CN118094450B - Fault early warning method and related equipment - Google Patents

Abstract

The application provides a fault early warning method and related equipment, wherein the fault early warning method comprises the following steps: acquiring a history spot detection signal corresponding to each of a plurality of vibration sensors; each vibration sensor corresponds to one detection point of the equipment to be detected, and the historical point detection signals correspond to a plurality of time stamps; determining an abnormal signal in each history point detection signal and a front signal corresponding to the abnormal signal according to the amplitude of the history point detection signal; determining the time difference and the amplitude difference degree of every two abnormal signals; determining a change trend curve of each abnormal signal, and calculating a first similarity of every two partial change trend curves; calculating the correlation of any two abnormal signals according to the time difference, the amplitude difference and the first similarity; calculating a second similarity between the monitoring signal and the front signal, which are acquired in real time; and outputting alarm information of each detection point according to the second similarity and the correlation. The application relates to the technical field of equipment operation and maintenance, and can improve the early warning efficiency of equipment.

Description

Fault early warning method and related equipment

Technical Field

The application relates to the technical field of equipment operation and maintenance, in particular to the technical field of fault early warning, and especially relates to a fault early warning method and related equipment.

Background

Along with development of industrial science and technology, in order to improve production efficiency, various enterprises or organizations pay more attention to operation and maintenance of equipment so as to ensure that equipment failure rate is reduced and stability of equipment operation is ensured. At present, vibration signals of a plurality of sampling points of equipment are obtained by using a sensor for monitoring the running signals of the equipment, and as the vibration signals are time sequence discrete signals, fault points and fault time cannot be predicted timely and accurately by manpower through vibration signal curves, equipment is usually stopped, and the operation and maintenance efficiency of the equipment is low.

Disclosure of Invention

In view of the foregoing, it is necessary to provide a fault early warning method and related devices, so as to solve the technical problem of low efficiency of fault early warning. The related equipment comprises a fault early warning device and electronic equipment.

The application provides a fault early warning method which is applied to electronic equipment, wherein the electronic equipment is in communication connection with a plurality of vibration sensors, and the method comprises the following steps: acquiring a history spot detection signal corresponding to each of the plurality of vibration sensors; wherein each vibration sensor corresponds to one detection point of the equipment to be detected, and the history point detection signal corresponds to a plurality of time stamps; determining an abnormal signal in each history point detection signal and a front signal corresponding to the abnormal signal according to the amplitude of the history point detection signal; determining the time difference of the abnormal signals of every two different historical point detection signals; determining the amplitude difference degree between every two abnormal signals belonging to different historical point detection signals; determining a change trend curve of each abnormal signal, and calculating first similarity of each two change trend curves which belong to different historical point detection signals; according to the time difference, the amplitude difference degree and the first similarity, calculating the correlation of any two abnormal signals which belong to different historical point detection signals; calculating a second similarity between the monitoring signal and the front signal, which are acquired in real time; and outputting alarm information of each detection point according to the second similarity and the correlation.

In some embodiments, the determining the anomaly signal in each of the historical click signals according to the amplitude of the historical click signal, and the pre-signal corresponding to the anomaly signal includes: determining a difference between amplitudes corresponding to each two time stamps; when the difference value is positive, determining any one of two time stamps corresponding to the difference value as a starting time point; when the difference value is a negative number, determining any one of two time stamps corresponding to the difference value as a termination time point; according to the sequence from the early to the late of the time stamp, determining that the history point detection signals between the adjacent starting time point and the termination time point are abnormal signals; and determining the history point detection signals between the adjacent ending time points and the starting time points as the prepositive signals corresponding to the abnormal signals according to the sequence from the early to the late of the time stamps.

In some embodiments, determining the degree of amplitude difference between any two of the anomaly signals belonging to different ones of the historical click signals comprises: determining a first amplitude corresponding to each time stamp in any one of the abnormal signals; determining a second amplitude corresponding to each time stamp in another abnormal signal; determining a difference between each of the first amplitudes and each of the second amplitudes; determining a minimum difference value corresponding to each time stamp; and determining the sum of all the minimum differences as the amplitude difference degree.

In some embodiments, said determining a trend curve for each of said anomaly signals comprises: a, marking a maximum amplitude point and a minimum amplitude point in the abnormal signal, and fitting the maximum amplitude point and the minimum amplitude point by using a preset interpolation function to obtain an upper envelope curve and a lower envelope curve; b, calculating the average value of the upper envelope curve and the lower envelope curve to be used as an average value curve; c, calculating the difference value between the abnormal signal and the mean curve to be used as an update signal; d, taking the updating signal as the abnormal signal if the updating signal meets a preset first judging condition, and repeating the steps a-c, otherwise taking the updating signal as a comprehensive curve and carrying out the step e; e, calculating the difference value between the abnormal signal and the comprehensive curve to obtain a data sequence; f, if the data sequence meets a preset second judging condition, taking the data sequence as an abnormal signal and repeating the steps a-f, otherwise, determining the comprehensive curve as a change trend curve.

In some embodiments, the calculating the first similarity of the trend curve for each two points belonging to different historical click signals comprises: randomly collecting a plurality of first amplitude data from any one of the change trend curves; randomly acquiring a plurality of second amplitude data from another variation trend curve; determining a first vector from the plurality of first amplitude data; determining a second vector from the plurality of second amplitude data; and determining the similarity of the first vector and the second vector as the first similarity.

In some embodiments, the calculating the correlation of any two abnormal signals belonging to different historical click signals according to the time difference, the amplitude difference degree and the first similarity degree includes: respectively carrying out normalization processing on the time difference, the amplitude difference degree and the first similarity to obtain a normalized time difference, a normalized amplitude difference degree and a normalized first similarity; determining the correlation from the normalized time difference, the normalized amplitude difference, and the normalized first similarity comprises: Wherein R represents the correlation; t represents the normalized time difference; d represents the normalized amplitude variance; s represents the normalized first similarity; e represents a natural constant.

In some embodiments, said outputting alert information for each of said detection points according to said second similarity and said correlation comprises: determining the alarm probability of each detection point according to the second similarity and the correlation; generating alarm information according to the alarm probability, the ID corresponding to each detection point and a pre-stored alarm template; and outputting the alarm information.

In some embodiments, the determining the alarm probability of each detection point according to the second similarity and the correlation satisfies the following relation: Wherein R represents the correlation; f represents the second similarity; p represents the alarm probability; e represents a natural constant.

The embodiment of the application also provides a fault early warning device, which comprises: the acquisition module is used for acquiring historical spot detection signals corresponding to each of the plurality of vibration sensors; wherein each vibration sensor corresponds to one detection point of the equipment to be detected, and the history point detection signal corresponds to a plurality of time stamps; the determining module is used for determining an abnormal signal in each history point detection signal and a front signal corresponding to the abnormal signal according to the amplitude of the history point detection signal; the determining module is further used for determining the time difference of the abnormal signals of each two different historical point detection signals; the determining module is further used for determining the amplitude difference degree between every two abnormal signals belonging to different historical spot detection signals; the determining module is further used for determining a change trend curve of each abnormal signal, and calculating first similarity of the change trend curves of every two different historical point detection signals; the determining module is further configured to calculate, according to the time difference, the amplitude difference degree, and the first similarity, correlation of any two abnormal signals that belong to different historical point detection signals; the determining module is further used for calculating a second similarity between the monitoring signal acquired in real time and the front signal; and the early warning module is used for outputting warning information of each detection point according to the second similarity and the correlation.

The embodiment of the application also provides electronic equipment, which comprises: a memory storing at least one instruction; and the processor executes the instructions stored in the memory to realize the fault early warning method.

The embodiment of the application also provides a computer readable storage medium, wherein at least one instruction is stored in the computer readable storage medium, and the at least one instruction is executed by a processor in electronic equipment to realize the fault early warning method.

According to the technical scheme, the vibration signal of each detection point in the equipment to be detected is obtained by acquiring the historical point detection signal corresponding to each vibration sensor. And determining an anomaly signal and a preamble signal in each of the history spot signals according to the amplitudes. And determining a change trend curve of each abnormal signal, and calculating the correlation of each two abnormal signals according to the time difference and the amplitude difference of the two abnormal signals and the first similarity of the change trend curve. Therefore, the association degree between every two detection points in the equipment to be detected is represented by quantized data, and the probability of the faults of the other detection points can be predicted through the quantized data. And finally, calculating a second similarity between the monitoring signal and the front signal, which are acquired in real time, and outputting alarm information of each detection point according to the second similarity and the correlation. Therefore, the fault condition of the detection point of the equipment to be detected is represented by the quantized data, and the accuracy and the efficiency of fault early warning can be improved.

Drawings

Fig. 1 is an application scenario diagram of a fault early warning method according to an embodiment of the present application.

Fig. 2 is a flowchart of a fault early warning method according to an embodiment of the present application.

Fig. 3 is a functional block diagram of a fault early warning device according to an embodiment of the present application.

Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

The application will be described in detail below with reference to the drawings and the specific embodiments thereof in order to more clearly understand the objects, features and advantages of the application. It should be noted that, without conflict, embodiments of the present application and features in the embodiments may be combined with each other. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, the described embodiments are merely some, rather than all, embodiments of the present application.

Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

The embodiment of the application provides a fault early warning method, which can be applied to one or more electronic devices, wherein the electronic devices are devices capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and the hardware comprises, but is not limited to, a microprocessor, an Application SPECIFIC INTEGRATED Circuit (ASIC), a Programmable gate array (Field-Programmable GATE ARRAY, FPGA), a digital Processor (DIGITAL SIGNAL Processor, DSP), an embedded device and the like.

The electronic device may be any electronic product that can interact with a customer in a human-computer manner, such as a Personal computer, a tablet computer, a smart phone, a Personal digital assistant (Personal DIGITAL ASSISTANT, PDA), a game console, an interactive internet protocol television (Internet Protocol Television, IPTV), a smart wearable device, etc.

The electronic device may also include a network device and/or a client device. Wherein the network device includes, but is not limited to, a single network server, a server group composed of a plurality of network servers, or a Cloud based Cloud Computing (Cloud Computing) composed of a large number of hosts or network servers.

The network in which the electronic device is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (Virtual Private Network, VPN), and the like.

As shown in fig. 1, the fault early warning method provided by the application can be applied to an electronic device 100, and the electronic device 100 is in communication connection with a server 200. The electronic device 100 is configured to obtain, from the server 200, a history click signal corresponding to each of the plurality of vibration sensors; wherein each vibration sensor corresponds to one detection point of the equipment to be detected, and the history point detection signal corresponds to a plurality of time stamps; determining an abnormal signal in each history point detection signal and a front signal corresponding to the abnormal signal according to the amplitude of the history point detection signal; determining the time difference of the abnormal signals of every two different historical point detection signals; determining the amplitude difference degree between every two abnormal signals belonging to different historical point detection signals; determining a change trend curve of each abnormal signal, and calculating first similarity of each two change trend curves which belong to different historical point detection signals; according to the time difference, the amplitude difference degree and the first similarity, calculating the correlation of any two abnormal signals which belong to different historical point detection signals; calculating a second similarity between the monitoring signal and the front signal, which are acquired in real time; and outputting alarm information of each detection point according to the second similarity and the correlation.

Fig. 2 is a flowchart of a fault early warning method according to an embodiment of the present application. The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs. The fault early warning method provided by the embodiment of the application comprises the following steps of.

S20, acquiring a history spot detection signal corresponding to each of the plurality of vibration sensors.

In an embodiment of the present application, each vibration sensor corresponds to a detection point of the device to be detected, and the historical detection signal corresponds to a plurality of time stamps. Each vibration sensor is used for detecting a vibration signal at a corresponding detection point, the vibration signal is used for representing the running condition of equipment to be detected, and when the vibration signal of a certain detection point is subjected to strong change, the probability of fault at the detection point is higher.

S21, determining an abnormal signal in each history point detection signal and a preamble signal corresponding to the abnormal signal according to the amplitude of the history point detection signal.

In an embodiment of the present application, the anomaly signal is used to characterize a signal with a higher amplitude than the mean value and a high rate of change in the historical click signal. The pre-signal is used to characterize the signal before each anomaly signal in the history spot signal occurs.

In an embodiment of the present application, the determining the abnormal signal in each historical click signal according to the amplitude of the historical click signal, and the pre-signal corresponding to the abnormal signal includes: determining a difference between amplitudes corresponding to each two time stamps; when the difference value is positive, determining any one of two time stamps corresponding to the difference value as a starting time point; when the difference value is a negative number, determining any one of two time stamps corresponding to the difference value as a termination time point; according to the sequence from the early to the late of the time stamp, determining that the history point detection signals between the adjacent starting time point and the termination time point are abnormal signals; and determining the history point detection signals between the adjacent ending time points and the starting time points as the prepositive signals corresponding to the abnormal signals according to the sequence from the early to the late of the time stamps.

S22, determining the time difference of the abnormal signals of every two points belonging to different historical point detection signals.

In an embodiment of the present application, in order to determine a time delay between every two abnormal signals, thereby determining an influence on the remaining detection points in a time dimension when different detection points fail, a time difference between every two abnormal signals belonging to different historical point detection signals may be determined. When the time difference is smaller, the mutual influence degree between the detection points corresponding to the two abnormal signals is higher.

In an embodiment of the present application, it may be determined that a time difference between starting time points of two abnormal signals is a time difference of two abnormal signals; it is also possible to determine the time difference between the termination time points of the two abnormal signals as the time difference of the two abnormal signals; it is also possible to determine the average value between the time difference at the start time point and the time difference at the end time point as the time difference of the two abnormal signals. The application is not limited in this regard.

S23, determining the amplitude difference degree between every two abnormal signals belonging to different historical point detection signals.

In an embodiment of the present application, in order to determine the correlation between every two detection points, the degree of amplitude difference between every two abnormal signals belonging to different historical detection signals may be determined first. The larger the amplitude difference degree is, the smaller the correlation between two abnormal signals belonging to different historical point detection signals is, and the smaller the correlation between the corresponding two detection points is; the smaller the amplitude difference degree is, the greater the correlation between the abnormal signals of the two points belonging to different history point detection signals is, and the greater the correlation between the corresponding two detection points is.

In an embodiment of the present application, determining the degree of amplitude difference between any two of the abnormal signals belonging to different historical click signals includes: determining a first amplitude corresponding to each time stamp in any one of the abnormal signals; determining a second amplitude corresponding to each time stamp in another abnormal signal; determining a difference between each of the first amplitudes and each of the second amplitudes; determining a minimum difference value corresponding to each time stamp; and determining the sum of all the minimum differences as the amplitude difference degree.

S24, determining a change trend curve of each abnormal signal, and calculating first similarity of the change trend curves of every two different historical point detection signals.

In an embodiment of the present application, the determining a trend curve of each of the abnormal signals includes: a, marking a maximum amplitude point and a minimum amplitude point in the abnormal signal, and fitting the maximum amplitude point and the minimum amplitude point by using a preset interpolation function to obtain an upper envelope curve and a lower envelope curve; b, calculating the average value of the upper envelope curve and the lower envelope curve to be used as an average value curve; c, calculating the difference value between the abnormal signal and the mean curve to be used as an update signal; d, if the first updating signal meets a preset first judging condition, taking the first updating signal as the abnormal signal, and repeating the steps a-c, otherwise taking the first updating signal as a comprehensive curve and carrying out the step e; e, calculating the difference value between the abnormal signal and the comprehensive curve to obtain a second updating signal; f, if the second updating signal meets a preset second judging condition, taking the second updating signal as an abnormal signal and repeating the steps a-f, otherwise, determining the comprehensive curve as a change trend curve.

Specifically, the first criterion may be that a negative local maximum value and a positive local minimum value exist in the first update signal; the second criterion may be that the number of extreme points in the second update signal is greater than a preset threshold. The preset threshold may be a natural number of 2, 3, or 4, for example, which is not limited in the present application.

Taking an abnormal signal X as an example, the specific implementation steps for obtaining the change trend curve of the abnormal signal X are as follows:

a1: marking maximum amplitude points and minimum amplitude points in an abnormal signal X, and fitting the maximum amplitude points and the minimum amplitude points by using a preset interpolation function to obtain an upper envelope curve and a lower envelope curve; the upper envelope is obtained by fitting extremely-large amplitude points, and the lower envelope is obtained by fitting extremely-small amplitude points; the preset interpolation function may be an interpolation function algorithm such as a cubic spline interpolation algorithm, which is not limited in the present application;

A2: calculating a mean value sequence of the upper envelope curve and the lower envelope curve and recording the mean value sequence as a mean value curve;

a3: calculating the difference value between the abnormal signal X and the mean curve to obtain a first updating signal Y;

A4: if the negative local maximum value and the positive local minimum value exist in the updating signal Y, the first updating signal Y is taken as an original data sequence, and the steps A1, A2 and A3 are repeated until the negative local maximum value and the positive local minimum value do not exist in the first updating signal Y, and the first updating signal Y is determined to be a first comprehensive curve Z;

a5: calculating the difference value between the abnormal signal X and the comprehensive curve Z to obtain a second updating signal W;

A6: if the number of extreme points of the second updating signal W is more than 3, the second updating signal W is used as an abnormal signal, and the steps A1 to A4 are repeated to obtain a second comprehensive curve, otherwise, the iteration is terminated, and the comprehensive curve Z is determined to be a change trend curve corresponding to the abnormal signal X.

In an embodiment of the present application, the calculating the first similarity of the change trend curves of each two points belonging to different historical point detection signals includes: randomly collecting a plurality of first amplitude data from any one of the change trend curves; randomly acquiring a plurality of second amplitude data from another variation trend curve; determining a first vector from the plurality of first amplitude data; determining a second vector from the plurality of second amplitude data; and determining the similarity of the first vector and the second vector as the first similarity. Wherein the number of first amplitude data and the number of second amplitude data may be the same or different.

Specifically, the first amplitude data may be sequentially arranged according to the order from early to late of each first amplitude data, and the normalization processing may be performed on the first amplitude data, so as to determine that the sequentially arranged normalized first amplitude data is the first vector data; the second amplitude data may be sequentially arranged according to the order of the second amplitude data from early to late, and the normalization processing may be performed on the second amplitude data, and it may be determined that the sequentially arranged normalized second amplitude data is the second vector data.

Specifically, the cosine similarity of the first vector and the second vector may be determined as the first similarity, and the inverse of the euclidean distance between the first vector and the second vector may be determined as the first similarity. The higher the first similarity is, the more similar the change trend curves of the two different historical point detection signals are, and the higher the correlation of the detection points corresponding to the two different historical point detection signals is.

S25, calculating the correlation of any two abnormal signals which belong to different historical point detection signals according to the time difference, the amplitude difference degree and the first similarity.

In an embodiment of the present application, the calculating the correlation of any two abnormal signals belonging to different historical point detection signals according to the time difference, the amplitude difference degree and the first similarity includes: respectively carrying out normalization processing on the time difference, the amplitude difference degree and the first similarity to obtain a normalized time difference, a normalized amplitude difference degree and a normalized first similarity; determining the correlation from the normalized time difference, the normalized amplitude difference, and the normalized first similarity comprises: Wherein R represents the correlation; t represents the normalized time difference; d represents the normalized amplitude variance; s represents the normalized first similarity; e represents a natural constant.

Specifically, when the normalized time difference is smaller, the mutual influence degree between detection points corresponding to two abnormal signals is higher; the smaller the normalized amplitude difference degree is, the larger the correlation between two abnormal signals which belong to different historical point detection signals is, and the larger the correlation between the corresponding two detection points is; the higher the first similarity is, the more similar the change trend curves of the two different historical point detection signals are, and the higher the correlation of the detection points corresponding to the two different historical point detection signals is.

S26, calculating the second similarity between the monitoring signal acquired in real time and the front signal.

In an embodiment of the present application, a euclidean distance between the monitoring signal and the preamble signal may be calculated, and an inverse of the euclidean distance is determined as the second similarity; the cosine similarity between the monitoring signal and the pre-signal can also be determined as the second similarity, and the method for obtaining the second similarity is not limited in the application.

And S27, outputting alarm information of each detection point according to the second similarity and the correlation.

In an embodiment of the present application, the outputting the alarm information of each detection point according to the second similarity and the correlation includes: determining the alarm probability of each detection point according to the second similarity and the correlation; generating alarm information according to the alarm probability, the ID corresponding to each detection point and a pre-stored alarm template; and outputting the alarm information.

Specifically, the alarm probability is used for representing the probability of the corresponding detection point to fail, and when the alarm probability is higher, the probability of the corresponding detection point to fail is higher. The ID corresponding to the detection point is used to represent the unique identifier of the detection point in the device to be detected, for example, the ID of the detection point may be in the form of a natural number such as 1,2,3,4, or A, B, C, D. The pre-stored alarm templates are used for formatting the ID and alarm probability corresponding to each detection point, and the alarm probability and the ID of the detection point are uniformly formatted into text information to be output. Illustratively, the pre-stored alert templates may be: note that the detection point ID: ; alarm probability: ". For example, when the alarm probability corresponding to the detection point with the ID a is 0.8, the alarm information may be: note that the detection point ID: a, A is as follows; alarm probability: 0.8".

In an embodiment of the present application, the alarm probability of each detection point determined according to the second similarity and the correlation satisfies the following relationship: Wherein R represents the correlation; f represents the second similarity; p represents the alarm probability; e represents a natural constant.

When the correlation between any two abnormal signals belonging to different historical point detection signals is higher, the correlation between the signal corresponding to any one detection point and the signal corresponding to the other detection point is higher, and when any one detection point fails, the probability of the other detection point fails is also higher; when the second similarity between the monitoring signal of any one detection point and any one preamble signal obtained in real time is higher, the probability that the abnormal signal corresponding to the preamble signal appears at the detection point is higher, and the probability that the detection point fails is higher. Therefore, the alarm probability of the corresponding detection point is determined based on the correlation and the second similarity according to the formula, and the alarm probability is mapped to be between 0 and 1, so that the accuracy of fault early warning according to the alarm probability can be improved.

According to the technical scheme, the vibration signal of each detection point in the equipment to be detected is obtained by acquiring the historical point detection signal corresponding to each vibration sensor. And determining an anomaly signal and a preamble signal in each of the history spot signals according to the amplitudes. And determining a change trend curve of each abnormal signal, and calculating the correlation of each two abnormal signals according to the time difference and the amplitude difference of the two abnormal signals and the first similarity of the change trend curve. Therefore, the association degree between every two detection points in the equipment to be detected is represented by quantized data, and the probability of the faults of the other detection points can be predicted through the quantized data. And finally, calculating a second similarity between the monitoring signal and the front signal, which are acquired in real time, and outputting alarm information of each detection point according to the second similarity and the correlation. Therefore, the fault condition of the detection point of the equipment to be detected is represented by the quantized data, and the accuracy and the efficiency of fault early warning can be improved.

Referring to fig. 3, fig. 3 is a functional block diagram of a fault early warning device according to an embodiment of the application. The fault early warning device 31 comprises an acquisition module 310, a determination module 311 and an early warning module 312. The module/unit referred to herein is a series of computer readable instructions capable of being executed by the processor 13 and of performing a fixed function, stored in the memory 12. In the present embodiment, the functions of the respective modules/units will be described in detail in the following embodiments.

The acquiring module 310 is configured to acquire a history spot detection signal corresponding to each of the plurality of vibration sensors; each vibration sensor corresponds to one detection point of equipment to be detected, and the historical point detection signal corresponds to a plurality of time stamps.

The determining module 311 is configured to determine an abnormal signal in each history point detection signal and a preamble signal corresponding to the abnormal signal according to the amplitude of the history point detection signal.

The determining module 311 is further configured to determine a time difference between the abnormal signals belonging to each two different historical click signals.

The determining module 311 is further configured to determine an amplitude difference between the abnormal signals belonging to each two different historical click signals.

The determining module 311 is further configured to determine a change trend curve of each abnormal signal, and calculate a first similarity of the change trend curves of each two different historical point detection signals.

The determining module 311 is further configured to calculate a correlation of any two abnormal signals that belong to different historical point detection signals according to the time difference, the amplitude difference degree, and the first similarity.

The determining module 311 is further configured to calculate a second similarity between the monitoring signal and the preamble signal, where the second similarity is obtained in real time.

The early warning module 312 is configured to output warning information of each detection point according to the second similarity and the correlation.

Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device 100 comprises a memory 12 and a processor 13. The memory 12 is used for storing computer readable instructions, and the processor 13 is used for executing the computer readable instructions stored in the memory to implement a fault early warning method according to any one of the embodiments.

In an embodiment of the application, the electronic device 100 further comprises a bus, a computer program stored in said memory 12 and executable on said processor 13, for example a fault warning program.

Fig. 4 shows only an electronic device 100 having a memory 12 and a processor 13, it will be understood by those skilled in the art that the configuration shown in fig. 4 is not limiting of the electronic device 100 and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.

In connection with fig. 2, the memory 12 in the electronic device 100 stores a plurality of computer readable instructions to implement a fault warning method, the processor 13 being executable to implement: acquiring a history spot detection signal corresponding to each of the plurality of vibration sensors; wherein each vibration sensor corresponds to one detection point of the equipment to be detected, and the history point detection signal corresponds to a plurality of time stamps; determining an abnormal signal in each history point detection signal and a front signal corresponding to the abnormal signal according to the amplitude of the history point detection signal; determining the time difference of the abnormal signals of every two different historical point detection signals; determining the amplitude difference degree between every two abnormal signals belonging to different historical point detection signals; determining a change trend curve of each abnormal signal, and calculating first similarity of each two change trend curves which belong to different historical point detection signals; according to the time difference, the amplitude difference degree and the first similarity, calculating the correlation of any two abnormal signals which belong to different historical point detection signals; calculating a second similarity between the monitoring signal and the front signal, which are acquired in real time; and outputting alarm information of each detection point according to the second similarity and the correlation.

Specifically, the specific implementation method of the above instructions by the processor 13 may refer to the description of the relevant steps in the corresponding embodiment of fig. 2, which is not repeated herein.

Those skilled in the art will appreciate that the schematic diagram is merely an example of the electronic device 100, and is not meant to limit the electronic device 100, and the electronic device 100 may be a bus-type structure, a star-type structure, other hardware or software, or a different arrangement of components than illustrated, where the electronic device 100 may include more or less hardware or software, and where the electronic device 100 may include an input/output device, a network access device, etc.

It should be noted that the electronic device 100 is only an example, and other electronic products that may be present in the present application or may be present in the future are also included in the scope of the present application by way of reference.

The memory 12 includes at least one type of readable storage medium, which may be non-volatile or volatile. The readable storage medium includes flash memory, a removable hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 12 may in some embodiments be an internal storage unit of the electronic device 100, such as a removable hard disk of the electronic device 100. The memory 12 may also be an external storage device of the electronic device 100 in other embodiments, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), etc. that are provided on the electronic device 100. The memory 12 may be used not only for storing application software installed in the electronic device 100 and various types of data, such as a code of a malfunction early warning program, etc., but also for temporarily storing data that has been output or is to be output.

The processor 13 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, various control chips, and the like. The processor 13 is a Control Unit (Control Unit) of the electronic device 100, connects the respective components of the entire electronic device 100 using various interfaces and lines, and executes various functions of the electronic device 100 and processes data by running or executing programs or modules (for example, executing a malfunction alerting program or the like) stored in the memory 12, and calling data stored in the memory 12.

The processor 13 executes the operating system of the electronic device 100 and various types of applications installed. The processor 13 executes the application program to implement the steps of each of the above-described embodiments of a fault warning method, such as the steps shown in fig. 2.

Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory 12 and executed by the processor 13 to complete the present application. The one or more modules/units may be a series of computer readable instruction segments capable of performing particular functions for describing the execution of the computer program in the electronic device 100. For example, the computer program may be divided into an acquisition module 310, a determination module 311, and an early warning module 310.

The integrated units implemented in the form of software functional modules described above may be stored in a computer readable storage medium. The software functional modules are stored in a storage medium and include instructions for causing a computer device (which may be a personal computer, a computer device, or a network device, etc.) or a Processor (Processor) to perform portions of a fault pre-warning method according to various embodiments of the present application.

The modules/units integrated with the electronic device 100 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as a stand alone product. Based on this understanding, the present application may also be implemented by a computer program for instructing a relevant hardware device to implement all or part of the procedures of the above-mentioned embodiment method, where the computer program may be stored in a computer readable storage medium and the computer program may be executed by a processor to implement the steps of each of the above-mentioned method embodiments.

Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory, other memories, and the like.

Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.

The bus may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. For ease of illustration, only one arrow is shown in FIG. 4, but only one bus or one type of bus is not shown. The bus is arranged to enable a connection communication between the memory 12 and at least one processor 13 or the like.

The embodiment of the application also provides a computer readable storage medium (not shown), in which computer readable instructions are stored, and the computer readable instructions are executed by a processor in an electronic device to implement a fault early warning method according to any one of the above embodiments.

In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.

The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.

Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Several of the elements or devices described in the specification may be embodied by one and the same item of software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.

Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present application and not for limiting the same, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present application without departing from the spirit and scope of the technical solution of the present application.

Claims (9)

1. A fault pre-warning method applied to an electronic device, the electronic device being communicatively connected to a plurality of vibration sensors, the method comprising:

acquiring a history spot detection signal corresponding to each of the plurality of vibration sensors; wherein each vibration sensor corresponds to one detection point of the equipment to be detected, and the history point detection signal corresponds to a plurality of time stamps;

determining an abnormal signal in each history point detection signal and a front signal corresponding to the abnormal signal according to the amplitude of the history point detection signal;

determining the time difference of the abnormal signals of every two different historical point detection signals;

determining the amplitude difference degree between every two abnormal signals belonging to different historical point detection signals;

determining a change trend curve of each abnormal signal, and calculating first similarity of each two change trend curves which belong to different historical point detection signals;

According to the time difference, the amplitude difference degree and the first similarity, calculating the correlation of any two abnormal signals respectively belonging to different historical point detection signals, wherein the correlation comprises the following steps: respectively carrying out normalization processing on the time difference, the amplitude difference degree and the first similarity to obtain a normalized time difference, a normalized amplitude difference degree and a normalized first similarity; determining the correlation from the normalized time difference, the normalized amplitude difference, and the normalized first similarity comprises:

wherein R represents the correlation; t represents the normalized time difference; d represents the normalized amplitude variance; s represents the normalized first similarity; e represents a natural constant;

Calculating a second similarity between the monitoring signal and the front signal, which are acquired in real time;

And outputting alarm information of each detection point according to the second similarity and the correlation.

2. The fault pre-warning method as claimed in claim 1, wherein said determining an anomaly signal in each of the historical click signals based on the amplitudes of the historical click signals, and the pre-signal corresponding to the anomaly signal comprises:

determining a difference between amplitudes corresponding to each two time stamps;

When the difference value is positive, determining any one of two time stamps corresponding to the difference value as a starting time point;

when the difference value is a negative number, determining any one of two time stamps corresponding to the difference value as a termination time point;

According to the sequence from the early to the late of the time stamp, determining that the history point detection signals between the adjacent starting time point and the termination time point are abnormal signals;

and determining the history point detection signals between the adjacent ending time points and the starting time points as the prepositive signals corresponding to the abnormal signals according to the sequence from the early to the late of the time stamps.

3. The fault pre-warning method of claim 1, wherein determining the degree of amplitude difference between any two of the anomaly signals belonging to different ones of the historical click signals comprises:

determining a first amplitude corresponding to each time stamp in any one of the abnormal signals;

determining a second amplitude corresponding to each time stamp in another abnormal signal;

determining a difference between each of the first amplitudes and each of the second amplitudes;

determining a minimum difference value corresponding to each time stamp;

and determining the sum of all the minimum differences as the amplitude difference degree.

4. The fault pre-warning method as claimed in claim 1, wherein said determining a trend curve of each of said abnormal signals comprises:

a, marking a maximum amplitude point and a minimum amplitude point in the abnormal signal, and fitting the maximum amplitude point and the minimum amplitude point by using a preset interpolation function to obtain an upper envelope curve and a lower envelope curve;

b, calculating the average value of the upper envelope curve and the lower envelope curve to be used as an average value curve;

c, calculating the difference value between the abnormal signal and the mean curve to be used as an update signal;

d, taking the updating signal as the abnormal signal if the updating signal meets a preset first judging condition, and repeating the steps a-c, otherwise taking the updating signal as a comprehensive curve and carrying out the step e;

e, calculating the difference value between the abnormal signal and the comprehensive curve to obtain a data sequence;

f, if the data sequence meets a preset second judging condition, taking the data sequence as an abnormal signal and repeating the steps a-f, otherwise, determining the comprehensive curve as a change trend curve.

5. The method of claim 4, wherein calculating the first similarity of the trend curves for each two different historical click signals comprises:

randomly collecting a plurality of first amplitude data from any one of the change trend curves;

Randomly acquiring a plurality of second amplitude data from another variation trend curve;

determining a first vector from the plurality of first amplitude data;

determining a second vector from the plurality of second amplitude data;

And determining the similarity of the first vector and the second vector as the first similarity.

6. The fault pre-warning method as claimed in claim 1, wherein said outputting the warning information of each of the detection points according to the second similarity and the correlation comprises:

Determining the alarm probability of each detection point according to the second similarity and the correlation;

Generating alarm information according to the alarm probability, the ID corresponding to each detection and a pre-stored alarm template;

And outputting the alarm information.

7. The fault pre-warning method as claimed in claim 6, wherein said determining the alarm probability of each of said detection points according to said second similarity and said correlation satisfies the following relation:

Wherein R represents the correlation; f represents the second similarity; p represents the alarm probability; e represents a natural constant.

8. A fault pre-warning device, characterized in that the device comprises a module for implementing a fault pre-warning method according to any one of claims 1 to 7, the device comprising:

The acquisition module is used for acquiring historical spot detection signals corresponding to each of the plurality of vibration sensors; wherein each vibration sensor corresponds to one detection point of the equipment to be detected, and the history point detection signal corresponds to a plurality of time stamps;

The determining module is used for determining an abnormal signal in each history point detection signal and a front signal corresponding to the abnormal signal according to the amplitude of the history point detection signal;

the determining module is further used for determining the time difference of the abnormal signals of each two different historical point detection signals;

the determining module is further used for determining the amplitude difference degree between every two abnormal signals belonging to different historical spot detection signals;

the determining module is further used for determining a change trend curve of each abnormal signal, and calculating first similarity of the change trend curves of every two different historical point detection signals;

The determining module is further configured to calculate, according to the time difference, the amplitude difference degree, and the first similarity, correlation of any two abnormal signals that belong to different historical point detection signals;

The determining module is further used for calculating a second similarity between the monitoring signal acquired in real time and the front signal;

And the early warning module is used for outputting warning information of each detection point according to the second similarity and the correlation.

9. An electronic device comprising a processor and a memory, wherein the processor is configured to implement a fault warning method according to any one of claims 1 to 7 when executing a computer program stored in the memory.