A cloud computing-based bearing analysis method for cone float collar drill bits

Abstract

The present application belongs to the field of industrial control, and particularly relates to a bearing analysis method for a cone float-over bushing drill bit based on cloud computing, aiming at solving the problem of systematic errors in fault diagnosis caused by neglecting the influence of actual working conditions in related technologies. The method comprises: obtaining a bearing vibration signal; determining a working parameter and performing a signal decomposition operation according to the working parameter to obtain a bearing characteristic component; determining an equivalent stiffness tensor of the cone float-over bushing drill bit bearing when working in a preset working condition according to the bearing characteristic component and an environmental parameter, and solving a bearing system dynamics model according to the equivalent stiffness tensor to obtain evolution field data; determining a target feature vector according to the bearing characteristic component and the evolution field data, and mapping the target feature vector to a preset fault feature space; determining the distance between the target feature vector and a plurality of typical fault feature vectors, and determining a bearing control strategy for the cone float-over bushing drill bit according to the distance. The method has higher accuracy when applied to bearing analysis.

Description

Technical Field

[0001] This invention belongs to the field of industrial control, and specifically relates to a cloud computing-based analysis method for cone floating sleeve drill bit bearings. Background Technology

[0002] The tapered floating sleeve drill bit is a key tool for deep well drilling. Its core structure consists of a tapered drill bit body, a floating sleeve assembly, and a bearing system. The bearing system, as the core transmission component of the drilling tool system, directly determines the safety and timeliness of drilling operations. Currently, the industry commonly uses a "field data acquisition + cloud platform online analysis" approach to monitor the bearing system. This involves collecting and analyzing bearing operating data through multiple sensors deployed on-site to determine the bearing stiffness model, thereby determining the current operating status of the bearing and making adjustments accordingly.

[0003] However, this approach has significant limitations. It relies on a static bearing stiffness model for analysis, neglecting the continuous and dynamic impact of complex operating parameters during drilling on the bearing's equivalent stiffness tensor. For example, the axial load during drilling is not constant but fluctuates significantly with drill bit rock breaking, drill pressure adjustments, and equipment vibrations, continuously affecting the contact state between the bearing rollers and raceways. This leads to dynamic changes in the internal contact stress distribution of the bearing, which the static bearing stiffness model cannot respond to in a timely manner. This results in discrepancies between the calculated bearing internal clearance and the actual operating conditions, failing to accurately reflect the bearing's true stress state and early damage signals. Furthermore, factors such as rock hardness and cutting fluid temperature also simultaneously affect the bearing's equivalent stiffness tensor, ultimately distorting the analysis results on the cloud platform and generating systematic errors. Summary of the Invention

[0004] To address the aforementioned problems in existing technologies, namely, the use of static stiffness models for bearing analysis that neglects the dynamic influence of actual operating parameters on the equivalent stiffness tensor, leading to systematic errors in the cloud platform, this invention, in its first aspect, proposes a cloud-based method for analyzing cone-shaped floating sleeve drill bit bearings. This method is applied to a cloud platform that is communicatively connected to multiple cone-shaped floating sleeve drill bits. The method includes:

[0005] Acquire bearing vibration signals of the tapered floating sleeve drill bit under preset working conditions;

[0006] 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;

[0007] 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.

[0008] 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.

[0009] The distance between the target feature vector and multiple typical fault feature vectors is determined, and the bearing control strategy of the cone floating sleeve drill bit is determined based on the distance.

[0010] In some preferred embodiments, performing signal decomposition operation on the bearing vibration signal according to the operating parameters includes:

[0011] Based on the aforementioned operating parameters, determine the bearing physical structure parameters of the tapered floating sleeve drill bit;

[0012] Based on the physical structural parameters of the bearing, the signal decomposition frequency band is determined;

[0013] 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.

[0014] The bearing characteristic components are obtained by processing the specified frequency band signal through envelope demodulation analysis.

[0015] In some preferred embodiments, 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:

[0016] Determine the stiffness correction coefficient based on the bearing characteristic components;

[0017] Determine the environmental parameter vector based on the environmental parameters;

[0018] The equivalent stiffness tensor is determined by a preset stiffness mapping function based on the stiffness correction coefficient and the environmental parameter vector.

[0019] In some preferred embodiments, 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; and 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.

[0020] In some preferred embodiments, 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:

[0021] 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.

[0022] 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.

[0023] In some preferred embodiments, determining the target feature vector based on the bearing feature components and the evolution field data includes:

[0024] Extract the temporal kurtosis and envelope spectrum peak factor from the bearing characteristic components;

[0025] The radial clearance change rate and axial clearance standard deviation are extracted from the evolution field data;

[0026] 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.

[0027] In some preferred embodiments, the distance between the target feature vector and each typical fault feature vector in the fault feature space satisfies the Mahalanobis distance formula, and determining the distance between the target feature vector and each typical fault feature vector in the fault feature space includes:

[0028] 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;

[0029] Determine the difference vector between the target feature vector and the target typical fault feature vector;

[0030] Determine the inverse of the covariance matrix of historical fault samples;

[0031] 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.

[0032] In some preferred embodiments, determining the bearing maintenance strategy for the tapered floating sleeve drill bit based on the distance includes:

[0033] Determine a target threshold, which is used to indicate the standard operating state of the tapered floating sleeve drill bit bearing;

[0034] The bearing control strategy of the cone floating sleeve drill bit is determined by comparing the target threshold with the distance.

[0035] 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.

[0036] In some preferred embodiments, determining the bearing control strategy for the tapered floating sleeve drill bit by comparing the target threshold with the distance includes:

[0037] The distance is compared with the value of the target threshold, which includes a safety threshold, a warning threshold, and a critical threshold.

[0038] 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.

[0039] 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.

[0040] If the distance is greater than the critical threshold, a disassembly and replacement instruction for the tapered floating sleeve drill bit bearing is determined.

[0041] The beneficial effects of this invention are:

[0042] (1) The present invention constrains the signal decomposition process by setting the working parameters under the preset working conditions, ensuring that the separated bearing feature components avoid the frequency band dominated by the working condition noise, thereby effectively eliminating feature confusion caused by environmental interference and solving the problem of insufficient signal-to-noise ratio of traditional blind decomposition methods under varying working conditions.

[0043] (2) The present invention determines the equivalent stiffness tensor by integrating the bearing characteristic components of the drill bit bearing and the environmental parameters corresponding to the working conditions, thereby avoiding clearance deviation caused by stiffness inaccuracy. The equivalent stiffness tensor can better adapt to different working conditions, making the dynamic change effect of the internal clearance of the tapered floating sleeve drill bit bearing captured based on this more accurate, thereby improving the reliability of the final bearing control strategy relative to the current working conditions.

[0044] (3) The present invention constructs a common target feature vector based on evolution field data and bearing vibration characteristics, and uses the target feature vector to measure the distance between vectors in the fault feature space, and uses this as a benchmark to make hierarchical decisions, so that the final output bearing control strategy has higher practicality. Attached Figure Description

[0045] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0046] Figure 1 This is a flowchart illustrating a cloud computing-based analysis method for cone floating sleeve drill bit bearings proposed in an embodiment of the present invention.

[0047] Figure 2 This 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;

[0048] 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

[0049] 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.

[0050] 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.

[0051] 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:

[0052] Step S10: Obtain the bearing vibration signal of the cone floating sleeve drill bit under preset working conditions;

[0053] 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.

[0054] 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.

[0055] 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;

[0056] In this embodiment, the working parameters of the tapered floating sleeve drill bit under preset working conditions include at least the physical structural parameters of the bearing of the tapered floating sleeve drill bit (such as the number of bearing rollers, pitch circle diameter, etc.) and its kinematic constraint parameters during operation (such as bearing speed, axial load, etc.). The signal decomposition operation referred to in this embodiment refers to processing the vibration signal using established signal decomposition techniques (such as empirical mode decomposition EMD, variational mode decomposition VMD, or wavelet packet decomposition, etc.) to extract feature components that can effectively characterize the dynamic behavior of the bearing body from the bearing signal containing various noises.

[0057] Step S30: Based on the bearing characteristic components and the environmental parameters corresponding to the preset working condition, determine the equivalent stiffness tensor of the tapered floating sleeve drill bit bearing when it is working under the preset working condition, and solve the bearing system dynamic model for the transient deformation effect of the tapered floating sleeve drill bit bearing based on the equivalent stiffness tensor 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.

[0058] In this embodiment, the environmental parameters corresponding to the preset working conditions refer to the relevant parameters that affect the drill bit bearings when the cone floating sleeve drill bit is working under the preset working conditions, such as ambient temperature, rock hardness, etc.

[0059] It is easy to understand that the equivalent stiffness tensor is essentially a matrix used to describe the multi-directional stiffness characteristics of a bearing system. Compared with the equivalent stiffness tensor in the prior art, the equivalent stiffness tensor in this embodiment is calculated based on environmental parameters and bearing characteristic components, which has stronger dynamic adaptability to real-time operating conditions.

[0060] Specifically, based on the bearing characteristic components and the environmental parameters corresponding to the preset working condition, the equivalent stiffness tensor of the tapered floating sleeve drill bit bearing when operating under the preset working condition is determined, including:

[0061] Based on the bearing characteristic components, a stiffness correction coefficient is determined; based on the environmental parameters, an environmental parameter vector is determined; based on the stiffness correction coefficient and the environmental parameter vector, the equivalent stiffness tensor is determined through a preset stiffness mapping function.

[0062] Specifically, 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.

[0063] More specifically, the stiffness correction factor satisfies:

[0064] ;

[0065] In the formula, δ is the stiffness correction coefficient, k1 is the bearing material attenuation coefficient, and E s E0 represents the total energy of the envelope spectrum of the bearing's characteristic components, and E0 is the reference energy value for the bearing's health state.

[0066] The environmental parameter vector satisfies:

[0067] ;

[0068] F a ω is the axial load, ω is the rotational speed, T is the cutting fluid temperature, and H is the cutting fluid temperature. r The rock layer hardness coefficient;

[0069] The preset stiffness mapping function satisfies:

[0070] ;

[0071] In the formula, K eq Let K0 be the equivalent stiffness tensor of the bearing system, K0 be the initial stiffness matrix of the bearing system, and Φ() be the nonlinear response function corresponding to the environmental parameters.

[0072] The nonlinear response function corresponding to the environmental parameters refers to the response function determined based on several parameters that have the greatest impact on the bearing system. Its specific form can be determined by those skilled in the art based on the actual conditions of various operating conditions. This embodiment provides a specific form of the nonlinear response function, which satisfies the following:

[0073] ;

[0074] Where A is the maximum stiffness gain coefficient, B is the load saturation factor, and F a Where ω is the axial load, C is the oil film strengthening coefficient, ω is the rotational speed, D is the material thermal sensitivity coefficient, λ is the temperature decay index, and H is the axial load. r λ represents the dimensionless rock hardness, λ represents the stiffness temperature decay rate, and T represents the cutting fluid temperature.

[0075] Step S40: Determine the target feature vector based on the bearing feature components and the evolution field data, and map the feature vector to a preset fault feature space, wherein the fault feature space stores multiple typical fault feature vectors.

[0076] Specifically, the step 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:

[0077] 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 describing the transient deformation effect of the tapered floating sleeve drill bit bearing system is constructed. The dynamic model of the bearing system is solved 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 the three-dimensional spatiotemporal matrix is ​​used as the evolution field data.

[0078] More specifically, the calculation formula for the dynamic model of the bearing system satisfies:

[0079] ;

[0080] In the formula, M is the mass matrix of the bearing-drill pipe system, C is the system damping matrix, X is the system displacement vector, F(t) is the dynamic working load of the bearing, and t is time. G(X) is the floating sleeve deformation gradient function, β is the floating sleeve deformation coupling coefficient, and F(t) is the dynamic load generated based on the drill pressure curve.

[0081] More specifically, determining the target feature vector based on the bearing feature components and the evolution field data includes:

[0082] The temporal kurtosis and envelope spectrum peak factor are extracted 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, envelope spectrum peak factor, radial clearance change rate, and axial clearance standard deviation, wherein the target feature vector is sequentially composed of the temporal kurtosis, envelope spectrum peak factor, radial clearance change rate, and axial clearance standard deviation, satisfying:

[0083] .

[0084] Step S50: Determine the distance between the target feature vector and the multiple typical fault feature vectors, and determine the bearing control strategy of the cone floating sleeve drill bit based on the distance.

[0085] The distance between the target feature vector and multiple typical fault feature vectors includes, but is not limited to, Euclidean distance, cosine similarity, Mahalanobis distance, etc.

[0086] As a feasible implementation, the distance between the target feature vector and each typical fault feature vector in the fault feature space satisfies the Mahalanobis distance. Determining the distance between the target feature vector and each typical fault feature vector in the fault feature space includes:

[0087] Determine the target typical fault feature vector, which 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 matrix 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.

[0088] Taking Mahalanobis distance as an example, the distance between the target feature vector and the multiple typical fault feature vectors satisfies:

[0089] ;

[0090] In the formula, DM is the distance between the target feature vector and any typical fault feature vector, Σ-1 is the inverse matrix of the covariance matrix of historical fault samples, Vt is the target feature vector, and Vi is the i-th typical fault feature vector in the preset fault feature space.

[0091] More specifically, determining the bearing maintenance strategy for the tapered floating sleeve drill bit based on the distance includes:

[0092] A target threshold is determined, which is used to indicate the standard operating state of the tapered floating sleeve drill bit bearing; a bearing control strategy for the tapered 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.

[0093] The step of determining the bearing control strategy for the tapered floating sleeve drill bit by comparing the target threshold with the distance includes:

[0094] 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 tapered floating sleeve drill bit bearing is determined to monitor the bearing's working status 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 bearing. If the distance is greater than the critical threshold, a disassembly and replacement command for the tapered floating sleeve drill bit bearing is determined.

[0095] Furthermore, in the above embodiments, the step of performing signal decomposition operation on the bearing vibration signal according to the operating parameters includes:

[0096] Based on the operating parameters, the bearing physical structure parameters of the tapered floating sleeve drill bit are determined; based on the bearing physical structure parameters, the signal decomposition frequency band is determined; based on the signal decomposition frequency band, the bearing vibration signal is divided into frequency bands using wavelet packet decomposition, and the specified frequency band signal where the bearing characteristic component is located is extracted; the specified frequency band signal is processed by envelope demodulation analysis to obtain the bearing characteristic component.

[0097] In this embodiment, wavelet packet decomposition is used to decompose the bearing vibration signal. In addition, empirical mode decomposition or independent component analysis can also be used. This embodiment does not impose too many limitations on these methods.

[0098] Although the steps in the above embodiments are described in the above order, those skilled in the art will understand that in order to achieve the effect of this embodiment, different steps do not need to be executed in such an order. They can be executed simultaneously (in parallel) or in a reverse order. These simple variations are all within the protection scope of this invention.

[0099] The second embodiment of the present invention proposes a cloud computing-based cone floating sleeve drill bit bearing analysis device to implement the method involved in the first embodiment above.

[0100] The cloud-based tapered floating sleeve drill bit bearing analysis device can be a software module, comprising several instructions stored in memory. The processor can access this memory, call the instructions, and execute them to complete the cloud-based tapered floating sleeve drill bit bearing analysis method described in the various embodiments above. In some embodiments, the cloud-based tapered floating sleeve drill bit bearing analysis device can also be built from hardware devices. For example, it can be built from one or more chips, which can coordinate with each other to complete the cloud-based tapered floating sleeve drill bit bearing analysis method described in the various embodiments above. Furthermore, the cloud-based tapered floating sleeve drill bit bearing analysis device can also be built from various logic devices, such as general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), microcontrollers, ARM (AcornRISC) or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or any combination of these components.

[0101] Please refer to Figure 2 , Figure 2 The diagram shows the structure of the cloud-based tapered floating sleeve drill bit bearing analysis device. As shown in the figure, the cloud-based tapered floating sleeve drill bit bearing analysis device includes:

[0102] Data acquisition module 210 is used to acquire bearing vibration signals of the cone floating sleeve drill bit under preset working conditions;

[0103] The signal separation module 220 is used to determine the working parameters of the tapered floating sleeve drill bit under the preset working conditions, and to 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.

[0104] The first calculation module 230 is used to determine the equivalent stiffness tensor of the tapered floating sleeve drill bit bearing when it is working under the preset working condition based on the bearing characteristic components and the environmental parameters corresponding to the preset working condition, and to solve the bearing system dynamic model with respect to the transient deformation effect of the tapered floating sleeve drill bit bearing based on the equivalent stiffness tensor, so as 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.

[0105] The second calculation module 240 is used to determine the target feature vector based on the bearing feature components and the evolution field data, and to map the feature vector to a preset fault feature space, wherein the fault feature space stores multiple typical fault feature vectors.

[0106] The strategy output module 250 is used to determine the distance between the target feature vector and multiple typical fault feature vectors, and to determine the bearing control strategy of the cone floating sleeve drill bit based on the distance.

[0107] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the device described above can be found in the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0108] It should be noted that the cloud computing-based cone floating sleeve drill bit bearing analysis device provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be merged into one module, or further divided 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 invention are only for distinguishing the various modules or steps and are not considered as an improper limitation of the present invention.

[0109] The third embodiment of the present invention provides a computer-readable storage medium storing computer instructions, which are executed by the computer to implement the above-described cloud computing-based analysis method for cone floating sleeve drill bit bearings.

[0110] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the electronic device and computer-readable storage medium described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0111] Those skilled in the art will recognize that the modules and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. The programs corresponding to the software modules and method steps can be placed in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. To clearly illustrate the interchangeability of electronic hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented 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 invention.

[0112] The following is for reference. Figure 3 It shows a schematic diagram of the structure of a computer system for implementing the methods and apparatus embodiments of this application. Figure 3 The server shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0113] like Figure 3 As shown, the computer system includes a Central Processing Unit (CPU) 301, which can perform various appropriate actions and processes based on programs stored in Read Only Memory (ROM) 302 or programs loaded from storage section 308 into Random Access Memory (RAM) 303. The RAM 303 also stores various programs and data required for system operation. The CPU 301, ROM 302, and RAM 303 are interconnected via a bus 304. An Input / Output (I / O) interface 305 is also connected to the bus 304.

[0114] The following components are connected to I / O interface 305: an input section 303 including a keyboard, mouse, etc.; an output section 307 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a 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, modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A drive 310 is also connected to I / O interface 305 as needed. Removable media 311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 310 as needed so that computer programs read from them can be installed into storage section 308 as needed.

[0115] Specifically, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 309, and / or installed from removable medium 311. When the computer program is executed by central processing unit (CPU) 301, it performs the functions defined in the methods of this application. It should be noted that the computer-readable medium described above in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0116] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0117] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0118] The terms “first”, “second”, etc., are used to distinguish similar objects, not to describe or indicate a specific order or sequence.

[0119] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0120] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

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. Determine the distance between the target feature vector and each typical fault feature vector in the fault feature space, and determine the bearing control strategy of the cone floating sleeve drill bit based on the distance; 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.

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 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 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.

7. 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.

8. The cloud computing-based analysis method for cone floating sleeve drill bit bearings according to claim 7, 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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