in the prior art, that is, the related art adopts a static stiffness model for bearing analysis, ignoring the dynamic influence of actual working condition parameters on the equivalent stiffness tensor, and causing systematic errors in the cloud platform, in a first aspect, the present application proposes a bearing analysis method for a cone floating sleeve drill bit based on cloud computing, applied to a cloud platform, the cloud platform being in communication connection with a plurality of cone floating sleeve drill bits, the method comprising: obtaining a bearing vibration signal of the cone floating sleeve drill bit under a preset working condition; determining a working parameter of the cone floating sleeve drill bit under the preset working condition, and performing a signal decomposition operation on the bearing vibration signal according to the working parameter to obtain a bearing characteristic component separated from the bearing signal; determine an equivalent stiffness tensor of the bearing of the cone float collar drill bit when working in the preset working condition according to the bearing characteristic component and the environmental parameter corresponding to the preset working condition, and solve a bearing system dynamics model about a transient deformation effect of the bearing of the cone float collar drill bit according to the equivalent stiffness tensor to obtain evolution field data, the evolution field data being used to represent a dynamic change effect of an internal clearance of the bearing of the cone float collar drill bit; determine a target feature vector according to the bearing characteristic component and the evolution field data, and map the feature vector to a preset fault feature space, the fault feature space storing a plurality of typical fault feature vectors; determine a distance between the target feature vector and the plurality of typical fault feature vectors, and determine a bearing control strategy of the cone float collar drill bit according to the distance.

[0005] In some preferred embodiments, the signal decomposition operation performed on the bearing vibration signal according to the working parameter includes: determining a bearing physical structure parameter of the cone float collar drill bit according to the working parameter; determining a signal decomposition frequency band according to the bearing physical structure parameter; performing frequency band division on the bearing vibration signal by using a wavelet packet decomposition method according to the signal decomposition frequency band to extract a designated frequency band signal in which the bearing characteristic component is located; obtaining the bearing characteristic component by envelope demodulation analysis processing the designated frequency band signal.

[0006] In some preferred embodiments, the determination of the equivalent stiffness tensor of the bearing of the cone float collar drill bit when working in the preset working condition according to the bearing characteristic component and the environmental parameter corresponding to the preset working condition includes: determining a stiffness correction coefficient according to the bearing characteristic component; determining an environmental parameter vector according to the environmental parameter; determining the equivalent stiffness tensor by a preset stiffness mapping function according to the stiffness correction coefficient and the environmental parameter vector.

[0007] In some preferred embodiments, the stiffness correction coefficient is determined by an attenuation coefficient of bearing material, envelope spectrum total energy of the bearing characteristic component and a bearing health state benchmark energy value, the environmental parameter vector is determined by an axial load, a rotating speed, a cutting fluid temperature and a rock stratum hardness coefficient, and the preset stiffness mapping function is used to indicate a corresponding relationship between the equivalent stiffness tensor and the stiffness correction coefficient and the environmental parameter vector.

[0008] In some preferred embodiments, the solving a bearing system dynamics model about the transient deformation effect of the cone float collar drill bit bearing according to the equivalent stiffness tensor comprises: taking the equivalent stiffness tensor as a stiffness characteristic parameter of the bearing system, and combining inertia characteristics and damping characteristics of the bearing-drill pipe system, a bearing dynamic working load generated by a weight on bit curve, and a coupling relationship between a float collar dynamic deformation and a system displacement, a bearing system dynamics model for describing the transient deformation effect of the cone float collar drill bit bearing system is constructed; solving the bearing system dynamics model to obtain a three-dimensional space-time matrix containing an axial gap value and a longitudinal gap value of the cone float collar drill bit bearing, and taking the three-dimensional space-time matrix as the evolution field data.

[0009] In some preferred embodiments, the determining a target feature vector according to the bearing characteristic component and the evolution field data comprises: extracting a time domain kurtosis and an envelope spectrum peak value factor from the bearing characteristic component; extracting a radial gap change rate and an axial gap standard deviation from the evolution field data; determining the target feature vector according to the time domain kurtosis, the envelope spectrum peak value factor, the radial gap change rate, and the axial gap standard deviation, the target feature vector being sequentially composed of the time domain kurtosis, the envelope spectrum peak value factor, the radial gap change rate, and the axial gap standard deviation.

[0010] In some preferred embodiments, a distance between the target feature vector and each typical fault feature vector in the fault feature space satisfies a Mahalanobis distance formula, and the determining the distance between the target feature vector and each typical fault feature vector in the fault feature space comprises: determining a target typical fault feature vector, the target typical fault feature vector being any one of the typical fault feature vectors in the fault feature space; determining a difference vector between the target feature vector and the target typical fault feature vector; determining an inverse matrix of a historical fault sample covariance matrix; determining a product result of the difference vector and the inverse matrix, and multiplying the product result by a transpose matrix of the difference vector to obtain the distance between the target feature vector and the target typical fault feature vector.

[0011] In some preferred embodiments, the determining a bearing maintenance strategy of the cone float collar drill bit according to the distance comprises: determining a target threshold value, the target threshold value being used to indicate a standard working state of the cone float collar drill bit bearing; determining a bearing control strategy of the cone float collar drill bit by comparing the target threshold value with the distance; sending the bearing control strategy to a control center of the cone float collar drill bit, so that the control center performs corresponding operations according to the maintenance strategy.

[0012] In some preferred embodiments, the determining a bearing control strategy of the cone float collar drill bit by comparing the target threshold value with the distance comprises: comparing the distance with numerical values of the target threshold value, the target threshold value including a safety threshold value, a pre-warning threshold value and a critical threshold value; if the distance is greater than the safety threshold value, determining a monitoring instruction of the cone float collar drill bit bearing to monitor the working state of the cone float collar drill bit bearing in real time; if the distance is greater than the pre-warning threshold value, determining a pre-warning instruction of the cone float collar drill bit bearing to monitor the stress concentration area of the cone float collar drill bit bearing; if the distance is greater than the critical threshold value, determining a dismounting and replacing instruction of the cone float collar drill bit bearing.

[0013] The present application has the following beneficial effects: (1) The present application ensures that the separated bearing characteristic component avoids the dominant frequency band of the working condition noise by decomposing the signal process under the preset working condition, thereby effectively eliminating the feature confusion caused by environmental interference and solving the problem of insufficient signal-to-noise ratio of the traditional blind decomposition method under variable working conditions; (2) The present application determines the equivalent stiffness tensor by fusing the bearing characteristic component of the drill bit bearing and the environmental parameters corresponding to the working condition, avoids the gap deviation caused by stiffness misalignment, and the equivalent stiffness tensor can better adapt to different working conditions, so that the dynamic change effect of the captured internal gap of the cone float collar drill bit bearing is more accurate, thereby improving the reliability of the final bearing control strategy relative to the current working condition; (3) The present application constructs a common target feature vector based on the evolution field data and the bearing vibration characteristics, and uses the target feature vector to measure the distance between vectors in the fault feature space, so that the final output bearing control strategy has higher practicability. BRIEF DESCRIPTION OF DRAWINGS

[0014] Other features, objects and advantages of the present application will become more apparent from the following detailed description of non-limiting embodiments, made with reference to the accompanying drawings: Figure 1 is a flowchart of a cone float collar drill bit bearing analysis method based on cloud computing according to an embodiment of the present application; Figure 2This is a schematic diagram of the structure of a cloud computing-based cone floating sleeve drill bit bearing analysis device proposed in an embodiment of the present invention; Figure 3 This is a schematic diagram of the structure of a computer system used to implement the methods and apparatus embodiments of this application. Detailed Implementation

[0015] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0016] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0017] like Figure 1 As shown, the first embodiment of the present invention provides a cloud computing-based method for analyzing tapered floating sleeve drill bit bearings. The method includes steps S10-S50, each of which is described in detail below: Step S10: Obtain the bearing vibration signal of the cone floating sleeve drill bit under preset working conditions; In this embodiment, the preset working condition refers to the actual working conditions of the test cone floating sleeve drill bit at the current time (such as the drill bit working in a granite layer, Fa=25.3kN). Its bearing vibration signal refers to the periodic data obtained by collecting the vibration signal of the bearing at a certain frequency, which can effectively characterize the physical information of the bearing's operating state.

[0018] In this embodiment, the vibration signal of the bearing can be acquired in real time by a sensor installed in the bearing housing. The system includes at least a vibration sensor, a signal conditioner and a corresponding communication module. The vibration sensor can be a piezoelectric accelerometer or a magnetoelectric velocity sensor, etc. It can be installed in the bearing housing of the tapered floating sleeve drill bit or in the rigid structure of the drill bit housing. It should at least acquire the radial (horizontal and perpendicular to the drill bit axis) and axial (along the drill bit axis) vibration signals of the bearing to comprehensively reflect the vibration state of the bearing in different directions.

[0019] Step S20: Determine the working parameters of the cone floating sleeve drill bit under the preset working conditions, and perform a signal decomposition operation on the bearing vibration signal according to the working parameters to obtain the bearing characteristic components separated from the bearing signal; In this embodiment, the working parameters of the cone float collar bit in the preset working condition at least include the bearing physical structure parameters (such as the number of rollers of the bearing, the pitch circle diameter, etc.) of the cone float collar bit and the kinematic constraint parameters (such as the rotational speed of the bearing, the axial load, etc.) thereof in the working state. The signal decomposition operation referred to in this embodiment refers to processing the vibration signal by using a given signal decomposition technology (for example, empirical mode decomposition (EMD), variational mode decomposition (VMD) or wavelet packet decomposition, etc.) and then extracting a characteristic component capable of effectively representing the dynamic behavior of the bearing body from the bearing signal containing multiple noises.

[0020] In step S30, the equivalent stiffness tensor of the bearing of the cone float collar bit in the working state in the preset working condition is determined according to the bearing characteristic component and the environmental parameter corresponding to the preset working condition, and a bearing system dynamics model about the transient deformation effect of the bearing of the cone float collar bit is solved according to the equivalent stiffness tensor to obtain evolution field data, which is used to represent the dynamic change effect of the internal clearance of the bearing of the cone float collar bit. In this embodiment, the environmental parameter corresponding to the preset working condition refers to a related parameter (such as the environmental temperature, the rock hardness, etc.) that has an effect on the bearing of the cone float collar bit in the working state in the preset working condition.

[0021] It is easy to understand that the essence of the equivalent stiffness tensor is a matrix used to describe the multidirectional stiffness characteristics of the bearing system, and compared with the equivalent stiffness tensor in the prior art, the equivalent stiffness tensor in this embodiment is calculated according to the environmental parameter and the bearing characteristic component, which has stronger dynamic adaptability to real-time working conditions.

[0022] Specifically, the equivalent stiffness tensor of the bearing of the cone float collar bit in the working state in the preset working condition is determined according to the bearing characteristic component and the environmental parameter corresponding to the preset working condition, which includes: A stiffness correction coefficient is determined according to the bearing characteristic component, an environmental parameter vector is determined according to the environmental parameter, and the equivalent stiffness tensor is determined by a preset stiffness mapping function according to the stiffness correction coefficient and the environmental parameter vector.

[0023] Specifically, the stiffness correction coefficient is determined by the attenuation coefficient of the bearing material, the envelope spectrum total energy of the bearing characteristic component and the bearing health state reference energy value, the environmental parameter vector is determined by the axial load, the rotational speed, the cutting fluid temperature and the rock hardness coefficient, and the preset stiffness mapping function is used to indicate the corresponding relationship between the equivalent stiffness tensor and the stiffness correction coefficient and the environmental parameter vector.

[0024] More specifically, the stiffness correction coefficient satisfies: ; Wherein, δ is a stiffness correction coefficient, k1 is a bearing material attenuation coefficient, E s is the envelope spectrum total energy of bearing characteristic components, E0 is the bearing health state reference energy value; The environmental parameter vector satisfies: ; F a is the axial load, ω is the rotating speed, T is the cutting fluid temperature, H r is the rock hardness coefficient; The preset stiffness mapping function satisfies: ; Wherein, K eq is the equivalent stiffness tensor of the bearing system, K0 is the initial stiffness matrix of the bearing system, and Φ() is the nonlinear response function corresponding to the environmental parameters; The nonlinear response function corresponding to the environmental parameters refers to the response function determined according to the largest number of parameters in the environmental parameters that have the greatest influence on the bearing system, and the specific form can be determined by a person skilled in the art according to the actual situation of various working conditions. In this embodiment, a specific form of the nonlinear response function is given, which satisfies: ; Wherein, A is the maximum stiffness gain coefficient, B is the load saturation factor, F a is the axial load, C is the oil film strengthening coefficient, ω is the rotating speed, D is the material thermal sensitivity coefficient, λ is the temperature attenuation index, H r is the dimensionless rock hardness, λ is the stiffness temperature attenuation rate, and T is the cutting fluid temperature.

[0025] Step S40, determining a target feature vector according to the bearing characteristic components and the evolution field data, and mapping the feature vector to a preset fault feature space, wherein a plurality of typical fault feature vectors are stored in the fault feature space; Specifically, the bearing system dynamics model about the transient deformation effect of the cone floating sleeve drill bit bearing is solved according to the equivalent stiffness tensor, including: The equivalent stiffness tensor is taken as the stiffness characteristic parameter of the bearing system, and the inertia characteristic, the damping characteristic of the bearing-drill rod system, the bearing dynamic working load generated by the drilling pressure curve, and the coupling relationship between the floating sleeve dynamic deformation and the system displacement are combined to construct a bearing system dynamics model for describing the transient deformation effect of the cone floating sleeve drill bit bearing system. The bearing system dynamics model is solved to obtain a three-dimensional space-time matrix containing the axial gap value and the longitudinal gap value of the cone floating sleeve drill bit bearing, and the three-dimensional space-time matrix is taken as the evolution field data.

[0026] More specifically, the bearing system dynamics model, the calculation formula satisfies: ; In the formula, M is the bearing-drill pipe system mass matrix, C is the system damping matrix, X is the system displacement vector, F(t) is the dynamic working load of the bearing, t is time, G(X) is the floating sleeve deformation gradient function, β is the floating sleeve deformation coupling coefficient, F(t) is the dynamic load generated according to the drilling pressure curve.

[0027] More specifically, according to the bearing characteristic component and the evolution field data, a target feature vector is determined, comprising: The time domain kurtosis and the envelope spectrum peak value factor are extracted from the bearing characteristic component; the radial gap change rate and the axial gap standard deviation are extracted from the evolution field data; the target feature vector is determined according to the time domain kurtosis, the envelope spectrum peak value factor, the radial gap change rate and the axial gap standard deviation, the target feature vector is sequentially composed of the time domain kurtosis, the envelope spectrum peak value factor, the radial gap change rate and the axial gap standard deviation, and satisfies: .

[0028] Step S50, the distance between the target feature vector and a plurality of the typical fault feature vectors is determined, and a bearing control strategy of the cone floating sleeve drill bit is determined according to the distance.

[0029] Among them, the distance between the target feature vector and a plurality of the typical fault feature vectors includes but is not limited to, such as: Euclidean distance, cosine similarity, Mahalanobis distance, etc.

[0030] As a feasible implementation manner, the distance between the target feature vector and each typical fault feature vector in the fault feature space satisfies the Mahalanobis distance, and the distance between the target feature vector and each typical fault feature vector in the fault feature space is determined, comprising: A target typical fault feature vector is determined, the target typical fault feature vector is any one of the typical fault feature vectors in the fault feature space; a difference vector between the target feature vector and the target typical fault feature vector is determined; an inverse matrix of a historical fault sample covariance matrix is determined; a product result of the difference vector and the inverse matrix is determined, and the product result is multiplied by a transpose matrix of the difference vector to obtain the distance between the target feature vector and the target typical fault feature vector.

[0031] Taking the Mahalanobis distance as an example, the distance between the target feature vector and a plurality of the typical fault feature vectors satisfies: ; In the formula, DM is the distance between the target feature vector and any one typical fault feature vector, Σ-1 is the inverse matrix of the historical fault sample covariance matrix, Vt is the target feature vector, and Vi is the i-th typical fault feature vector in the preset fault feature space.

[0032] More specifically, the determining the bearing maintenance strategy of the cone float collar drill bit according to the distance comprises: determining a target threshold value for indicating a standard working state of the cone float collar drill bit bearing; determining the bearing control strategy of the cone float collar drill bit by comparing the target threshold value with the distance; and sending the bearing control strategy to a control center of the cone float collar drill bit so that the control center performs corresponding operations according to the maintenance strategy.

[0033] The determining the bearing control strategy of the cone float collar drill bit by comparing the target threshold value with the distance comprises: comparing the distance with the numerical value of the target threshold value, the target threshold value including a safety threshold value, a pre-warning threshold value, and a critical threshold value; if the distance is greater than the safety threshold value, determining a monitoring instruction of the cone float collar drill bit bearing to monitor the working state of the cone float collar drill bit bearing in real time; if the distance is greater than the pre-warning threshold value, determining a pre-warning instruction of the cone float collar drill bit bearing to monitor the stress concentration area of the cone float collar drill bit bearing; and if the distance is greater than the critical threshold value, determining a dismounting and replacing instruction of the cone float collar drill bit bearing.

[0034] Further, in the above embodiment, the performing signal decomposition operation on the bearing vibration signal according to the working parameter comprises: determining a bearing physical structure parameter of the cone float collar drill bit according to the working parameter; determining a signal decomposition frequency band according to the bearing physical structure parameter; performing frequency band division on the bearing vibration signal by using a wavelet packet decomposition method according to the signal decomposition frequency band, and extracting a designated frequency band signal in which the bearing characteristic component is located; and obtaining the bearing characteristic component by processing the designated frequency band signal through envelope demodulation analysis.

[0035] In addition to the wavelet packet decomposition technology, methods such as empirical mode decomposition or independent component analysis can also be used for signal decomposition in the bearing vibration signal, and the present embodiment does not make too many limitations in this regard.

[0036] Although the above steps are described in the above order in the above embodiment, those skilled in the art can understand that, in order to achieve the effect of the present embodiment, the different steps do not have to be executed in such an order, and they can be executed simultaneously (in parallel) or in a reversed order, and these simple changes are within the protection scope of the present application.

[0037] The second embodiment of the present application provides a cloud computing-based bearing analysis device for a cone float collar drill bit, which is used to implement the method related to the first embodiment.

[0038] In some embodiments, the cloud computing-based bearing analysis device for the cone float collar drill bit can also be built by hardware devices, for example, the cloud computing-based bearing analysis device for the cone float collar drill bit can be built by one or more chips, and each chip can work in coordination to complete the cloud computing-based bearing analysis method for the cone float collar drill bit as described in each embodiment. For another example, the cloud computing-based bearing analysis device for the cone float collar drill bit can also be built by various logic devices, such as a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a single-chip microcomputer, an ARM (Acorn RISC Machine), or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, or any combination of these components.

[0039] Please refer to Figure 2 , Figure 2 The structure of the cloud computing-based bearing analysis device for the cone float collar drill bit is shown in FIG. 2. As shown in the figure, the cloud computing-based bearing analysis device for the cone float collar drill bit includes: A data acquisition module 210 is configured to acquire a bearing vibration signal of a cone float collar drill bit under a preset working condition. A signal separation module 220 is configured to determine a working parameter of the cone float collar drill bit under the preset working condition, and perform a signal decomposition operation on the bearing vibration signal according to the working parameter to obtain a bearing characteristic component separated from the bearing signal. A first calculation module 230 is configured to determine an equivalent stiffness tensor of a cone float collar drill bit bearing when the bearing works under the preset working condition according to the bearing characteristic component and an environmental parameter corresponding to the preset working condition, and solve a bearing system dynamics model about a transient deformation effect of the cone float collar drill bit bearing according to the equivalent stiffness tensor to obtain evolution field data, which is used to represent a dynamic change effect of an internal clearance of the cone float collar drill bit bearing. A second calculation module 240 is configured to determine a target feature vector according to the bearing characteristic component and the evolution field data, and map the feature vector to a preset fault feature space in which a plurality of typical fault feature vectors are stored. The strategy output module 250 is configured to determine distances between the target feature vector and a plurality of the typical fault feature vectors, and determine a bearing control strategy of the cone float collar drill bit according to the distances.

[0040] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process and related description of the device described above can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0041] It should be noted that the cone float collar drill bit bearing analysis device based on cloud computing provided in the above embodiments is only exemplified by the division of the above functional modules, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the modules or steps in the embodiments of the present application are further decomposed or combined, for example, the modules of the above embodiments can be combined into one module, or can be further split into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present application are only for distinguishing the respective modules and steps, and should not be considered as an improper limitation of the present application.

[0042] The third embodiment of the present application is a computer readable storage medium, which stores computer instructions for being executed by the computer to implement the above-mentioned cone float collar drill bit bearing analysis method based on cloud computing.

[0043] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process and related description of the electronic device and the computer readable storage medium described above can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0044] Those skilled in the art should be aware that the modules and method steps of each example described in connection with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. The programs corresponding to the software modules and method steps can be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. In order to clearly illustrate the interchangeability of electronic hardware and software, the components and steps of each example have been described in the foregoing description in general terms. Whether the functions are performed in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0045] Reference will now be made to the drawings Figure 3 which shows a structural schematic diagram of a computer system of a server for implementing the method and device embodiments of the present application. Figure 3 The server shown is merely an example and should not bring any limitation to the functions and usage range of the embodiments of the present application.

[0046] As shown in Figure 3 , the computer system includes a central processing unit (CPU) 301 which can perform various appropriate actions and processes according to programs stored in a read only memory (ROM) 302 or loaded from a storage section 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, the ROM 302 and the RAM 303 are connected to each other through a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0047] The following components are connected to the I / O interface 305: an input section 306 including a keyboard, a mouse, etc.; an output section 307 including a display such as a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; the storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to the I / O interface 305 as necessary. A removable recording medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 310 as necessary, so that a computer program read therefrom is installed into the storage section 308 as necessary.

[0048] In particular, the processes described above with reference to the flowcharts can be implemented as a computer software program according to embodiments of the present disclosure. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a computer readable medium, the computer program comprising program code for performing the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, the above-described functions defined in the methods of the present application are performed. It should be noted that the computer readable medium of the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. The computer readable storage medium may, for example, be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any suitable combination of the above. More specific examples of the computer readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus or device. In the present application, the computer readable signal medium can include a data signal carried in a baseband or as part of a carrier wave, in which the computer readable program code is carried. Such a propagated data signal can take any of a variety of forms, including but not limited to electro-magnetic, optical, or any suitable combination thereof. The computer readable signal medium can also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate or transport a program for use by or in connection with an instruction execution system, apparatus or device. Program code contained on the computer readable medium can be transmitted by any suitable medium, including but not limited to wireless, wire line, optical fiber cable, RF, etc., or any suitable combination of the above.

[0049] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0050] The computer program instructions can also be loaded onto a computer or other programmable information processing apparatus to cause a series of operations to be performed on the computer or other programmable information processing apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable information processing apparatus implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0051] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. For example, singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0052] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. For example, singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0053] The technical scheme of the present application has been described in combination with the preferred embodiments shown in the drawings, but it is easy for those skilled in the art to understand that the protection scope of the present application is obviously not limited to these specific embodiments. Those skilled in the art can make equivalent changes or replacements to the related technical features without departing from the principles of the present application, and the technical schemes after the changes or replacements will all fall within the protection scope of the present application.

Claims

1. A cloud computing-based analysis method for cone floating sleeve drill bit bearings, characterized in that, The method is applied to a cloud platform, which is communicatively connected to multiple tapered floating sleeve drill bits, and includes: Acquire bearing vibration signals of the tapered floating sleeve drill bit under preset working conditions; The working parameters of the tapered floating sleeve drill bit under the preset working conditions are determined, and a signal decomposition operation is performed on the bearing vibration signal according to the working parameters to obtain the bearing characteristic components separated from the bearing signal; Based on the bearing characteristic components and the environmental parameters corresponding to the preset working conditions, the equivalent stiffness tensor of the tapered floating sleeve drill bit bearing when working under the preset working conditions is determined. Based on the equivalent stiffness tensor, the dynamic model of the bearing system regarding the transient deformation effect of the tapered floating sleeve drill bit bearing is solved to obtain evolution field data. The evolution field data is used to characterize the dynamic change effect of the internal clearance of the tapered floating sleeve drill bit bearing. Based on the bearing characteristic components and the evolution field data, a target feature vector is determined, and the feature vector is mapped to a preset fault feature space, which stores multiple typical fault feature vectors. The distance between the target feature vector and each typical fault feature vector in the fault feature space is determined, and the bearing control strategy of the cone floating sleeve drill bit is determined based on the distance.

2. The cloud computing-based analysis method for cone floating sleeve drill bit bearings according to claim 1, characterized in that, The step of performing signal decomposition operation on the bearing vibration signal according to the operating parameters includes: Based on the aforementioned operating parameters, determine the bearing physical structure parameters of the tapered floating sleeve drill bit; Based on the physical structural parameters of the bearing, the signal decomposition frequency band is determined; Based on the signal decomposition frequency band, the wavelet packet decomposition method is used to divide the bearing vibration signal into frequency bands and extract the specified frequency band signal where the bearing characteristic component is located. The bearing characteristic components are obtained by processing the specified frequency band signal through envelope demodulation analysis.

3. The cloud computing-based analysis method for cone floating sleeve drill bit bearings according to claim 1, characterized in that, The step of determining the equivalent stiffness tensor of the tapered floating sleeve drill bit bearing under the preset working condition based on the bearing characteristic components and the environmental parameters corresponding to the preset working condition includes: Determine the stiffness correction coefficient based on the bearing characteristic components; Determine the environmental parameter vector based on the environmental parameters; The equivalent stiffness tensor is determined by a preset stiffness mapping function based on the stiffness correction coefficient and the environmental parameter vector.

4. The cloud computing-based analysis method for cone floating sleeve drill bit bearings according to claim 3, characterized in that, The stiffness correction coefficient is determined by the attenuation coefficient of the bearing material, the total energy of the envelope spectrum of the bearing characteristic components, and the reference energy value of the bearing health state. The environmental parameter vector is determined by the axial load, rotational speed, cutting fluid temperature, and rock hardness coefficient. The preset stiffness mapping function is used to indicate the correspondence between the equivalent stiffness tensor, the stiffness correction coefficient, and the environmental parameter vector.

5. The cloud computing-based analysis method for cone floating sleeve drill bit bearings according to claim 1, characterized in that, The process of solving the bearing system dynamic model for the transient deformation effect of the tapered floating sleeve drill bit bearing based on the equivalent stiffness tensor includes: Using the equivalent stiffness tensor as the stiffness characteristic parameter of the bearing system, and combining the inertial characteristics and damping characteristics of the bearing-drill pipe system, the dynamic working load of the bearing generated by the drill pressure curve, and the coupling relationship between the dynamic deformation of the floating sleeve and the system displacement, a dynamic model of the bearing system for describing the transient deformation effect of the tapered floating sleeve drill bit bearing system is constructed. Solve the dynamic model of the bearing system to obtain a three-dimensional spatiotemporal matrix containing the axial clearance value and longitudinal clearance value of the tapered floating sleeve drill bit bearing, and use the three-dimensional spatiotemporal matrix as the evolution field data.

6. The cloud computing-based analysis method for cone floating sleeve drill bit bearings according to claim 1, characterized in that, The step of determining the target feature vector based on the bearing feature components and the evolution field data includes: Extract the temporal kurtosis and envelope spectrum peak factor from the bearing characteristic components; The radial clearance change rate and axial clearance standard deviation are extracted from the evolution field data; The target feature vector is determined based on the temporal kurtosis, the envelope spectrum peak factor, the radial gap change rate, and the axial gap standard deviation. The target feature vector is composed of the temporal kurtosis, the envelope spectrum peak factor, the radial gap change rate, and the axial gap standard deviation in sequence.

7. The cloud computing-based analysis method for cone floating sleeve drill bit bearings according to claim 1, characterized in that, The distance between the target feature vector and each typical fault feature vector in the fault feature space satisfies Mahalanobis distance. Determining the distance between the target feature vector and each typical fault feature vector in the fault feature space includes: Determine the target typical fault feature vector, wherein the target typical fault feature vector is any typical fault feature vector in the fault feature space; Determine the difference vector between the target feature vector and the target typical fault feature vector; Determine the inverse of the covariance matrix of historical fault samples; Determine the product of the difference vector and the inverse matrix, and multiply the product by the transpose of the difference vector to obtain the distance between the target feature vector and the target typical fault feature vector.

8. The cloud computing-based analysis method for cone floating sleeve drill bit bearings according to claim 1, characterized in that, The method for determining the bearing maintenance strategy for the tapered floating sleeve drill bit based on the distance includes: Determine a target threshold, which is used to indicate the standard operating state of the tapered floating sleeve drill bit bearing; The bearing control strategy of the cone floating sleeve drill bit is determined by comparing the target threshold with the distance. The bearing control strategy is sent to the control center of the tapered floating sleeve drill bit, so that the control center can perform corresponding operations according to the maintenance strategy.

9. The cloud computing-based analysis method for cone floating sleeve drill bit bearings according to claim 8, characterized in that, The step of determining the bearing control strategy for the tapered floating sleeve drill bit by comparing the target threshold with the distance includes: The distance is compared with the value of the target threshold, which includes a safety threshold, a warning threshold, and a critical threshold. If the distance is greater than the safety threshold, a monitoring command for the cone floating sleeve drill bit bearing is determined to monitor the bearing working status of the cone floating sleeve drill bit in real time. If the distance is greater than the warning threshold, a warning command for the tapered floating sleeve drill bit bearing is determined to monitor the stress concentration area of ​​the tapered floating sleeve drill bit bearing. If the distance is greater than the critical threshold, a disassembly and replacement instruction for the tapered floating sleeve drill bit bearing is determined.

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