Self-elevating platform sensor health index evaluation method, device, server and medium

Abstract

The embodiment of the present invention discloses a method, device, server and medium for evaluating the health index of a self-elevating platform sensor, wherein the method comprises: obtaining the numerical sequence of each sensor of the equipment component to be evaluated; matching the component attributes corresponding to the numerical sequence of the sensor with the component attributes, and determining the initial change trend of the sensor according to the component attributes; setting the random disturbance term of the numerical sequence of the sensor according to the initial change trend; filtering the numerical sequence of the sensor according to the random disturbance term, and determining the actual change trend of the sensor according to the numerical sequence after filtering; judging whether the initial change trend and the actual change trend are consistent, and when they are consistent, calculating the change rate of the numerical sequence after filtering; adjusting the component weight value of the sensor according to the change rate; and evaluating the health index of the component according to the adjusted component weight value. The health index of the component can be accurately evaluated according to the current motion state.

Description

Technical Field

[0001] The present invention relates to the technical field of health index evaluation, and in particular to a method, device, server and medium for evaluating the health index of a self-elevating platform sensor. Background Art

[0002] The jack-up platform is designed and built for offshore oil fields. It is usually a rectangular box-shaped structure. The bow is equipped with a living building with four cylindrical legs, and the lower end of each leg is equipped with a rectangular pile shoe. It is also equipped with an electric frame and a pinion lifting device, and the platform can be raised and lowered to meet different usage requirements.

[0003] The self-elevating platform is equipped with an intelligent management system that integrates ship equipment operation and maintenance, status monitoring, fault diagnosis and decision support. The system collects real-time status data of the ship's main propulsion system, generator and distribution system, as well as the stress state of the pile shoe and hull structure. Then, through mathematical modeling and big data analysis, the health status of the equipment is monitored in real time, abnormal conditions are alarmed, and fault diagnosis and analysis are performed. Among them, component health index assessment is one of the important tasks. Health assessment is an important basis for the system to carry out intelligent maintenance, and it is also an indispensable key step before predicting the remaining life of the system.

[0004] At present, existing health assessment mainly adopts two methods: qualitative assessment and quantitative assessment. Among them, the health index method can be used in quantitative assessment, that is, by using multiple indicators and parameters, each indicator can correspond to a corresponding weight, comprehensively assess the health status of the equipment, and give a quantitative health index.

[0005] In the process of implementing the present invention, the inventors found the following technical problems: the weight of each indicator in the health index method is usually set based on experience. However, there are many states of self-elevating platform components and many motion states. It is obviously impossible to accurately evaluate the health index of the component by using the same weight for each state. Summary of the invention

[0006] The embodiments of the present invention provide a self-elevating platform sensor health index assessment method, device, server and medium to solve the technical problem in the prior art that the health index of the components of the self-elevating platform cannot be accurately assessed according to the motion state of the components.

[0007] In a first aspect, an embodiment of the present invention provides a method for evaluating the health index of a jack-up platform sensor, comprising:

[0008] Obtaining a sequence of values ​​of each sensor of the equipment component to be evaluated;

[0009] Matching the value sequence of the sensor with the component attributes corresponding to the sensor, and determining the initial change trend of the sensor according to the component attributes;

[0010] According to the initial change trend, a random disturbance term of the numerical sequence of the sensor is set;

[0011] Performing filtering processing on the sensor value sequence according to the random disturbance term, and determining an actual change trend of the sensor according to the filtered value sequence;

[0012] Determine whether the initial change trend is consistent with the actual change trend, and if they are consistent, calculate the change rate of the filtered numerical sequence;

[0013] adjusting component weight values ​​of the sensor according to the rate of change;

[0014] The health index of the component is evaluated based on the adjusted component weight value.

[0015] In a second aspect, an embodiment of the present invention further provides a self-elevating platform sensor health index assessment device, comprising:

[0016] An acquisition module, used to acquire a sequence of values ​​of each sensor of the equipment component to be evaluated;

[0017] A matching module, used to match the numerical sequence of the sensor with the component attributes corresponding to the sensor, and determine the initial change trend of the sensor according to the component attributes;

[0018] A setting module, used for setting a random disturbance term of the numerical sequence of the sensor according to the initial change trend;

[0019] A filtering module, used for filtering the sensor value sequence according to the random disturbance term, and determining the actual change trend of the sensor according to the filtered value sequence;

[0020] A judgment module, used to judge whether the initial change trend is consistent with the actual change trend, and if they are consistent, calculate the change rate of the filtered numerical sequence;

[0021] an adjustment module, configured to adjust component weight values ​​of the sensor according to the change rate;

[0022] The evaluation module is used to evaluate and obtain the health index of the component according to the adjusted component weight value.

[0023] In a third aspect, an embodiment of the present invention further provides a server, including:

[0024] one or more processors;

[0025] a storage device for storing one or more programs,

[0026] When the one or more programs are executed by the one or more processors, the one or more processors implement the self-elevating platform sensor health index assessment method provided in the above embodiment.

[0027] In a fourth aspect, an embodiment of the present invention further provides a storage medium comprising computer executable instructions, which, when executed by a computer processor, are used to execute the self-elevating platform sensor health index assessment method provided in the above embodiment.

[0028] The self-elevating platform sensor health index evaluation method, device, server and storage medium provided by the embodiment of the present invention obtain the numerical sequence of each sensor of the equipment component to be evaluated; match the numerical sequence of the sensor with the component attribute corresponding to the sensor, and determine the initial change trend of the sensor according to the component attribute; set the random disturbance term of the numerical sequence of the sensor according to the initial change trend; filter the numerical sequence of the sensor according to the random disturbance term, and determine the actual change trend of the sensor according to the numerical sequence after filtering; judge whether the initial change trend and the actual change trend are consistent, and when they are consistent, calculate the change rate of the numerical sequence after filtering; adjust the component weight value of the sensor according to the change rate; and evaluate the component health index according to the adjusted component weight value. Sensors of different parts and properties in the component can be used to obtain accurate measurement values ​​according to the motion state, and the motion change trend of each unit in the component is determined. The weight value is adjusted in combination with the change trend, and the health index of the component can be accurately evaluated according to the current motion state. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0030] Figure 1 It is a flow chart of a method for evaluating the health index of a jack-up platform sensor provided in the first embodiment of the present invention;

[0031] Figure 2 It is a flow chart of a method for evaluating the health index of a jack-up platform sensor provided in Embodiment 2 of the present invention;

[0032] Figure 3 It is a structural schematic diagram of a self-elevating platform sensor health index assessment device provided in Embodiment 3 of the present invention;

[0033] Figure 4 It is a structural diagram of a server provided in Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0034] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for ease of description, only parts related to the present invention, rather than all structures, are shown in the accompanying drawings.

[0035] Embodiment 1

[0036] Figure 1 : is a flow chart of a method for evaluating the health index of a jack-up platform sensor provided in Embodiment 1 of the present invention. This embodiment is applicable to the situation where the health index of a jack-up platform component is accurately evaluated when the jack-up platform component is in motion. The method can be executed by a jack-up platform sensor health index evaluation device, and specifically includes the following steps:

[0037] Step 110: Acquire the value sequence of each sensor of the equipment component to be evaluated.

[0038] In this embodiment, the component may be a subsystem in the self-elevating platform, or a component that realizes a specific function independently. For example, a propulsion motor includes a bearing, a mixer and other components. The parameters measured by the sensor may include bearing temperature, vibration coefficient, cooling temperature, etc. In order to obtain its operating status, multiple types of sensors are usually configured on it, as well as sensors of the same type configured in different parts. The corresponding data are collected by the sensor, and a numerical sequence of each sensor is generated according to the collection time.

[0039] Step 120 , matching the value sequence of the sensor with the component attributes corresponding to the sensor, and determining the initial change trend of the sensor according to the component attributes.

[0040] Exemplarily, determining the initial change trend of the sensor according to the component attributes may include: obtaining the current motion state of the component; determining the measurement position and measurement data type of the sensor in the component according to the component attributes; determining the initial change trend of the sensor according to the current motion state and the measurement position and measurement data type of the sensor in the component. First, the motion state of the component is determined according to the current state of the self-elevating platform, such as: navigation, lifting, descending or normal operation. The initial change trend of the sensor can be determined according to the simulated correspondence between the motion state of the component and the data collected by each sensor during design, that is, the physical quantity corresponding to the measurement value and the mathematical model of the motion state, which is equivalent to the change function of the physical quantity corresponding to the sensor. The position and type of the sensor are matched as attributes with the numerical sequence of the sensor obtained in the above steps.

[0041] Step 130: setting a random disturbance term of the sensor's numerical sequence according to the initial variation trend.

[0042] In this embodiment, if the data collected by the sensor is closer to the real value, the accuracy of the component health index assessment is higher. However, due to various influences, the collected data will inevitably have certain errors. Specifically, these errors may come from resolution, temperature and humidity, electromagnetic interference and linearity. In this embodiment, the above errors can be defined as random disturbance terms.

[0043] Exemplarily, it may include: determining at least two influencing factors according to the component attributes corresponding to the sensor, and sorting them according to the importance; selecting an influencing factor in order from the sorting results, and generating a corresponding random differential equation according to the influencing factor as the random disturbance term of the influencing factor; judging whether the random disturbance term of the influencing factor is less than the preset loss standard value, and if not less than, returning to the step of selecting an influencing factor in order from the sorting results, until whether the random disturbance term of the influencing factor is less than the preset loss standard value. The influencing factor may be the aforementioned factor affecting the accuracy. In this embodiment, the error may be decomposed step by step into random disturbance terms corresponding to multiple influencing factors. And the number of influencing factors required, that is, the number of influencing factors, is calculated by using the loss function. The loss function may be: the difference function of the time integral function corresponding to the measured value and the random disturbance term and the initial change parameter. At this time, the loss function not only has an undetermined error term, but also includes the deviation between the theoretical value and the actual value. At the same time. The unpredictable error is converted into a prediction formula that can change with time by fitting the time integral function in the above manner, thereby improving the calculation accuracy of the random disturbance term.

[0044] Step 140: filtering the sensor value sequence according to the random disturbance term, and determining the actual change trend of the sensor according to the filtered value sequence.

[0045] After the random disturbance term, i.e., the algorithm that is closer to the possible error, is determined through the above steps, the random disturbance term, i.e., the possible error, is calculated respectively, and each element in the sensor value sequence is filtered according to the corresponding time using the error, and the actual change trend of the sensor is determined based on the filtered value sequence. Exemplarily, the actual change trend of the sensor is determined based on the filtered value sequence, which may include: determining whether the filtered value sequence is evenly distributed on both sides of the time-sensor value curve; when evenly distributed, determining the actual change trend of the sensor based on the filtered value sequence.

[0046] The filtered value is used to determine whether it conforms to the probability distribution characteristics. If it conforms, it can be said that the value processed by the above steps is close to the actual value. Therefore, the actual change trend of the sensor can be determined based on it. For example, the actual change trend can be determined by the least squares fitting method.

[0047] Step 150, determining whether the initial change trend is consistent with the actual change trend, and if they are consistent, calculating the change rate of the filtered numerical sequence.

[0048] If the initial change trend is consistent with the actual change trend, it can be said that the sensor is in a normal state and the collected data is credible. If it is credible, the change rate of the filtered numerical sequence is calculated, and the change rate reflects the degree of change of the collected data per unit time.

[0049] Step 160: adjusting the component weight value of the sensor according to the change rate; and evaluating and obtaining the component health index according to the adjusted component weight value.

[0050] Exemplarily, the rate of change may be multiplied by the component weight value corresponding to the sensor to adjust the weight value. Furthermore, the adjusted weight value may be evaluated using the health index method to obtain the health index of the component.

[0051] This embodiment obtains the numerical sequence of each sensor of the device component to be evaluated; matches the numerical sequence of the sensor with the component attribute corresponding to the sensor, and determines the initial change trend of the sensor according to the component attribute; sets the random disturbance term of the numerical sequence of the sensor according to the initial change trend; filters the numerical sequence of the sensor according to the random disturbance term, and determines the actual change trend of the sensor according to the numerical sequence after filtering; determines whether the initial change trend and the actual change trend are consistent, and when they are consistent, calculates the change rate of the numerical sequence after filtering; adjusts the component weight value of the sensor according to the change rate; and obtains the component health index according to the adjusted component weight value. Sensors of different parts and properties in the component can be used to obtain accurate measurement values ​​according to the motion state, and the motion change trend of each unit in the component is determined. The weight value is adjusted in combination with the change trend, and the component health index can be accurately evaluated according to the current motion state.

[0052] Embodiment 2

[0053] Figure 2A flow chart of a method for evaluating the health index of a self-elevating platform sensor provided in Embodiment 2 of the present invention. This embodiment is optimized based on the above embodiment. In this embodiment, the health index of the component is obtained by evaluating the adjusted component weight value. The specific optimization is as follows: the normal coefficient of the component is calculated according to the adjusted component weight value; the current life stage of the component is determined according to the normal coefficient under different states; and the health index of the component is evaluated based on the life stage and the normal coefficient.

[0054] Accordingly, the self-elevating platform sensor health index assessment method provided in this embodiment specifically includes:

[0055] Step 210: Acquire the value sequence of each sensor of the equipment component to be evaluated.

[0056] Step 220: Match the value sequence of the sensor with the component attributes corresponding to the sensor, and determine the initial change trend of the sensor according to the component attributes.

[0057] Step 230: setting a random disturbance term of the sensor's numerical sequence according to the initial variation trend.

[0058] Step 240: filtering the sensor value sequence according to the random disturbance term, and determining the actual change trend of the sensor according to the filtered value sequence.

[0059] Step 250: determine whether the initial change trend is consistent with the actual change trend. If they are consistent, calculate the change rate of the filtered numerical sequence.

[0060] Step 260: adjusting the component weight value of the sensor according to the change rate, and calculating the normal coefficient of the component according to the adjusted component weight value.

[0061] In this embodiment, the value calculated by the health index method can be used as the normal coefficient of the component.

[0062] Step 270, determining the current life stage of the component according to the normal coefficients under different states; and evaluating and obtaining the health index of the component based on the life stage and the normal coefficients.

[0063] Optionally, in this embodiment, the components can be divided into three life stages: initial stage, mature stage and aging stage. Since there may be an approximate normal coefficient in each stage, it may not be possible to obtain an accurate health index using only the normal coefficient. Therefore, in this embodiment, the current life stage of the component can be determined based on the normal coefficient in the previous state and the normal coefficient calculated currently. Adding the previous normal coefficient can effectively determine the corresponding life stage. In particular, if the previous state is a different state, for example, this time it is a sailing motion state, and the previous time was a lifting stage, the current life stage of the component can be determined more accurately by using the state difference.

[0064] Correspondingly, the health index of the component obtained by evaluating the life stage and the normal coefficient may include: obtaining the normal coefficient curve in the corresponding life stage; calculating the median or average value of the normal coefficient based on the normal coefficient in different states; and obtaining the corresponding health index by comparing the normal coefficient curve with the median or average value. The calculation of the median or average value of the normal coefficient based on the normal coefficient in different states may include: when the number of normal coefficients in the different states exceeds a first threshold, calculating the median value of the normal coefficient, otherwise, calculating the average value of the normal coefficient. The above method can be used to more accurately determine the corresponding life stage using the normal coefficients in multiple states, thereby better evaluating the health index of the component.

[0065] This embodiment optimizes the health index of the component obtained by evaluating the adjusted component weight value as follows: calculating the normal coefficient of the component according to the adjusted component weight value; determining the current life stage of the component according to the normal coefficient under different states; and evaluating the health index of the component based on the life stage and the normal coefficient. By introducing the life stage and determining the corresponding life stage in combination with the normal coefficient under different states, the health index of the component can be evaluated better and more accurately.

[0066] Embodiment 3

[0067] Figure 3 is a schematic diagram of the structure of the self-elevating platform sensor health index evaluation device provided in the third embodiment of the present invention, such as Figure 3 As shown, the device comprises:

[0068] An acquisition module 310 is used to acquire a sequence of values ​​of each sensor of a device component to be evaluated;

[0069] A matching module 320, for matching the numerical sequence of the sensor with the component attributes corresponding to the sensor, and determining the initial change trend of the sensor according to the component attributes;

[0070] A setting module 330, configured to set a random disturbance term of the sensor's numerical sequence according to the initial change trend;

[0071] A filtering module 340, configured to filter the sensor value sequence according to the random disturbance term, and determine an actual change trend of the sensor according to the filtered value sequence;

[0072] A judgment module 350 is used to judge whether the initial change trend is consistent with the actual change trend, and if they are consistent, calculate the change rate of the filtered numerical sequence;

[0073] An adjustment module 360, configured to adjust the component weight value of the sensor according to the change rate;

[0074] The evaluation module 370 is used to evaluate and obtain the health index of the component according to the adjusted component weight value.

[0075] The self-elevating platform sensor health index evaluation device provided in this embodiment obtains the numerical sequence of each sensor of the equipment component to be evaluated; matches the numerical sequence of the sensor with the component attribute corresponding to the sensor, and determines the initial change trend of the sensor according to the component attribute; sets the random disturbance term of the numerical sequence of the sensor according to the initial change trend; filters the numerical sequence of the sensor according to the random disturbance term, and determines the actual change trend of the sensor according to the numerical sequence after filtering; determines whether the initial change trend and the actual change trend are consistent, and when they are consistent, calculates the change rate of the numerical sequence after filtering; adjusts the component weight value of the sensor according to the change rate; and evaluates the health index of the component according to the adjusted component weight value. Sensors of different parts and properties in the component can be used to obtain accurate measurement values ​​according to the motion state, and the motion change trend of each unit in the component is determined. The weight value is adjusted in combination with the change trend, and the health index of the component can be accurately evaluated according to the current motion state.

[0076] Based on the above embodiments, the setting module includes:

[0077] A sorting unit, used to determine at least two influencing factors according to the component attributes corresponding to the sensor, and sort them according to their importance;

[0078] A selection unit, used for selecting an influencing factor from the sorting results in order, and generating a corresponding stochastic differential equation according to the influencing factor as a random perturbation term of the influencing factor;

[0079] The judgment unit is used to judge whether the random disturbance term of the influencing factor is less than the preset loss standard value. If it is not less than, it returns to select an influencing factor step in sequence from the sorting results until the random disturbance term of the influencing factor is less than the preset loss standard value.

[0080] Based on the above embodiments, the filtering module includes:

[0081] A judging unit, used to judge whether the numerical sequence after filtering is evenly distributed on both sides of the time-sensor numerical curve;

[0082] The determination unit is used to determine the actual change trend of the sensor according to the numerical sequence after filtering when the distribution is uniform.

[0083] Based on the above embodiments, the matching module includes:

[0084] An acquisition unit, used to acquire the current motion state of the component;

[0085] A determination unit, configured to determine a measurement position and a measurement data type of the sensor in the component according to the component attributes;

[0086] A trend determination unit is used to determine an initial change trend of the sensor according to the current motion state and a measurement position and measurement data type of the sensor in the component.

[0087] Based on the above embodiments, the evaluation module includes:

[0088] A calculation unit, used for calculating a normal coefficient of a component according to the adjusted component weight value;

[0089] A life stage determination unit, used for determining the current life stage of the component according to normal coefficients in different states;

[0090] An evaluation unit is used to evaluate and obtain a health index of a component based on the life stage and the normal coefficient.

[0091] Based on the above embodiments, the life stage determination unit includes:

[0092] A curve acquisition subunit is used to obtain a normal coefficient curve in a corresponding life stage;

[0093] A median or average value calculation subunit, used for calculating the median or average value of the normal coefficient based on the normal coefficients in different states;

[0094] The search subunit is used to search for a corresponding health index by comparing the median value or the average value with the normal coefficient curve.

[0095] Based on the above embodiments, the median or average value calculation subunit is used to:

[0096] When the number of normal coefficients under the different states exceeds the first threshold, the median value of the normal coefficients is calculated, otherwise, the average value of the normal coefficients is calculated.

[0097] The self-elevating platform sensor health index assessment device provided in the embodiment of the present invention can execute the self-elevating platform sensor health index assessment method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0098] Embodiment 4

[0099] Figure 4 A schematic diagram of the structure of a server provided in Embodiment 6 of the present invention. Figure 4 A block diagram of an exemplary server 12 suitable for use in implementing embodiments of the present invention is shown. Figure 4 The server 12 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0100] like Figure 4 As shown, the server 12 is in the form of a general purpose computing device. The components of the server 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0101] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0102] The server 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the server 12, including volatile and non-volatile media, removable and non-removable media.

[0103] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The server 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 4not shown, usually called a "hard drive"). Although Figure 4 Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0104] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in the memory 28, such program modules 42 including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. The program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0105] The server 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), may communicate with one or more devices that enable users to interact with the server 12, and / or may communicate with any device that enables the server 12 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via an input / output (I / O) interface 22. In addition, the server 12 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the server 12 via a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the server 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0106] The processing unit 16 executes various functional applications and data processing by running the programs stored in the system memory 28, such as implementing the self-elevating platform sensor health index assessment method provided in the embodiment of the present invention.

[0107] Embodiment 5

[0108] Embodiment 5 of the present invention further provides a storage medium containing computer executable instructions, and when the computer executable instructions are executed by a computer processor, the method for evaluating the health index of a sensor of a jack-up platform as provided in the above embodiment is used to execute.

[0109] The computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination thereof. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device.

[0110] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0111] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0112] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may 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 may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0113] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for evaluating the health index of a self-elevating platform sensor, characterized in that: include: Obtaining a sequence of values ​​of each sensor of the equipment component to be evaluated; Matching the value sequence of the sensor with the component attribute corresponding to the sensor to obtain the current motion state of the component; Determine the measurement position and measurement data type of the sensor in the component according to the component attributes; Determine an initial change trend of the sensor according to the current motion state and a measurement position and a measurement data type of the sensor in the component; According to the initial change trend, a random disturbance term of the numerical sequence of the sensor is set; Performing filtering processing on the sensor value sequence according to the random disturbance term, and determining an actual change trend of the sensor according to the filtered value sequence; Determine whether the initial change trend is consistent with the actual change trend, and if they are consistent, calculate the change rate of the filtered numerical sequence, where the change rate reflects the degree of change of the collected data per unit time; adjusting component weight values ​​of the sensor according to the rate of change; The health index of the component is evaluated based on the adjusted component weight value.

2. The method according to claim 1, characterized in that: The step of setting the random disturbance term of the value sequence of the sensor according to the initial change trend includes: Determine at least two influencing factors according to the component attributes corresponding to the sensor, and sort them according to their importance; Select an impact factor from the sorting results in order, and generate a corresponding stochastic differential equation according to the impact factor as a random perturbation term of the impact factor; Determine whether the random perturbation term of the influencing factor is less than the preset loss standard value. If it is not less than, return to select an influencing factor step in order from the sorting results until the random perturbation term of the influencing factor is less than the preset loss standard value.

3. The method according to claim 1, characterized in that Determining the actual change trend of the sensor according to the filtered numerical sequence includes: Determine whether the value sequence after filtering is evenly distributed on both sides of the time-sensor value curve; When the distribution is uniform, the actual change trend of the sensor is determined according to the value sequence after filtering. Momentum.

4. The method according to claim 1, characterized in that: The health index of the component is obtained by evaluating the adjusted component weight value, including: The normal coefficient of the component is calculated according to the adjusted component weight value; determining the current life stage of the component according to normal coefficients under different states; A health index of the component is evaluated based on the life stage and the normal coefficient.

5. The method according to claim 4, characterized in that The health index of the component is obtained based on the life stage and the normal coefficient evaluation, including: Obtain the normal coefficient curve in the corresponding life stage; Calculate the median or average value of the normal coefficient based on the normal coefficients under different conditions; The corresponding health index is obtained by comparing the median value or the average value with the normal coefficient curve.

6. The method according to claim 5, characterized in that The calculating of the median or average value of the normal coefficient based on the normal coefficients in different states includes: When the number of normal coefficients under the different states exceeds the first threshold, the median value of the normal coefficients is calculated, otherwise, the average value of the normal coefficients is calculated.

7. A self-elevating platform sensor health index assessment device, characterized in that: include: An acquisition module, used to acquire a sequence of values ​​of each sensor of the equipment component to be evaluated; A matching module, used to match the numerical sequence of the sensor with the component attribute corresponding to the sensor to obtain the current motion state of the component; and determine the measurement position and measurement data type of the sensor in the component according to the component attribute; Determine an initial change trend of the sensor according to the current motion state and a measurement position and a measurement data type of the sensor in the component; A setting module, used for setting a random disturbance term of the numerical sequence of the sensor according to the initial change trend; A filtering module, used for filtering the sensor value sequence according to the random disturbance term, and determining the actual change trend of the sensor according to the filtered value sequence; A judgment module, used to judge whether the initial change trend is consistent with the actual change trend, and if they are consistent, calculate the change rate of the filtered numerical sequence, wherein the change rate reflects the degree of change of the collected data per unit time; an adjustment module, configured to adjust component weight values ​​of the sensor according to the change rate; The evaluation module is used to evaluate and obtain the health index of the component according to the adjusted component weight value.

8. A server, characterized in that: The server comprises: one or more processors; a storage device for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the self-elevating platform sensor health index assessment method as described in any one of claims 1-6.

9. A storage medium comprising computer executable instructions, wherein the computer executable instructions are used to execute the self-elevating platform sensor health index assessment method according to any one of claims 1 to 6 when executed by a computer processor.

Images