CN111538723A

CN111538723A - Monitoring data processing method and device and electronic equipment

Info

Publication number: CN111538723A
Application number: CN202010359888.3A
Authority: CN
Inventors: 王建辉; 韦福东; 刘朋鹏; 金晶; 王辉; 姚丙雷
Original assignee: Shanghai Electrical Apparatus Research Institute Group Co Ltd; Shanghai Motor System Energy Saving Engineering Technology Research Center Co Ltd
Current assignee: Shanghai Electrical Apparatus Research Institute Group Co Ltd; Shanghai Motor System Energy Saving Engineering Technology Research Center Co Ltd
Priority date: 2020-04-29
Filing date: 2020-04-29
Publication date: 2020-08-14

Abstract

The application provides a monitoring data processing method, a monitoring data processing device and electronic equipment, wherein the method comprises the following steps: obtaining a variation trend function corresponding to the monitoring data according to the collected monitoring data; extracting data from the variation trend function according to a set time sequence to form an initial data set; and carrying out validity processing on each data point in the initial data set to obtain target monitoring data. By processing the monitoring data, the monitoring data can reflect the state of the object more accurately.

Description

Monitoring data processing method and device and electronic equipment

Technical Field

The invention relates to the technical field of data processing, in particular to a monitoring data processing method and device and electronic equipment.

Background

When fault detection or operation maintenance of the equipment is required, some measurable parameters of the monitored equipment need to be monitored on line. For example, when the monitored device is a motor, the measurable data typically includes temperature, voltage, current, vibration, etc. Based on these measurable data, a time-varying data sequence can be obtained, such as a time-varying temperature sequence, a voltage-effective value sequence, a current-effective value sequence, a vibration-effective value sequence. From these data sequences, the health and fault trends of the monitored equipment, etc. can be further analyzed.

However, during the sampling and recording process, the monitoring device may occasionally be disturbed, power is cut off or communication is interrupted, and therefore, the collected data sequence may cause the subsequent misjudgment of the state evaluation and fault diagnosis of the monitored device.

Disclosure of Invention

The invention aims to provide a monitoring data processing method, a device and an electronic device, which can enable monitoring data to reflect the state of an object more accurately.

In a first aspect, an embodiment of the present invention provides a monitoring data processing method, including:

obtaining a variation trend function corresponding to the monitoring data according to the collected monitoring data of the monitored equipment;

extracting data from the change trend function according to a set time sequence to form an initial data set;

and carrying out effectiveness processing on each data point in the initial data set to obtain target monitoring data.

In an alternative embodiment, the performing validity processing on each data point in the initial data set to obtain target monitoring data includes:

judging whether each data point in the initial data set is an invalid data point or not according to a preset fluctuation interval and a numerical limit interval;

and if an invalid data point exists in the initial data set, replacing the value of the invalid data point with the value of a designated data point so as to update the initial data set to obtain target monitoring data.

According to the monitoring data processing method provided by the embodiment of the application, the invalid points in the initial data group are replaced, so that the probability that the data in the obtained target monitoring data are invalid data points is greatly reduced, and the target monitoring data can better represent the condition of the monitored equipment.

determining an initial data point from the initial data set according to a preset numerical limit interval;

judging whether a data point after the initial data point in the initial data set is an invalid data point or not according to a preset fluctuation interval, wherein if a judgment value determined by a current data point in the initial data set and a previous data point of the current data point is outside the preset fluctuation interval, the current data point is represented as an invalid data point, and if the judgment value determined by the current data point in the initial data set and the previous data point of the current data point is within the preset fluctuation interval, the current data point is represented as an effective data point;

According to the monitoring data processing method provided by the embodiment of the application, the effective initial data points are determined, and after the effective data points are determined, the effectiveness in the subsequent initial data is determined one by one, so that the subsequent data points can be judged more accurately, and the target monitoring data can better represent the condition of the monitored equipment.

In an alternative embodiment, the replacing the value of the invalid data point with the value of the designated data point includes:

replacing the value of the invalid data point with the value of a valid data point that is closest in time distance to the invalid data point; or,

and fitting an objective function according to the determined valid data points, and replacing the values of the invalid data points with the values in the objective function.

According to the monitoring data processing method provided by the embodiment of the application, an effective data point closest to the invalid data point in time is probably closest to the true value of the invalid data point, so that the value of the invalid data point is replaced by the value of the effective data point closest to the invalid data point in time, the data condition of the invalid data point can be better represented, and finally obtained target monitoring data can be more effective. Or, a data fitting mode is used to determine a replacement value of the invalid data point, so that the value of the invalid data point after replacement can better represent the condition of the monitored equipment, and the finally obtained target monitoring data can be more effective.

In an alternative embodiment, the trend function is a function of time and a measured parameter; the extracting data from the variation trend function according to the set time sequence to form an initial data group comprises the following steps:

and extracting a plurality of data points with equal time distance from the variation trend function to form an initial data set.

According to the monitoring data processing method provided by the embodiment of the application, the detection data with indefinite step length is determined as the monitoring data with equidistant fixed step length, so that the fluctuation range standard of the monitoring data can be determined. The change in the amount of the monitored equipment generally has an inherent time constant, and the change in the amount of the monitored equipment does not exceed a certain value or the change speed does not exceed a certain value within a certain time. Therefore, the data transformation such as mean value calculation of different time periods and the like through the monitoring data is facilitated through the equidistant processing.

In an optional embodiment, the obtaining a variation trend function corresponding to the monitoring data according to the collected monitoring data of the monitored device includes:

and carrying out data fitting processing on the collected monitoring data to obtain a variation trend function corresponding to the monitoring data.

In an optional embodiment, the performing data fitting processing on the collected monitoring data to obtain a variation trend function corresponding to the monitoring data includes:

linearly connecting every two adjacent data points in the collected monitoring data to obtain a variation trend function comprising a piecewise linear function; or,

and performing curve fitting on each data point in the collected monitoring data to obtain a variation trend function.

According to the monitoring data processing method provided by the embodiment of the application, the function corresponding to the monitoring data is determined through the various fitting modes, so that the subsequent extracted initial data set is data based on the monitoring data and cannot be separated from the monitoring data, and the finally determined target monitoring data can well represent the condition of the monitored object.

In an optional embodiment, the monitoring data processing method further includes:

determining a status score of the monitored equipment according to the target monitoring data;

and determining a target alarm signal according to the state score.

The monitoring data processing method provided by the embodiment of the application can also use the processed target monitoring data to determine the state of the monitored equipment, and the state of the monitored equipment can be more accurately monitored by using the processed monitoring data.

In a second aspect, an embodiment of the present invention provides a monitoring data processing apparatus, including:

the obtaining module is used for obtaining a change trend function corresponding to the monitoring data according to the collected monitoring data of the monitored equipment;

the extraction module is used for extracting data from the change trend function according to a set time sequence to form an initial data set;

and the processing module is used for carrying out effectiveness processing on each data point in the initial data set so as to obtain target monitoring data.

In a third aspect, an embodiment of the present invention provides an electronic device, including: a processor, a memory storing machine readable instructions executable by the processor, the machine readable instructions when executed by the processor perform the steps of the method of any of the preceding embodiments when the electronic device is run.

In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the method according to any one of the foregoing embodiments.

The monitoring data processing method, the monitoring data processing device, the electronic equipment and the computer readable storage medium have the advantages that:

drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.

Fig. 1 is a block diagram of an electronic device according to an embodiment of the present disclosure.

Fig. 2 is a flowchart of a monitoring data processing method according to an embodiment of the present application.

Fig. 3 is a schematic diagram of a data change trend in a data processing process in the monitoring data processing method according to the embodiment of the present application.

Fig. 4 is a detailed flowchart of step 203 in the monitoring data processing method according to the embodiment of the present application.

Fig. 5 is a schematic diagram illustrating another data change trend in the data processing process in the monitoring data processing method according to the embodiment of the present application.

Fig. 6 is a schematic diagram of a data change trend in another data processing process in the monitoring data processing method according to the embodiment of the present application.

Fig. 7 is a schematic functional block diagram of a monitoring data processing apparatus according to an embodiment of the present application.

Detailed Description

The technical solution in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.

Example one

To facilitate understanding of the present embodiment, first, an electronic device executing the monitoring data processing method disclosed in the embodiments of the present application will be described in detail.

As shown in fig. 1, is a block schematic diagram of an electronic device. The electronic device 100 may include a memory 111, a memory controller 112, a processor 113, a peripheral interface 114, an input-output unit 115, and a display unit 116. It will be understood by those of ordinary skill in the art that the structure shown in fig. 1 is merely exemplary and is not intended to limit the structure of the electronic device 100. For example, electronic device 100 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.

The above-mentioned elements of the memory 111, the memory controller 112, the processor 113, the peripheral interface 114, the input/output unit 115 and the display unit 116 are electrically connected to each other directly or indirectly, so as to implement data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The processor 113 is used to execute the executable modules stored in the memory.

The Memory 111 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 111 is configured to store a program, and the processor 113 executes the program after receiving an execution instruction, and the method executed by the electronic device 100 defined by the process disclosed in any embodiment of the present application may be applied to the processor 113, or implemented by the processor 113.

The processor 113 may be an integrated circuit chip having signal processing capability. The Processor 113 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

The peripheral interface 114 couples various input/output devices to the processor 113 and memory 111. In some embodiments, the peripheral interface 114, the processor 113, and the memory controller 112 may be implemented in a single chip. In other examples, they may be implemented separately from the individual chips.

The input/output unit 115 is used for inputting data from a sensing device such as a sensor and outputting the processed result data to a computer, a server, or the like. The input/output unit 115 may be, but is not limited to, a communication port, etc.

The display unit 116 provides an interactive interface (e.g., a user operation interface) between the electronic device 100 and the user or is used for displaying image data to the user for reference. In this embodiment, the display unit may be a liquid crystal display or a touch display. In the case of a touch display, the display can be a capacitive touch screen or a resistive touch screen, which supports single-point and multi-point touch operations. The support of single-point and multi-point touch operations means that the touch display can sense touch operations simultaneously generated from one or more positions on the touch display, and the sensed touch operations are sent to the processor for calculation and processing.

The electronic device 100 in this embodiment may be configured to perform each step in each method provided in this embodiment. The following describes in detail the implementation of the monitoring data processing method by means of several embodiments.

Example two

Please refer to fig. 2, which is a flowchart illustrating a monitoring data processing method according to an embodiment of the present disclosure. The specific process shown in fig. 2 will be described in detail below.

Step 201, obtaining a variation trend function corresponding to the monitoring data according to the collected monitoring data of the monitored equipment.

In one embodiment, when the monitored device is an electric motor, the monitored data may include one or more of temperature, current amplitude or effective value, voltage amplitude or effective value, vibration displacement amplitude or effective value, vibration velocity amplitude or effective value, vibration acceleration amplitude or effective value, and the like.

Taking the monitoring data as an example, the data sequence corresponding to the sampled monitoring data may be t0[ k0] and x0[ k0 ]. Where k0 is 1 to m, where m is a positive integer greater than one. Where t0[ k0] represents an arbitrary time point, and x0[ k0] represents a t0[ k0] sampled monitored data value.

In one example, data sequences t0[ k0] and x0[ k0], k0 ═ 1 to m, of the monitored data are sampled. If the currently sampled monitoring data is the vibration speed amplitude. Wherein m is 8. The data sequence of the monitoring data is shown in table 1 below.

TABLE 1

k0	t0[k]	x0[k]
			1	0	1.523
2	24	0.2614
			3	51	0.7087
4	73	0.6943
			5	102	1.000
6	128	0.6752
			7	148	0.679
8	175	0.6801

Where t0 is in seconds(s) and x0 is in millimeters per second (mm/s).

Optionally, data fitting processing is performed on the collected monitoring data to obtain a variation trend function corresponding to the monitoring data.

In one embodiment, every two adjacent data points in the collected monitoring data are connected in a straight line to obtain a variation trend function including a piecewise linear function.

Taking the above example as an example, as shown in fig. 3, each data point of the monitoring data is indicated by a "o". Every two adjacent data points are connected in a straight line, and a unary linear function can be formed. The independent variable of each unary linear function is a time variable, and the dependent variable of the function is a vibration speed amplitude variable.

In another embodiment, each data point in the collected monitoring data is curve-fitted to obtain a trend function.

Alternatively, a polynomial curve may be used to achieve a curve fit to the monitored data.

And step 202, extracting data from the change trend function according to a set time sequence to form an initial data set.

Optionally, a plurality of time-equally spaced data points are extracted from the trend function to form an initial data set.

For example, an equidistant time sequence t [ k ] may be given, where t is 1 to n, where n is a positive integer greater than one. In this embodiment, the value of n may be greater than the value of m.

In one example, t [1] ═ t0[1], t [ n ] ═ t0[ m ], and t [ k ] time series intervals dt ═ t [ n ] -t [1])/(n-1), n ≧ 2. And taking t [ k ] as an independent variable, and obtaining a strain x [ k ] by a change trend function formed by the data sequences t0[ k0] and x [ k0], thereby obtaining the data sequences t [ k ] and x [ k ] of the initial data set.

In one example, n may be 11, t [1] ═ t0[1] ═ 0, t [11] ═ t0[8] ═ 175, and the time series interval dt ═ 175/10 ═ 17.5. And obtaining the strain x [ k ] by a change trend function formed by connecting lines of the data sequence t0[ k0] and x0[ k0], thereby obtaining interpolated sequences t [ k ] and x [ k ].

As shown in fig. 3, each data point in the extracted initial data set is denoted by a. The value of the monitored data corresponding to t k can be taken from the trend function, and an initial data set can be formed.

The data sequence x k of the initial data set may be, as shown in Table 2 below.

TABLE 2

k	t[k]	x[k]
			1	0	1.523
2	17.5	0.603
			3	35	0.444
4	52.5	0.708
			5	70	0.696
6	87.5	0.847
			7	105	0.963
8	122.5	0.744
			9	140	0.677
10	157.5	0.679
			11	175	0.680

Where t is in units of seconds(s) and x is in units of millimeters per second (mm/s).

In this embodiment, the initial data set is extracted from the above change trend function, so that the monitoring data with indefinite step length can be changed into the equidistant initial data set with definite step length, and data transformation such as mean value calculation of different time periods can be performed through the extracted data.

In the case where the device being monitored is an electric motor, the change due to the impaired health of the motor may be considered a gradual change in the data, and the range of change and the rate of change are within certain limits. The change of each item of data of the motor has an inherent time constant, and the change of each item of data within a certain time does not exceed a certain value or the change speed does not exceed a certain value. The jump data generated by the sudden and serious failure of the motor is not suitable for being used as data for motor failure diagnosis and predictive maintenance. Therefore, each data point in the initial data set can be processed for validity, and invalid data points in the initial data set can be removed or replaced.

And step 203, performing validity processing on each data point in the initial data set to obtain target monitoring data.

In one embodiment, as shown in FIG. 4, step 203 may include the following steps.

Step 2031, according to a preset numerical limit interval, determining an initial data point from the initial data set.

Alternatively, the above-described numerical limit interval may be set to different values according to different monitored devices. For example, when the monitored device is a motor and the monitored data is the vibration velocity amplitude of the motor, the numerical limit section may include a limit upper limit and a limit lower limit, respectively. For example, the upper limit of the limit x _ max may be 1.0, 1.1, 1.2, etc., and the lower limit of the limit x _ min may be 0.3, 0.25, 0.28, 0.32, etc.

In this embodiment, the initial data point may be the first valid data point in the initial data set.

In this embodiment, the data point before the initial data point in the initial data set is determined to be an invalid data point.

Step 2032, according to a preset fluctuation interval, determining whether a data point subsequent to the initial data point in the initial data set is an invalid data point.

And if the difference value between the current data point and the previous data point in the initial data set is outside the preset fluctuation interval, representing that the current data point is an invalid data point.

If the decision value determined by the current data point in the initial data set and the previous data point of the current data point is outside the preset fluctuation interval, the current data point is represented as an invalid data point, and if the decision value determined by the current data point in the initial data set and the previous data point of the current data point is within the preset fluctuation interval, the current data point is represented as an effective data point.

Alternatively, the fluctuation interval described above may include a numerical fluctuation interval, a slope fluctuation interval, and a power function ratio fluctuation interval.

Wherein the power function ratio can be expressed as: km ^ n/(t2-t1) (x2-x 1). Where the previous data point is represented as (t1, x1), the current data point is represented as (t2, x2), and n is a real number greater than 0.

In this embodiment, the determination value determined by the current data point and the previous data point may be a difference value between the value of the current data point and the value of the previous valid data point; the decision value determined for the current data point and the previous data point in the initial data set may also be the slope of the line segment formed by the current data point and the previous valid data point.

Optionally, it is determined whether a difference between a value of the current data point and a value of a previous valid data point is within a value fluctuation interval, and if the difference between the value of the current data point and the value of the previous valid data point is within the value fluctuation interval, the current data point is a valid data point.

Optionally, it is determined whether a slope of a line segment formed by the current data point and the previous valid data point is within a slope fluctuation interval, and if the slope of the line segment formed by the current data point and the previous valid data point is within the slope fluctuation interval, the current data point is a valid data point.

Optionally, it is determined whether a power function ratio formed by the current data point and the previous valid data point is within a power function ratio fluctuation interval, and if the power function ratio formed by the current data point and the previous valid data point is within the power function ratio fluctuation interval, it indicates that the current data point is a valid data point.

Optionally, determining whether a difference between a value of the current data point and a value of a previous valid data point is within a value fluctuation interval, and whether a slope of a line segment formed by the current data point and the previous valid data point is within a slope fluctuation interval; and if the difference between the value of the current data point and the value of the previous valid data point is within a numerical fluctuation interval and the slope of a line segment formed by the current data point and the previous valid data point is within a slope fluctuation interval, indicating that the current data point is a valid data point.

Optionally, determining whether a difference between a value of the current data point and a value of a previous valid data point is within a power function ratio fluctuation interval, and whether a power function ratio formed by the current data point and the previous valid data point is within the power function ratio fluctuation interval; and if the difference between the value of the current data point and the value of the previous effective data point is within the numerical fluctuation interval, and the power function ratio formed by the current data point and the previous effective data point is within the power function ratio fluctuation interval, indicating that the current data point is an effective data point.

For example, the value fluctuation interval may include an upper value limit and a lower value limit. In one example, the upper numerical limit is denoted as xw _ max ═ a and the lower numerical limit is denoted as xw _ min ═ b. And the values of a and b may be different according to different collected monitoring data. For example, when the collected monitoring data is the vibration velocity amplitude of the motor, the values of a may be 0.3, 0.35, 0.4, etc., and the values of b may be-0.3, -0.35, -0.4, etc.

Illustratively, the slope fluctuation interval may include an upper slope limit and a lower slope limit. In one example, the upper slope limit is represented as kxw _ max ═ c and the lower slope limit kxw _ min ═ d. And the values of c and d may be different according to different collected monitoring data. For example, when the collected monitoring data is the vibration velocity amplitude of the motor, the value of c may be 0.01, 0.013, 0.018, 0.015, etc., and the value of d may be-0.01, -0.013, -0.018, -0.015, etc.

Alternatively, whether each data point in the initial data set is a valid data point may be recorded by a recording array. Illustratively, if one value in the record array is assigned as the first value, it means that the data point corresponding to the value is a valid data point; and if one value in the record array is assigned as the second value, the data point corresponding to the value is represented as an invalid data point. In one example, the first value is a non-zero value; the second value is zero.

In one example, the record array may be flag [ k ], where k is 1 to n, and the flag [ k ] indicates the validity of the kth data. When flag [ k1] is 1, the k1 th data point t [ k1] and x [ k1] in the corresponding initial data group are valid data points. When flag [ k2] is 0, the corresponding k2 th data point t [ k2] and x [ k2] in the initial data group are invalid data points.

Step 2033, if there is an invalid data point in the initial data set, replacing the value of the invalid data point with the value of the designated data point, so as to update the initial data set to obtain the target monitoring data.

In one embodiment, step 2033 may be implemented as: replacing the value of the invalid data point with the value of a valid data point that is the closest in time distance from the invalid data point.

Alternatively, a new array may be used to store the target monitoring data. For example, target monitoring data may be recorded using t [ k ] and y [ k ].

Illustratively, when flag [ i ] ═ 1, then y [ i ] ═ x [ i ], where i is a positive integer. Illustratively, when flag [ j ] ═ 0, then y [ j ] ═ y [ j-1], where j is a positive integer greater than two.

Illustratively, when flag [1] ═ 0, and flag [ c ] ═ 1; then y [1] ═ x [ c ]. Wherein c is a positive integer greater than one, and r is zero, and the value of r is 1 to c-1.

The processing flow of steps 2031-2033 is described below by a specific logic:

p is from 1 to n: if x _ min ≦ x [ p ] ≦ x _ max, then x _ z ═ x [ p ], t _ z ═ t [ p ], and the loop is skipped; otherwise, p +1 continues to loop. The first valid data point is determined and recorded as t _ z and x _ z, and t [ p ] and x [ p ] are marked as valid points, i.e. flag [ p ] is 1.

The preprocessed data values are y k, 1-n, and the initial value is assigned as 0.

k is from 1 to p: y [ k ] ═ x _ z. And determining the number of the front P in the target monitoring data.

The data points t _ z and x _ z are temporary variables used in the judgment process and used for storing the currently valid data points in the judgment process.

According to the above processing flow, the upper limit and the lower limit of the numerical limit interval are x _ max to 1.0 and x _ min to 0.3, respectively, using the data in table 2; the upper limit and the lower limit of the numerical value fluctuation interval are xw _ max being 0.3 and xw _ min being-0.3 respectively; and the upper and lower slope limits of the slope fluctuation interval are kxw _ max being 0.01 and kxw _ min being-0.01, respectively, as an example:

when p is 1, x1 is 1.523, 1.523>1.0, and the cycle continues; when p is 2, x [2] ═ 0.603, 0.3 ≦ 0.603 ≦ 1.0, x _ z ≦ 0.603, t _ z ≦ 17.5, flag [2] ═ 1, y [1] ═ x _ z ═ 0.603, and y [2] ═ x _ z ≦ 0.603. Thus, the second data point can be determined to be the initial data point.

After the initial data point is determined, whether a data point subsequent to the initial data point in the initial data set is an invalid data point can be judged according to a preset fluctuation interval. Exemplaryly,

then, let p ═ p +1, t _ n ═ t [ p ], x _ n ═ x [ p ]; calculating the slope: kxw ═ x _ n-x _ z)/(t _ n-t _ z); calculating the deviation xw-x _ n-x _ z; when kxw _ min ≦ kxw ≦ kxw _ max and w _ min ≦ xw _ max, then t _ z ═ t [ p ], x _ z ═ x [ p ], flag [ p ] ═ 1; otherwise, flag [ p ] is 0 and y [ p ] is x _ z.

According to the above processing flow, the upper limit and the lower limit of the numerical limit interval are x _ max to 1.0 and x _ min to 0.3, respectively, using the data in table 2; the upper limit and the lower limit of the numerical value fluctuation interval are xw _ max being 0.3 and xw _ min being-0.3 respectively; and the upper limit and the lower limit of the slope fluctuation interval are kxw _ max being 0.01 and kxw _ min being-0.01 respectively, as an example, the process flow of determining the data point after the second data point in the initial data set may be represented as:

let p be 3, t _ n be t [3] ═ 35, x _ n be x [3] ═ 0.444;

calculating the slope: kxw- (x _ n-x _ z)/(t _ n-t _ z) - (0.444-0.603)/(35-17.5) - (0.009086);

calculating deviation: xw ═ 0.159;

t _ z ═ tp ═ t 3 ≦ 35 because-0.01 ≦ -0.009086 ≦ 0.01 and-0.3 ≦ -0.159 ≦ 0.3; x _ z ═ x [ p ] ═ x [3] ═ 0.444; flag [ p ] ═ flag [3] ═ 1; y [ p ] ═ y [3] ═ x _ z = 0.444.

Then, the next loop is executed: let p +1 be 4, t _ n be 52.5, x _ n be 0.708;

calculating the slope: kxw (x _ n-x _ z)/(t _ n-t _ z) (0.708-0.444)/(52.5-35) ((0.0151));

calculating deviation; xw is 0.264;

since 0.0151 ≧ 0.01 does not hold, flag [ p ] ═ flag [4] ═ 0; y [ p ] ═ y [4] ═ x _ z = 0.444;

this is cycled through until p > n.

As described by way of example above, the determined array of flag [ k ] s may be as shown in Table 3 below.

TABLE 3

As shown in fig. 5, the initial data set shown in table 2 is processed through the above steps to obtain target monitoring data. In this case, ". smallcircle" shown in FIG. 5 indicates a data point in the initial data set, and ". DELTA" shown in FIG. 5 indicates a data point in the target monitored data.

In another embodiment, step 2033 may be implemented as: fitting a target function according to the determined effective data points; replacing a value in the objective function with a value of the invalid data point.

In this embodiment, the objective function may be a linear function or a curved function.

The following description takes the initial data set as the data in table 2 as an example:

alternatively, eight valid data points in the initial data set in this example can be fit to a first linear function with the second data point and the third data point; then determining a replacement value corresponding to the first data point according to the first linear function;

the fifth data point, the sixth data point, and the seventh data point are fitted to form a second straight-line function, or the fifth data point and the sixth data point are fitted to form a second straight-line function; then determining a replacement value corresponding to the fourth data point according to the second straight-line function;

fitting the ninth data point, the tenth data point and the eleventh data point to form a third linear function, or fitting the ninth data point and the tenth data point to form a third linear function; and then determining a replacement value corresponding to the eighth data point according to the third straight-line function.

Optionally, a curve fitting is performed with the second data point, the third data point, the fifth data point, the sixth data point, the seventh data point, the ninth data point, the tenth data point, and the eleventh data point to obtain a curve function. Then the value corresponding to the first data point, the value corresponding to the fourth data point and the replacement value corresponding to the eighth data point can be determined according to the curve function.

As shown in fig. 6, the initial data set shown in table 2 is processed through the above steps to obtain target monitoring data. Where ". smallcircle" shown in FIG. 6 indicates a valid data point in the initial data set and ". DELTA" shown in FIG. 6 indicates a data point in the target monitored data.

In another embodiment, step 203 may also include the following steps.

Step 2034, determining whether each data point in the initial data set is an invalid data point according to a preset fluctuation interval and a numerical limit interval.

For example, the numerical limitation interval may represent a limitation of the value of each data point in the initial data set. If the value of each data point in the initial data set is within the value limit interval, the data point may be a valid data point. If the value of each data point in the initial data set is within the value limit interval, the data point is represented as an invalid data point.

Alternatively, the fluctuation interval described above may include a numerical fluctuation interval and a slope fluctuation interval.

Optionally, judging whether the value of the current data point is within a value limit interval; if the value of each data point in the initial data set is within the value limit interval, the data point is represented as a valid data point. If the value of each data point in the initial data set is within the value limit interval, the data point is represented as an invalid data point.

Optionally, determining whether a difference between a value of the current data point and a value of a previous valid data point is within a value fluctuation interval, whether a slope of a line segment formed by the current data point and the previous valid data point is within a slope fluctuation interval, and whether the value of the current data point is within a value limit interval; and if the value of each data point in the initial data set is within the value limit interval, the difference between the value of the current data point and the value of the previous valid data point is within the value fluctuation interval, and the slope of the line segment formed by the current data point and the previous valid data point is within the slope fluctuation interval, indicating that the current data point is a valid data point.

Step 2035, if there is an invalid data point in the initial data set, replacing the value of the invalid data point with the value of the designated data point, so as to update the initial data set to obtain the target monitoring data.

Illustratively, when flag [1] ═ 0, and flag [ c ] ═ 1; then y [1] ═ x [ c ]. Wherein c is a positive integer greater than one, and r is zero, and r is a positive integer from 1 to c-1. Further, if c is greater than two, let y [ s ] ═ x [ c ], where s is a positive integer from 1 to c-1.

Through the steps, the monitoring data of the monitored equipment can be preprocessed, so that the processed target monitoring data can better represent the condition of the monitored equipment.

In this embodiment, the status of the monitored equipment may be identified according to the target monitoring data.

After step 203, may further include: determining a status score of the monitored equipment according to the target monitoring data; and determining a target alarm signal according to the state score.

Illustratively, the above-mentioned target alarm signal may be a no alarm signal; or may be a different level of alarm signal.

Taking the temperature rise data T of the motor as the monitored equipment and the monitored data as the motor as an example, the evaluation score is obtained through the following piecewise linear evaluation function.

In one example, where each threshold parameter T in the above equation is₁、T₂、T₃、T₄、T₅The correspondence between the values and the evaluation scores is shown in table 4 below.

TABLE 4

Threshold parameter value	T₁	T₂	T₃	T₄	T₅
						Evaluating a score	100	75	50	25	0

In one example, for one motor, the threshold parameters may be given as shown in table 5 below.

TABLE 5

Threshold parameter	T₁	T₂	T₃	T₄	T₅
						Value taking	60	65	70	75	80

In this embodiment, the number of the threshold parameters and the values of the threshold parameters are not limited to the above examples, and the threshold parameters may be determined according to the monitored device and the monitoring data.

Alternatively, the operating state of the motor may be divided into: normal state, suspicious state, bad state and dangerous state. Different alarms can be output according to different states.

For example, different states of the motor may correspond to different criteria, as follows:

1) the normal state refers to a state that the evaluation score is between 75 and 100 points, and no alarm is given;

2) the suspicious state refers to a state that the evaluation score is between 50 and 75 (not 75), and an intelligent early warning signal is triggered;

3) the bad state refers to a state that the evaluation score is between 25 and 50 (not 50), and a common early warning signal is triggered;

4) the dangerous state refers to a state in which the evaluation score is between 0 and 25 points (excluding 25 points), and an emergency alert signal is triggered.

Alternatively, when the target alarm signal is an alarm signal of different levels, the target alarm signal may be sent to a server or a designated communication account.

The alarm signal should be immediately uploaded to the cloud platform and be notified to the operation and maintenance personnel through the cloud platform.

By way of example, it can be seen that:

if the corresponding temperature rise is 65K or below, an evaluation score not less than 75 points is obtained, and no alarm is given;

if the corresponding temperature rise is 70K or below and is more than 65K (or 65K is not contained), an evaluation score of 50-75 points (or 75 points is not contained) is obtained, and an intelligent early warning signal is triggered;

if the corresponding temperature rise is 75K or below and more than 70K (without 70K), an evaluation score of 25-50 points (without 50 points) is obtained, and a common early warning signal is triggered;

if the corresponding temperature rise is 80K or below and more than 75K (without 75K), an evaluation score of 0-25 points (without 25 points) is obtained, and an emergency alarm signal is triggered.

Where K represents Kelvin, a temperature unit in International systems of units.

It can be known that if the monitoring data of temperature rise is not preprocessed in the

above step

201 and 203, if an interference signal with temperature rise greater than 75K occurs, false alarm such as emergency alarm may be caused, which affects the operation of the device.

Further, the operation and maintenance service system of the motor can predict the specific time when the motor should be maintained according to the trend of the evaluation parameter score related to the loss characteristic of the motor.

Illustratively, the evaluation parameter evaluation score related to the loss characteristic may include a current evaluation score, a voltage evaluation score, a vibration evaluation score, a temperature evaluation score, and the like. And maintaining a threshold parameter according to an evaluation score preset by the operation and maintenance service system, and giving specific contents and time of next maintenance. The evaluation score maintenance threshold may be a value between the intelligent warning threshold and the general warning threshold.

Wherein, the trend of the evaluation score refers to the change rate of the evaluation score in a certain period and is the slope of the linear regression of the previous operation period. The operation period refers to the main cycle period of the motor work, and can be the period of day, week, month, quarter, year and the like.

For example, in the online monitoring of the motor temperature rise, a sudden high value of the motor temperature rise due to interference may cause an excessively large change rate of the evaluation score for a certain period of time, so that the system may calculate that the system may reach a low score in a short time to falsely trigger an alarm for maintenance.

In addition, the occurrence of an interference signal in which the monitoring data suddenly becomes larger or smaller may also affect the fault diagnosis. The fault diagnosis of the motor will be based primarily on a comparison between monitored data of the motor and predetermined threshold parameters. Once an abnormally large or small signal occurs, the fault diagnosis of the motor is misjudged. For example, an excessive temperature rise due to the disturbance may be determined as a fault such as a phase loss, an overload, a wind path blockage, a fan damage, or a turn-to-turn short circuit of the motor.

By the monitoring data processing method in the embodiment of the application, the abnormity caused by non-motor faults can be eliminated, for example, invalid data caused by faults of interference, power failure or communication interruption and the like of acquisition and transmission equipment can be eliminated, and therefore the effectiveness of target monitoring data can be improved.

EXAMPLE III

Based on the same application concept, a monitoring data processing apparatus corresponding to the monitoring data processing method is also provided in the embodiments of the present application, and since the principle of solving the problem of the apparatus in the embodiments of the present application is similar to that in the embodiments of the monitoring data processing method, the apparatus in the embodiments of the present application may be implemented by referring to the description in the embodiments of the method, and repeated details are omitted.

Please refer to fig. 7, which is a schematic diagram of functional modules of a monitoring data processing apparatus according to an embodiment of the present disclosure. Each module in the monitoring data processing apparatus in this embodiment is configured to perform each step in the above-described method embodiment. The monitoring data processing device comprises an obtaining module 301, an extracting module 302 and a processing module 303; wherein,

an obtaining module 301, configured to obtain a variation trend function corresponding to monitoring data according to the collected monitoring data of the monitored device;

an extraction module 302, configured to extract data from the trend function according to a set time sequence to form an initial data set;

the processing module 303 is configured to perform validity processing on each data point in the initial data set to obtain target monitoring data.

In one possible implementation, the processing module 303 includes: a first judgment unit and a first replacement unit;

a first judging unit, configured to judge whether each data point in the initial data set is an invalid data point according to a preset fluctuation interval and a numerical limit interval, where if a determination value determined by a current data point in the initial data set and a previous data point of the current data point is outside the preset fluctuation interval, the current data point is represented as an invalid data point, and if the determination value determined by the current data point in the initial data set and the previous data point of the current data point is within the preset fluctuation interval, the current data point is represented as a valid data point;

and the first replacing unit is used for replacing the value of the invalid data point with the value of the designated data point to update the initial data group to obtain the target monitoring data if the invalid data point exists in the initial data group.

In one possible embodiment, the first replacement unit is configured to:

replacing the value of the invalid data point with the value of a valid data point that is the closest in time distance from the invalid data point.

In one possible embodiment, the first replacement unit is configured to:

fitting a target function according to the determined effective data points;

replacing a value in the objective function with a value of the invalid data point.

In one possible implementation, the processing module 303 includes: a determination unit, a second judgment unit, and a second replacement unit:

a determining unit, configured to determine an initial data point from the initial data set according to a preset numerical limit interval;

a second judging unit, configured to judge whether a data point in the initial data set after the initial data point is an invalid data point according to a preset fluctuation interval, where if a judgment value determined by a current data point in the initial data set and a previous data point of the current data point is outside the preset fluctuation interval, the current data point is represented as an invalid data point, and if the judgment value determined by the current data point in the initial data set and the previous data point of the current data point is within the preset fluctuation interval, the current data point is represented as a valid data point;

and the second replacing unit is used for replacing the value of the invalid data point with the value of the designated data point to update the initial data group to obtain the target monitoring data if the invalid data point exists in the initial data group.

In one possible embodiment, the second replacement unit is configured to:

fitting a target function according to the determined effective data points;

In a possible implementation, the extracting module 302 is configured to extract a plurality of data points with equal time distance from the trend function to form an initial data set.

In a possible implementation manner, the obtaining module 301 is configured to perform data fitting processing on the acquired monitoring data to obtain a variation trend function corresponding to the monitoring data.

In one possible implementation, a module 301 is obtained for:

In one possible embodiment, the monitoring data processing apparatus may further include:

a first determination module for determining a status score of the monitored device based on the target monitoring data;

and the second determining module is used for determining a target alarm signal according to the state score.

In addition, an embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the monitoring data processing method in the foregoing method embodiment.

The computer program product of the monitoring data processing method provided in the embodiment of the present application includes a computer-readable storage medium storing a program code, where instructions included in the program code may be used to execute the steps of the monitoring data processing method described in the above method embodiment, which may be specifically referred to in the above method embodiment, and are not described herein again.

In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.

The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.

The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for processing monitoring data, comprising:

2. The method of claim 1, wherein the validation processing of each data point in the initial data set to obtain target monitoring data comprises:

3. The method of claim 1, wherein the validation processing of each data point in the initial data set to obtain target monitoring data comprises:

4. The method of claim 2 or 3, wherein replacing the value of the invalid data point with the value of the designated data point comprises:

5. The method of claim 1, wherein the trend function is a function of time and a measured parameter; the extracting data from the variation trend function according to the set time sequence to form an initial data group comprises the following steps:

6. The method according to claim 1, wherein the obtaining a variation trend function corresponding to the monitoring data according to the collected monitoring data of the monitored equipment comprises:

7. The method of claim 1, further comprising:

and determining a target alarm signal according to the state score.

8. A monitoring data processing apparatus, comprising:

9. An electronic device, comprising: a processor, a memory storing machine-readable instructions executable by the processor, the machine-readable instructions when executed by the processor performing the steps of the method of any of claims 1 to 7 when the electronic device is run.

10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, which computer program, when being executed by a processor, performs the steps of the method of any one of claims 1 to 7.